module Tensorflow::RawOps
Public Class Methods
_arg(typeT: nil, index: nil, name: "_Arg")
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# File lib/tensorflow/ops/raw_ops.rb, line 5011 def self._arg(typeT: nil, index: nil, name: "_Arg") self.execute("_Arg", [], T: typeT, index: index, name: name) end
_array_to_list(input, typeT: nil, n: nil, out_types: nil, name: "_ArrayToList")
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# File lib/tensorflow/ops/raw_ops.rb, line 5015 def self._array_to_list(input, typeT: nil, n: nil, out_types: nil, name: "_ArrayToList") self.execute("_ArrayToList", [input], T: typeT, N: n, out_types: out_types, name: name) end
_configure_distributed_tpu(inputs, n: nil, enable_whole_mesh_compilations: false, name: "_ConfigureDistributedTPU")
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# File lib/tensorflow/ops/raw_ops.rb, line 5019 def self._configure_distributed_tpu(inputs, n: nil, enable_whole_mesh_compilations: false, name: "_ConfigureDistributedTPU") self.execute("_ConfigureDistributedTPU", [inputs], N: n, enable_whole_mesh_compilations: enable_whole_mesh_compilations, name: name) end
_device_arg(typeT: nil, index: nil, name: "_DeviceArg")
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# File lib/tensorflow/ops/raw_ops.rb, line 5023 def self._device_arg(typeT: nil, index: nil, name: "_DeviceArg") self.execute("_DeviceArg", [], T: typeT, index: index, name: name) end
_device_retval(input, typeT: nil, index: nil, name: "_DeviceRetval")
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# File lib/tensorflow/ops/raw_ops.rb, line 5027 def self._device_retval(input, typeT: nil, index: nil, name: "_DeviceRetval") self.execute("_DeviceRetval", [input], T: typeT, index: index, name: name) end
_disconnect_host_from_distributed_tpu_system(name: "_DisconnectHostFromDistributedTPUSystem")
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# File lib/tensorflow/ops/raw_ops.rb, line 5031 def self._disconnect_host_from_distributed_tpu_system(name: "_DisconnectHostFromDistributedTPUSystem") self.execute("_DisconnectHostFromDistributedTPUSystem", [], name: name) end
_fused_batch_norm_ex(x, scale, offset, mean, variance, side_input, typeT: nil, u: nil, epsilon: 9.999999747378752e-05, num_side_inputs: 0, activation_mode: "Identity", data_format: "NHWC", is_training: true, name: "_FusedBatchNormEx")
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# File lib/tensorflow/ops/raw_ops.rb, line 5035 def self._fused_batch_norm_ex(x, scale, offset, mean, variance, side_input, typeT: nil, u: nil, epsilon: 9.999999747378752e-05, num_side_inputs: 0, activation_mode: "Identity", data_format: "NHWC", is_training: true, name: "_FusedBatchNormEx") self.execute("_FusedBatchNormEx", [x, scale, offset, mean, variance, side_input], T: typeT, U: u, epsilon: epsilon, num_side_inputs: num_side_inputs, activation_mode: activation_mode, data_format: data_format, is_training: is_training, name: name) end
_fused_conv2_d(input, filter, args, typeT: nil, num_args: nil, strides: nil, padding: nil, explicit_paddings: [], data_format: "NHWC", dilations: [], use_cudnn_on_gpu: true, fused_ops: [], epsilon: 9.999999747378752e-05, name: "_FusedConv2D")
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# File lib/tensorflow/ops/raw_ops.rb, line 5039 def self._fused_conv2_d(input, filter, args, typeT: nil, num_args: nil, strides: nil, padding: nil, explicit_paddings: [], data_format: "NHWC", dilations: [], use_cudnn_on_gpu: true, fused_ops: [], epsilon: 9.999999747378752e-05, name: "_FusedConv2D") self.execute("_FusedConv2D", [input, filter, args], T: typeT, num_args: num_args, strides: strides, padding: padding, explicit_paddings: explicit_paddings, data_format: data_format, dilations: dilations, use_cudnn_on_gpu: use_cudnn_on_gpu, fused_ops: fused_ops, epsilon: epsilon, name: name) end
_fused_mat_mul(a, b, args, transpose_a: false, transpose_b: false, typeT: nil, num_args: nil, fused_ops: [], epsilon: 9.999999747378752e-05, name: "_FusedMatMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 5043 def self._fused_mat_mul(a, b, args, transpose_a: false, transpose_b: false, typeT: nil, num_args: nil, fused_ops: [], epsilon: 9.999999747378752e-05, name: "_FusedMatMul") self.execute("_FusedMatMul", [a, b, args], transpose_a: transpose_a, transpose_b: transpose_b, T: typeT, num_args: num_args, fused_ops: fused_ops, epsilon: epsilon, name: name) end
_host_cast(x, srct: nil, dstt: nil, truncate: false, name: "_HostCast")
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# File lib/tensorflow/ops/raw_ops.rb, line 5047 def self._host_cast(x, srct: nil, dstt: nil, truncate: false, name: "_HostCast") self.execute("_HostCast", [x], SrcT: srct, DstT: dstt, Truncate: truncate, name: name) end
_host_recv(tensor_type: nil, tensor_name: "", send_device: "", send_device_incarnation: nil, recv_device: "", client_terminated: false, name: "_HostRecv")
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# File lib/tensorflow/ops/raw_ops.rb, line 5051 def self._host_recv(tensor_type: nil, tensor_name: "", send_device: "", send_device_incarnation: nil, recv_device: "", client_terminated: false, name: "_HostRecv") self.execute("_HostRecv", [], tensor_type: tensor_type, tensor_name: tensor_name, send_device: send_device, send_device_incarnation: send_device_incarnation, recv_device: recv_device, client_terminated: client_terminated, name: name) end
_host_send(tensor, typeT: nil, tensor_name: "", send_device: "", send_device_incarnation: nil, recv_device: "", client_terminated: false, name: "_HostSend")
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# File lib/tensorflow/ops/raw_ops.rb, line 5055 def self._host_send(tensor, typeT: nil, tensor_name: "", send_device: "", send_device_incarnation: nil, recv_device: "", client_terminated: false, name: "_HostSend") self.execute("_HostSend", [tensor], T: typeT, tensor_name: tensor_name, send_device: send_device, send_device_incarnation: send_device_incarnation, recv_device: recv_device, client_terminated: client_terminated, name: name) end
_if(cond, input, tcond: nil, tin: nil, tout: nil, then_branch: nil, else_branch: nil, name: "_If")
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# File lib/tensorflow/ops/raw_ops.rb, line 5059 def self._if(cond, input, tcond: nil, tin: nil, tout: nil, then_branch: nil, else_branch: nil, name: "_If") self.execute("_If", [cond, input], Tcond: tcond, Tin: tin, Tout: tout, then_branch: then_branch, else_branch: else_branch, name: name) end
_initialize_host_for_distributed_tpu(input, enable_whole_mesh_compilations: false, name: "_InitializeHostForDistributedTPU")
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# File lib/tensorflow/ops/raw_ops.rb, line 5063 def self._initialize_host_for_distributed_tpu(input, enable_whole_mesh_compilations: false, name: "_InitializeHostForDistributedTPU") self.execute("_InitializeHostForDistributedTPU", [input], enable_whole_mesh_compilations: enable_whole_mesh_compilations, name: name) end
_list_to_array(input, tin: nil, typeT: nil, n: nil, name: "_ListToArray")
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# File lib/tensorflow/ops/raw_ops.rb, line 5067 def self._list_to_array(input, tin: nil, typeT: nil, n: nil, name: "_ListToArray") self.execute("_ListToArray", [input], Tin: tin, T: typeT, N: n, name: name) end
_mkl_maximum(x, y, mkl_x, mkl_y, typeT: nil, name: "_MklMaximum")
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# File lib/tensorflow/ops/raw_ops.rb, line 5071 def self._mkl_maximum(x, y, mkl_x, mkl_y, typeT: nil, name: "_MklMaximum") self.execute("_MklMaximum", [x, y, mkl_x, mkl_y], T: typeT, name: name) end
_mkl_mul(x, y, mkl_x, mkl_y, typeT: nil, name: "_MklMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 5075 def self._mkl_mul(x, y, mkl_x, mkl_y, typeT: nil, name: "_MklMul") self.execute("_MklMul", [x, y, mkl_x, mkl_y], T: typeT, name: name) end
_mkl_squared_difference(x, y, mkl_x, mkl_y, typeT: nil, name: "_MklSquaredDifference")
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# File lib/tensorflow/ops/raw_ops.rb, line 5079 def self._mkl_squared_difference(x, y, mkl_x, mkl_y, typeT: nil, name: "_MklSquaredDifference") self.execute("_MklSquaredDifference", [x, y, mkl_x, mkl_y], T: typeT, name: name) end
_mkl_sub(x, y, mkl_x, mkl_y, typeT: nil, name: "_MklSub")
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# File lib/tensorflow/ops/raw_ops.rb, line 5083 def self._mkl_sub(x, y, mkl_x, mkl_y, typeT: nil, name: "_MklSub") self.execute("_MklSub", [x, y, mkl_x, mkl_y], T: typeT, name: name) end
_nccl_broadcast_recv(shape, typeT: nil, num_devices: nil, shared_name: "", name: "_NcclBroadcastRecv")
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# File lib/tensorflow/ops/raw_ops.rb, line 5087 def self._nccl_broadcast_recv(shape, typeT: nil, num_devices: nil, shared_name: "", name: "_NcclBroadcastRecv") self.execute("_NcclBroadcastRecv", [shape], T: typeT, num_devices: num_devices, shared_name: shared_name, name: name) end
_nccl_broadcast_send(input, typeT: nil, num_devices: nil, shared_name: "", name: "_NcclBroadcastSend")
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# File lib/tensorflow/ops/raw_ops.rb, line 5091 def self._nccl_broadcast_send(input, typeT: nil, num_devices: nil, shared_name: "", name: "_NcclBroadcastSend") self.execute("_NcclBroadcastSend", [input], T: typeT, num_devices: num_devices, shared_name: shared_name, name: name) end
_nccl_reduce_recv(input, reduction: nil, typeT: nil, num_devices: nil, shared_name: "", name: "_NcclReduceRecv")
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# File lib/tensorflow/ops/raw_ops.rb, line 5095 def self._nccl_reduce_recv(input, reduction: nil, typeT: nil, num_devices: nil, shared_name: "", name: "_NcclReduceRecv") self.execute("_NcclReduceRecv", [input], reduction: reduction, T: typeT, num_devices: num_devices, shared_name: shared_name, name: name) end
_nccl_reduce_send(input, reduction: nil, typeT: nil, num_devices: nil, shared_name: "", name: "_NcclReduceSend")
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# File lib/tensorflow/ops/raw_ops.rb, line 5099 def self._nccl_reduce_send(input, reduction: nil, typeT: nil, num_devices: nil, shared_name: "", name: "_NcclReduceSend") self.execute("_NcclReduceSend", [input], reduction: reduction, T: typeT, num_devices: num_devices, shared_name: shared_name, name: name) end
_parallel_concat_start(shape: nil, dtype: nil, name: "_ParallelConcatStart")
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# File lib/tensorflow/ops/raw_ops.rb, line 5103 def self._parallel_concat_start(shape: nil, dtype: nil, name: "_ParallelConcatStart") self.execute("_ParallelConcatStart", [], shape: shape, dtype: dtype, name: name) end
_parallel_concat_update(value, update, typeT: nil, loc: nil, name: "_ParallelConcatUpdate")
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# File lib/tensorflow/ops/raw_ops.rb, line 5107 def self._parallel_concat_update(value, update, typeT: nil, loc: nil, name: "_ParallelConcatUpdate") self.execute("_ParallelConcatUpdate", [value, update], T: typeT, loc: loc, name: name) end
_read_variables_op(resources, n: nil, dtypes: nil, name: "_ReadVariablesOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 5111 def self._read_variables_op(resources, n: nil, dtypes: nil, name: "_ReadVariablesOp") self.execute("_ReadVariablesOp", [resources], N: n, dtypes: dtypes, name: name) end
_recv(tensor_type: nil, tensor_name: "", send_device: "", send_device_incarnation: nil, recv_device: "", client_terminated: false, name: "_Recv")
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# File lib/tensorflow/ops/raw_ops.rb, line 5115 def self._recv(tensor_type: nil, tensor_name: "", send_device: "", send_device_incarnation: nil, recv_device: "", client_terminated: false, name: "_Recv") self.execute("_Recv", [], tensor_type: tensor_type, tensor_name: tensor_name, send_device: send_device, send_device_incarnation: send_device_incarnation, recv_device: recv_device, client_terminated: client_terminated, name: name) end
_retval(input, typeT: nil, index: nil, name: "_Retval")
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# File lib/tensorflow/ops/raw_ops.rb, line 5119 def self._retval(input, typeT: nil, index: nil, name: "_Retval") self.execute("_Retval", [input], T: typeT, index: index, name: name) end
_scoped_allocator(shapes: nil, shape: nil, typeT: nil, sa_name: "", id: nil, expected_call_count: nil, name: "_ScopedAllocator")
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# File lib/tensorflow/ops/raw_ops.rb, line 5123 def self._scoped_allocator(shapes: nil, shape: nil, typeT: nil, sa_name: "", id: nil, expected_call_count: nil, name: "_ScopedAllocator") self.execute("_ScopedAllocator", [], shapes: shapes, shape: shape, T: typeT, sa_name: sa_name, id: id, expected_call_count: expected_call_count, name: name) end
_scoped_allocator_concat(backing, inputs, shape: nil, typeT: nil, reshape: false, sa_name: "", id: nil, n: nil, name: "_ScopedAllocatorConcat")
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# File lib/tensorflow/ops/raw_ops.rb, line 5127 def self._scoped_allocator_concat(backing, inputs, shape: nil, typeT: nil, reshape: false, sa_name: "", id: nil, n: nil, name: "_ScopedAllocatorConcat") self.execute("_ScopedAllocatorConcat", [backing, inputs], shape: shape, T: typeT, reshape: reshape, sa_name: sa_name, id: id, N: n, name: name) end
_scoped_allocator_split(concat, split, typeT: nil, sa_name: "", id: nil, n: nil, shapes: nil, name: "_ScopedAllocatorSplit")
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# File lib/tensorflow/ops/raw_ops.rb, line 5131 def self._scoped_allocator_split(concat, split, typeT: nil, sa_name: "", id: nil, n: nil, shapes: nil, name: "_ScopedAllocatorSplit") self.execute("_ScopedAllocatorSplit", [concat, split], T: typeT, sa_name: sa_name, id: id, N: n, shapes: shapes, name: name) end
_send(tensor, typeT: nil, tensor_name: "", send_device: "", send_device_incarnation: nil, recv_device: "", client_terminated: false, name: "_Send")
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# File lib/tensorflow/ops/raw_ops.rb, line 5135 def self._send(tensor, typeT: nil, tensor_name: "", send_device: "", send_device_incarnation: nil, recv_device: "", client_terminated: false, name: "_Send") self.execute("_Send", [tensor], T: typeT, tensor_name: tensor_name, send_device: send_device, send_device_incarnation: send_device_incarnation, recv_device: recv_device, client_terminated: client_terminated, name: name) end
_set_global_tpu_array(topology, name: "_SetGlobalTPUArray")
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# File lib/tensorflow/ops/raw_ops.rb, line 5139 def self._set_global_tpu_array(topology, name: "_SetGlobalTPUArray") self.execute("_SetGlobalTPUArray", [topology], name: name) end
_shutdown_distributed_tpu(name: "_ShutdownDistributedTPU")
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# File lib/tensorflow/ops/raw_ops.rb, line 5143 def self._shutdown_distributed_tpu(name: "_ShutdownDistributedTPU") self.execute("_ShutdownDistributedTPU", [], name: name) end
_switch_n(data, output_index, num_outs: nil, typeT: nil, name: "_SwitchN")
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# File lib/tensorflow/ops/raw_ops.rb, line 5147 def self._switch_n(data, output_index, num_outs: nil, typeT: nil, name: "_SwitchN") self.execute("_SwitchN", [data, output_index], num_outs: num_outs, T: typeT, name: name) end
_tpu_replicate(inputs, broadcast_inputs, variables, guaranteed_constants, computation: nil, num_replicas: nil, num_cores_per_replica: 1, topology: "", use_tpu: true, device_assignment: [], host_compute_core: [], tinputs: nil, tbroadcast_inputs: nil, numvariables: nil, tguaranteed_constants: nil, output_types: nil, padding_map: [], step_marker_location: "STEP_MARK_AT_ENTRY", allow_soft_placement: false, name: "_TPUReplicate")
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# File lib/tensorflow/ops/raw_ops.rb, line 5151 def self._tpu_replicate(inputs, broadcast_inputs, variables, guaranteed_constants, computation: nil, num_replicas: nil, num_cores_per_replica: 1, topology: "", use_tpu: true, device_assignment: [], host_compute_core: [], tinputs: nil, tbroadcast_inputs: nil, numvariables: nil, tguaranteed_constants: nil, output_types: nil, padding_map: [], step_marker_location: "STEP_MARK_AT_ENTRY", allow_soft_placement: false, name: "_TPUReplicate") self.execute("_TPUReplicate", [inputs, broadcast_inputs, variables, guaranteed_constants], computation: computation, num_replicas: num_replicas, num_cores_per_replica: num_cores_per_replica, topology: topology, use_tpu: use_tpu, device_assignment: device_assignment, host_compute_core: host_compute_core, Tinputs: tinputs, Tbroadcast_inputs: tbroadcast_inputs, NumVariables: numvariables, Tguaranteed_constants: tguaranteed_constants, output_types: output_types, padding_map: padding_map, step_marker_location: step_marker_location, allow_soft_placement: allow_soft_placement, name: name) end
_unary_ops_composition(x, typeT: nil, op_names: nil, name: "_UnaryOpsComposition")
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# File lib/tensorflow/ops/raw_ops.rb, line 5155 def self._unary_ops_composition(x, typeT: nil, op_names: nil, name: "_UnaryOpsComposition") self.execute("_UnaryOpsComposition", [x], T: typeT, op_names: op_names, name: name) end
_var_handles_op(containers: nil, shared_names: nil, n: nil, dtypes: nil, shapes: nil, name: "_VarHandlesOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 5159 def self._var_handles_op(containers: nil, shared_names: nil, n: nil, dtypes: nil, shapes: nil, name: "_VarHandlesOp") self.execute("_VarHandlesOp", [], containers: containers, shared_names: shared_names, N: n, dtypes: dtypes, shapes: shapes, name: name) end
_wait_for_distributed_tpu(inputs, startup_timeout_sec: 20, n: nil, name: "_WaitForDistributedTPU")
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# File lib/tensorflow/ops/raw_ops.rb, line 5163 def self._wait_for_distributed_tpu(inputs, startup_timeout_sec: 20, n: nil, name: "_WaitForDistributedTPU") self.execute("_WaitForDistributedTPU", [inputs], startup_timeout_sec: startup_timeout_sec, N: n, name: name) end
_while(input, typeT: nil, cond: nil, body: nil, name: "_While")
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# File lib/tensorflow/ops/raw_ops.rb, line 5167 def self._while(input, typeT: nil, cond: nil, body: nil, name: "_While") self.execute("_While", [input], T: typeT, cond: cond, body: body, name: name) end
_xla_recv_at_host(dynamic_key, toutputs: nil, key: "", device_ordinal: nil, name: "_XlaRecvAtHost")
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# File lib/tensorflow/ops/raw_ops.rb, line 5171 def self._xla_recv_at_host(dynamic_key, toutputs: nil, key: "", device_ordinal: nil, name: "_XlaRecvAtHost") self.execute("_XlaRecvAtHost", [dynamic_key], Toutputs: toutputs, key: key, device_ordinal: device_ordinal, name: name) end
_xla_send_from_host(inputs, dynamic_key, tinputs: nil, key: "", device_ordinal: nil, name: "_XlaSendFromHost")
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# File lib/tensorflow/ops/raw_ops.rb, line 5175 def self._xla_send_from_host(inputs, dynamic_key, tinputs: nil, key: "", device_ordinal: nil, name: "_XlaSendFromHost") self.execute("_XlaSendFromHost", [inputs, dynamic_key], Tinputs: tinputs, key: key, device_ordinal: device_ordinal, name: name) end
abort(error_msg: "", exit_without_error: false, name: "Abort")
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# File lib/tensorflow/ops/raw_ops.rb, line 18 def self.abort(error_msg: "", exit_without_error: false, name: "Abort") self.execute("Abort", [], error_msg: error_msg, exit_without_error: exit_without_error, name: name) end
abs(x, typeT: nil, name: "Abs")
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# File lib/tensorflow/ops/raw_ops.rb, line 22 def self.abs(x, typeT: nil, name: "Abs") self.execute("Abs", [x], T: typeT, name: name) end
accumulate_nv2(inputs, n: nil, typeT: nil, shape: nil, name: "AccumulateNV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 26 def self.accumulate_nv2(inputs, n: nil, typeT: nil, shape: nil, name: "AccumulateNV2") self.execute("AccumulateNV2", [inputs], N: n, T: typeT, shape: shape, name: name) end
accumulator_apply_gradient(handle, local_step, gradient, dtype: nil, name: "AccumulatorApplyGradient")
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# File lib/tensorflow/ops/raw_ops.rb, line 30 def self.accumulator_apply_gradient(handle, local_step, gradient, dtype: nil, name: "AccumulatorApplyGradient") self.execute("AccumulatorApplyGradient", [handle, local_step, gradient], dtype: dtype, name: name) end
accumulator_num_accumulated(handle, name: "AccumulatorNumAccumulated")
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# File lib/tensorflow/ops/raw_ops.rb, line 34 def self.accumulator_num_accumulated(handle, name: "AccumulatorNumAccumulated") self.execute("AccumulatorNumAccumulated", [handle], name: name) end
accumulator_set_global_step(handle, new_global_step, name: "AccumulatorSetGlobalStep")
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# File lib/tensorflow/ops/raw_ops.rb, line 38 def self.accumulator_set_global_step(handle, new_global_step, name: "AccumulatorSetGlobalStep") self.execute("AccumulatorSetGlobalStep", [handle, new_global_step], name: name) end
accumulator_take_gradient(handle, num_required, dtype: nil, name: "AccumulatorTakeGradient")
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# File lib/tensorflow/ops/raw_ops.rb, line 42 def self.accumulator_take_gradient(handle, num_required, dtype: nil, name: "AccumulatorTakeGradient") self.execute("AccumulatorTakeGradient", [handle, num_required], dtype: dtype, name: name) end
acos(x, typeT: nil, name: "Acos")
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# File lib/tensorflow/ops/raw_ops.rb, line 46 def self.acos(x, typeT: nil, name: "Acos") self.execute("Acos", [x], T: typeT, name: name) end
acosh(x, typeT: nil, name: "Acosh")
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# File lib/tensorflow/ops/raw_ops.rb, line 50 def self.acosh(x, typeT: nil, name: "Acosh") self.execute("Acosh", [x], T: typeT, name: name) end
add(x, y, typeT: nil, name: "Add")
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# File lib/tensorflow/ops/raw_ops.rb, line 54 def self.add(x, y, typeT: nil, name: "Add") self.execute("Add", [x, y], T: typeT, name: name) end
add_many_sparse_to_tensors_map(sparse_indices, sparse_values, sparse_shape, typeT: nil, container: "", shared_name: "", name: "AddManySparseToTensorsMap")
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# File lib/tensorflow/ops/raw_ops.rb, line 58 def self.add_many_sparse_to_tensors_map(sparse_indices, sparse_values, sparse_shape, typeT: nil, container: "", shared_name: "", name: "AddManySparseToTensorsMap") self.execute("AddManySparseToTensorsMap", [sparse_indices, sparse_values, sparse_shape], T: typeT, container: container, shared_name: shared_name, name: name) end
add_n(inputs, n: nil, typeT: nil, name: "AddN")
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# File lib/tensorflow/ops/raw_ops.rb, line 62 def self.add_n(inputs, n: nil, typeT: nil, name: "AddN") self.execute("AddN", [inputs], N: n, T: typeT, name: name) end
add_sparse_to_tensors_map(sparse_indices, sparse_values, sparse_shape, typeT: nil, container: "", shared_name: "", name: "AddSparseToTensorsMap")
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# File lib/tensorflow/ops/raw_ops.rb, line 66 def self.add_sparse_to_tensors_map(sparse_indices, sparse_values, sparse_shape, typeT: nil, container: "", shared_name: "", name: "AddSparseToTensorsMap") self.execute("AddSparseToTensorsMap", [sparse_indices, sparse_values, sparse_shape], T: typeT, container: container, shared_name: shared_name, name: name) end
add_v2(x, y, typeT: nil, name: "AddV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 70 def self.add_v2(x, y, typeT: nil, name: "AddV2") self.execute("AddV2", [x, y], T: typeT, name: name) end
adjust_contrast(images, contrast_factor, min_value, max_value, typeT: nil, name: "AdjustContrast")
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# File lib/tensorflow/ops/raw_ops.rb, line 74 def self.adjust_contrast(images, contrast_factor, min_value, max_value, typeT: nil, name: "AdjustContrast") self.execute("AdjustContrast", [images, contrast_factor, min_value, max_value], T: typeT, name: name) end
adjust_contrastv2(images, contrast_factor, typeT: :float, name: "AdjustContrastv2")
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# File lib/tensorflow/ops/raw_ops.rb, line 78 def self.adjust_contrastv2(images, contrast_factor, typeT: :float, name: "AdjustContrastv2") self.execute("AdjustContrastv2", [images, contrast_factor], T: typeT, name: name) end
adjust_hue(images, delta, typeT: :float, name: "AdjustHue")
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# File lib/tensorflow/ops/raw_ops.rb, line 82 def self.adjust_hue(images, delta, typeT: :float, name: "AdjustHue") self.execute("AdjustHue", [images, delta], T: typeT, name: name) end
adjust_saturation(images, scale, typeT: :float, name: "AdjustSaturation")
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# File lib/tensorflow/ops/raw_ops.rb, line 86 def self.adjust_saturation(images, scale, typeT: :float, name: "AdjustSaturation") self.execute("AdjustSaturation", [images, scale], T: typeT, name: name) end
all(input, reduction_indices, keep_dims: false, tidx: :int32, name: "All")
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# File lib/tensorflow/ops/raw_ops.rb, line 90 def self.all(input, reduction_indices, keep_dims: false, tidx: :int32, name: "All") self.execute("All", [input, reduction_indices], keep_dims: keep_dims, Tidx: tidx, name: name) end
all_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, seed: 0, seed2: 0, name: "AllCandidateSampler")
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# File lib/tensorflow/ops/raw_ops.rb, line 94 def self.all_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, seed: 0, seed2: 0, name: "AllCandidateSampler") self.execute("AllCandidateSampler", [true_classes], num_true: num_true, num_sampled: num_sampled, unique: unique, seed: seed, seed2: seed2, name: name) end
all_to_all(input, group_assignment, typeT: nil, concat_dimension: nil, split_dimension: nil, split_count: nil, name: "AllToAll")
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# File lib/tensorflow/ops/raw_ops.rb, line 98 def self.all_to_all(input, group_assignment, typeT: nil, concat_dimension: nil, split_dimension: nil, split_count: nil, name: "AllToAll") self.execute("AllToAll", [input, group_assignment], T: typeT, concat_dimension: concat_dimension, split_dimension: split_dimension, split_count: split_count, name: name) end
angle(input, typeT: :complex64, tout: :float, name: "Angle")
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# File lib/tensorflow/ops/raw_ops.rb, line 102 def self.angle(input, typeT: :complex64, tout: :float, name: "Angle") self.execute("Angle", [input], T: typeT, Tout: tout, name: name) end
anonymous_iterator(output_types: nil, output_shapes: nil, name: "AnonymousIterator")
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# File lib/tensorflow/ops/raw_ops.rb, line 106 def self.anonymous_iterator(output_types: nil, output_shapes: nil, name: "AnonymousIterator") self.execute("AnonymousIterator", [], output_types: output_types, output_shapes: output_shapes, name: name) end
anonymous_iterator_v2(output_types: nil, output_shapes: nil, name: "AnonymousIteratorV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 110 def self.anonymous_iterator_v2(output_types: nil, output_shapes: nil, name: "AnonymousIteratorV2") self.execute("AnonymousIteratorV2", [], output_types: output_types, output_shapes: output_shapes, name: name) end
anonymous_memory_cache(name: "AnonymousMemoryCache")
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# File lib/tensorflow/ops/raw_ops.rb, line 114 def self.anonymous_memory_cache(name: "AnonymousMemoryCache") self.execute("AnonymousMemoryCache", [], name: name) end
anonymous_multi_device_iterator(devices: nil, output_types: nil, output_shapes: nil, name: "AnonymousMultiDeviceIterator")
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# File lib/tensorflow/ops/raw_ops.rb, line 118 def self.anonymous_multi_device_iterator(devices: nil, output_types: nil, output_shapes: nil, name: "AnonymousMultiDeviceIterator") self.execute("AnonymousMultiDeviceIterator", [], devices: devices, output_types: output_types, output_shapes: output_shapes, name: name) end
anonymous_random_seed_generator(seed, seed2, name: "AnonymousRandomSeedGenerator")
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# File lib/tensorflow/ops/raw_ops.rb, line 122 def self.anonymous_random_seed_generator(seed, seed2, name: "AnonymousRandomSeedGenerator") self.execute("AnonymousRandomSeedGenerator", [seed, seed2], name: name) end
any(input, reduction_indices, keep_dims: false, tidx: :int32, name: "Any")
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# File lib/tensorflow/ops/raw_ops.rb, line 126 def self.any(input, reduction_indices, keep_dims: false, tidx: :int32, name: "Any") self.execute("Any", [input, reduction_indices], keep_dims: keep_dims, Tidx: tidx, name: name) end
apply_ada_max(var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, typeT: nil, use_locking: false, name: "ApplyAdaMax")
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# File lib/tensorflow/ops/raw_ops.rb, line 130 def self.apply_ada_max(var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, typeT: nil, use_locking: false, name: "ApplyAdaMax") self.execute("ApplyAdaMax", [var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad], T: typeT, use_locking: use_locking, name: name) end
apply_adadelta(var, accum, accum_update, lr, rho, epsilon, grad, typeT: nil, use_locking: false, name: "ApplyAdadelta")
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# File lib/tensorflow/ops/raw_ops.rb, line 134 def self.apply_adadelta(var, accum, accum_update, lr, rho, epsilon, grad, typeT: nil, use_locking: false, name: "ApplyAdadelta") self.execute("ApplyAdadelta", [var, accum, accum_update, lr, rho, epsilon, grad], T: typeT, use_locking: use_locking, name: name) end
apply_adagrad(var, accum, lr, grad, typeT: nil, use_locking: false, update_slots: true, name: "ApplyAdagrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 138 def self.apply_adagrad(var, accum, lr, grad, typeT: nil, use_locking: false, update_slots: true, name: "ApplyAdagrad") self.execute("ApplyAdagrad", [var, accum, lr, grad], T: typeT, use_locking: use_locking, update_slots: update_slots, name: name) end
apply_adagrad_da(var, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step, typeT: nil, use_locking: false, name: "ApplyAdagradDA")
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# File lib/tensorflow/ops/raw_ops.rb, line 142 def self.apply_adagrad_da(var, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step, typeT: nil, use_locking: false, name: "ApplyAdagradDA") self.execute("ApplyAdagradDA", [var, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step], T: typeT, use_locking: use_locking, name: name) end
apply_adagrad_v2(var, accum, lr, epsilon, grad, typeT: nil, use_locking: false, update_slots: true, name: "ApplyAdagradV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 146 def self.apply_adagrad_v2(var, accum, lr, epsilon, grad, typeT: nil, use_locking: false, update_slots: true, name: "ApplyAdagradV2") self.execute("ApplyAdagradV2", [var, accum, lr, epsilon, grad], T: typeT, use_locking: use_locking, update_slots: update_slots, name: name) end
apply_adam(var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, typeT: nil, use_locking: false, use_nesterov: false, name: "ApplyAdam")
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# File lib/tensorflow/ops/raw_ops.rb, line 150 def self.apply_adam(var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, typeT: nil, use_locking: false, use_nesterov: false, name: "ApplyAdam") self.execute("ApplyAdam", [var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad], T: typeT, use_locking: use_locking, use_nesterov: use_nesterov, name: name) end
apply_add_sign(var, m, lr, alpha, sign_decay, beta, grad, typeT: nil, use_locking: false, name: "ApplyAddSign")
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# File lib/tensorflow/ops/raw_ops.rb, line 154 def self.apply_add_sign(var, m, lr, alpha, sign_decay, beta, grad, typeT: nil, use_locking: false, name: "ApplyAddSign") self.execute("ApplyAddSign", [var, m, lr, alpha, sign_decay, beta, grad], T: typeT, use_locking: use_locking, name: name) end
apply_centered_rms_prop(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, typeT: nil, use_locking: false, name: "ApplyCenteredRMSProp")
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# File lib/tensorflow/ops/raw_ops.rb, line 158 def self.apply_centered_rms_prop(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, typeT: nil, use_locking: false, name: "ApplyCenteredRMSProp") self.execute("ApplyCenteredRMSProp", [var, mg, ms, mom, lr, rho, momentum, epsilon, grad], T: typeT, use_locking: use_locking, name: name) end
apply_ftrl(var, accum, linear, grad, lr, l1, l2, lr_power, typeT: nil, use_locking: false, name: "ApplyFtrl")
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# File lib/tensorflow/ops/raw_ops.rb, line 162 def self.apply_ftrl(var, accum, linear, grad, lr, l1, l2, lr_power, typeT: nil, use_locking: false, name: "ApplyFtrl") self.execute("ApplyFtrl", [var, accum, linear, grad, lr, l1, l2, lr_power], T: typeT, use_locking: use_locking, name: name) end
apply_ftrl_v2(var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, typeT: nil, use_locking: false, name: "ApplyFtrlV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 166 def self.apply_ftrl_v2(var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, typeT: nil, use_locking: false, name: "ApplyFtrlV2") self.execute("ApplyFtrlV2", [var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power], T: typeT, use_locking: use_locking, name: name) end
apply_gradient_descent(var, alpha, delta, typeT: nil, use_locking: false, name: "ApplyGradientDescent")
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# File lib/tensorflow/ops/raw_ops.rb, line 170 def self.apply_gradient_descent(var, alpha, delta, typeT: nil, use_locking: false, name: "ApplyGradientDescent") self.execute("ApplyGradientDescent", [var, alpha, delta], T: typeT, use_locking: use_locking, name: name) end
apply_momentum(var, accum, lr, grad, momentum, typeT: nil, use_locking: false, use_nesterov: false, name: "ApplyMomentum")
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# File lib/tensorflow/ops/raw_ops.rb, line 174 def self.apply_momentum(var, accum, lr, grad, momentum, typeT: nil, use_locking: false, use_nesterov: false, name: "ApplyMomentum") self.execute("ApplyMomentum", [var, accum, lr, grad, momentum], T: typeT, use_locking: use_locking, use_nesterov: use_nesterov, name: name) end
apply_power_sign(var, m, lr, logbase, sign_decay, beta, grad, typeT: nil, use_locking: false, name: "ApplyPowerSign")
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# File lib/tensorflow/ops/raw_ops.rb, line 178 def self.apply_power_sign(var, m, lr, logbase, sign_decay, beta, grad, typeT: nil, use_locking: false, name: "ApplyPowerSign") self.execute("ApplyPowerSign", [var, m, lr, logbase, sign_decay, beta, grad], T: typeT, use_locking: use_locking, name: name) end
apply_proximal_adagrad(var, accum, lr, l1, l2, grad, typeT: nil, use_locking: false, name: "ApplyProximalAdagrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 182 def self.apply_proximal_adagrad(var, accum, lr, l1, l2, grad, typeT: nil, use_locking: false, name: "ApplyProximalAdagrad") self.execute("ApplyProximalAdagrad", [var, accum, lr, l1, l2, grad], T: typeT, use_locking: use_locking, name: name) end
apply_proximal_gradient_descent(var, alpha, l1, l2, delta, typeT: nil, use_locking: false, name: "ApplyProximalGradientDescent")
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# File lib/tensorflow/ops/raw_ops.rb, line 186 def self.apply_proximal_gradient_descent(var, alpha, l1, l2, delta, typeT: nil, use_locking: false, name: "ApplyProximalGradientDescent") self.execute("ApplyProximalGradientDescent", [var, alpha, l1, l2, delta], T: typeT, use_locking: use_locking, name: name) end
apply_rms_prop(var, ms, mom, lr, rho, momentum, epsilon, grad, typeT: nil, use_locking: false, name: "ApplyRMSProp")
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# File lib/tensorflow/ops/raw_ops.rb, line 190 def self.apply_rms_prop(var, ms, mom, lr, rho, momentum, epsilon, grad, typeT: nil, use_locking: false, name: "ApplyRMSProp") self.execute("ApplyRMSProp", [var, ms, mom, lr, rho, momentum, epsilon, grad], T: typeT, use_locking: use_locking, name: name) end
approximate_equal(x, y, typeT: nil, tolerance: 9.999999747378752e-06, name: "ApproximateEqual")
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# File lib/tensorflow/ops/raw_ops.rb, line 194 def self.approximate_equal(x, y, typeT: nil, tolerance: 9.999999747378752e-06, name: "ApproximateEqual") self.execute("ApproximateEqual", [x, y], T: typeT, tolerance: tolerance, name: name) end
arg_max(input, dimension, typeT: nil, tidx: :int32, output_type: :int64, name: "ArgMax")
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# File lib/tensorflow/ops/raw_ops.rb, line 198 def self.arg_max(input, dimension, typeT: nil, tidx: :int32, output_type: :int64, name: "ArgMax") self.execute("ArgMax", [input, dimension], T: typeT, Tidx: tidx, output_type: output_type, name: name) end
arg_min(input, dimension, typeT: nil, tidx: :int32, output_type: :int64, name: "ArgMin")
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# File lib/tensorflow/ops/raw_ops.rb, line 202 def self.arg_min(input, dimension, typeT: nil, tidx: :int32, output_type: :int64, name: "ArgMin") self.execute("ArgMin", [input, dimension], T: typeT, Tidx: tidx, output_type: output_type, name: name) end
as_string(input, typeT: nil, precision: -1, scientific: false, shortest: false, width: -1, fill: "", name: "AsString")
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# File lib/tensorflow/ops/raw_ops.rb, line 206 def self.as_string(input, typeT: nil, precision: -1, scientific: false, shortest: false, width: -1, fill: "", name: "AsString") self.execute("AsString", [input], T: typeT, precision: precision, scientific: scientific, shortest: shortest, width: width, fill: fill, name: name) end
asin(x, typeT: nil, name: "Asin")
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# File lib/tensorflow/ops/raw_ops.rb, line 210 def self.asin(x, typeT: nil, name: "Asin") self.execute("Asin", [x], T: typeT, name: name) end
asinh(x, typeT: nil, name: "Asinh")
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# File lib/tensorflow/ops/raw_ops.rb, line 214 def self.asinh(x, typeT: nil, name: "Asinh") self.execute("Asinh", [x], T: typeT, name: name) end
assert(condition, data, typeT: nil, summarize: 3, name: "Assert")
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# File lib/tensorflow/ops/raw_ops.rb, line 218 def self.assert(condition, data, typeT: nil, summarize: 3, name: "Assert") self.execute("Assert", [condition, data], T: typeT, summarize: summarize, name: name) end
assert_next_dataset(input_dataset, transformations, output_types: nil, output_shapes: nil, name: "AssertNextDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 222 def self.assert_next_dataset(input_dataset, transformations, output_types: nil, output_shapes: nil, name: "AssertNextDataset") self.execute("AssertNextDataset", [input_dataset, transformations], output_types: output_types, output_shapes: output_shapes, name: name) end
assign(ref, value, typeT: nil, validate_shape: true, use_locking: true, name: "Assign")
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# File lib/tensorflow/ops/raw_ops.rb, line 226 def self.assign(ref, value, typeT: nil, validate_shape: true, use_locking: true, name: "Assign") self.execute("Assign", [ref, value], T: typeT, validate_shape: validate_shape, use_locking: use_locking, name: name) end
assign_add(ref, value, typeT: nil, use_locking: false, name: "AssignAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 230 def self.assign_add(ref, value, typeT: nil, use_locking: false, name: "AssignAdd") self.execute("AssignAdd", [ref, value], T: typeT, use_locking: use_locking, name: name) end
assign_add_variable_op(resource, value, dtype: nil, name: "AssignAddVariableOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 234 def self.assign_add_variable_op(resource, value, dtype: nil, name: "AssignAddVariableOp") self.execute("AssignAddVariableOp", [resource, value], dtype: dtype, name: name) end
assign_sub(ref, value, typeT: nil, use_locking: false, name: "AssignSub")
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# File lib/tensorflow/ops/raw_ops.rb, line 238 def self.assign_sub(ref, value, typeT: nil, use_locking: false, name: "AssignSub") self.execute("AssignSub", [ref, value], T: typeT, use_locking: use_locking, name: name) end
assign_sub_variable_op(resource, value, dtype: nil, name: "AssignSubVariableOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 242 def self.assign_sub_variable_op(resource, value, dtype: nil, name: "AssignSubVariableOp") self.execute("AssignSubVariableOp", [resource, value], dtype: dtype, name: name) end
assign_variable_op(resource, value, dtype: nil, name: "AssignVariableOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 246 def self.assign_variable_op(resource, value, dtype: nil, name: "AssignVariableOp") self.execute("AssignVariableOp", [resource, value], dtype: dtype, name: name) end
atan(x, typeT: nil, name: "Atan")
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# File lib/tensorflow/ops/raw_ops.rb, line 250 def self.atan(x, typeT: nil, name: "Atan") self.execute("Atan", [x], T: typeT, name: name) end
atan2(y, x, typeT: nil, name: "Atan2")
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# File lib/tensorflow/ops/raw_ops.rb, line 254 def self.atan2(y, x, typeT: nil, name: "Atan2") self.execute("Atan2", [y, x], T: typeT, name: name) end
atanh(x, typeT: nil, name: "Atanh")
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# File lib/tensorflow/ops/raw_ops.rb, line 258 def self.atanh(x, typeT: nil, name: "Atanh") self.execute("Atanh", [x], T: typeT, name: name) end
audio_spectrogram(input, window_size: nil, stride: nil, magnitude_squared: false, name: "AudioSpectrogram")
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# File lib/tensorflow/ops/raw_ops.rb, line 262 def self.audio_spectrogram(input, window_size: nil, stride: nil, magnitude_squared: false, name: "AudioSpectrogram") self.execute("AudioSpectrogram", [input], window_size: window_size, stride: stride, magnitude_squared: magnitude_squared, name: name) end
audio_summary(tag, tensor, sample_rate: nil, max_outputs: 3, name: "AudioSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 266 def self.audio_summary(tag, tensor, sample_rate: nil, max_outputs: 3, name: "AudioSummary") self.execute("AudioSummary", [tag, tensor], sample_rate: sample_rate, max_outputs: max_outputs, name: name) end
audio_summary_v2(tag, tensor, sample_rate, max_outputs: 3, name: "AudioSummaryV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 270 def self.audio_summary_v2(tag, tensor, sample_rate, max_outputs: 3, name: "AudioSummaryV2") self.execute("AudioSummaryV2", [tag, tensor, sample_rate], max_outputs: max_outputs, name: name) end
auto_shard_dataset(input_dataset, num_workers, index, auto_shard_policy: 0, output_types: nil, output_shapes: nil, name: "AutoShardDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 274 def self.auto_shard_dataset(input_dataset, num_workers, index, auto_shard_policy: 0, output_types: nil, output_shapes: nil, name: "AutoShardDataset") self.execute("AutoShardDataset", [input_dataset, num_workers, index], auto_shard_policy: auto_shard_policy, output_types: output_types, output_shapes: output_shapes, name: name) end
avg_pool(value, ksize: nil, strides: nil, padding: nil, data_format: "NHWC", typeT: nil, name: "AvgPool")
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# File lib/tensorflow/ops/raw_ops.rb, line 278 def self.avg_pool(value, ksize: nil, strides: nil, padding: nil, data_format: "NHWC", typeT: nil, name: "AvgPool") self.execute("AvgPool", [value], ksize: ksize, strides: strides, padding: padding, data_format: data_format, T: typeT, name: name) end
avg_pool3_d(input, ksize: nil, strides: nil, padding: nil, data_format: "NDHWC", typeT: nil, name: "AvgPool3D")
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# File lib/tensorflow/ops/raw_ops.rb, line 282 def self.avg_pool3_d(input, ksize: nil, strides: nil, padding: nil, data_format: "NDHWC", typeT: nil, name: "AvgPool3D") self.execute("AvgPool3D", [input], ksize: ksize, strides: strides, padding: padding, data_format: data_format, T: typeT, name: name) end
avg_pool3_d_grad(orig_input_shape, grad, ksize: nil, strides: nil, padding: nil, data_format: "NDHWC", typeT: nil, name: "AvgPool3DGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 286 def self.avg_pool3_d_grad(orig_input_shape, grad, ksize: nil, strides: nil, padding: nil, data_format: "NDHWC", typeT: nil, name: "AvgPool3DGrad") self.execute("AvgPool3DGrad", [orig_input_shape, grad], ksize: ksize, strides: strides, padding: padding, data_format: data_format, T: typeT, name: name) end
avg_pool_grad(orig_input_shape, grad, ksize: nil, strides: nil, padding: nil, data_format: "NHWC", typeT: nil, name: "AvgPoolGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 290 def self.avg_pool_grad(orig_input_shape, grad, ksize: nil, strides: nil, padding: nil, data_format: "NHWC", typeT: nil, name: "AvgPoolGrad") self.execute("AvgPoolGrad", [orig_input_shape, grad], ksize: ksize, strides: strides, padding: padding, data_format: data_format, T: typeT, name: name) end
barrier(component_types: nil, shapes: [], capacity: -1, container: "", shared_name: "", name: "Barrier")
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# File lib/tensorflow/ops/raw_ops.rb, line 294 def self.barrier(component_types: nil, shapes: [], capacity: -1, container: "", shared_name: "", name: "Barrier") self.execute("Barrier", [], component_types: component_types, shapes: shapes, capacity: capacity, container: container, shared_name: shared_name, name: name) end
barrier_close(handle, cancel_pending_enqueues: false, name: "BarrierClose")
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# File lib/tensorflow/ops/raw_ops.rb, line 298 def self.barrier_close(handle, cancel_pending_enqueues: false, name: "BarrierClose") self.execute("BarrierClose", [handle], cancel_pending_enqueues: cancel_pending_enqueues, name: name) end
barrier_incomplete_size(handle, name: "BarrierIncompleteSize")
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# File lib/tensorflow/ops/raw_ops.rb, line 302 def self.barrier_incomplete_size(handle, name: "BarrierIncompleteSize") self.execute("BarrierIncompleteSize", [handle], name: name) end
barrier_insert_many(handle, keys, values, typeT: nil, component_index: nil, name: "BarrierInsertMany")
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# File lib/tensorflow/ops/raw_ops.rb, line 306 def self.barrier_insert_many(handle, keys, values, typeT: nil, component_index: nil, name: "BarrierInsertMany") self.execute("BarrierInsertMany", [handle, keys, values], T: typeT, component_index: component_index, name: name) end
barrier_ready_size(handle, name: "BarrierReadySize")
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# File lib/tensorflow/ops/raw_ops.rb, line 310 def self.barrier_ready_size(handle, name: "BarrierReadySize") self.execute("BarrierReadySize", [handle], name: name) end
barrier_take_many(handle, num_elements, component_types: nil, allow_small_batch: false, wait_for_incomplete: false, timeout_ms: -1, name: "BarrierTakeMany")
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# File lib/tensorflow/ops/raw_ops.rb, line 314 def self.barrier_take_many(handle, num_elements, component_types: nil, allow_small_batch: false, wait_for_incomplete: false, timeout_ms: -1, name: "BarrierTakeMany") self.execute("BarrierTakeMany", [handle, num_elements], component_types: component_types, allow_small_batch: allow_small_batch, wait_for_incomplete: wait_for_incomplete, timeout_ms: timeout_ms, name: name) end
batch(in_tensors, num_batch_threads: nil, max_batch_size: nil, max_enqueued_batches: 10, batch_timeout_micros: nil, allowed_batch_sizes: [], grad_timeout_micros: nil, container: "", shared_name: "", batching_queue: "", typeT: nil, name: "Batch")
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# File lib/tensorflow/ops/raw_ops.rb, line 318 def self.batch(in_tensors, num_batch_threads: nil, max_batch_size: nil, max_enqueued_batches: 10, batch_timeout_micros: nil, allowed_batch_sizes: [], grad_timeout_micros: nil, container: "", shared_name: "", batching_queue: "", typeT: nil, name: "Batch") self.execute("Batch", [in_tensors], num_batch_threads: num_batch_threads, max_batch_size: max_batch_size, max_enqueued_batches: max_enqueued_batches, batch_timeout_micros: batch_timeout_micros, allowed_batch_sizes: allowed_batch_sizes, grad_timeout_micros: grad_timeout_micros, container: container, shared_name: shared_name, batching_queue: batching_queue, T: typeT, name: name) end
batch_cholesky(input, typeT: nil, name: "BatchCholesky")
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# File lib/tensorflow/ops/raw_ops.rb, line 322 def self.batch_cholesky(input, typeT: nil, name: "BatchCholesky") self.execute("BatchCholesky", [input], T: typeT, name: name) end
batch_cholesky_grad(l, grad, typeT: nil, name: "BatchCholeskyGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 326 def self.batch_cholesky_grad(l, grad, typeT: nil, name: "BatchCholeskyGrad") self.execute("BatchCholeskyGrad", [l, grad], T: typeT, name: name) end
batch_dataset(input_dataset, batch_size, output_types: nil, output_shapes: nil, name: "BatchDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 330 def self.batch_dataset(input_dataset, batch_size, output_types: nil, output_shapes: nil, name: "BatchDataset") self.execute("BatchDataset", [input_dataset, batch_size], output_types: output_types, output_shapes: output_shapes, name: name) end
batch_dataset_v2(input_dataset, batch_size, drop_remainder, parallel_copy: false, output_types: nil, output_shapes: nil, name: "BatchDatasetV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 334 def self.batch_dataset_v2(input_dataset, batch_size, drop_remainder, parallel_copy: false, output_types: nil, output_shapes: nil, name: "BatchDatasetV2") self.execute("BatchDatasetV2", [input_dataset, batch_size, drop_remainder], parallel_copy: parallel_copy, output_types: output_types, output_shapes: output_shapes, name: name) end
batch_fft(input, name: "BatchFFT")
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# File lib/tensorflow/ops/raw_ops.rb, line 338 def self.batch_fft(input, name: "BatchFFT") self.execute("BatchFFT", [input], name: name) end
batch_fft2_d(input, name: "BatchFFT2D")
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# File lib/tensorflow/ops/raw_ops.rb, line 342 def self.batch_fft2_d(input, name: "BatchFFT2D") self.execute("BatchFFT2D", [input], name: name) end
batch_fft3_d(input, name: "BatchFFT3D")
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# File lib/tensorflow/ops/raw_ops.rb, line 346 def self.batch_fft3_d(input, name: "BatchFFT3D") self.execute("BatchFFT3D", [input], name: name) end
batch_function(in_tensors, captured_tensors, f: nil, num_batch_threads: nil, max_batch_size: nil, batch_timeout_micros: nil, max_enqueued_batches: 10, allowed_batch_sizes: [], container: "", shared_name: "", batching_queue: "", tin: nil, tcaptured: nil, tout: nil, name: "BatchFunction")
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# File lib/tensorflow/ops/raw_ops.rb, line 350 def self.batch_function(in_tensors, captured_tensors, f: nil, num_batch_threads: nil, max_batch_size: nil, batch_timeout_micros: nil, max_enqueued_batches: 10, allowed_batch_sizes: [], container: "", shared_name: "", batching_queue: "", tin: nil, tcaptured: nil, tout: nil, name: "BatchFunction") self.execute("BatchFunction", [in_tensors, captured_tensors], f: f, num_batch_threads: num_batch_threads, max_batch_size: max_batch_size, batch_timeout_micros: batch_timeout_micros, max_enqueued_batches: max_enqueued_batches, allowed_batch_sizes: allowed_batch_sizes, container: container, shared_name: shared_name, batching_queue: batching_queue, Tin: tin, Tcaptured: tcaptured, Tout: tout, name: name) end
batch_ifft(input, name: "BatchIFFT")
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# File lib/tensorflow/ops/raw_ops.rb, line 354 def self.batch_ifft(input, name: "BatchIFFT") self.execute("BatchIFFT", [input], name: name) end
batch_ifft2_d(input, name: "BatchIFFT2D")
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# File lib/tensorflow/ops/raw_ops.rb, line 358 def self.batch_ifft2_d(input, name: "BatchIFFT2D") self.execute("BatchIFFT2D", [input], name: name) end
batch_ifft3_d(input, name: "BatchIFFT3D")
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# File lib/tensorflow/ops/raw_ops.rb, line 362 def self.batch_ifft3_d(input, name: "BatchIFFT3D") self.execute("BatchIFFT3D", [input], name: name) end
batch_mat_mul(x, y, typeT: nil, adj_x: false, adj_y: false, name: "BatchMatMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 366 def self.batch_mat_mul(x, y, typeT: nil, adj_x: false, adj_y: false, name: "BatchMatMul") self.execute("BatchMatMul", [x, y], T: typeT, adj_x: adj_x, adj_y: adj_y, name: name) end
batch_mat_mul_v2(x, y, typeT: nil, adj_x: false, adj_y: false, name: "BatchMatMulV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 370 def self.batch_mat_mul_v2(x, y, typeT: nil, adj_x: false, adj_y: false, name: "BatchMatMulV2") self.execute("BatchMatMulV2", [x, y], T: typeT, adj_x: adj_x, adj_y: adj_y, name: name) end
batch_matrix_band_part(input, num_lower, num_upper, typeT: nil, name: "BatchMatrixBandPart")
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# File lib/tensorflow/ops/raw_ops.rb, line 374 def self.batch_matrix_band_part(input, num_lower, num_upper, typeT: nil, name: "BatchMatrixBandPart") self.execute("BatchMatrixBandPart", [input, num_lower, num_upper], T: typeT, name: name) end
batch_matrix_determinant(input, typeT: nil, name: "BatchMatrixDeterminant")
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# File lib/tensorflow/ops/raw_ops.rb, line 378 def self.batch_matrix_determinant(input, typeT: nil, name: "BatchMatrixDeterminant") self.execute("BatchMatrixDeterminant", [input], T: typeT, name: name) end
batch_matrix_diag(diagonal, typeT: nil, name: "BatchMatrixDiag")
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# File lib/tensorflow/ops/raw_ops.rb, line 382 def self.batch_matrix_diag(diagonal, typeT: nil, name: "BatchMatrixDiag") self.execute("BatchMatrixDiag", [diagonal], T: typeT, name: name) end
batch_matrix_diag_part(input, typeT: nil, name: "BatchMatrixDiagPart")
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# File lib/tensorflow/ops/raw_ops.rb, line 386 def self.batch_matrix_diag_part(input, typeT: nil, name: "BatchMatrixDiagPart") self.execute("BatchMatrixDiagPart", [input], T: typeT, name: name) end
batch_matrix_inverse(input, adjoint: false, typeT: nil, name: "BatchMatrixInverse")
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# File lib/tensorflow/ops/raw_ops.rb, line 390 def self.batch_matrix_inverse(input, adjoint: false, typeT: nil, name: "BatchMatrixInverse") self.execute("BatchMatrixInverse", [input], adjoint: adjoint, T: typeT, name: name) end
batch_matrix_set_diag(input, diagonal, typeT: nil, name: "BatchMatrixSetDiag")
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# File lib/tensorflow/ops/raw_ops.rb, line 394 def self.batch_matrix_set_diag(input, diagonal, typeT: nil, name: "BatchMatrixSetDiag") self.execute("BatchMatrixSetDiag", [input, diagonal], T: typeT, name: name) end
batch_matrix_solve(matrix, rhs, adjoint: false, typeT: nil, name: "BatchMatrixSolve")
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# File lib/tensorflow/ops/raw_ops.rb, line 398 def self.batch_matrix_solve(matrix, rhs, adjoint: false, typeT: nil, name: "BatchMatrixSolve") self.execute("BatchMatrixSolve", [matrix, rhs], adjoint: adjoint, T: typeT, name: name) end
batch_matrix_solve_ls(matrix, rhs, l2_regularizer, typeT: nil, fast: true, name: "BatchMatrixSolveLs")
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# File lib/tensorflow/ops/raw_ops.rb, line 402 def self.batch_matrix_solve_ls(matrix, rhs, l2_regularizer, typeT: nil, fast: true, name: "BatchMatrixSolveLs") self.execute("BatchMatrixSolveLs", [matrix, rhs, l2_regularizer], T: typeT, fast: fast, name: name) end
batch_matrix_triangular_solve(matrix, rhs, lower: true, adjoint: false, typeT: nil, name: "BatchMatrixTriangularSolve")
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# File lib/tensorflow/ops/raw_ops.rb, line 406 def self.batch_matrix_triangular_solve(matrix, rhs, lower: true, adjoint: false, typeT: nil, name: "BatchMatrixTriangularSolve") self.execute("BatchMatrixTriangularSolve", [matrix, rhs], lower: lower, adjoint: adjoint, T: typeT, name: name) end
batch_norm_with_global_normalization(t, m, v, beta, gamma, typeT: nil, variance_epsilon: nil, scale_after_normalization: nil, name: "BatchNormWithGlobalNormalization")
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# File lib/tensorflow/ops/raw_ops.rb, line 410 def self.batch_norm_with_global_normalization(t, m, v, beta, gamma, typeT: nil, variance_epsilon: nil, scale_after_normalization: nil, name: "BatchNormWithGlobalNormalization") self.execute("BatchNormWithGlobalNormalization", [t, m, v, beta, gamma], T: typeT, variance_epsilon: variance_epsilon, scale_after_normalization: scale_after_normalization, name: name) end
batch_norm_with_global_normalization_grad(t, m, v, gamma, backprop, typeT: nil, variance_epsilon: nil, scale_after_normalization: nil, name: "BatchNormWithGlobalNormalizationGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 414 def self.batch_norm_with_global_normalization_grad(t, m, v, gamma, backprop, typeT: nil, variance_epsilon: nil, scale_after_normalization: nil, name: "BatchNormWithGlobalNormalizationGrad") self.execute("BatchNormWithGlobalNormalizationGrad", [t, m, v, gamma, backprop], T: typeT, variance_epsilon: variance_epsilon, scale_after_normalization: scale_after_normalization, name: name) end
batch_self_adjoint_eig(input, typeT: nil, name: "BatchSelfAdjointEig")
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# File lib/tensorflow/ops/raw_ops.rb, line 418 def self.batch_self_adjoint_eig(input, typeT: nil, name: "BatchSelfAdjointEig") self.execute("BatchSelfAdjointEig", [input], T: typeT, name: name) end
batch_self_adjoint_eig_v2(input, compute_v: true, typeT: nil, name: "BatchSelfAdjointEigV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 422 def self.batch_self_adjoint_eig_v2(input, compute_v: true, typeT: nil, name: "BatchSelfAdjointEigV2") self.execute("BatchSelfAdjointEigV2", [input], compute_v: compute_v, T: typeT, name: name) end
batch_svd(input, compute_uv: true, full_matrices: false, typeT: nil, name: "BatchSvd")
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# File lib/tensorflow/ops/raw_ops.rb, line 426 def self.batch_svd(input, compute_uv: true, full_matrices: false, typeT: nil, name: "BatchSvd") self.execute("BatchSvd", [input], compute_uv: compute_uv, full_matrices: full_matrices, T: typeT, name: name) end
batch_to_space(input, crops, typeT: nil, block_size: nil, tidx: :int32, name: "BatchToSpace")
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# File lib/tensorflow/ops/raw_ops.rb, line 430 def self.batch_to_space(input, crops, typeT: nil, block_size: nil, tidx: :int32, name: "BatchToSpace") self.execute("BatchToSpace", [input, crops], T: typeT, block_size: block_size, Tidx: tidx, name: name) end
batch_to_space_nd(input, block_shape, crops, typeT: nil, tblock_shape: :int32, tcrops: :int32, name: "BatchToSpaceND")
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# File lib/tensorflow/ops/raw_ops.rb, line 434 def self.batch_to_space_nd(input, block_shape, crops, typeT: nil, tblock_shape: :int32, tcrops: :int32, name: "BatchToSpaceND") self.execute("BatchToSpaceND", [input, block_shape, crops], T: typeT, Tblock_shape: tblock_shape, Tcrops: tcrops, name: name) end
bessel_i0e(x, typeT: nil, name: "BesselI0e")
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# File lib/tensorflow/ops/raw_ops.rb, line 438 def self.bessel_i0e(x, typeT: nil, name: "BesselI0e") self.execute("BesselI0e", [x], T: typeT, name: name) end
bessel_i1e(x, typeT: nil, name: "BesselI1e")
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# File lib/tensorflow/ops/raw_ops.rb, line 442 def self.bessel_i1e(x, typeT: nil, name: "BesselI1e") self.execute("BesselI1e", [x], T: typeT, name: name) end
betainc(a, b, x, typeT: nil, name: "Betainc")
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# File lib/tensorflow/ops/raw_ops.rb, line 446 def self.betainc(a, b, x, typeT: nil, name: "Betainc") self.execute("Betainc", [a, b, x], T: typeT, name: name) end
bias_add(value, bias, typeT: nil, data_format: "NHWC", name: "BiasAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 450 def self.bias_add(value, bias, typeT: nil, data_format: "NHWC", name: "BiasAdd") self.execute("BiasAdd", [value, bias], T: typeT, data_format: data_format, name: name) end
bias_add_grad(out_backprop, typeT: nil, data_format: "NHWC", name: "BiasAddGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 454 def self.bias_add_grad(out_backprop, typeT: nil, data_format: "NHWC", name: "BiasAddGrad") self.execute("BiasAddGrad", [out_backprop], T: typeT, data_format: data_format, name: name) end
bias_add_v1(value, bias, typeT: nil, name: "BiasAddV1")
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# File lib/tensorflow/ops/raw_ops.rb, line 458 def self.bias_add_v1(value, bias, typeT: nil, name: "BiasAddV1") self.execute("BiasAddV1", [value, bias], T: typeT, name: name) end
bincount(arr, size, weights, typeT: nil, name: "Bincount")
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# File lib/tensorflow/ops/raw_ops.rb, line 462 def self.bincount(arr, size, weights, typeT: nil, name: "Bincount") self.execute("Bincount", [arr, size, weights], T: typeT, name: name) end
bitcast(input, typeT: nil, type: nil, name: "Bitcast")
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# File lib/tensorflow/ops/raw_ops.rb, line 466 def self.bitcast(input, typeT: nil, type: nil, name: "Bitcast") self.execute("Bitcast", [input], T: typeT, type: type, name: name) end
bitwise_and(x, y, typeT: nil, name: "BitwiseAnd")
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# File lib/tensorflow/ops/raw_ops.rb, line 470 def self.bitwise_and(x, y, typeT: nil, name: "BitwiseAnd") self.execute("BitwiseAnd", [x, y], T: typeT, name: name) end
bitwise_or(x, y, typeT: nil, name: "BitwiseOr")
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# File lib/tensorflow/ops/raw_ops.rb, line 474 def self.bitwise_or(x, y, typeT: nil, name: "BitwiseOr") self.execute("BitwiseOr", [x, y], T: typeT, name: name) end
bitwise_xor(x, y, typeT: nil, name: "BitwiseXor")
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# File lib/tensorflow/ops/raw_ops.rb, line 478 def self.bitwise_xor(x, y, typeT: nil, name: "BitwiseXor") self.execute("BitwiseXor", [x, y], T: typeT, name: name) end
block_lstm(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias: 1.0, cell_clip: 3.0, use_peephole: false, typeT: nil, name: "BlockLSTM")
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# File lib/tensorflow/ops/raw_ops.rb, line 482 def self.block_lstm(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias: 1.0, cell_clip: 3.0, use_peephole: false, typeT: nil, name: "BlockLSTM") self.execute("BlockLSTM", [seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b], forget_bias: forget_bias, cell_clip: cell_clip, use_peephole: use_peephole, T: typeT, name: name) end
block_lstm_grad(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole: nil, typeT: nil, name: "BlockLSTMGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 486 def self.block_lstm_grad(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole: nil, typeT: nil, name: "BlockLSTMGrad") self.execute("BlockLSTMGrad", [seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad], use_peephole: use_peephole, T: typeT, name: name) end
block_lstm_grad_v2(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole: nil, typeT: nil, name: "BlockLSTMGradV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 490 def self.block_lstm_grad_v2(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad, use_peephole: nil, typeT: nil, name: "BlockLSTMGradV2") self.execute("BlockLSTMGradV2", [seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, h, cs_grad, h_grad], use_peephole: use_peephole, T: typeT, name: name) end
block_lstmv2(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, cell_clip: 0.0, use_peephole: false, typeT: nil, name: "BlockLSTMV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 494 def self.block_lstmv2(seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b, cell_clip: 0.0, use_peephole: false, typeT: nil, name: "BlockLSTMV2") self.execute("BlockLSTMV2", [seq_len_max, x, cs_prev, h_prev, w, wci, wcf, wco, b], cell_clip: cell_clip, use_peephole: use_peephole, T: typeT, name: name) end
boosted_trees_aggregate_stats(node_ids, gradients, hessians, feature, max_splits: nil, num_buckets: nil, name: "BoostedTreesAggregateStats")
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# File lib/tensorflow/ops/raw_ops.rb, line 498 def self.boosted_trees_aggregate_stats(node_ids, gradients, hessians, feature, max_splits: nil, num_buckets: nil, name: "BoostedTreesAggregateStats") self.execute("BoostedTreesAggregateStats", [node_ids, gradients, hessians, feature], max_splits: max_splits, num_buckets: num_buckets, name: name) end
boosted_trees_bucketize(float_values, bucket_boundaries, num_features: nil, name: "BoostedTreesBucketize")
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# File lib/tensorflow/ops/raw_ops.rb, line 502 def self.boosted_trees_bucketize(float_values, bucket_boundaries, num_features: nil, name: "BoostedTreesBucketize") self.execute("BoostedTreesBucketize", [float_values, bucket_boundaries], num_features: num_features, name: name) end
boosted_trees_calculate_best_feature_split(node_id_range, stats_summary, l1, l2, tree_complexity, min_node_weight, logits_dimension: nil, split_type: "inequality", name: "BoostedTreesCalculateBestFeatureSplit")
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# File lib/tensorflow/ops/raw_ops.rb, line 506 def self.boosted_trees_calculate_best_feature_split(node_id_range, stats_summary, l1, l2, tree_complexity, min_node_weight, logits_dimension: nil, split_type: "inequality", name: "BoostedTreesCalculateBestFeatureSplit") self.execute("BoostedTreesCalculateBestFeatureSplit", [node_id_range, stats_summary, l1, l2, tree_complexity, min_node_weight], logits_dimension: logits_dimension, split_type: split_type, name: name) end
boosted_trees_calculate_best_gains_per_feature(node_id_range, stats_summary_list, l1, l2, tree_complexity, min_node_weight, max_splits: nil, num_features: nil, name: "BoostedTreesCalculateBestGainsPerFeature")
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# File lib/tensorflow/ops/raw_ops.rb, line 510 def self.boosted_trees_calculate_best_gains_per_feature(node_id_range, stats_summary_list, l1, l2, tree_complexity, min_node_weight, max_splits: nil, num_features: nil, name: "BoostedTreesCalculateBestGainsPerFeature") self.execute("BoostedTreesCalculateBestGainsPerFeature", [node_id_range, stats_summary_list, l1, l2, tree_complexity, min_node_weight], max_splits: max_splits, num_features: num_features, name: name) end
boosted_trees_center_bias(tree_ensemble_handle, mean_gradients, mean_hessians, l1, l2, name: "BoostedTreesCenterBias")
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# File lib/tensorflow/ops/raw_ops.rb, line 514 def self.boosted_trees_center_bias(tree_ensemble_handle, mean_gradients, mean_hessians, l1, l2, name: "BoostedTreesCenterBias") self.execute("BoostedTreesCenterBias", [tree_ensemble_handle, mean_gradients, mean_hessians, l1, l2], name: name) end
boosted_trees_create_ensemble(tree_ensemble_handle, stamp_token, tree_ensemble_serialized, name: "BoostedTreesCreateEnsemble")
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# File lib/tensorflow/ops/raw_ops.rb, line 518 def self.boosted_trees_create_ensemble(tree_ensemble_handle, stamp_token, tree_ensemble_serialized, name: "BoostedTreesCreateEnsemble") self.execute("BoostedTreesCreateEnsemble", [tree_ensemble_handle, stamp_token, tree_ensemble_serialized], name: name) end
boosted_trees_create_quantile_stream_resource(quantile_stream_resource_handle, epsilon, num_streams, max_elements: 1099511627776, name: "BoostedTreesCreateQuantileStreamResource")
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# File lib/tensorflow/ops/raw_ops.rb, line 522 def self.boosted_trees_create_quantile_stream_resource(quantile_stream_resource_handle, epsilon, num_streams, max_elements: 1099511627776, name: "BoostedTreesCreateQuantileStreamResource") self.execute("BoostedTreesCreateQuantileStreamResource", [quantile_stream_resource_handle, epsilon, num_streams], max_elements: max_elements, name: name) end
boosted_trees_deserialize_ensemble(tree_ensemble_handle, stamp_token, tree_ensemble_serialized, name: "BoostedTreesDeserializeEnsemble")
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# File lib/tensorflow/ops/raw_ops.rb, line 526 def self.boosted_trees_deserialize_ensemble(tree_ensemble_handle, stamp_token, tree_ensemble_serialized, name: "BoostedTreesDeserializeEnsemble") self.execute("BoostedTreesDeserializeEnsemble", [tree_ensemble_handle, stamp_token, tree_ensemble_serialized], name: name) end
boosted_trees_ensemble_resource_handle_op(container: "", shared_name: "", name: "BoostedTreesEnsembleResourceHandleOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 530 def self.boosted_trees_ensemble_resource_handle_op(container: "", shared_name: "", name: "BoostedTreesEnsembleResourceHandleOp") self.execute("BoostedTreesEnsembleResourceHandleOp", [], container: container, shared_name: shared_name, name: name) end
boosted_trees_example_debug_outputs(tree_ensemble_handle, bucketized_features, num_bucketized_features: nil, logits_dimension: nil, name: "BoostedTreesExampleDebugOutputs")
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# File lib/tensorflow/ops/raw_ops.rb, line 534 def self.boosted_trees_example_debug_outputs(tree_ensemble_handle, bucketized_features, num_bucketized_features: nil, logits_dimension: nil, name: "BoostedTreesExampleDebugOutputs") self.execute("BoostedTreesExampleDebugOutputs", [tree_ensemble_handle, bucketized_features], num_bucketized_features: num_bucketized_features, logits_dimension: logits_dimension, name: name) end
boosted_trees_flush_quantile_summaries(quantile_stream_resource_handle, num_features: nil, name: "BoostedTreesFlushQuantileSummaries")
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# File lib/tensorflow/ops/raw_ops.rb, line 538 def self.boosted_trees_flush_quantile_summaries(quantile_stream_resource_handle, num_features: nil, name: "BoostedTreesFlushQuantileSummaries") self.execute("BoostedTreesFlushQuantileSummaries", [quantile_stream_resource_handle], num_features: num_features, name: name) end
boosted_trees_get_ensemble_states(tree_ensemble_handle, name: "BoostedTreesGetEnsembleStates")
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# File lib/tensorflow/ops/raw_ops.rb, line 542 def self.boosted_trees_get_ensemble_states(tree_ensemble_handle, name: "BoostedTreesGetEnsembleStates") self.execute("BoostedTreesGetEnsembleStates", [tree_ensemble_handle], name: name) end
boosted_trees_make_quantile_summaries(float_values, example_weights, epsilon, num_features: nil, name: "BoostedTreesMakeQuantileSummaries")
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# File lib/tensorflow/ops/raw_ops.rb, line 546 def self.boosted_trees_make_quantile_summaries(float_values, example_weights, epsilon, num_features: nil, name: "BoostedTreesMakeQuantileSummaries") self.execute("BoostedTreesMakeQuantileSummaries", [float_values, example_weights, epsilon], num_features: num_features, name: name) end
boosted_trees_make_stats_summary(node_ids, gradients, hessians, bucketized_features_list, max_splits: nil, num_buckets: nil, num_features: nil, name: "BoostedTreesMakeStatsSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 550 def self.boosted_trees_make_stats_summary(node_ids, gradients, hessians, bucketized_features_list, max_splits: nil, num_buckets: nil, num_features: nil, name: "BoostedTreesMakeStatsSummary") self.execute("BoostedTreesMakeStatsSummary", [node_ids, gradients, hessians, bucketized_features_list], max_splits: max_splits, num_buckets: num_buckets, num_features: num_features, name: name) end
boosted_trees_predict(tree_ensemble_handle, bucketized_features, num_bucketized_features: nil, logits_dimension: nil, name: "BoostedTreesPredict")
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# File lib/tensorflow/ops/raw_ops.rb, line 554 def self.boosted_trees_predict(tree_ensemble_handle, bucketized_features, num_bucketized_features: nil, logits_dimension: nil, name: "BoostedTreesPredict") self.execute("BoostedTreesPredict", [tree_ensemble_handle, bucketized_features], num_bucketized_features: num_bucketized_features, logits_dimension: logits_dimension, name: name) end
boosted_trees_quantile_stream_resource_add_summaries(quantile_stream_resource_handle, summaries, num_features: nil, name: "BoostedTreesQuantileStreamResourceAddSummaries")
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# File lib/tensorflow/ops/raw_ops.rb, line 558 def self.boosted_trees_quantile_stream_resource_add_summaries(quantile_stream_resource_handle, summaries, num_features: nil, name: "BoostedTreesQuantileStreamResourceAddSummaries") self.execute("BoostedTreesQuantileStreamResourceAddSummaries", [quantile_stream_resource_handle, summaries], num_features: num_features, name: name) end
boosted_trees_quantile_stream_resource_deserialize(quantile_stream_resource_handle, bucket_boundaries, num_streams: nil, name: "BoostedTreesQuantileStreamResourceDeserialize")
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# File lib/tensorflow/ops/raw_ops.rb, line 562 def self.boosted_trees_quantile_stream_resource_deserialize(quantile_stream_resource_handle, bucket_boundaries, num_streams: nil, name: "BoostedTreesQuantileStreamResourceDeserialize") self.execute("BoostedTreesQuantileStreamResourceDeserialize", [quantile_stream_resource_handle, bucket_boundaries], num_streams: num_streams, name: name) end
boosted_trees_quantile_stream_resource_flush(quantile_stream_resource_handle, num_buckets, generate_quantiles: false, name: "BoostedTreesQuantileStreamResourceFlush")
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# File lib/tensorflow/ops/raw_ops.rb, line 566 def self.boosted_trees_quantile_stream_resource_flush(quantile_stream_resource_handle, num_buckets, generate_quantiles: false, name: "BoostedTreesQuantileStreamResourceFlush") self.execute("BoostedTreesQuantileStreamResourceFlush", [quantile_stream_resource_handle, num_buckets], generate_quantiles: generate_quantiles, name: name) end
boosted_trees_quantile_stream_resource_get_bucket_boundaries(quantile_stream_resource_handle, num_features: nil, name: "BoostedTreesQuantileStreamResourceGetBucketBoundaries")
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# File lib/tensorflow/ops/raw_ops.rb, line 570 def self.boosted_trees_quantile_stream_resource_get_bucket_boundaries(quantile_stream_resource_handle, num_features: nil, name: "BoostedTreesQuantileStreamResourceGetBucketBoundaries") self.execute("BoostedTreesQuantileStreamResourceGetBucketBoundaries", [quantile_stream_resource_handle], num_features: num_features, name: name) end
boosted_trees_quantile_stream_resource_handle_op(container: "", shared_name: "", name: "BoostedTreesQuantileStreamResourceHandleOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 574 def self.boosted_trees_quantile_stream_resource_handle_op(container: "", shared_name: "", name: "BoostedTreesQuantileStreamResourceHandleOp") self.execute("BoostedTreesQuantileStreamResourceHandleOp", [], container: container, shared_name: shared_name, name: name) end
boosted_trees_serialize_ensemble(tree_ensemble_handle, name: "BoostedTreesSerializeEnsemble")
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# File lib/tensorflow/ops/raw_ops.rb, line 578 def self.boosted_trees_serialize_ensemble(tree_ensemble_handle, name: "BoostedTreesSerializeEnsemble") self.execute("BoostedTreesSerializeEnsemble", [tree_ensemble_handle], name: name) end
boosted_trees_sparse_aggregate_stats(node_ids, gradients, hessians, feature_indices, feature_values, feature_shape, max_splits: nil, num_buckets: nil, name: "BoostedTreesSparseAggregateStats")
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# File lib/tensorflow/ops/raw_ops.rb, line 582 def self.boosted_trees_sparse_aggregate_stats(node_ids, gradients, hessians, feature_indices, feature_values, feature_shape, max_splits: nil, num_buckets: nil, name: "BoostedTreesSparseAggregateStats") self.execute("BoostedTreesSparseAggregateStats", [node_ids, gradients, hessians, feature_indices, feature_values, feature_shape], max_splits: max_splits, num_buckets: num_buckets, name: name) end
boosted_trees_sparse_calculate_best_feature_split(node_id_range, stats_summary_indices, stats_summary_values, stats_summary_shape, l1, l2, tree_complexity, min_node_weight, logits_dimension: nil, split_type: "inequality", name: "BoostedTreesSparseCalculateBestFeatureSplit")
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# File lib/tensorflow/ops/raw_ops.rb, line 586 def self.boosted_trees_sparse_calculate_best_feature_split(node_id_range, stats_summary_indices, stats_summary_values, stats_summary_shape, l1, l2, tree_complexity, min_node_weight, logits_dimension: nil, split_type: "inequality", name: "BoostedTreesSparseCalculateBestFeatureSplit") self.execute("BoostedTreesSparseCalculateBestFeatureSplit", [node_id_range, stats_summary_indices, stats_summary_values, stats_summary_shape, l1, l2, tree_complexity, min_node_weight], logits_dimension: logits_dimension, split_type: split_type, name: name) end
boosted_trees_training_predict(tree_ensemble_handle, cached_tree_ids, cached_node_ids, bucketized_features, num_bucketized_features: nil, logits_dimension: nil, name: "BoostedTreesTrainingPredict")
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# File lib/tensorflow/ops/raw_ops.rb, line 590 def self.boosted_trees_training_predict(tree_ensemble_handle, cached_tree_ids, cached_node_ids, bucketized_features, num_bucketized_features: nil, logits_dimension: nil, name: "BoostedTreesTrainingPredict") self.execute("BoostedTreesTrainingPredict", [tree_ensemble_handle, cached_tree_ids, cached_node_ids, bucketized_features], num_bucketized_features: num_bucketized_features, logits_dimension: logits_dimension, name: name) end
boosted_trees_update_ensemble(tree_ensemble_handle, feature_ids, node_ids, gains, thresholds, left_node_contribs, right_node_contribs, max_depth, learning_rate, pruning_mode: nil, num_features: nil, name: "BoostedTreesUpdateEnsemble")
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# File lib/tensorflow/ops/raw_ops.rb, line 594 def self.boosted_trees_update_ensemble(tree_ensemble_handle, feature_ids, node_ids, gains, thresholds, left_node_contribs, right_node_contribs, max_depth, learning_rate, pruning_mode: nil, num_features: nil, name: "BoostedTreesUpdateEnsemble") self.execute("BoostedTreesUpdateEnsemble", [tree_ensemble_handle, feature_ids, node_ids, gains, thresholds, left_node_contribs, right_node_contribs, max_depth, learning_rate], pruning_mode: pruning_mode, num_features: num_features, name: name) end
boosted_trees_update_ensemble_v2(tree_ensemble_handle, feature_ids, dimension_ids, node_ids, gains, thresholds, left_node_contribs, right_node_contribs, split_types, max_depth, learning_rate, pruning_mode, num_features: nil, logits_dimension: 1, name: "BoostedTreesUpdateEnsembleV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 598 def self.boosted_trees_update_ensemble_v2(tree_ensemble_handle, feature_ids, dimension_ids, node_ids, gains, thresholds, left_node_contribs, right_node_contribs, split_types, max_depth, learning_rate, pruning_mode, num_features: nil, logits_dimension: 1, name: "BoostedTreesUpdateEnsembleV2") self.execute("BoostedTreesUpdateEnsembleV2", [tree_ensemble_handle, feature_ids, dimension_ids, node_ids, gains, thresholds, left_node_contribs, right_node_contribs, split_types, max_depth, learning_rate, pruning_mode], num_features: num_features, logits_dimension: logits_dimension, name: name) end
broadcast_args(s0, s1, typeT: :int32, name: "BroadcastArgs")
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# File lib/tensorflow/ops/raw_ops.rb, line 602 def self.broadcast_args(s0, s1, typeT: :int32, name: "BroadcastArgs") self.execute("BroadcastArgs", [s0, s1], T: typeT, name: name) end
broadcast_gradient_args(s0, s1, typeT: :int32, name: "BroadcastGradientArgs")
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# File lib/tensorflow/ops/raw_ops.rb, line 606 def self.broadcast_gradient_args(s0, s1, typeT: :int32, name: "BroadcastGradientArgs") self.execute("BroadcastGradientArgs", [s0, s1], T: typeT, name: name) end
broadcast_to(input, shape, typeT: nil, tidx: :int32, name: "BroadcastTo")
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# File lib/tensorflow/ops/raw_ops.rb, line 610 def self.broadcast_to(input, shape, typeT: nil, tidx: :int32, name: "BroadcastTo") self.execute("BroadcastTo", [input, shape], T: typeT, Tidx: tidx, name: name) end
bucketize(input, typeT: nil, boundaries: nil, name: "Bucketize")
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# File lib/tensorflow/ops/raw_ops.rb, line 614 def self.bucketize(input, typeT: nil, boundaries: nil, name: "Bucketize") self.execute("Bucketize", [input], T: typeT, boundaries: boundaries, name: name) end
bytes_produced_stats_dataset(input_dataset, tag, output_types: nil, output_shapes: nil, name: "BytesProducedStatsDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 618 def self.bytes_produced_stats_dataset(input_dataset, tag, output_types: nil, output_shapes: nil, name: "BytesProducedStatsDataset") self.execute("BytesProducedStatsDataset", [input_dataset, tag], output_types: output_types, output_shapes: output_shapes, name: name) end
cache_dataset(input_dataset, filename, output_types: nil, output_shapes: nil, name: "CacheDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 650 def self.cache_dataset(input_dataset, filename, output_types: nil, output_shapes: nil, name: "CacheDataset") self.execute("CacheDataset", [input_dataset, filename], output_types: output_types, output_shapes: output_shapes, name: name) end
cache_dataset_v2(input_dataset, filename, cache, output_types: nil, output_shapes: nil, name: "CacheDatasetV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 654 def self.cache_dataset_v2(input_dataset, filename, cache, output_types: nil, output_shapes: nil, name: "CacheDatasetV2") self.execute("CacheDatasetV2", [input_dataset, filename, cache], output_types: output_types, output_shapes: output_shapes, name: name) end
case(branch_index, input, tin: nil, tout: nil, branches: nil, output_shapes: [], name: "Case")
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# File lib/tensorflow/ops/raw_ops.rb, line 658 def self.case(branch_index, input, tin: nil, tout: nil, branches: nil, output_shapes: [], name: "Case") self.execute("Case", [branch_index, input], Tin: tin, Tout: tout, branches: branches, output_shapes: output_shapes, name: name) end
cast(x, srct: nil, dstt: nil, truncate: false, name: "Cast")
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# File lib/tensorflow/ops/raw_ops.rb, line 662 def self.cast(x, srct: nil, dstt: nil, truncate: false, name: "Cast") self.execute("Cast", [x], SrcT: srct, DstT: dstt, Truncate: truncate, name: name) end
ceil(x, typeT: nil, name: "Ceil")
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# File lib/tensorflow/ops/raw_ops.rb, line 666 def self.ceil(x, typeT: nil, name: "Ceil") self.execute("Ceil", [x], T: typeT, name: name) end
check_numerics(tensor, typeT: nil, message: "", name: "CheckNumerics")
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# File lib/tensorflow/ops/raw_ops.rb, line 670 def self.check_numerics(tensor, typeT: nil, message: "", name: "CheckNumerics") self.execute("CheckNumerics", [tensor], T: typeT, message: message, name: name) end
cholesky(input, typeT: nil, name: "Cholesky")
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# File lib/tensorflow/ops/raw_ops.rb, line 674 def self.cholesky(input, typeT: nil, name: "Cholesky") self.execute("Cholesky", [input], T: typeT, name: name) end
cholesky_grad(l, grad, typeT: nil, name: "CholeskyGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 678 def self.cholesky_grad(l, grad, typeT: nil, name: "CholeskyGrad") self.execute("CholeskyGrad", [l, grad], T: typeT, name: name) end
choose_fastest_branch_dataset(input_dataset, ratio_numerator, ratio_denominator, other_arguments, targuments: nil, num_elements_per_branch: nil, branches: nil, other_arguments_lengths: nil, output_types: nil, output_shapes: nil, name: "ChooseFastestBranchDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 682 def self.choose_fastest_branch_dataset(input_dataset, ratio_numerator, ratio_denominator, other_arguments, targuments: nil, num_elements_per_branch: nil, branches: nil, other_arguments_lengths: nil, output_types: nil, output_shapes: nil, name: "ChooseFastestBranchDataset") self.execute("ChooseFastestBranchDataset", [input_dataset, ratio_numerator, ratio_denominator, other_arguments], Targuments: targuments, num_elements_per_branch: num_elements_per_branch, branches: branches, other_arguments_lengths: other_arguments_lengths, output_types: output_types, output_shapes: output_shapes, name: name) end
choose_fastest_dataset(input_datasets, n: nil, num_experiments: nil, output_types: nil, output_shapes: nil, name: "ChooseFastestDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 686 def self.choose_fastest_dataset(input_datasets, n: nil, num_experiments: nil, output_types: nil, output_shapes: nil, name: "ChooseFastestDataset") self.execute("ChooseFastestDataset", [input_datasets], N: n, num_experiments: num_experiments, output_types: output_types, output_shapes: output_shapes, name: name) end
clip_by_value(t, clip_value_min, clip_value_max, typeT: nil, name: "ClipByValue")
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# File lib/tensorflow/ops/raw_ops.rb, line 690 def self.clip_by_value(t, clip_value_min, clip_value_max, typeT: nil, name: "ClipByValue") self.execute("ClipByValue", [t, clip_value_min, clip_value_max], T: typeT, name: name) end
close_summary_writer(writer, name: "CloseSummaryWriter")
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# File lib/tensorflow/ops/raw_ops.rb, line 694 def self.close_summary_writer(writer, name: "CloseSummaryWriter") self.execute("CloseSummaryWriter", [writer], name: name) end
collective_bcast_recv(typeT: nil, group_size: nil, group_key: nil, instance_key: nil, shape: nil, communication_hint: "auto", name: "CollectiveBcastRecv")
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# File lib/tensorflow/ops/raw_ops.rb, line 698 def self.collective_bcast_recv(typeT: nil, group_size: nil, group_key: nil, instance_key: nil, shape: nil, communication_hint: "auto", name: "CollectiveBcastRecv") self.execute("CollectiveBcastRecv", [], T: typeT, group_size: group_size, group_key: group_key, instance_key: instance_key, shape: shape, communication_hint: communication_hint, name: name) end
collective_bcast_send(input, typeT: nil, group_size: nil, group_key: nil, instance_key: nil, shape: nil, communication_hint: "auto", name: "CollectiveBcastSend")
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# File lib/tensorflow/ops/raw_ops.rb, line 702 def self.collective_bcast_send(input, typeT: nil, group_size: nil, group_key: nil, instance_key: nil, shape: nil, communication_hint: "auto", name: "CollectiveBcastSend") self.execute("CollectiveBcastSend", [input], T: typeT, group_size: group_size, group_key: group_key, instance_key: instance_key, shape: shape, communication_hint: communication_hint, name: name) end
collective_gather(input, typeT: nil, group_size: nil, group_key: nil, instance_key: nil, shape: nil, communication_hint: "auto", name: "CollectiveGather")
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# File lib/tensorflow/ops/raw_ops.rb, line 706 def self.collective_gather(input, typeT: nil, group_size: nil, group_key: nil, instance_key: nil, shape: nil, communication_hint: "auto", name: "CollectiveGather") self.execute("CollectiveGather", [input], T: typeT, group_size: group_size, group_key: group_key, instance_key: instance_key, shape: shape, communication_hint: communication_hint, name: name) end
collective_permute(input, source_target_pairs, typeT: nil, name: "CollectivePermute")
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# File lib/tensorflow/ops/raw_ops.rb, line 710 def self.collective_permute(input, source_target_pairs, typeT: nil, name: "CollectivePermute") self.execute("CollectivePermute", [input, source_target_pairs], T: typeT, name: name) end
collective_reduce(input, typeT: nil, group_size: nil, group_key: nil, instance_key: nil, merge_op: nil, final_op: nil, subdiv_offsets: nil, wait_for: [], communication_hint: "auto", name: "CollectiveReduce")
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# File lib/tensorflow/ops/raw_ops.rb, line 714 def self.collective_reduce(input, typeT: nil, group_size: nil, group_key: nil, instance_key: nil, merge_op: nil, final_op: nil, subdiv_offsets: nil, wait_for: [], communication_hint: "auto", name: "CollectiveReduce") self.execute("CollectiveReduce", [input], T: typeT, group_size: group_size, group_key: group_key, instance_key: instance_key, merge_op: merge_op, final_op: final_op, subdiv_offsets: subdiv_offsets, wait_for: wait_for, communication_hint: communication_hint, name: name) end
combined_non_max_suppression(boxes, scores, max_output_size_per_class, max_total_size, iou_threshold, score_threshold, pad_per_class: false, clip_boxes: true, name: "CombinedNonMaxSuppression")
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# File lib/tensorflow/ops/raw_ops.rb, line 718 def self.combined_non_max_suppression(boxes, scores, max_output_size_per_class, max_total_size, iou_threshold, score_threshold, pad_per_class: false, clip_boxes: true, name: "CombinedNonMaxSuppression") self.execute("CombinedNonMaxSuppression", [boxes, scores, max_output_size_per_class, max_total_size, iou_threshold, score_threshold], pad_per_class: pad_per_class, clip_boxes: clip_boxes, name: name) end
compare_and_bitpack(input, threshold, typeT: nil, name: "CompareAndBitpack")
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# File lib/tensorflow/ops/raw_ops.rb, line 722 def self.compare_and_bitpack(input, threshold, typeT: nil, name: "CompareAndBitpack") self.execute("CompareAndBitpack", [input, threshold], T: typeT, name: name) end
complex(real, imag, typeT: :float, tout: :complex64, name: "Complex")
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# File lib/tensorflow/ops/raw_ops.rb, line 726 def self.complex(real, imag, typeT: :float, tout: :complex64, name: "Complex") self.execute("Complex", [real, imag], T: typeT, Tout: tout, name: name) end
complex_abs(x, typeT: :complex64, tout: :float, name: "ComplexAbs")
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# File lib/tensorflow/ops/raw_ops.rb, line 730 def self.complex_abs(x, typeT: :complex64, tout: :float, name: "ComplexAbs") self.execute("ComplexAbs", [x], T: typeT, Tout: tout, name: name) end
compute_accidental_hits(true_classes, sampled_candidates, num_true: nil, seed: 0, seed2: 0, name: "ComputeAccidentalHits")
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# File lib/tensorflow/ops/raw_ops.rb, line 734 def self.compute_accidental_hits(true_classes, sampled_candidates, num_true: nil, seed: 0, seed2: 0, name: "ComputeAccidentalHits") self.execute("ComputeAccidentalHits", [true_classes, sampled_candidates], num_true: num_true, seed: seed, seed2: seed2, name: name) end
concat(concat_dim, values, n: nil, typeT: nil, name: "Concat")
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# File lib/tensorflow/ops/raw_ops.rb, line 738 def self.concat(concat_dim, values, n: nil, typeT: nil, name: "Concat") self.execute("Concat", [concat_dim, values], N: n, T: typeT, name: name) end
concat_offset(concat_dim, shape, n: nil, name: "ConcatOffset")
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# File lib/tensorflow/ops/raw_ops.rb, line 742 def self.concat_offset(concat_dim, shape, n: nil, name: "ConcatOffset") self.execute("ConcatOffset", [concat_dim, shape], N: n, name: name) end
concat_v2(values, axis, n: nil, typeT: nil, tidx: :int32, name: "ConcatV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 746 def self.concat_v2(values, axis, n: nil, typeT: nil, tidx: :int32, name: "ConcatV2") self.execute("ConcatV2", [values, axis], N: n, T: typeT, Tidx: tidx, name: name) end
concatenate_dataset(input_dataset, another_dataset, output_types: nil, output_shapes: nil, name: "ConcatenateDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 750 def self.concatenate_dataset(input_dataset, another_dataset, output_types: nil, output_shapes: nil, name: "ConcatenateDataset") self.execute("ConcatenateDataset", [input_dataset, another_dataset], output_types: output_types, output_shapes: output_shapes, name: name) end
conditional_accumulator(dtype: nil, shape: nil, container: "", shared_name: "", reduction_type: "MEAN", name: "ConditionalAccumulator")
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# File lib/tensorflow/ops/raw_ops.rb, line 754 def self.conditional_accumulator(dtype: nil, shape: nil, container: "", shared_name: "", reduction_type: "MEAN", name: "ConditionalAccumulator") self.execute("ConditionalAccumulator", [], dtype: dtype, shape: shape, container: container, shared_name: shared_name, reduction_type: reduction_type, name: name) end
configure_distributed_tpu(embedding_config: "", tpu_embedding_config: "", is_global_init: false, enable_whole_mesh_compilations: false, name: "ConfigureDistributedTPU")
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# File lib/tensorflow/ops/raw_ops.rb, line 758 def self.configure_distributed_tpu(embedding_config: "", tpu_embedding_config: "", is_global_init: false, enable_whole_mesh_compilations: false, name: "ConfigureDistributedTPU") self.execute("ConfigureDistributedTPU", [], embedding_config: embedding_config, tpu_embedding_config: tpu_embedding_config, is_global_init: is_global_init, enable_whole_mesh_compilations: enable_whole_mesh_compilations, name: name) end
configure_tpu_embedding(config: "", name: "ConfigureTPUEmbedding")
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# File lib/tensorflow/ops/raw_ops.rb, line 762 def self.configure_tpu_embedding(config: "", name: "ConfigureTPUEmbedding") self.execute("ConfigureTPUEmbedding", [], config: config, name: name) end
conj(input, typeT: :complex64, name: "Conj")
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# File lib/tensorflow/ops/raw_ops.rb, line 766 def self.conj(input, typeT: :complex64, name: "Conj") self.execute("Conj", [input], T: typeT, name: name) end
conjugate_transpose(x, perm, typeT: nil, tperm: :int32, name: "ConjugateTranspose")
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# File lib/tensorflow/ops/raw_ops.rb, line 770 def self.conjugate_transpose(x, perm, typeT: nil, tperm: :int32, name: "ConjugateTranspose") self.execute("ConjugateTranspose", [x, perm], T: typeT, Tperm: tperm, name: name) end
const(value: nil, dtype: nil, name: "Const")
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# File lib/tensorflow/ops/raw_ops.rb, line 774 def self.const(value: nil, dtype: nil, name: "Const") self.execute("Const", [], value: value, dtype: dtype, name: name) end
consume_mutex_lock(mutex_lock, name: "ConsumeMutexLock")
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# File lib/tensorflow/ops/raw_ops.rb, line 778 def self.consume_mutex_lock(mutex_lock, name: "ConsumeMutexLock") self.execute("ConsumeMutexLock", [mutex_lock], name: name) end
control_trigger(name: "ControlTrigger")
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# File lib/tensorflow/ops/raw_ops.rb, line 782 def self.control_trigger(name: "ControlTrigger") self.execute("ControlTrigger", [], name: name) end
conv2_d(input, filter, typeT: nil, strides: nil, use_cudnn_on_gpu: true, padding: nil, explicit_paddings: [], data_format: "NHWC", dilations: [], name: "Conv2D")
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# File lib/tensorflow/ops/raw_ops.rb, line 786 def self.conv2_d(input, filter, typeT: nil, strides: nil, use_cudnn_on_gpu: true, padding: nil, explicit_paddings: [], data_format: "NHWC", dilations: [], name: "Conv2D") self.execute("Conv2D", [input, filter], T: typeT, strides: strides, use_cudnn_on_gpu: use_cudnn_on_gpu, padding: padding, explicit_paddings: explicit_paddings, data_format: data_format, dilations: dilations, name: name) end
conv2_d_backprop_filter(input, filter_sizes, out_backprop, typeT: nil, strides: nil, use_cudnn_on_gpu: true, padding: nil, explicit_paddings: [], data_format: "NHWC", dilations: [], name: "Conv2DBackpropFilter")
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# File lib/tensorflow/ops/raw_ops.rb, line 790 def self.conv2_d_backprop_filter(input, filter_sizes, out_backprop, typeT: nil, strides: nil, use_cudnn_on_gpu: true, padding: nil, explicit_paddings: [], data_format: "NHWC", dilations: [], name: "Conv2DBackpropFilter") self.execute("Conv2DBackpropFilter", [input, filter_sizes, out_backprop], T: typeT, strides: strides, use_cudnn_on_gpu: use_cudnn_on_gpu, padding: padding, explicit_paddings: explicit_paddings, data_format: data_format, dilations: dilations, name: name) end
conv2_d_backprop_input(input_sizes, filter, out_backprop, typeT: nil, strides: nil, use_cudnn_on_gpu: true, padding: nil, explicit_paddings: [], data_format: "NHWC", dilations: [], name: "Conv2DBackpropInput")
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# File lib/tensorflow/ops/raw_ops.rb, line 794 def self.conv2_d_backprop_input(input_sizes, filter, out_backprop, typeT: nil, strides: nil, use_cudnn_on_gpu: true, padding: nil, explicit_paddings: [], data_format: "NHWC", dilations: [], name: "Conv2DBackpropInput") self.execute("Conv2DBackpropInput", [input_sizes, filter, out_backprop], T: typeT, strides: strides, use_cudnn_on_gpu: use_cudnn_on_gpu, padding: padding, explicit_paddings: explicit_paddings, data_format: data_format, dilations: dilations, name: name) end
conv3_d(input, filter, typeT: nil, strides: nil, padding: nil, data_format: "NDHWC", dilations: [], name: "Conv3D")
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# File lib/tensorflow/ops/raw_ops.rb, line 798 def self.conv3_d(input, filter, typeT: nil, strides: nil, padding: nil, data_format: "NDHWC", dilations: [], name: "Conv3D") self.execute("Conv3D", [input, filter], T: typeT, strides: strides, padding: padding, data_format: data_format, dilations: dilations, name: name) end
conv3_d_backprop_filter(input, filter, out_backprop, typeT: nil, strides: nil, padding: nil, dilations: [], name: "Conv3DBackpropFilter")
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# File lib/tensorflow/ops/raw_ops.rb, line 802 def self.conv3_d_backprop_filter(input, filter, out_backprop, typeT: nil, strides: nil, padding: nil, dilations: [], name: "Conv3DBackpropFilter") self.execute("Conv3DBackpropFilter", [input, filter, out_backprop], T: typeT, strides: strides, padding: padding, dilations: dilations, name: name) end
conv3_d_backprop_filter_v2(input, filter_sizes, out_backprop, typeT: nil, strides: nil, padding: nil, data_format: "NDHWC", dilations: [], name: "Conv3DBackpropFilterV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 806 def self.conv3_d_backprop_filter_v2(input, filter_sizes, out_backprop, typeT: nil, strides: nil, padding: nil, data_format: "NDHWC", dilations: [], name: "Conv3DBackpropFilterV2") self.execute("Conv3DBackpropFilterV2", [input, filter_sizes, out_backprop], T: typeT, strides: strides, padding: padding, data_format: data_format, dilations: dilations, name: name) end
conv3_d_backprop_input(input, filter, out_backprop, typeT: nil, strides: nil, padding: nil, dilations: [], name: "Conv3DBackpropInput")
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# File lib/tensorflow/ops/raw_ops.rb, line 810 def self.conv3_d_backprop_input(input, filter, out_backprop, typeT: nil, strides: nil, padding: nil, dilations: [], name: "Conv3DBackpropInput") self.execute("Conv3DBackpropInput", [input, filter, out_backprop], T: typeT, strides: strides, padding: padding, dilations: dilations, name: name) end
conv3_d_backprop_input_v2(input_sizes, filter, out_backprop, typeT: nil, strides: nil, padding: nil, data_format: "NDHWC", dilations: [], tshape: :int32, name: "Conv3DBackpropInputV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 814 def self.conv3_d_backprop_input_v2(input_sizes, filter, out_backprop, typeT: nil, strides: nil, padding: nil, data_format: "NDHWC", dilations: [], tshape: :int32, name: "Conv3DBackpropInputV2") self.execute("Conv3DBackpropInputV2", [input_sizes, filter, out_backprop], T: typeT, strides: strides, padding: padding, data_format: data_format, dilations: dilations, Tshape: tshape, name: name) end
copy(input, typeT: nil, tensor_name: "", debug_ops_spec: [], name: "Copy")
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# File lib/tensorflow/ops/raw_ops.rb, line 818 def self.copy(input, typeT: nil, tensor_name: "", debug_ops_spec: [], name: "Copy") self.execute("Copy", [input], T: typeT, tensor_name: tensor_name, debug_ops_spec: debug_ops_spec, name: name) end
copy_host(input, typeT: nil, tensor_name: "", debug_ops_spec: [], name: "CopyHost")
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# File lib/tensorflow/ops/raw_ops.rb, line 822 def self.copy_host(input, typeT: nil, tensor_name: "", debug_ops_spec: [], name: "CopyHost") self.execute("CopyHost", [input], T: typeT, tensor_name: tensor_name, debug_ops_spec: debug_ops_spec, name: name) end
cos(x, typeT: nil, name: "Cos")
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# File lib/tensorflow/ops/raw_ops.rb, line 826 def self.cos(x, typeT: nil, name: "Cos") self.execute("Cos", [x], T: typeT, name: name) end
cosh(x, typeT: nil, name: "Cosh")
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# File lib/tensorflow/ops/raw_ops.rb, line 830 def self.cosh(x, typeT: nil, name: "Cosh") self.execute("Cosh", [x], T: typeT, name: name) end
count_up_to(ref, limit: nil, typeT: nil, name: "CountUpTo")
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# File lib/tensorflow/ops/raw_ops.rb, line 834 def self.count_up_to(ref, limit: nil, typeT: nil, name: "CountUpTo") self.execute("CountUpTo", [ref], limit: limit, T: typeT, name: name) end
create_summary_db_writer(writer, db_uri, experiment_name, run_name, user_name, name: "CreateSummaryDbWriter")
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# File lib/tensorflow/ops/raw_ops.rb, line 838 def self.create_summary_db_writer(writer, db_uri, experiment_name, run_name, user_name, name: "CreateSummaryDbWriter") self.execute("CreateSummaryDbWriter", [writer, db_uri, experiment_name, run_name, user_name], name: name) end
create_summary_file_writer(writer, logdir, max_queue, flush_millis, filename_suffix, name: "CreateSummaryFileWriter")
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# File lib/tensorflow/ops/raw_ops.rb, line 842 def self.create_summary_file_writer(writer, logdir, max_queue, flush_millis, filename_suffix, name: "CreateSummaryFileWriter") self.execute("CreateSummaryFileWriter", [writer, logdir, max_queue, flush_millis, filename_suffix], name: name) end
crop_and_resize(image, boxes, box_ind, crop_size, typeT: nil, method: "bilinear", extrapolation_value: 0.0, name: "CropAndResize")
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# File lib/tensorflow/ops/raw_ops.rb, line 846 def self.crop_and_resize(image, boxes, box_ind, crop_size, typeT: nil, method: "bilinear", extrapolation_value: 0.0, name: "CropAndResize") self.execute("CropAndResize", [image, boxes, box_ind, crop_size], T: typeT, method: method, extrapolation_value: extrapolation_value, name: name) end
crop_and_resize_grad_boxes(grads, image, boxes, box_ind, typeT: nil, method: "bilinear", name: "CropAndResizeGradBoxes")
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# File lib/tensorflow/ops/raw_ops.rb, line 850 def self.crop_and_resize_grad_boxes(grads, image, boxes, box_ind, typeT: nil, method: "bilinear", name: "CropAndResizeGradBoxes") self.execute("CropAndResizeGradBoxes", [grads, image, boxes, box_ind], T: typeT, method: method, name: name) end
crop_and_resize_grad_image(grads, boxes, box_ind, image_size, typeT: nil, method: "bilinear", name: "CropAndResizeGradImage")
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# File lib/tensorflow/ops/raw_ops.rb, line 854 def self.crop_and_resize_grad_image(grads, boxes, box_ind, image_size, typeT: nil, method: "bilinear", name: "CropAndResizeGradImage") self.execute("CropAndResizeGradImage", [grads, boxes, box_ind, image_size], T: typeT, method: method, name: name) end
cross(a, b, typeT: nil, name: "Cross")
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# File lib/tensorflow/ops/raw_ops.rb, line 858 def self.cross(a, b, typeT: nil, name: "Cross") self.execute("Cross", [a, b], T: typeT, name: name) end
cross_replica_sum(input, group_assignment, typeT: nil, name: "CrossReplicaSum")
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# File lib/tensorflow/ops/raw_ops.rb, line 862 def self.cross_replica_sum(input, group_assignment, typeT: nil, name: "CrossReplicaSum") self.execute("CrossReplicaSum", [input, group_assignment], T: typeT, name: name) end
csr_sparse_matrix_components(csr_sparse_matrix, index, type: nil, name: "CSRSparseMatrixComponents")
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# File lib/tensorflow/ops/raw_ops.rb, line 622 def self.csr_sparse_matrix_components(csr_sparse_matrix, index, type: nil, name: "CSRSparseMatrixComponents") self.execute("CSRSparseMatrixComponents", [csr_sparse_matrix, index], type: type, name: name) end
csr_sparse_matrix_to_dense(sparse_input, type: nil, name: "CSRSparseMatrixToDense")
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# File lib/tensorflow/ops/raw_ops.rb, line 626 def self.csr_sparse_matrix_to_dense(sparse_input, type: nil, name: "CSRSparseMatrixToDense") self.execute("CSRSparseMatrixToDense", [sparse_input], type: type, name: name) end
csr_sparse_matrix_to_sparse_tensor(sparse_matrix, type: nil, name: "CSRSparseMatrixToSparseTensor")
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# File lib/tensorflow/ops/raw_ops.rb, line 630 def self.csr_sparse_matrix_to_sparse_tensor(sparse_matrix, type: nil, name: "CSRSparseMatrixToSparseTensor") self.execute("CSRSparseMatrixToSparseTensor", [sparse_matrix], type: type, name: name) end
csv_dataset(filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols, record_defaults, output_types: nil, output_shapes: nil, name: "CSVDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 634 def self.csv_dataset(filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols, record_defaults, output_types: nil, output_shapes: nil, name: "CSVDataset") self.execute("CSVDataset", [filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols, record_defaults], output_types: output_types, output_shapes: output_shapes, name: name) end
ctc_beam_search_decoder(inputs, sequence_length, beam_width: nil, top_paths: nil, merge_repeated: true, typeT: :float, name: "CTCBeamSearchDecoder")
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# File lib/tensorflow/ops/raw_ops.rb, line 638 def self.ctc_beam_search_decoder(inputs, sequence_length, beam_width: nil, top_paths: nil, merge_repeated: true, typeT: :float, name: "CTCBeamSearchDecoder") self.execute("CTCBeamSearchDecoder", [inputs, sequence_length], beam_width: beam_width, top_paths: top_paths, merge_repeated: merge_repeated, T: typeT, name: name) end
ctc_greedy_decoder(inputs, sequence_length, merge_repeated: false, typeT: :float, name: "CTCGreedyDecoder")
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# File lib/tensorflow/ops/raw_ops.rb, line 642 def self.ctc_greedy_decoder(inputs, sequence_length, merge_repeated: false, typeT: :float, name: "CTCGreedyDecoder") self.execute("CTCGreedyDecoder", [inputs, sequence_length], merge_repeated: merge_repeated, T: typeT, name: name) end
ctc_loss(inputs, labels_indices, labels_values, sequence_length, preprocess_collapse_repeated: false, ctc_merge_repeated: true, ignore_longer_outputs_than_inputs: false, typeT: :float, name: "CTCLoss")
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# File lib/tensorflow/ops/raw_ops.rb, line 646 def self.ctc_loss(inputs, labels_indices, labels_values, sequence_length, preprocess_collapse_repeated: false, ctc_merge_repeated: true, ignore_longer_outputs_than_inputs: false, typeT: :float, name: "CTCLoss") self.execute("CTCLoss", [inputs, labels_indices, labels_values, sequence_length], preprocess_collapse_repeated: preprocess_collapse_repeated, ctc_merge_repeated: ctc_merge_repeated, ignore_longer_outputs_than_inputs: ignore_longer_outputs_than_inputs, T: typeT, name: name) end
cudnn_rnn(input, input_h, input_c, params, typeT: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, is_training: true, name: "CudnnRNN")
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# File lib/tensorflow/ops/raw_ops.rb, line 866 def self.cudnn_rnn(input, input_h, input_c, params, typeT: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, is_training: true, name: "CudnnRNN") self.execute("CudnnRNN", [input, input_h, input_c, params], T: typeT, rnn_mode: rnn_mode, input_mode: input_mode, direction: direction, dropout: dropout, seed: seed, seed2: seed2, is_training: is_training, name: name) end
cudnn_rnn_backprop(input, input_h, input_c, params, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space, typeT: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, name: "CudnnRNNBackprop")
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# File lib/tensorflow/ops/raw_ops.rb, line 870 def self.cudnn_rnn_backprop(input, input_h, input_c, params, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space, typeT: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, name: "CudnnRNNBackprop") self.execute("CudnnRNNBackprop", [input, input_h, input_c, params, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space], T: typeT, rnn_mode: rnn_mode, input_mode: input_mode, direction: direction, dropout: dropout, seed: seed, seed2: seed2, name: name) end
cudnn_rnn_backprop_v2(input, input_h, input_c, params, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space, host_reserved, typeT: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, name: "CudnnRNNBackpropV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 874 def self.cudnn_rnn_backprop_v2(input, input_h, input_c, params, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space, host_reserved, typeT: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, name: "CudnnRNNBackpropV2") self.execute("CudnnRNNBackpropV2", [input, input_h, input_c, params, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space, host_reserved], T: typeT, rnn_mode: rnn_mode, input_mode: input_mode, direction: direction, dropout: dropout, seed: seed, seed2: seed2, name: name) end
cudnn_rnn_backprop_v3(input, input_h, input_c, params, sequence_lengths, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space, host_reserved, typeT: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, num_proj: 0, time_major: true, name: "CudnnRNNBackpropV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 878 def self.cudnn_rnn_backprop_v3(input, input_h, input_c, params, sequence_lengths, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space, host_reserved, typeT: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, num_proj: 0, time_major: true, name: "CudnnRNNBackpropV3") self.execute("CudnnRNNBackpropV3", [input, input_h, input_c, params, sequence_lengths, output, output_h, output_c, output_backprop, output_h_backprop, output_c_backprop, reserve_space, host_reserved], T: typeT, rnn_mode: rnn_mode, input_mode: input_mode, direction: direction, dropout: dropout, seed: seed, seed2: seed2, num_proj: num_proj, time_major: time_major, name: name) end
cudnn_rnn_canonical_to_params(num_layers, num_units, input_size, weights, biases, typeT: nil, num_params: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, name: "CudnnRNNCanonicalToParams")
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# File lib/tensorflow/ops/raw_ops.rb, line 882 def self.cudnn_rnn_canonical_to_params(num_layers, num_units, input_size, weights, biases, typeT: nil, num_params: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, name: "CudnnRNNCanonicalToParams") self.execute("CudnnRNNCanonicalToParams", [num_layers, num_units, input_size, weights, biases], T: typeT, num_params: num_params, rnn_mode: rnn_mode, input_mode: input_mode, direction: direction, dropout: dropout, seed: seed, seed2: seed2, name: name) end
cudnn_rnn_canonical_to_params_v2(num_layers, num_units, input_size, weights, biases, typeT: nil, num_params_weights: nil, num_params_biases: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, num_proj: 0, name: "CudnnRNNCanonicalToParamsV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 886 def self.cudnn_rnn_canonical_to_params_v2(num_layers, num_units, input_size, weights, biases, typeT: nil, num_params_weights: nil, num_params_biases: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, num_proj: 0, name: "CudnnRNNCanonicalToParamsV2") self.execute("CudnnRNNCanonicalToParamsV2", [num_layers, num_units, input_size, weights, biases], T: typeT, num_params_weights: num_params_weights, num_params_biases: num_params_biases, rnn_mode: rnn_mode, input_mode: input_mode, direction: direction, dropout: dropout, seed: seed, seed2: seed2, num_proj: num_proj, name: name) end
cudnn_rnn_params_size(num_layers, num_units, input_size, typeT: nil, s: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, num_proj: 0, name: "CudnnRNNParamsSize")
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# File lib/tensorflow/ops/raw_ops.rb, line 890 def self.cudnn_rnn_params_size(num_layers, num_units, input_size, typeT: nil, s: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, num_proj: 0, name: "CudnnRNNParamsSize") self.execute("CudnnRNNParamsSize", [num_layers, num_units, input_size], T: typeT, S: s, rnn_mode: rnn_mode, input_mode: input_mode, direction: direction, dropout: dropout, seed: seed, seed2: seed2, num_proj: num_proj, name: name) end
cudnn_rnn_params_to_canonical(num_layers, num_units, input_size, params, typeT: nil, num_params: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, name: "CudnnRNNParamsToCanonical")
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# File lib/tensorflow/ops/raw_ops.rb, line 894 def self.cudnn_rnn_params_to_canonical(num_layers, num_units, input_size, params, typeT: nil, num_params: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, name: "CudnnRNNParamsToCanonical") self.execute("CudnnRNNParamsToCanonical", [num_layers, num_units, input_size, params], T: typeT, num_params: num_params, rnn_mode: rnn_mode, input_mode: input_mode, direction: direction, dropout: dropout, seed: seed, seed2: seed2, name: name) end
cudnn_rnn_params_to_canonical_v2(num_layers, num_units, input_size, params, typeT: nil, num_params_weights: nil, num_params_biases: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, num_proj: 0, name: "CudnnRNNParamsToCanonicalV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 898 def self.cudnn_rnn_params_to_canonical_v2(num_layers, num_units, input_size, params, typeT: nil, num_params_weights: nil, num_params_biases: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, num_proj: 0, name: "CudnnRNNParamsToCanonicalV2") self.execute("CudnnRNNParamsToCanonicalV2", [num_layers, num_units, input_size, params], T: typeT, num_params_weights: num_params_weights, num_params_biases: num_params_biases, rnn_mode: rnn_mode, input_mode: input_mode, direction: direction, dropout: dropout, seed: seed, seed2: seed2, num_proj: num_proj, name: name) end
cudnn_rnnv2(input, input_h, input_c, params, typeT: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, is_training: true, name: "CudnnRNNV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 902 def self.cudnn_rnnv2(input, input_h, input_c, params, typeT: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, is_training: true, name: "CudnnRNNV2") self.execute("CudnnRNNV2", [input, input_h, input_c, params], T: typeT, rnn_mode: rnn_mode, input_mode: input_mode, direction: direction, dropout: dropout, seed: seed, seed2: seed2, is_training: is_training, name: name) end
cudnn_rnnv3(input, input_h, input_c, params, sequence_lengths, typeT: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, num_proj: 0, is_training: true, time_major: true, name: "CudnnRNNV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 906 def self.cudnn_rnnv3(input, input_h, input_c, params, sequence_lengths, typeT: nil, rnn_mode: "lstm", input_mode: "linear_input", direction: "unidirectional", dropout: 0.0, seed: 0, seed2: 0, num_proj: 0, is_training: true, time_major: true, name: "CudnnRNNV3") self.execute("CudnnRNNV3", [input, input_h, input_c, params, sequence_lengths], T: typeT, rnn_mode: rnn_mode, input_mode: input_mode, direction: direction, dropout: dropout, seed: seed, seed2: seed2, num_proj: num_proj, is_training: is_training, time_major: time_major, name: name) end
cumprod(x, axis, exclusive: false, reverse: false, typeT: nil, tidx: :int32, name: "Cumprod")
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# File lib/tensorflow/ops/raw_ops.rb, line 910 def self.cumprod(x, axis, exclusive: false, reverse: false, typeT: nil, tidx: :int32, name: "Cumprod") self.execute("Cumprod", [x, axis], exclusive: exclusive, reverse: reverse, T: typeT, Tidx: tidx, name: name) end
cumsum(x, axis, exclusive: false, reverse: false, typeT: nil, tidx: :int32, name: "Cumsum")
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# File lib/tensorflow/ops/raw_ops.rb, line 914 def self.cumsum(x, axis, exclusive: false, reverse: false, typeT: nil, tidx: :int32, name: "Cumsum") self.execute("Cumsum", [x, axis], exclusive: exclusive, reverse: reverse, T: typeT, Tidx: tidx, name: name) end
cumulative_logsumexp(x, axis, exclusive: false, reverse: false, typeT: nil, tidx: :int32, name: "CumulativeLogsumexp")
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# File lib/tensorflow/ops/raw_ops.rb, line 918 def self.cumulative_logsumexp(x, axis, exclusive: false, reverse: false, typeT: nil, tidx: :int32, name: "CumulativeLogsumexp") self.execute("CumulativeLogsumexp", [x, axis], exclusive: exclusive, reverse: reverse, T: typeT, Tidx: tidx, name: name) end
data_format_dim_map(x, typeT: :int32, src_format: "NHWC", dst_format: "NCHW", name: "DataFormatDimMap")
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# File lib/tensorflow/ops/raw_ops.rb, line 922 def self.data_format_dim_map(x, typeT: :int32, src_format: "NHWC", dst_format: "NCHW", name: "DataFormatDimMap") self.execute("DataFormatDimMap", [x], T: typeT, src_format: src_format, dst_format: dst_format, name: name) end
data_format_vec_permute(x, typeT: :int32, src_format: "NHWC", dst_format: "NCHW", name: "DataFormatVecPermute")
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# File lib/tensorflow/ops/raw_ops.rb, line 926 def self.data_format_vec_permute(x, typeT: :int32, src_format: "NHWC", dst_format: "NCHW", name: "DataFormatVecPermute") self.execute("DataFormatVecPermute", [x], T: typeT, src_format: src_format, dst_format: dst_format, name: name) end
dataset_cardinality(input_dataset, name: "DatasetCardinality")
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# File lib/tensorflow/ops/raw_ops.rb, line 930 def self.dataset_cardinality(input_dataset, name: "DatasetCardinality") self.execute("DatasetCardinality", [input_dataset], name: name) end
dataset_from_graph(graph_def, name: "DatasetFromGraph")
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# File lib/tensorflow/ops/raw_ops.rb, line 934 def self.dataset_from_graph(graph_def, name: "DatasetFromGraph") self.execute("DatasetFromGraph", [graph_def], name: name) end
dataset_to_graph(input_dataset, stateful_whitelist: [], allow_stateful: false, strip_device_assignment: false, name: "DatasetToGraph")
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# File lib/tensorflow/ops/raw_ops.rb, line 938 def self.dataset_to_graph(input_dataset, stateful_whitelist: [], allow_stateful: false, strip_device_assignment: false, name: "DatasetToGraph") self.execute("DatasetToGraph", [input_dataset], stateful_whitelist: stateful_whitelist, allow_stateful: allow_stateful, strip_device_assignment: strip_device_assignment, name: name) end
dataset_to_graph_v2(input_dataset, external_state_policy: 0, strip_device_assignment: false, name: "DatasetToGraphV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 942 def self.dataset_to_graph_v2(input_dataset, external_state_policy: 0, strip_device_assignment: false, name: "DatasetToGraphV2") self.execute("DatasetToGraphV2", [input_dataset], external_state_policy: external_state_policy, strip_device_assignment: strip_device_assignment, name: name) end
dataset_to_single_element(dataset, output_types: nil, output_shapes: nil, name: "DatasetToSingleElement")
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# File lib/tensorflow/ops/raw_ops.rb, line 946 def self.dataset_to_single_element(dataset, output_types: nil, output_shapes: nil, name: "DatasetToSingleElement") self.execute("DatasetToSingleElement", [dataset], output_types: output_types, output_shapes: output_shapes, name: name) end
dataset_to_tf_record(input_dataset, filename, compression_type, name: "DatasetToTFRecord")
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# File lib/tensorflow/ops/raw_ops.rb, line 950 def self.dataset_to_tf_record(input_dataset, filename, compression_type, name: "DatasetToTFRecord") self.execute("DatasetToTFRecord", [input_dataset, filename, compression_type], name: name) end
debug_gradient_identity(input, typeT: nil, name: "DebugGradientIdentity")
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# File lib/tensorflow/ops/raw_ops.rb, line 954 def self.debug_gradient_identity(input, typeT: nil, name: "DebugGradientIdentity") self.execute("DebugGradientIdentity", [input], T: typeT, name: name) end
debug_gradient_ref_identity(input, typeT: nil, name: "DebugGradientRefIdentity")
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# File lib/tensorflow/ops/raw_ops.rb, line 958 def self.debug_gradient_ref_identity(input, typeT: nil, name: "DebugGradientRefIdentity") self.execute("DebugGradientRefIdentity", [input], T: typeT, name: name) end
debug_identity(input, typeT: nil, device_name: "", tensor_name: "", debug_urls: [], gated_grpc: false, name: "DebugIdentity")
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# File lib/tensorflow/ops/raw_ops.rb, line 962 def self.debug_identity(input, typeT: nil, device_name: "", tensor_name: "", debug_urls: [], gated_grpc: false, name: "DebugIdentity") self.execute("DebugIdentity", [input], T: typeT, device_name: device_name, tensor_name: tensor_name, debug_urls: debug_urls, gated_grpc: gated_grpc, name: name) end
debug_identity_v2(input, typeT: nil, tfdbg_context_id: "", op_name: "", output_slot: -1, tensor_debug_mode: -1, debug_urls: [], name: "DebugIdentityV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 966 def self.debug_identity_v2(input, typeT: nil, tfdbg_context_id: "", op_name: "", output_slot: -1, tensor_debug_mode: -1, debug_urls: [], name: "DebugIdentityV2") self.execute("DebugIdentityV2", [input], T: typeT, tfdbg_context_id: tfdbg_context_id, op_name: op_name, output_slot: output_slot, tensor_debug_mode: tensor_debug_mode, debug_urls: debug_urls, name: name) end
debug_nan_count(input, typeT: nil, device_name: "", tensor_name: "", debug_urls: [], gated_grpc: false, name: "DebugNanCount")
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# File lib/tensorflow/ops/raw_ops.rb, line 970 def self.debug_nan_count(input, typeT: nil, device_name: "", tensor_name: "", debug_urls: [], gated_grpc: false, name: "DebugNanCount") self.execute("DebugNanCount", [input], T: typeT, device_name: device_name, tensor_name: tensor_name, debug_urls: debug_urls, gated_grpc: gated_grpc, name: name) end
debug_numeric_summary(input, typeT: nil, device_name: "", tensor_name: "", debug_urls: [], lower_bound: -Infinity, upper_bound: Infinity, mute_if_healthy: false, gated_grpc: false, name: "DebugNumericSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 974 def self.debug_numeric_summary(input, typeT: nil, device_name: "", tensor_name: "", debug_urls: [], lower_bound: -Infinity, upper_bound: Infinity, mute_if_healthy: false, gated_grpc: false, name: "DebugNumericSummary") self.execute("DebugNumericSummary", [input], T: typeT, device_name: device_name, tensor_name: tensor_name, debug_urls: debug_urls, lower_bound: lower_bound, upper_bound: upper_bound, mute_if_healthy: mute_if_healthy, gated_grpc: gated_grpc, name: name) end
decode_and_crop_jpeg(contents, crop_window, channels: 0, ratio: 1, fancy_upscaling: true, try_recover_truncated: false, acceptable_fraction: 1.0, dct_method: "", name: "DecodeAndCropJpeg")
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# File lib/tensorflow/ops/raw_ops.rb, line 978 def self.decode_and_crop_jpeg(contents, crop_window, channels: 0, ratio: 1, fancy_upscaling: true, try_recover_truncated: false, acceptable_fraction: 1.0, dct_method: "", name: "DecodeAndCropJpeg") self.execute("DecodeAndCropJpeg", [contents, crop_window], channels: channels, ratio: ratio, fancy_upscaling: fancy_upscaling, try_recover_truncated: try_recover_truncated, acceptable_fraction: acceptable_fraction, dct_method: dct_method, name: name) end
decode_base64(input, name: "DecodeBase64")
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# File lib/tensorflow/ops/raw_ops.rb, line 982 def self.decode_base64(input, name: "DecodeBase64") self.execute("DecodeBase64", [input], name: name) end
decode_bmp(contents, channels: 0, name: "DecodeBmp")
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# File lib/tensorflow/ops/raw_ops.rb, line 986 def self.decode_bmp(contents, channels: 0, name: "DecodeBmp") self.execute("DecodeBmp", [contents], channels: channels, name: name) end
decode_compressed(bytes, compression_type: "", name: "DecodeCompressed")
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# File lib/tensorflow/ops/raw_ops.rb, line 994 def self.decode_compressed(bytes, compression_type: "", name: "DecodeCompressed") self.execute("DecodeCompressed", [bytes], compression_type: compression_type, name: name) end
decode_csv(records, record_defaults, out_type: nil, field_delim: ",", use_quote_delim: true, na_value: "", select_cols: [], name: "DecodeCSV")
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# File lib/tensorflow/ops/raw_ops.rb, line 990 def self.decode_csv(records, record_defaults, out_type: nil, field_delim: ",", use_quote_delim: true, na_value: "", select_cols: [], name: "DecodeCSV") self.execute("DecodeCSV", [records, record_defaults], OUT_TYPE: out_type, field_delim: field_delim, use_quote_delim: use_quote_delim, na_value: na_value, select_cols: select_cols, name: name) end
decode_gif(contents, name: "DecodeGif")
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# File lib/tensorflow/ops/raw_ops.rb, line 998 def self.decode_gif(contents, name: "DecodeGif") self.execute("DecodeGif", [contents], name: name) end
decode_jpeg(contents, channels: 0, ratio: 1, fancy_upscaling: true, try_recover_truncated: false, acceptable_fraction: 1.0, dct_method: "", name: "DecodeJpeg")
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# File lib/tensorflow/ops/raw_ops.rb, line 1006 def self.decode_jpeg(contents, channels: 0, ratio: 1, fancy_upscaling: true, try_recover_truncated: false, acceptable_fraction: 1.0, dct_method: "", name: "DecodeJpeg") self.execute("DecodeJpeg", [contents], channels: channels, ratio: ratio, fancy_upscaling: fancy_upscaling, try_recover_truncated: try_recover_truncated, acceptable_fraction: acceptable_fraction, dct_method: dct_method, name: name) end
decode_json_example(json_examples, name: "DecodeJSONExample")
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# File lib/tensorflow/ops/raw_ops.rb, line 1002 def self.decode_json_example(json_examples, name: "DecodeJSONExample") self.execute("DecodeJSONExample", [json_examples], name: name) end
decode_padded_raw(input_bytes, fixed_length, out_type: nil, little_endian: true, name: "DecodePaddedRaw")
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# File lib/tensorflow/ops/raw_ops.rb, line 1010 def self.decode_padded_raw(input_bytes, fixed_length, out_type: nil, little_endian: true, name: "DecodePaddedRaw") self.execute("DecodePaddedRaw", [input_bytes, fixed_length], out_type: out_type, little_endian: little_endian, name: name) end
decode_png(contents, channels: 0, dtype: :uint8, name: "DecodePng")
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# File lib/tensorflow/ops/raw_ops.rb, line 1014 def self.decode_png(contents, channels: 0, dtype: :uint8, name: "DecodePng") self.execute("DecodePng", [contents], channels: channels, dtype: dtype, name: name) end
decode_proto_v2(bytes, message_type: "", field_names: nil, output_types: nil, descriptor_source: "local://", message_format: "binary", sanitize: false, name: "DecodeProtoV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1018 def self.decode_proto_v2(bytes, message_type: "", field_names: nil, output_types: nil, descriptor_source: "local://", message_format: "binary", sanitize: false, name: "DecodeProtoV2") self.execute("DecodeProtoV2", [bytes], message_type: message_type, field_names: field_names, output_types: output_types, descriptor_source: descriptor_source, message_format: message_format, sanitize: sanitize, name: name) end
decode_raw(bytes, out_type: nil, little_endian: true, name: "DecodeRaw")
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# File lib/tensorflow/ops/raw_ops.rb, line 1022 def self.decode_raw(bytes, out_type: nil, little_endian: true, name: "DecodeRaw") self.execute("DecodeRaw", [bytes], out_type: out_type, little_endian: little_endian, name: name) end
decode_wav(contents, desired_channels: -1, desired_samples: -1, name: "DecodeWav")
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# File lib/tensorflow/ops/raw_ops.rb, line 1026 def self.decode_wav(contents, desired_channels: -1, desired_samples: -1, name: "DecodeWav") self.execute("DecodeWav", [contents], desired_channels: desired_channels, desired_samples: desired_samples, name: name) end
deep_copy(x, typeT: nil, name: "DeepCopy")
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# File lib/tensorflow/ops/raw_ops.rb, line 1030 def self.deep_copy(x, typeT: nil, name: "DeepCopy") self.execute("DeepCopy", [x], T: typeT, name: name) end
delete_iterator(handle, deleter, name: "DeleteIterator")
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# File lib/tensorflow/ops/raw_ops.rb, line 1034 def self.delete_iterator(handle, deleter, name: "DeleteIterator") self.execute("DeleteIterator", [handle, deleter], name: name) end
delete_memory_cache(handle, deleter, name: "DeleteMemoryCache")
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# File lib/tensorflow/ops/raw_ops.rb, line 1038 def self.delete_memory_cache(handle, deleter, name: "DeleteMemoryCache") self.execute("DeleteMemoryCache", [handle, deleter], name: name) end
delete_multi_device_iterator(multi_device_iterator, iterators, deleter, n: nil, name: "DeleteMultiDeviceIterator")
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# File lib/tensorflow/ops/raw_ops.rb, line 1042 def self.delete_multi_device_iterator(multi_device_iterator, iterators, deleter, n: nil, name: "DeleteMultiDeviceIterator") self.execute("DeleteMultiDeviceIterator", [multi_device_iterator, iterators, deleter], N: n, name: name) end
delete_random_seed_generator(handle, deleter, name: "DeleteRandomSeedGenerator")
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# File lib/tensorflow/ops/raw_ops.rb, line 1046 def self.delete_random_seed_generator(handle, deleter, name: "DeleteRandomSeedGenerator") self.execute("DeleteRandomSeedGenerator", [handle, deleter], name: name) end
delete_session_tensor(handle, name: "DeleteSessionTensor")
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# File lib/tensorflow/ops/raw_ops.rb, line 1050 def self.delete_session_tensor(handle, name: "DeleteSessionTensor") self.execute("DeleteSessionTensor", [handle], name: name) end
dense_to_csr_sparse_matrix(dense_input, indices, typeT: nil, name: "DenseToCSRSparseMatrix")
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# File lib/tensorflow/ops/raw_ops.rb, line 1054 def self.dense_to_csr_sparse_matrix(dense_input, indices, typeT: nil, name: "DenseToCSRSparseMatrix") self.execute("DenseToCSRSparseMatrix", [dense_input, indices], T: typeT, name: name) end
dense_to_dense_set_operation(set1, set2, set_operation: "", validate_indices: true, typeT: nil, name: "DenseToDenseSetOperation")
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# File lib/tensorflow/ops/raw_ops.rb, line 1058 def self.dense_to_dense_set_operation(set1, set2, set_operation: "", validate_indices: true, typeT: nil, name: "DenseToDenseSetOperation") self.execute("DenseToDenseSetOperation", [set1, set2], set_operation: set_operation, validate_indices: validate_indices, T: typeT, name: name) end
dense_to_sparse_batch_dataset(input_dataset, batch_size, row_shape, output_types: nil, output_shapes: nil, name: "DenseToSparseBatchDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1062 def self.dense_to_sparse_batch_dataset(input_dataset, batch_size, row_shape, output_types: nil, output_shapes: nil, name: "DenseToSparseBatchDataset") self.execute("DenseToSparseBatchDataset", [input_dataset, batch_size, row_shape], output_types: output_types, output_shapes: output_shapes, name: name) end
dense_to_sparse_set_operation(set1, set2_indices, set2_values, set2_shape, set_operation: "", validate_indices: true, typeT: nil, name: "DenseToSparseSetOperation")
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# File lib/tensorflow/ops/raw_ops.rb, line 1066 def self.dense_to_sparse_set_operation(set1, set2_indices, set2_values, set2_shape, set_operation: "", validate_indices: true, typeT: nil, name: "DenseToSparseSetOperation") self.execute("DenseToSparseSetOperation", [set1, set2_indices, set2_values, set2_shape], set_operation: set_operation, validate_indices: validate_indices, T: typeT, name: name) end
depth_to_space(input, typeT: nil, block_size: nil, data_format: "NHWC", name: "DepthToSpace")
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# File lib/tensorflow/ops/raw_ops.rb, line 1070 def self.depth_to_space(input, typeT: nil, block_size: nil, data_format: "NHWC", name: "DepthToSpace") self.execute("DepthToSpace", [input], T: typeT, block_size: block_size, data_format: data_format, name: name) end
depthwise_conv2d_native(input, filter, typeT: nil, strides: nil, padding: nil, data_format: "NHWC", dilations: [], name: "DepthwiseConv2dNative")
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# File lib/tensorflow/ops/raw_ops.rb, line 1074 def self.depthwise_conv2d_native(input, filter, typeT: nil, strides: nil, padding: nil, data_format: "NHWC", dilations: [], name: "DepthwiseConv2dNative") self.execute("DepthwiseConv2dNative", [input, filter], T: typeT, strides: strides, padding: padding, data_format: data_format, dilations: dilations, name: name) end
depthwise_conv2d_native_backprop_filter(input, filter_sizes, out_backprop, typeT: nil, strides: nil, padding: nil, data_format: "NHWC", dilations: [], name: "DepthwiseConv2dNativeBackpropFilter")
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# File lib/tensorflow/ops/raw_ops.rb, line 1078 def self.depthwise_conv2d_native_backprop_filter(input, filter_sizes, out_backprop, typeT: nil, strides: nil, padding: nil, data_format: "NHWC", dilations: [], name: "DepthwiseConv2dNativeBackpropFilter") self.execute("DepthwiseConv2dNativeBackpropFilter", [input, filter_sizes, out_backprop], T: typeT, strides: strides, padding: padding, data_format: data_format, dilations: dilations, name: name) end
depthwise_conv2d_native_backprop_input(input_sizes, filter, out_backprop, typeT: nil, strides: nil, padding: nil, data_format: "NHWC", dilations: [], name: "DepthwiseConv2dNativeBackpropInput")
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# File lib/tensorflow/ops/raw_ops.rb, line 1082 def self.depthwise_conv2d_native_backprop_input(input_sizes, filter, out_backprop, typeT: nil, strides: nil, padding: nil, data_format: "NHWC", dilations: [], name: "DepthwiseConv2dNativeBackpropInput") self.execute("DepthwiseConv2dNativeBackpropInput", [input_sizes, filter, out_backprop], T: typeT, strides: strides, padding: padding, data_format: data_format, dilations: dilations, name: name) end
dequantize(input, min_range, max_range, typeT: nil, mode: "MIN_COMBINED", narrow_range: false, axis: -1, name: "Dequantize")
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# File lib/tensorflow/ops/raw_ops.rb, line 1086 def self.dequantize(input, min_range, max_range, typeT: nil, mode: "MIN_COMBINED", narrow_range: false, axis: -1, name: "Dequantize") self.execute("Dequantize", [input, min_range, max_range], T: typeT, mode: mode, narrow_range: narrow_range, axis: axis, name: name) end
deserialize_iterator(resource_handle, serialized, name: "DeserializeIterator")
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# File lib/tensorflow/ops/raw_ops.rb, line 1090 def self.deserialize_iterator(resource_handle, serialized, name: "DeserializeIterator") self.execute("DeserializeIterator", [resource_handle, serialized], name: name) end
deserialize_many_sparse(serialized_sparse, dtype: nil, name: "DeserializeManySparse")
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# File lib/tensorflow/ops/raw_ops.rb, line 1094 def self.deserialize_many_sparse(serialized_sparse, dtype: nil, name: "DeserializeManySparse") self.execute("DeserializeManySparse", [serialized_sparse], dtype: dtype, name: name) end
deserialize_sparse(serialized_sparse, dtype: nil, tserialized: :string, name: "DeserializeSparse")
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# File lib/tensorflow/ops/raw_ops.rb, line 1098 def self.deserialize_sparse(serialized_sparse, dtype: nil, tserialized: :string, name: "DeserializeSparse") self.execute("DeserializeSparse", [serialized_sparse], dtype: dtype, Tserialized: tserialized, name: name) end
destroy_resource_op(resource, ignore_lookup_error: true, name: "DestroyResourceOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 1102 def self.destroy_resource_op(resource, ignore_lookup_error: true, name: "DestroyResourceOp") self.execute("DestroyResourceOp", [resource], ignore_lookup_error: ignore_lookup_error, name: name) end
destroy_temporary_variable(ref, typeT: nil, var_name: "", name: "DestroyTemporaryVariable")
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# File lib/tensorflow/ops/raw_ops.rb, line 1106 def self.destroy_temporary_variable(ref, typeT: nil, var_name: "", name: "DestroyTemporaryVariable") self.execute("DestroyTemporaryVariable", [ref], T: typeT, var_name: var_name, name: name) end
diag(diagonal, typeT: nil, name: "Diag")
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# File lib/tensorflow/ops/raw_ops.rb, line 1110 def self.diag(diagonal, typeT: nil, name: "Diag") self.execute("Diag", [diagonal], T: typeT, name: name) end
diag_part(input, typeT: nil, name: "DiagPart")
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# File lib/tensorflow/ops/raw_ops.rb, line 1114 def self.diag_part(input, typeT: nil, name: "DiagPart") self.execute("DiagPart", [input], T: typeT, name: name) end
digamma(x, typeT: nil, name: "Digamma")
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# File lib/tensorflow/ops/raw_ops.rb, line 1118 def self.digamma(x, typeT: nil, name: "Digamma") self.execute("Digamma", [x], T: typeT, name: name) end
dilation2_d(input, filter, typeT: nil, strides: nil, rates: nil, padding: nil, name: "Dilation2D")
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# File lib/tensorflow/ops/raw_ops.rb, line 1122 def self.dilation2_d(input, filter, typeT: nil, strides: nil, rates: nil, padding: nil, name: "Dilation2D") self.execute("Dilation2D", [input, filter], T: typeT, strides: strides, rates: rates, padding: padding, name: name) end
dilation2_d_backprop_filter(input, filter, out_backprop, typeT: nil, strides: nil, rates: nil, padding: nil, name: "Dilation2DBackpropFilter")
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# File lib/tensorflow/ops/raw_ops.rb, line 1126 def self.dilation2_d_backprop_filter(input, filter, out_backprop, typeT: nil, strides: nil, rates: nil, padding: nil, name: "Dilation2DBackpropFilter") self.execute("Dilation2DBackpropFilter", [input, filter, out_backprop], T: typeT, strides: strides, rates: rates, padding: padding, name: name) end
dilation2_d_backprop_input(input, filter, out_backprop, typeT: nil, strides: nil, rates: nil, padding: nil, name: "Dilation2DBackpropInput")
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# File lib/tensorflow/ops/raw_ops.rb, line 1130 def self.dilation2_d_backprop_input(input, filter, out_backprop, typeT: nil, strides: nil, rates: nil, padding: nil, name: "Dilation2DBackpropInput") self.execute("Dilation2DBackpropInput", [input, filter, out_backprop], T: typeT, strides: strides, rates: rates, padding: padding, name: name) end
directed_interleave_dataset(selector_input_dataset, data_input_datasets, output_types: nil, output_shapes: nil, n: nil, name: "DirectedInterleaveDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1134 def self.directed_interleave_dataset(selector_input_dataset, data_input_datasets, output_types: nil, output_shapes: nil, n: nil, name: "DirectedInterleaveDataset") self.execute("DirectedInterleaveDataset", [selector_input_dataset, data_input_datasets], output_types: output_types, output_shapes: output_shapes, N: n, name: name) end
div(x, y, typeT: nil, name: "Div")
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# File lib/tensorflow/ops/raw_ops.rb, line 1138 def self.div(x, y, typeT: nil, name: "Div") self.execute("Div", [x, y], T: typeT, name: name) end
div_no_nan(x, y, typeT: nil, name: "DivNoNan")
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# File lib/tensorflow/ops/raw_ops.rb, line 1142 def self.div_no_nan(x, y, typeT: nil, name: "DivNoNan") self.execute("DivNoNan", [x, y], T: typeT, name: name) end
draw_bounding_boxes(images, boxes, typeT: :float, name: "DrawBoundingBoxes")
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# File lib/tensorflow/ops/raw_ops.rb, line 1146 def self.draw_bounding_boxes(images, boxes, typeT: :float, name: "DrawBoundingBoxes") self.execute("DrawBoundingBoxes", [images, boxes], T: typeT, name: name) end
draw_bounding_boxes_v2(images, boxes, colors, typeT: :float, name: "DrawBoundingBoxesV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1150 def self.draw_bounding_boxes_v2(images, boxes, colors, typeT: :float, name: "DrawBoundingBoxesV2") self.execute("DrawBoundingBoxesV2", [images, boxes, colors], T: typeT, name: name) end
dynamic_partition(data, partitions, num_partitions: nil, typeT: nil, name: "DynamicPartition")
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# File lib/tensorflow/ops/raw_ops.rb, line 1154 def self.dynamic_partition(data, partitions, num_partitions: nil, typeT: nil, name: "DynamicPartition") self.execute("DynamicPartition", [data, partitions], num_partitions: num_partitions, T: typeT, name: name) end
dynamic_stitch(indices, data, n: nil, typeT: nil, name: "DynamicStitch")
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# File lib/tensorflow/ops/raw_ops.rb, line 1158 def self.dynamic_stitch(indices, data, n: nil, typeT: nil, name: "DynamicStitch") self.execute("DynamicStitch", [indices, data], N: n, T: typeT, name: name) end
eager_py_func(input, token: "", is_async: false, tin: nil, tout: nil, name: "EagerPyFunc")
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# File lib/tensorflow/ops/raw_ops.rb, line 1162 def self.eager_py_func(input, token: "", is_async: false, tin: nil, tout: nil, name: "EagerPyFunc") self.execute("EagerPyFunc", [input], token: token, is_async: is_async, Tin: tin, Tout: tout, name: name) end
edit_distance(hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices, truth_values, truth_shape, normalize: true, typeT: nil, name: "EditDistance")
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# File lib/tensorflow/ops/raw_ops.rb, line 1166 def self.edit_distance(hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices, truth_values, truth_shape, normalize: true, typeT: nil, name: "EditDistance") self.execute("EditDistance", [hypothesis_indices, hypothesis_values, hypothesis_shape, truth_indices, truth_values, truth_shape], normalize: normalize, T: typeT, name: name) end
eig(input, compute_v: true, typeT: nil, tout: nil, name: "Eig")
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# File lib/tensorflow/ops/raw_ops.rb, line 1170 def self.eig(input, compute_v: true, typeT: nil, tout: nil, name: "Eig") self.execute("Eig", [input], compute_v: compute_v, T: typeT, Tout: tout, name: name) end
einsum(inputs, equation: "", n: nil, typeT: nil, name: "Einsum")
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# File lib/tensorflow/ops/raw_ops.rb, line 1174 def self.einsum(inputs, equation: "", n: nil, typeT: nil, name: "Einsum") self.execute("Einsum", [inputs], equation: equation, N: n, T: typeT, name: name) end
elu(features, typeT: nil, name: "Elu")
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# File lib/tensorflow/ops/raw_ops.rb, line 1178 def self.elu(features, typeT: nil, name: "Elu") self.execute("Elu", [features], T: typeT, name: name) end
elu_grad(gradients, outputs, typeT: nil, name: "EluGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 1182 def self.elu_grad(gradients, outputs, typeT: nil, name: "EluGrad") self.execute("EluGrad", [gradients, outputs], T: typeT, name: name) end
empty(shape, dtype: nil, init: false, name: "Empty")
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# File lib/tensorflow/ops/raw_ops.rb, line 1186 def self.empty(shape, dtype: nil, init: false, name: "Empty") self.execute("Empty", [shape], dtype: dtype, init: init, name: name) end
empty_tensor_list(element_shape, max_num_elements, element_dtype: nil, shape_type: nil, name: "EmptyTensorList")
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# File lib/tensorflow/ops/raw_ops.rb, line 1190 def self.empty_tensor_list(element_shape, max_num_elements, element_dtype: nil, shape_type: nil, name: "EmptyTensorList") self.execute("EmptyTensorList", [element_shape, max_num_elements], element_dtype: element_dtype, shape_type: shape_type, name: name) end
encode_base64(input, pad: false, name: "EncodeBase64")
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# File lib/tensorflow/ops/raw_ops.rb, line 1194 def self.encode_base64(input, pad: false, name: "EncodeBase64") self.execute("EncodeBase64", [input], pad: pad, name: name) end
encode_jpeg(image, format: "", quality: 95, progressive: false, optimize_size: false, chroma_downsampling: true, density_unit: "in", x_density: 300, y_density: 300, xmp_metadata: "", name: "EncodeJpeg")
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# File lib/tensorflow/ops/raw_ops.rb, line 1198 def self.encode_jpeg(image, format: "", quality: 95, progressive: false, optimize_size: false, chroma_downsampling: true, density_unit: "in", x_density: 300, y_density: 300, xmp_metadata: "", name: "EncodeJpeg") self.execute("EncodeJpeg", [image], format: format, quality: quality, progressive: progressive, optimize_size: optimize_size, chroma_downsampling: chroma_downsampling, density_unit: density_unit, x_density: x_density, y_density: y_density, xmp_metadata: xmp_metadata, name: name) end
encode_jpeg_variable_quality(images, quality, name: "EncodeJpegVariableQuality")
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# File lib/tensorflow/ops/raw_ops.rb, line 1202 def self.encode_jpeg_variable_quality(images, quality, name: "EncodeJpegVariableQuality") self.execute("EncodeJpegVariableQuality", [images, quality], name: name) end
encode_png(image, compression: -1, typeT: :uint8, name: "EncodePng")
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# File lib/tensorflow/ops/raw_ops.rb, line 1206 def self.encode_png(image, compression: -1, typeT: :uint8, name: "EncodePng") self.execute("EncodePng", [image], compression: compression, T: typeT, name: name) end
encode_proto(sizes, values, field_names: nil, message_type: "", descriptor_source: "local://", tinput_types: nil, name: "EncodeProto")
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# File lib/tensorflow/ops/raw_ops.rb, line 1210 def self.encode_proto(sizes, values, field_names: nil, message_type: "", descriptor_source: "local://", tinput_types: nil, name: "EncodeProto") self.execute("EncodeProto", [sizes, values], field_names: field_names, message_type: message_type, descriptor_source: descriptor_source, Tinput_types: tinput_types, name: name) end
encode_wav(audio, sample_rate, name: "EncodeWav")
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# File lib/tensorflow/ops/raw_ops.rb, line 1214 def self.encode_wav(audio, sample_rate, name: "EncodeWav") self.execute("EncodeWav", [audio, sample_rate], name: name) end
enqueue_tpu_embedding_integer_batch(batch, mode_override, n: nil, device_ordinal: -1, name: "EnqueueTPUEmbeddingIntegerBatch")
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# File lib/tensorflow/ops/raw_ops.rb, line 1218 def self.enqueue_tpu_embedding_integer_batch(batch, mode_override, n: nil, device_ordinal: -1, name: "EnqueueTPUEmbeddingIntegerBatch") self.execute("EnqueueTPUEmbeddingIntegerBatch", [batch, mode_override], N: n, device_ordinal: device_ordinal, name: name) end
enqueue_tpu_embedding_sparse_batch(sample_indices, embedding_indices, aggregation_weights, mode_override, t1: :int32, t2: :int32, t3: :float, n: nil, device_ordinal: -1, combiners: [], name: "EnqueueTPUEmbeddingSparseBatch")
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# File lib/tensorflow/ops/raw_ops.rb, line 1222 def self.enqueue_tpu_embedding_sparse_batch(sample_indices, embedding_indices, aggregation_weights, mode_override, t1: :int32, t2: :int32, t3: :float, n: nil, device_ordinal: -1, combiners: [], name: "EnqueueTPUEmbeddingSparseBatch") self.execute("EnqueueTPUEmbeddingSparseBatch", [sample_indices, embedding_indices, aggregation_weights, mode_override], T1: t1, T2: t2, T3: t3, N: n, device_ordinal: device_ordinal, combiners: combiners, name: name) end
enqueue_tpu_embedding_sparse_tensor_batch(sample_indices, embedding_indices, aggregation_weights, mode_override, t1: :int32, t2: :int32, t3: :float, n: nil, device_ordinal: -1, combiners: [], table_ids: nil, max_sequence_lengths: [], name: "EnqueueTPUEmbeddingSparseTensorBatch")
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# File lib/tensorflow/ops/raw_ops.rb, line 1226 def self.enqueue_tpu_embedding_sparse_tensor_batch(sample_indices, embedding_indices, aggregation_weights, mode_override, t1: :int32, t2: :int32, t3: :float, n: nil, device_ordinal: -1, combiners: [], table_ids: nil, max_sequence_lengths: [], name: "EnqueueTPUEmbeddingSparseTensorBatch") self.execute("EnqueueTPUEmbeddingSparseTensorBatch", [sample_indices, embedding_indices, aggregation_weights, mode_override], T1: t1, T2: t2, T3: t3, N: n, device_ordinal: device_ordinal, combiners: combiners, table_ids: table_ids, max_sequence_lengths: max_sequence_lengths, name: name) end
ensure_shape(input, shape: nil, typeT: nil, name: "EnsureShape")
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# File lib/tensorflow/ops/raw_ops.rb, line 1230 def self.ensure_shape(input, shape: nil, typeT: nil, name: "EnsureShape") self.execute("EnsureShape", [input], shape: shape, T: typeT, name: name) end
enter(data, typeT: nil, frame_name: "", is_constant: false, parallel_iterations: 10, name: "Enter")
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# File lib/tensorflow/ops/raw_ops.rb, line 1234 def self.enter(data, typeT: nil, frame_name: "", is_constant: false, parallel_iterations: 10, name: "Enter") self.execute("Enter", [data], T: typeT, frame_name: frame_name, is_constant: is_constant, parallel_iterations: parallel_iterations, name: name) end
equal(x, y, typeT: nil, incompatible_shape_error: true, name: "Equal")
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# File lib/tensorflow/ops/raw_ops.rb, line 1238 def self.equal(x, y, typeT: nil, incompatible_shape_error: true, name: "Equal") self.execute("Equal", [x, y], T: typeT, incompatible_shape_error: incompatible_shape_error, name: name) end
erf(x, typeT: nil, name: "Erf")
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# File lib/tensorflow/ops/raw_ops.rb, line 1242 def self.erf(x, typeT: nil, name: "Erf") self.execute("Erf", [x], T: typeT, name: name) end
erfc(x, typeT: nil, name: "Erfc")
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# File lib/tensorflow/ops/raw_ops.rb, line 1246 def self.erfc(x, typeT: nil, name: "Erfc") self.execute("Erfc", [x], T: typeT, name: name) end
erfinv(x, typeT: nil, name: "Erfinv")
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# File lib/tensorflow/ops/raw_ops.rb, line 1250 def self.erfinv(x, typeT: nil, name: "Erfinv") self.execute("Erfinv", [x], T: typeT, name: name) end
euclidean_norm(input, reduction_indices, keep_dims: false, typeT: nil, tidx: :int32, name: "EuclideanNorm")
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# File lib/tensorflow/ops/raw_ops.rb, line 1254 def self.euclidean_norm(input, reduction_indices, keep_dims: false, typeT: nil, tidx: :int32, name: "EuclideanNorm") self.execute("EuclideanNorm", [input, reduction_indices], keep_dims: keep_dims, T: typeT, Tidx: tidx, name: name) end
execute(op_type, inputs=[], attrs={})
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# File lib/tensorflow/ops/raw_ops.rb, line 5 def self.execute(op_type, inputs=[], attrs={}) context = ExecutionContext.current(inputs) attrs = attrs.compact operation = context.create_operation(op_type, inputs, attrs) if context.is_a?(Graph::Graph) operation else context.execute(operation) end end
exit(data, typeT: nil, name: "Exit")
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# File lib/tensorflow/ops/raw_ops.rb, line 1258 def self.exit(data, typeT: nil, name: "Exit") self.execute("Exit", [data], T: typeT, name: name) end
exp(x, typeT: nil, name: "Exp")
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# File lib/tensorflow/ops/raw_ops.rb, line 1262 def self.exp(x, typeT: nil, name: "Exp") self.execute("Exp", [x], T: typeT, name: name) end
expand_dims(input, dim, typeT: nil, tdim: :int32, name: "ExpandDims")
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# File lib/tensorflow/ops/raw_ops.rb, line 1266 def self.expand_dims(input, dim, typeT: nil, tdim: :int32, name: "ExpandDims") self.execute("ExpandDims", [input, dim], T: typeT, Tdim: tdim, name: name) end
experimental_assert_next_dataset(input_dataset, transformations, output_types: nil, output_shapes: nil, name: "ExperimentalAssertNextDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1270 def self.experimental_assert_next_dataset(input_dataset, transformations, output_types: nil, output_shapes: nil, name: "ExperimentalAssertNextDataset") self.execute("ExperimentalAssertNextDataset", [input_dataset, transformations], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_auto_shard_dataset(input_dataset, num_workers, index, auto_shard_policy: 0, output_types: nil, output_shapes: nil, name: "ExperimentalAutoShardDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1274 def self.experimental_auto_shard_dataset(input_dataset, num_workers, index, auto_shard_policy: 0, output_types: nil, output_shapes: nil, name: "ExperimentalAutoShardDataset") self.execute("ExperimentalAutoShardDataset", [input_dataset, num_workers, index], auto_shard_policy: auto_shard_policy, output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_bytes_produced_stats_dataset(input_dataset, tag, output_types: nil, output_shapes: nil, name: "ExperimentalBytesProducedStatsDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1278 def self.experimental_bytes_produced_stats_dataset(input_dataset, tag, output_types: nil, output_shapes: nil, name: "ExperimentalBytesProducedStatsDataset") self.execute("ExperimentalBytesProducedStatsDataset", [input_dataset, tag], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_choose_fastest_dataset(input_datasets, n: nil, num_experiments: nil, output_types: nil, output_shapes: nil, name: "ExperimentalChooseFastestDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1286 def self.experimental_choose_fastest_dataset(input_datasets, n: nil, num_experiments: nil, output_types: nil, output_shapes: nil, name: "ExperimentalChooseFastestDataset") self.execute("ExperimentalChooseFastestDataset", [input_datasets], N: n, num_experiments: num_experiments, output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_csv_dataset(filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols, record_defaults, output_types: nil, output_shapes: nil, name: "ExperimentalCSVDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1282 def self.experimental_csv_dataset(filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols, record_defaults, output_types: nil, output_shapes: nil, name: "ExperimentalCSVDataset") self.execute("ExperimentalCSVDataset", [filenames, compression_type, buffer_size, header, field_delim, use_quote_delim, na_value, select_cols, record_defaults], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_dataset_cardinality(input_dataset, name: "ExperimentalDatasetCardinality")
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# File lib/tensorflow/ops/raw_ops.rb, line 1290 def self.experimental_dataset_cardinality(input_dataset, name: "ExperimentalDatasetCardinality") self.execute("ExperimentalDatasetCardinality", [input_dataset], name: name) end
experimental_dataset_to_tf_record(input_dataset, filename, compression_type, name: "ExperimentalDatasetToTFRecord")
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# File lib/tensorflow/ops/raw_ops.rb, line 1294 def self.experimental_dataset_to_tf_record(input_dataset, filename, compression_type, name: "ExperimentalDatasetToTFRecord") self.execute("ExperimentalDatasetToTFRecord", [input_dataset, filename, compression_type], name: name) end
experimental_dense_to_sparse_batch_dataset(input_dataset, batch_size, row_shape, output_types: nil, output_shapes: nil, name: "ExperimentalDenseToSparseBatchDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1298 def self.experimental_dense_to_sparse_batch_dataset(input_dataset, batch_size, row_shape, output_types: nil, output_shapes: nil, name: "ExperimentalDenseToSparseBatchDataset") self.execute("ExperimentalDenseToSparseBatchDataset", [input_dataset, batch_size, row_shape], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_directed_interleave_dataset(selector_input_dataset, data_input_datasets, output_types: nil, output_shapes: nil, n: nil, name: "ExperimentalDirectedInterleaveDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1302 def self.experimental_directed_interleave_dataset(selector_input_dataset, data_input_datasets, output_types: nil, output_shapes: nil, n: nil, name: "ExperimentalDirectedInterleaveDataset") self.execute("ExperimentalDirectedInterleaveDataset", [selector_input_dataset, data_input_datasets], output_types: output_types, output_shapes: output_shapes, N: n, name: name) end
experimental_group_by_reducer_dataset(input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments, key_func: nil, init_func: nil, reduce_func: nil, finalize_func: nil, tkey_func_other_arguments: nil, tinit_func_other_arguments: nil, treduce_func_other_arguments: nil, tfinalize_func_other_arguments: nil, output_types: nil, output_shapes: nil, name: "ExperimentalGroupByReducerDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1306 def self.experimental_group_by_reducer_dataset(input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments, key_func: nil, init_func: nil, reduce_func: nil, finalize_func: nil, tkey_func_other_arguments: nil, tinit_func_other_arguments: nil, treduce_func_other_arguments: nil, tfinalize_func_other_arguments: nil, output_types: nil, output_shapes: nil, name: "ExperimentalGroupByReducerDataset") self.execute("ExperimentalGroupByReducerDataset", [input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments], key_func: key_func, init_func: init_func, reduce_func: reduce_func, finalize_func: finalize_func, Tkey_func_other_arguments: tkey_func_other_arguments, Tinit_func_other_arguments: tinit_func_other_arguments, Treduce_func_other_arguments: treduce_func_other_arguments, Tfinalize_func_other_arguments: tfinalize_func_other_arguments, output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_group_by_window_dataset(input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments, key_func: nil, reduce_func: nil, window_size_func: nil, tkey_func_other_arguments: nil, treduce_func_other_arguments: nil, twindow_size_func_other_arguments: nil, output_types: nil, output_shapes: nil, name: "ExperimentalGroupByWindowDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1310 def self.experimental_group_by_window_dataset(input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments, key_func: nil, reduce_func: nil, window_size_func: nil, tkey_func_other_arguments: nil, treduce_func_other_arguments: nil, twindow_size_func_other_arguments: nil, output_types: nil, output_shapes: nil, name: "ExperimentalGroupByWindowDataset") self.execute("ExperimentalGroupByWindowDataset", [input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments], key_func: key_func, reduce_func: reduce_func, window_size_func: window_size_func, Tkey_func_other_arguments: tkey_func_other_arguments, Treduce_func_other_arguments: treduce_func_other_arguments, Twindow_size_func_other_arguments: twindow_size_func_other_arguments, output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_ignore_errors_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "ExperimentalIgnoreErrorsDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1314 def self.experimental_ignore_errors_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "ExperimentalIgnoreErrorsDataset") self.execute("ExperimentalIgnoreErrorsDataset", [input_dataset], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_iterator_get_device(resource, name: "ExperimentalIteratorGetDevice")
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# File lib/tensorflow/ops/raw_ops.rb, line 1318 def self.experimental_iterator_get_device(resource, name: "ExperimentalIteratorGetDevice") self.execute("ExperimentalIteratorGetDevice", [resource], name: name) end
experimental_latency_stats_dataset(input_dataset, tag, output_types: nil, output_shapes: nil, name: "ExperimentalLatencyStatsDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1326 def self.experimental_latency_stats_dataset(input_dataset, tag, output_types: nil, output_shapes: nil, name: "ExperimentalLatencyStatsDataset") self.execute("ExperimentalLatencyStatsDataset", [input_dataset, tag], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_lmdb_dataset(filenames, output_types: nil, output_shapes: nil, name: "ExperimentalLMDBDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1322 def self.experimental_lmdb_dataset(filenames, output_types: nil, output_shapes: nil, name: "ExperimentalLMDBDataset") self.execute("ExperimentalLMDBDataset", [filenames], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_map_and_batch_dataset(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f: nil, targuments: nil, output_types: nil, output_shapes: nil, preserve_cardinality: false, name: "ExperimentalMapAndBatchDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1330 def self.experimental_map_and_batch_dataset(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f: nil, targuments: nil, output_types: nil, output_shapes: nil, preserve_cardinality: false, name: "ExperimentalMapAndBatchDataset") self.execute("ExperimentalMapAndBatchDataset", [input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder], f: f, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, preserve_cardinality: preserve_cardinality, name: name) end
experimental_map_dataset(input_dataset, other_arguments, f: nil, targuments: nil, output_types: nil, output_shapes: nil, use_inter_op_parallelism: true, preserve_cardinality: false, name: "ExperimentalMapDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1334 def self.experimental_map_dataset(input_dataset, other_arguments, f: nil, targuments: nil, output_types: nil, output_shapes: nil, use_inter_op_parallelism: true, preserve_cardinality: false, name: "ExperimentalMapDataset") self.execute("ExperimentalMapDataset", [input_dataset, other_arguments], f: f, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, use_inter_op_parallelism: use_inter_op_parallelism, preserve_cardinality: preserve_cardinality, name: name) end
experimental_matching_files_dataset(patterns, name: "ExperimentalMatchingFilesDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1338 def self.experimental_matching_files_dataset(patterns, name: "ExperimentalMatchingFilesDataset") self.execute("ExperimentalMatchingFilesDataset", [patterns], name: name) end
experimental_max_intra_op_parallelism_dataset(input_dataset, max_intra_op_parallelism, output_types: nil, output_shapes: nil, name: "ExperimentalMaxIntraOpParallelismDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1342 def self.experimental_max_intra_op_parallelism_dataset(input_dataset, max_intra_op_parallelism, output_types: nil, output_shapes: nil, name: "ExperimentalMaxIntraOpParallelismDataset") self.execute("ExperimentalMaxIntraOpParallelismDataset", [input_dataset, max_intra_op_parallelism], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_non_serializable_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "ExperimentalNonSerializableDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1346 def self.experimental_non_serializable_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "ExperimentalNonSerializableDataset") self.execute("ExperimentalNonSerializableDataset", [input_dataset], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_parallel_interleave_dataset(input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements, f: nil, targuments: nil, output_types: nil, output_shapes: nil, name: "ExperimentalParallelInterleaveDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1350 def self.experimental_parallel_interleave_dataset(input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements, f: nil, targuments: nil, output_types: nil, output_shapes: nil, name: "ExperimentalParallelInterleaveDataset") self.execute("ExperimentalParallelInterleaveDataset", [input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements], f: f, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_parse_example_dataset(input_dataset, num_parallel_calls, dense_defaults, sparse_keys: nil, dense_keys: nil, sparse_types: nil, tdense: nil, dense_shapes: nil, output_types: nil, output_shapes: nil, sloppy: false, name: "ExperimentalParseExampleDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1354 def self.experimental_parse_example_dataset(input_dataset, num_parallel_calls, dense_defaults, sparse_keys: nil, dense_keys: nil, sparse_types: nil, tdense: nil, dense_shapes: nil, output_types: nil, output_shapes: nil, sloppy: false, name: "ExperimentalParseExampleDataset") self.execute("ExperimentalParseExampleDataset", [input_dataset, num_parallel_calls, dense_defaults], sparse_keys: sparse_keys, dense_keys: dense_keys, sparse_types: sparse_types, Tdense: tdense, dense_shapes: dense_shapes, output_types: output_types, output_shapes: output_shapes, sloppy: sloppy, name: name) end
experimental_private_thread_pool_dataset(input_dataset, num_threads, output_types: nil, output_shapes: nil, name: "ExperimentalPrivateThreadPoolDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1358 def self.experimental_private_thread_pool_dataset(input_dataset, num_threads, output_types: nil, output_shapes: nil, name: "ExperimentalPrivateThreadPoolDataset") self.execute("ExperimentalPrivateThreadPoolDataset", [input_dataset, num_threads], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_random_dataset(seed, seed2, output_types: nil, output_shapes: nil, name: "ExperimentalRandomDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1362 def self.experimental_random_dataset(seed, seed2, output_types: nil, output_shapes: nil, name: "ExperimentalRandomDataset") self.execute("ExperimentalRandomDataset", [seed, seed2], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_rebatch_dataset(input_dataset, num_replicas, output_types: nil, output_shapes: nil, use_fallback: true, name: "ExperimentalRebatchDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1366 def self.experimental_rebatch_dataset(input_dataset, num_replicas, output_types: nil, output_shapes: nil, use_fallback: true, name: "ExperimentalRebatchDataset") self.execute("ExperimentalRebatchDataset", [input_dataset, num_replicas], output_types: output_types, output_shapes: output_shapes, use_fallback: use_fallback, name: name) end
experimental_scan_dataset(input_dataset, initial_state, other_arguments, f: nil, tstate: nil, targuments: nil, output_types: nil, output_shapes: nil, preserve_cardinality: false, name: "ExperimentalScanDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1370 def self.experimental_scan_dataset(input_dataset, initial_state, other_arguments, f: nil, tstate: nil, targuments: nil, output_types: nil, output_shapes: nil, preserve_cardinality: false, name: "ExperimentalScanDataset") self.execute("ExperimentalScanDataset", [input_dataset, initial_state, other_arguments], f: f, Tstate: tstate, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, preserve_cardinality: preserve_cardinality, name: name) end
experimental_set_stats_aggregator_dataset(input_dataset, stats_aggregator, tag, counter_prefix, output_types: nil, output_shapes: nil, name: "ExperimentalSetStatsAggregatorDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1374 def self.experimental_set_stats_aggregator_dataset(input_dataset, stats_aggregator, tag, counter_prefix, output_types: nil, output_shapes: nil, name: "ExperimentalSetStatsAggregatorDataset") self.execute("ExperimentalSetStatsAggregatorDataset", [input_dataset, stats_aggregator, tag, counter_prefix], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_sleep_dataset(input_dataset, sleep_microseconds, output_types: nil, output_shapes: nil, name: "ExperimentalSleepDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1378 def self.experimental_sleep_dataset(input_dataset, sleep_microseconds, output_types: nil, output_shapes: nil, name: "ExperimentalSleepDataset") self.execute("ExperimentalSleepDataset", [input_dataset, sleep_microseconds], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_sliding_window_dataset(input_dataset, window_size, window_shift, window_stride, output_types: nil, output_shapes: nil, name: "ExperimentalSlidingWindowDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1382 def self.experimental_sliding_window_dataset(input_dataset, window_size, window_shift, window_stride, output_types: nil, output_shapes: nil, name: "ExperimentalSlidingWindowDataset") self.execute("ExperimentalSlidingWindowDataset", [input_dataset, window_size, window_shift, window_stride], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_sql_dataset(driver_name, data_source_name, query, output_types: nil, output_shapes: nil, name: "ExperimentalSqlDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1386 def self.experimental_sql_dataset(driver_name, data_source_name, query, output_types: nil, output_shapes: nil, name: "ExperimentalSqlDataset") self.execute("ExperimentalSqlDataset", [driver_name, data_source_name, query], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_stats_aggregator_handle(container: "", shared_name: "", name: "ExperimentalStatsAggregatorHandle")
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# File lib/tensorflow/ops/raw_ops.rb, line 1390 def self.experimental_stats_aggregator_handle(container: "", shared_name: "", name: "ExperimentalStatsAggregatorHandle") self.execute("ExperimentalStatsAggregatorHandle", [], container: container, shared_name: shared_name, name: name) end
experimental_stats_aggregator_summary(iterator, name: "ExperimentalStatsAggregatorSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 1394 def self.experimental_stats_aggregator_summary(iterator, name: "ExperimentalStatsAggregatorSummary") self.execute("ExperimentalStatsAggregatorSummary", [iterator], name: name) end
experimental_take_while_dataset(input_dataset, other_arguments, predicate: nil, targuments: nil, output_types: nil, output_shapes: nil, name: "ExperimentalTakeWhileDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1398 def self.experimental_take_while_dataset(input_dataset, other_arguments, predicate: nil, targuments: nil, output_types: nil, output_shapes: nil, name: "ExperimentalTakeWhileDataset") self.execute("ExperimentalTakeWhileDataset", [input_dataset, other_arguments], predicate: predicate, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_thread_pool_dataset(input_dataset, thread_pool, output_types: nil, output_shapes: nil, name: "ExperimentalThreadPoolDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1402 def self.experimental_thread_pool_dataset(input_dataset, thread_pool, output_types: nil, output_shapes: nil, name: "ExperimentalThreadPoolDataset") self.execute("ExperimentalThreadPoolDataset", [input_dataset, thread_pool], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_thread_pool_handle(num_threads: nil, max_intra_op_parallelism: 1, display_name: "", container: "", shared_name: "", name: "ExperimentalThreadPoolHandle")
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# File lib/tensorflow/ops/raw_ops.rb, line 1406 def self.experimental_thread_pool_handle(num_threads: nil, max_intra_op_parallelism: 1, display_name: "", container: "", shared_name: "", name: "ExperimentalThreadPoolHandle") self.execute("ExperimentalThreadPoolHandle", [], num_threads: num_threads, max_intra_op_parallelism: max_intra_op_parallelism, display_name: display_name, container: container, shared_name: shared_name, name: name) end
experimental_unbatch_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "ExperimentalUnbatchDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1410 def self.experimental_unbatch_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "ExperimentalUnbatchDataset") self.execute("ExperimentalUnbatchDataset", [input_dataset], output_types: output_types, output_shapes: output_shapes, name: name) end
experimental_unique_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "ExperimentalUniqueDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1414 def self.experimental_unique_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "ExperimentalUniqueDataset") self.execute("ExperimentalUniqueDataset", [input_dataset], output_types: output_types, output_shapes: output_shapes, name: name) end
expm1(x, typeT: nil, name: "Expm1")
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# File lib/tensorflow/ops/raw_ops.rb, line 1418 def self.expm1(x, typeT: nil, name: "Expm1") self.execute("Expm1", [x], T: typeT, name: name) end
extract_glimpse(input, size, offsets, centered: true, normalized: true, uniform_noise: true, noise: "uniform", name: "ExtractGlimpse")
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# File lib/tensorflow/ops/raw_ops.rb, line 1422 def self.extract_glimpse(input, size, offsets, centered: true, normalized: true, uniform_noise: true, noise: "uniform", name: "ExtractGlimpse") self.execute("ExtractGlimpse", [input, size, offsets], centered: centered, normalized: normalized, uniform_noise: uniform_noise, noise: noise, name: name) end
extract_image_patches(images, ksizes: nil, strides: nil, rates: nil, typeT: nil, padding: nil, name: "ExtractImagePatches")
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# File lib/tensorflow/ops/raw_ops.rb, line 1426 def self.extract_image_patches(images, ksizes: nil, strides: nil, rates: nil, typeT: nil, padding: nil, name: "ExtractImagePatches") self.execute("ExtractImagePatches", [images], ksizes: ksizes, strides: strides, rates: rates, T: typeT, padding: padding, name: name) end
extract_jpeg_shape(contents, output_type: :int32, name: "ExtractJpegShape")
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# File lib/tensorflow/ops/raw_ops.rb, line 1430 def self.extract_jpeg_shape(contents, output_type: :int32, name: "ExtractJpegShape") self.execute("ExtractJpegShape", [contents], output_type: output_type, name: name) end
extract_volume_patches(input, ksizes: nil, strides: nil, typeT: nil, padding: nil, name: "ExtractVolumePatches")
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# File lib/tensorflow/ops/raw_ops.rb, line 1434 def self.extract_volume_patches(input, ksizes: nil, strides: nil, typeT: nil, padding: nil, name: "ExtractVolumePatches") self.execute("ExtractVolumePatches", [input], ksizes: ksizes, strides: strides, T: typeT, padding: padding, name: name) end
fact(name: "Fact")
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# File lib/tensorflow/ops/raw_ops.rb, line 1458 def self.fact(name: "Fact") self.execute("Fact", [], name: name) end
fake_param(dtype: nil, shape: nil, name: "FakeParam")
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# File lib/tensorflow/ops/raw_ops.rb, line 1462 def self.fake_param(dtype: nil, shape: nil, name: "FakeParam") self.execute("FakeParam", [], dtype: dtype, shape: shape, name: name) end
fake_quant_with_min_max_args(inputs, min: -6.0, max: 6.0, num_bits: 8, narrow_range: false, name: "FakeQuantWithMinMaxArgs")
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# File lib/tensorflow/ops/raw_ops.rb, line 1466 def self.fake_quant_with_min_max_args(inputs, min: -6.0, max: 6.0, num_bits: 8, narrow_range: false, name: "FakeQuantWithMinMaxArgs") self.execute("FakeQuantWithMinMaxArgs", [inputs], min: min, max: max, num_bits: num_bits, narrow_range: narrow_range, name: name) end
fake_quant_with_min_max_args_gradient(gradients, inputs, min: -6.0, max: 6.0, num_bits: 8, narrow_range: false, name: "FakeQuantWithMinMaxArgsGradient")
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# File lib/tensorflow/ops/raw_ops.rb, line 1470 def self.fake_quant_with_min_max_args_gradient(gradients, inputs, min: -6.0, max: 6.0, num_bits: 8, narrow_range: false, name: "FakeQuantWithMinMaxArgsGradient") self.execute("FakeQuantWithMinMaxArgsGradient", [gradients, inputs], min: min, max: max, num_bits: num_bits, narrow_range: narrow_range, name: name) end
fake_quant_with_min_max_vars(inputs, min, max, num_bits: 8, narrow_range: false, name: "FakeQuantWithMinMaxVars")
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# File lib/tensorflow/ops/raw_ops.rb, line 1474 def self.fake_quant_with_min_max_vars(inputs, min, max, num_bits: 8, narrow_range: false, name: "FakeQuantWithMinMaxVars") self.execute("FakeQuantWithMinMaxVars", [inputs, min, max], num_bits: num_bits, narrow_range: narrow_range, name: name) end
fake_quant_with_min_max_vars_gradient(gradients, inputs, min, max, num_bits: 8, narrow_range: false, name: "FakeQuantWithMinMaxVarsGradient")
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# File lib/tensorflow/ops/raw_ops.rb, line 1478 def self.fake_quant_with_min_max_vars_gradient(gradients, inputs, min, max, num_bits: 8, narrow_range: false, name: "FakeQuantWithMinMaxVarsGradient") self.execute("FakeQuantWithMinMaxVarsGradient", [gradients, inputs, min, max], num_bits: num_bits, narrow_range: narrow_range, name: name) end
fake_quant_with_min_max_vars_per_channel(inputs, min, max, num_bits: 8, narrow_range: false, name: "FakeQuantWithMinMaxVarsPerChannel")
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# File lib/tensorflow/ops/raw_ops.rb, line 1482 def self.fake_quant_with_min_max_vars_per_channel(inputs, min, max, num_bits: 8, narrow_range: false, name: "FakeQuantWithMinMaxVarsPerChannel") self.execute("FakeQuantWithMinMaxVarsPerChannel", [inputs, min, max], num_bits: num_bits, narrow_range: narrow_range, name: name) end
fake_quant_with_min_max_vars_per_channel_gradient(gradients, inputs, min, max, num_bits: 8, narrow_range: false, name: "FakeQuantWithMinMaxVarsPerChannelGradient")
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# File lib/tensorflow/ops/raw_ops.rb, line 1486 def self.fake_quant_with_min_max_vars_per_channel_gradient(gradients, inputs, min, max, num_bits: 8, narrow_range: false, name: "FakeQuantWithMinMaxVarsPerChannelGradient") self.execute("FakeQuantWithMinMaxVarsPerChannelGradient", [gradients, inputs, min, max], num_bits: num_bits, narrow_range: narrow_range, name: name) end
fake_queue(resource, name: "FakeQueue")
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# File lib/tensorflow/ops/raw_ops.rb, line 1490 def self.fake_queue(resource, name: "FakeQueue") self.execute("FakeQueue", [resource], name: name) end
fft(input, tcomplex: :complex64, name: "FFT")
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# File lib/tensorflow/ops/raw_ops.rb, line 1438 def self.fft(input, tcomplex: :complex64, name: "FFT") self.execute("FFT", [input], Tcomplex: tcomplex, name: name) end
fft2_d(input, tcomplex: :complex64, name: "FFT2D")
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# File lib/tensorflow/ops/raw_ops.rb, line 1442 def self.fft2_d(input, tcomplex: :complex64, name: "FFT2D") self.execute("FFT2D", [input], Tcomplex: tcomplex, name: name) end
fft3_d(input, tcomplex: :complex64, name: "FFT3D")
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# File lib/tensorflow/ops/raw_ops.rb, line 1446 def self.fft3_d(input, tcomplex: :complex64, name: "FFT3D") self.execute("FFT3D", [input], Tcomplex: tcomplex, name: name) end
fifo_queue(component_types: nil, shapes: [], capacity: -1, container: "", shared_name: "", name: "FIFOQueue")
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# File lib/tensorflow/ops/raw_ops.rb, line 1450 def self.fifo_queue(component_types: nil, shapes: [], capacity: -1, container: "", shared_name: "", name: "FIFOQueue") self.execute("FIFOQueue", [], component_types: component_types, shapes: shapes, capacity: capacity, container: container, shared_name: shared_name, name: name) end
fifo_queue_v2(component_types: nil, shapes: [], capacity: -1, container: "", shared_name: "", name: "FIFOQueueV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1454 def self.fifo_queue_v2(component_types: nil, shapes: [], capacity: -1, container: "", shared_name: "", name: "FIFOQueueV2") self.execute("FIFOQueueV2", [], component_types: component_types, shapes: shapes, capacity: capacity, container: container, shared_name: shared_name, name: name) end
fill(dims, value, typeT: nil, index_type: :int32, name: "Fill")
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# File lib/tensorflow/ops/raw_ops.rb, line 1494 def self.fill(dims, value, typeT: nil, index_type: :int32, name: "Fill") self.execute("Fill", [dims, value], T: typeT, index_type: index_type, name: name) end
filter_by_last_component_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "FilterByLastComponentDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1498 def self.filter_by_last_component_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "FilterByLastComponentDataset") self.execute("FilterByLastComponentDataset", [input_dataset], output_types: output_types, output_shapes: output_shapes, name: name) end
filter_dataset(input_dataset, other_arguments, predicate: nil, targuments: nil, output_types: nil, output_shapes: nil, name: "FilterDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1502 def self.filter_dataset(input_dataset, other_arguments, predicate: nil, targuments: nil, output_types: nil, output_shapes: nil, name: "FilterDataset") self.execute("FilterDataset", [input_dataset, other_arguments], predicate: predicate, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, name: name) end
fingerprint(data, method, typeT: nil, name: "Fingerprint")
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# File lib/tensorflow/ops/raw_ops.rb, line 1506 def self.fingerprint(data, method, typeT: nil, name: "Fingerprint") self.execute("Fingerprint", [data, method], T: typeT, name: name) end
fixed_length_record_dataset(filenames, header_bytes, record_bytes, footer_bytes, buffer_size, name: "FixedLengthRecordDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1510 def self.fixed_length_record_dataset(filenames, header_bytes, record_bytes, footer_bytes, buffer_size, name: "FixedLengthRecordDataset") self.execute("FixedLengthRecordDataset", [filenames, header_bytes, record_bytes, footer_bytes, buffer_size], name: name) end
fixed_length_record_dataset_v2(filenames, header_bytes, record_bytes, footer_bytes, buffer_size, compression_type, name: "FixedLengthRecordDatasetV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1514 def self.fixed_length_record_dataset_v2(filenames, header_bytes, record_bytes, footer_bytes, buffer_size, compression_type, name: "FixedLengthRecordDatasetV2") self.execute("FixedLengthRecordDatasetV2", [filenames, header_bytes, record_bytes, footer_bytes, buffer_size, compression_type], name: name) end
fixed_length_record_reader(header_bytes: 0, record_bytes: nil, footer_bytes: 0, hop_bytes: 0, container: "", shared_name: "", name: "FixedLengthRecordReader")
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# File lib/tensorflow/ops/raw_ops.rb, line 1518 def self.fixed_length_record_reader(header_bytes: 0, record_bytes: nil, footer_bytes: 0, hop_bytes: 0, container: "", shared_name: "", name: "FixedLengthRecordReader") self.execute("FixedLengthRecordReader", [], header_bytes: header_bytes, record_bytes: record_bytes, footer_bytes: footer_bytes, hop_bytes: hop_bytes, container: container, shared_name: shared_name, name: name) end
fixed_length_record_reader_v2(header_bytes: 0, record_bytes: nil, footer_bytes: 0, hop_bytes: 0, container: "", shared_name: "", encoding: "", name: "FixedLengthRecordReaderV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1522 def self.fixed_length_record_reader_v2(header_bytes: 0, record_bytes: nil, footer_bytes: 0, hop_bytes: 0, container: "", shared_name: "", encoding: "", name: "FixedLengthRecordReaderV2") self.execute("FixedLengthRecordReaderV2", [], header_bytes: header_bytes, record_bytes: record_bytes, footer_bytes: footer_bytes, hop_bytes: hop_bytes, container: container, shared_name: shared_name, encoding: encoding, name: name) end
fixed_unigram_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, vocab_file: "", distortion: 1.0, num_reserved_ids: 0, num_shards: 1, shard: 0, unigrams: [], seed: 0, seed2: 0, name: "FixedUnigramCandidateSampler")
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# File lib/tensorflow/ops/raw_ops.rb, line 1526 def self.fixed_unigram_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, vocab_file: "", distortion: 1.0, num_reserved_ids: 0, num_shards: 1, shard: 0, unigrams: [], seed: 0, seed2: 0, name: "FixedUnigramCandidateSampler") self.execute("FixedUnigramCandidateSampler", [true_classes], num_true: num_true, num_sampled: num_sampled, unique: unique, range_max: range_max, vocab_file: vocab_file, distortion: distortion, num_reserved_ids: num_reserved_ids, num_shards: num_shards, shard: shard, unigrams: unigrams, seed: seed, seed2: seed2, name: name) end
flat_map_dataset(input_dataset, other_arguments, f: nil, targuments: nil, output_types: nil, output_shapes: nil, name: "FlatMapDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1530 def self.flat_map_dataset(input_dataset, other_arguments, f: nil, targuments: nil, output_types: nil, output_shapes: nil, name: "FlatMapDataset") self.execute("FlatMapDataset", [input_dataset, other_arguments], f: f, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, name: name) end
floor(x, typeT: nil, name: "Floor")
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# File lib/tensorflow/ops/raw_ops.rb, line 1534 def self.floor(x, typeT: nil, name: "Floor") self.execute("Floor", [x], T: typeT, name: name) end
floor_div(x, y, typeT: nil, name: "FloorDiv")
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# File lib/tensorflow/ops/raw_ops.rb, line 1538 def self.floor_div(x, y, typeT: nil, name: "FloorDiv") self.execute("FloorDiv", [x, y], T: typeT, name: name) end
floor_mod(x, y, typeT: nil, name: "FloorMod")
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# File lib/tensorflow/ops/raw_ops.rb, line 1542 def self.floor_mod(x, y, typeT: nil, name: "FloorMod") self.execute("FloorMod", [x, y], T: typeT, name: name) end
flush_summary_writer(writer, name: "FlushSummaryWriter")
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# File lib/tensorflow/ops/raw_ops.rb, line 1546 def self.flush_summary_writer(writer, name: "FlushSummaryWriter") self.execute("FlushSummaryWriter", [writer], name: name) end
for(start, limit, delta, input, typeT: nil, body: nil, name: "For")
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# File lib/tensorflow/ops/raw_ops.rb, line 1550 def self.for(start, limit, delta, input, typeT: nil, body: nil, name: "For") self.execute("For", [start, limit, delta, input], T: typeT, body: body, name: name) end
fractional_avg_pool(value, pooling_ratio: nil, pseudo_random: false, overlapping: false, deterministic: false, seed: 0, seed2: 0, typeT: nil, name: "FractionalAvgPool")
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# File lib/tensorflow/ops/raw_ops.rb, line 1554 def self.fractional_avg_pool(value, pooling_ratio: nil, pseudo_random: false, overlapping: false, deterministic: false, seed: 0, seed2: 0, typeT: nil, name: "FractionalAvgPool") self.execute("FractionalAvgPool", [value], pooling_ratio: pooling_ratio, pseudo_random: pseudo_random, overlapping: overlapping, deterministic: deterministic, seed: seed, seed2: seed2, T: typeT, name: name) end
fractional_avg_pool_grad(orig_input_tensor_shape, out_backprop, row_pooling_sequence, col_pooling_sequence, overlapping: false, typeT: nil, name: "FractionalAvgPoolGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 1558 def self.fractional_avg_pool_grad(orig_input_tensor_shape, out_backprop, row_pooling_sequence, col_pooling_sequence, overlapping: false, typeT: nil, name: "FractionalAvgPoolGrad") self.execute("FractionalAvgPoolGrad", [orig_input_tensor_shape, out_backprop, row_pooling_sequence, col_pooling_sequence], overlapping: overlapping, T: typeT, name: name) end
fractional_max_pool(value, pooling_ratio: nil, pseudo_random: false, overlapping: false, deterministic: false, seed: 0, seed2: 0, typeT: nil, name: "FractionalMaxPool")
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# File lib/tensorflow/ops/raw_ops.rb, line 1562 def self.fractional_max_pool(value, pooling_ratio: nil, pseudo_random: false, overlapping: false, deterministic: false, seed: 0, seed2: 0, typeT: nil, name: "FractionalMaxPool") self.execute("FractionalMaxPool", [value], pooling_ratio: pooling_ratio, pseudo_random: pseudo_random, overlapping: overlapping, deterministic: deterministic, seed: seed, seed2: seed2, T: typeT, name: name) end
fractional_max_pool_grad(orig_input, orig_output, out_backprop, row_pooling_sequence, col_pooling_sequence, overlapping: false, typeT: nil, name: "FractionalMaxPoolGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 1566 def self.fractional_max_pool_grad(orig_input, orig_output, out_backprop, row_pooling_sequence, col_pooling_sequence, overlapping: false, typeT: nil, name: "FractionalMaxPoolGrad") self.execute("FractionalMaxPoolGrad", [orig_input, orig_output, out_backprop, row_pooling_sequence, col_pooling_sequence], overlapping: overlapping, T: typeT, name: name) end
fused_batch_norm(x, scale, offset, mean, variance, typeT: nil, epsilon: 9.999999747378752e-05, data_format: "NHWC", is_training: true, name: "FusedBatchNorm")
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# File lib/tensorflow/ops/raw_ops.rb, line 1570 def self.fused_batch_norm(x, scale, offset, mean, variance, typeT: nil, epsilon: 9.999999747378752e-05, data_format: "NHWC", is_training: true, name: "FusedBatchNorm") self.execute("FusedBatchNorm", [x, scale, offset, mean, variance], T: typeT, epsilon: epsilon, data_format: data_format, is_training: is_training, name: name) end
fused_batch_norm_grad(y_backprop, x, scale, reserve_space_1, reserve_space_2, typeT: nil, epsilon: 9.999999747378752e-05, data_format: "NHWC", is_training: true, name: "FusedBatchNormGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 1574 def self.fused_batch_norm_grad(y_backprop, x, scale, reserve_space_1, reserve_space_2, typeT: nil, epsilon: 9.999999747378752e-05, data_format: "NHWC", is_training: true, name: "FusedBatchNormGrad") self.execute("FusedBatchNormGrad", [y_backprop, x, scale, reserve_space_1, reserve_space_2], T: typeT, epsilon: epsilon, data_format: data_format, is_training: is_training, name: name) end
fused_batch_norm_grad_v2(y_backprop, x, scale, reserve_space_1, reserve_space_2, typeT: nil, u: nil, epsilon: 9.999999747378752e-05, data_format: "NHWC", is_training: true, name: "FusedBatchNormGradV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1578 def self.fused_batch_norm_grad_v2(y_backprop, x, scale, reserve_space_1, reserve_space_2, typeT: nil, u: nil, epsilon: 9.999999747378752e-05, data_format: "NHWC", is_training: true, name: "FusedBatchNormGradV2") self.execute("FusedBatchNormGradV2", [y_backprop, x, scale, reserve_space_1, reserve_space_2], T: typeT, U: u, epsilon: epsilon, data_format: data_format, is_training: is_training, name: name) end
fused_batch_norm_grad_v3(y_backprop, x, scale, reserve_space_1, reserve_space_2, reserve_space_3, typeT: nil, u: nil, epsilon: 9.999999747378752e-05, data_format: "NHWC", is_training: true, name: "FusedBatchNormGradV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 1582 def self.fused_batch_norm_grad_v3(y_backprop, x, scale, reserve_space_1, reserve_space_2, reserve_space_3, typeT: nil, u: nil, epsilon: 9.999999747378752e-05, data_format: "NHWC", is_training: true, name: "FusedBatchNormGradV3") self.execute("FusedBatchNormGradV3", [y_backprop, x, scale, reserve_space_1, reserve_space_2, reserve_space_3], T: typeT, U: u, epsilon: epsilon, data_format: data_format, is_training: is_training, name: name) end
fused_batch_norm_v2(x, scale, offset, mean, variance, typeT: nil, u: nil, epsilon: 9.999999747378752e-05, data_format: "NHWC", is_training: true, name: "FusedBatchNormV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1586 def self.fused_batch_norm_v2(x, scale, offset, mean, variance, typeT: nil, u: nil, epsilon: 9.999999747378752e-05, data_format: "NHWC", is_training: true, name: "FusedBatchNormV2") self.execute("FusedBatchNormV2", [x, scale, offset, mean, variance], T: typeT, U: u, epsilon: epsilon, data_format: data_format, is_training: is_training, name: name) end
fused_batch_norm_v3(x, scale, offset, mean, variance, typeT: nil, u: nil, epsilon: 9.999999747378752e-05, data_format: "NHWC", is_training: true, name: "FusedBatchNormV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 1590 def self.fused_batch_norm_v3(x, scale, offset, mean, variance, typeT: nil, u: nil, epsilon: 9.999999747378752e-05, data_format: "NHWC", is_training: true, name: "FusedBatchNormV3") self.execute("FusedBatchNormV3", [x, scale, offset, mean, variance], T: typeT, U: u, epsilon: epsilon, data_format: data_format, is_training: is_training, name: name) end
fused_pad_conv2_d(input, paddings, filter, typeT: nil, mode: nil, strides: nil, padding: nil, name: "FusedPadConv2D")
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# File lib/tensorflow/ops/raw_ops.rb, line 1594 def self.fused_pad_conv2_d(input, paddings, filter, typeT: nil, mode: nil, strides: nil, padding: nil, name: "FusedPadConv2D") self.execute("FusedPadConv2D", [input, paddings, filter], T: typeT, mode: mode, strides: strides, padding: padding, name: name) end
fused_resize_and_pad_conv2_d(input, size, paddings, filter, typeT: nil, resize_align_corners: false, mode: nil, strides: nil, padding: nil, name: "FusedResizeAndPadConv2D")
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# File lib/tensorflow/ops/raw_ops.rb, line 1598 def self.fused_resize_and_pad_conv2_d(input, size, paddings, filter, typeT: nil, resize_align_corners: false, mode: nil, strides: nil, padding: nil, name: "FusedResizeAndPadConv2D") self.execute("FusedResizeAndPadConv2D", [input, size, paddings, filter], T: typeT, resize_align_corners: resize_align_corners, mode: mode, strides: strides, padding: padding, name: name) end
gather(params, indices, validate_indices: true, tparams: nil, tindices: nil, name: "Gather")
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# File lib/tensorflow/ops/raw_ops.rb, line 1610 def self.gather(params, indices, validate_indices: true, tparams: nil, tindices: nil, name: "Gather") self.execute("Gather", [params, indices], validate_indices: validate_indices, Tparams: tparams, Tindices: tindices, name: name) end
gather_nd(params, indices, tparams: nil, tindices: nil, name: "GatherNd")
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# File lib/tensorflow/ops/raw_ops.rb, line 1614 def self.gather_nd(params, indices, tparams: nil, tindices: nil, name: "GatherNd") self.execute("GatherNd", [params, indices], Tparams: tparams, Tindices: tindices, name: name) end
gather_v2(params, indices, axis, batch_dims: 0, tparams: nil, tindices: nil, taxis: nil, name: "GatherV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1618 def self.gather_v2(params, indices, axis, batch_dims: 0, tparams: nil, tindices: nil, taxis: nil, name: "GatherV2") self.execute("GatherV2", [params, indices, axis], batch_dims: batch_dims, Tparams: tparams, Tindices: tindices, Taxis: taxis, name: name) end
generate_vocab_remapping(new_vocab_file, old_vocab_file, new_vocab_offset: nil, num_new_vocab: nil, old_vocab_size: -1, name: "GenerateVocabRemapping")
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# File lib/tensorflow/ops/raw_ops.rb, line 1622 def self.generate_vocab_remapping(new_vocab_file, old_vocab_file, new_vocab_offset: nil, num_new_vocab: nil, old_vocab_size: -1, name: "GenerateVocabRemapping") self.execute("GenerateVocabRemapping", [new_vocab_file, old_vocab_file], new_vocab_offset: new_vocab_offset, num_new_vocab: num_new_vocab, old_vocab_size: old_vocab_size, name: name) end
generator_dataset(init_func_other_args, next_func_other_args, finalize_func_other_args, init_func: nil, next_func: nil, finalize_func: nil, tinit_func_args: nil, tnext_func_args: nil, tfinalize_func_args: nil, output_types: nil, output_shapes: nil, name: "GeneratorDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1626 def self.generator_dataset(init_func_other_args, next_func_other_args, finalize_func_other_args, init_func: nil, next_func: nil, finalize_func: nil, tinit_func_args: nil, tnext_func_args: nil, tfinalize_func_args: nil, output_types: nil, output_shapes: nil, name: "GeneratorDataset") self.execute("GeneratorDataset", [init_func_other_args, next_func_other_args, finalize_func_other_args], init_func: init_func, next_func: next_func, finalize_func: finalize_func, Tinit_func_args: tinit_func_args, Tnext_func_args: tnext_func_args, Tfinalize_func_args: tfinalize_func_args, output_types: output_types, output_shapes: output_shapes, name: name) end
get_session_handle(value, typeT: nil, name: "GetSessionHandle")
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# File lib/tensorflow/ops/raw_ops.rb, line 1630 def self.get_session_handle(value, typeT: nil, name: "GetSessionHandle") self.execute("GetSessionHandle", [value], T: typeT, name: name) end
get_session_handle_v2(value, typeT: nil, name: "GetSessionHandleV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1634 def self.get_session_handle_v2(value, typeT: nil, name: "GetSessionHandleV2") self.execute("GetSessionHandleV2", [value], T: typeT, name: name) end
get_session_tensor(handle, dtype: nil, name: "GetSessionTensor")
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# File lib/tensorflow/ops/raw_ops.rb, line 1638 def self.get_session_tensor(handle, dtype: nil, name: "GetSessionTensor") self.execute("GetSessionTensor", [handle], dtype: dtype, name: name) end
greater(x, y, typeT: nil, name: "Greater")
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# File lib/tensorflow/ops/raw_ops.rb, line 1642 def self.greater(x, y, typeT: nil, name: "Greater") self.execute("Greater", [x, y], T: typeT, name: name) end
greater_equal(x, y, typeT: nil, name: "GreaterEqual")
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# File lib/tensorflow/ops/raw_ops.rb, line 1646 def self.greater_equal(x, y, typeT: nil, name: "GreaterEqual") self.execute("GreaterEqual", [x, y], T: typeT, name: name) end
group_by_reducer_dataset(input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments, key_func: nil, init_func: nil, reduce_func: nil, finalize_func: nil, tkey_func_other_arguments: nil, tinit_func_other_arguments: nil, treduce_func_other_arguments: nil, tfinalize_func_other_arguments: nil, output_types: nil, output_shapes: nil, name: "GroupByReducerDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1650 def self.group_by_reducer_dataset(input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments, key_func: nil, init_func: nil, reduce_func: nil, finalize_func: nil, tkey_func_other_arguments: nil, tinit_func_other_arguments: nil, treduce_func_other_arguments: nil, tfinalize_func_other_arguments: nil, output_types: nil, output_shapes: nil, name: "GroupByReducerDataset") self.execute("GroupByReducerDataset", [input_dataset, key_func_other_arguments, init_func_other_arguments, reduce_func_other_arguments, finalize_func_other_arguments], key_func: key_func, init_func: init_func, reduce_func: reduce_func, finalize_func: finalize_func, Tkey_func_other_arguments: tkey_func_other_arguments, Tinit_func_other_arguments: tinit_func_other_arguments, Treduce_func_other_arguments: treduce_func_other_arguments, Tfinalize_func_other_arguments: tfinalize_func_other_arguments, output_types: output_types, output_shapes: output_shapes, name: name) end
group_by_window_dataset(input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments, key_func: nil, reduce_func: nil, window_size_func: nil, tkey_func_other_arguments: nil, treduce_func_other_arguments: nil, twindow_size_func_other_arguments: nil, output_types: nil, output_shapes: nil, name: "GroupByWindowDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1654 def self.group_by_window_dataset(input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments, key_func: nil, reduce_func: nil, window_size_func: nil, tkey_func_other_arguments: nil, treduce_func_other_arguments: nil, twindow_size_func_other_arguments: nil, output_types: nil, output_shapes: nil, name: "GroupByWindowDataset") self.execute("GroupByWindowDataset", [input_dataset, key_func_other_arguments, reduce_func_other_arguments, window_size_func_other_arguments], key_func: key_func, reduce_func: reduce_func, window_size_func: window_size_func, Tkey_func_other_arguments: tkey_func_other_arguments, Treduce_func_other_arguments: treduce_func_other_arguments, Twindow_size_func_other_arguments: twindow_size_func_other_arguments, output_types: output_types, output_shapes: output_shapes, name: name) end
gru_block_cell(x, h_prev, w_ru, w_c, b_ru, b_c, typeT: nil, name: "GRUBlockCell")
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# File lib/tensorflow/ops/raw_ops.rb, line 1602 def self.gru_block_cell(x, h_prev, w_ru, w_c, b_ru, b_c, typeT: nil, name: "GRUBlockCell") self.execute("GRUBlockCell", [x, h_prev, w_ru, w_c, b_ru, b_c], T: typeT, name: name) end
gru_block_cell_grad(x, h_prev, w_ru, w_c, b_ru, b_c, r, u, c, d_h, typeT: nil, name: "GRUBlockCellGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 1606 def self.gru_block_cell_grad(x, h_prev, w_ru, w_c, b_ru, b_c, r, u, c, d_h, typeT: nil, name: "GRUBlockCellGrad") self.execute("GRUBlockCellGrad", [x, h_prev, w_ru, w_c, b_ru, b_c, r, u, c, d_h], T: typeT, name: name) end
guarantee_const(input, typeT: nil, name: "GuaranteeConst")
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# File lib/tensorflow/ops/raw_ops.rb, line 1658 def self.guarantee_const(input, typeT: nil, name: "GuaranteeConst") self.execute("GuaranteeConst", [input], T: typeT, name: name) end
hash_table(container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, name: "HashTable")
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# File lib/tensorflow/ops/raw_ops.rb, line 1666 def self.hash_table(container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, name: "HashTable") self.execute("HashTable", [], container: container, shared_name: shared_name, use_node_name_sharing: use_node_name_sharing, key_dtype: key_dtype, value_dtype: value_dtype, name: name) end
hash_table_v2(container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, name: "HashTableV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1670 def self.hash_table_v2(container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, name: "HashTableV2") self.execute("HashTableV2", [], container: container, shared_name: shared_name, use_node_name_sharing: use_node_name_sharing, key_dtype: key_dtype, value_dtype: value_dtype, name: name) end
histogram_fixed_width(values, value_range, nbins, typeT: nil, dtype: :int32, name: "HistogramFixedWidth")
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# File lib/tensorflow/ops/raw_ops.rb, line 1674 def self.histogram_fixed_width(values, value_range, nbins, typeT: nil, dtype: :int32, name: "HistogramFixedWidth") self.execute("HistogramFixedWidth", [values, value_range, nbins], T: typeT, dtype: dtype, name: name) end
histogram_summary(tag, values, typeT: :float, name: "HistogramSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 1678 def self.histogram_summary(tag, values, typeT: :float, name: "HistogramSummary") self.execute("HistogramSummary", [tag, values], T: typeT, name: name) end
host_const(value: nil, dtype: nil, name: "HostConst")
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# File lib/tensorflow/ops/raw_ops.rb, line 1682 def self.host_const(value: nil, dtype: nil, name: "HostConst") self.execute("HostConst", [], value: value, dtype: dtype, name: name) end
hsv_to_rgb(images, typeT: :float, name: "HSVToRGB")
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# File lib/tensorflow/ops/raw_ops.rb, line 1662 def self.hsv_to_rgb(images, typeT: :float, name: "HSVToRGB") self.execute("HSVToRGB", [images], T: typeT, name: name) end
identity(input, typeT: nil, name: "Identity")
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# File lib/tensorflow/ops/raw_ops.rb, line 1710 def self.identity(input, typeT: nil, name: "Identity") self.execute("Identity", [input], T: typeT, name: name) end
identity_n(input, typeT: nil, name: "IdentityN")
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# File lib/tensorflow/ops/raw_ops.rb, line 1714 def self.identity_n(input, typeT: nil, name: "IdentityN") self.execute("IdentityN", [input], T: typeT, name: name) end
identity_reader(container: "", shared_name: "", name: "IdentityReader")
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# File lib/tensorflow/ops/raw_ops.rb, line 1718 def self.identity_reader(container: "", shared_name: "", name: "IdentityReader") self.execute("IdentityReader", [], container: container, shared_name: shared_name, name: name) end
identity_reader_v2(container: "", shared_name: "", name: "IdentityReaderV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1722 def self.identity_reader_v2(container: "", shared_name: "", name: "IdentityReaderV2") self.execute("IdentityReaderV2", [], container: container, shared_name: shared_name, name: name) end
if(cond, input, tcond: nil, tin: nil, tout: nil, then_branch: nil, else_branch: nil, output_shapes: [], name: "If")
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# File lib/tensorflow/ops/raw_ops.rb, line 1726 def self.if(cond, input, tcond: nil, tin: nil, tout: nil, then_branch: nil, else_branch: nil, output_shapes: [], name: "If") self.execute("If", [cond, input], Tcond: tcond, Tin: tin, Tout: tout, then_branch: then_branch, else_branch: else_branch, output_shapes: output_shapes, name: name) end
ifft(input, tcomplex: :complex64, name: "IFFT")
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# File lib/tensorflow/ops/raw_ops.rb, line 1686 def self.ifft(input, tcomplex: :complex64, name: "IFFT") self.execute("IFFT", [input], Tcomplex: tcomplex, name: name) end
ifft2_d(input, tcomplex: :complex64, name: "IFFT2D")
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# File lib/tensorflow/ops/raw_ops.rb, line 1690 def self.ifft2_d(input, tcomplex: :complex64, name: "IFFT2D") self.execute("IFFT2D", [input], Tcomplex: tcomplex, name: name) end
ifft3_d(input, tcomplex: :complex64, name: "IFFT3D")
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# File lib/tensorflow/ops/raw_ops.rb, line 1694 def self.ifft3_d(input, tcomplex: :complex64, name: "IFFT3D") self.execute("IFFT3D", [input], Tcomplex: tcomplex, name: name) end
igamma(a, x, typeT: nil, name: "Igamma")
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# File lib/tensorflow/ops/raw_ops.rb, line 1730 def self.igamma(a, x, typeT: nil, name: "Igamma") self.execute("Igamma", [a, x], T: typeT, name: name) end
igamma_grad_a(a, x, typeT: nil, name: "IgammaGradA")
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# File lib/tensorflow/ops/raw_ops.rb, line 1734 def self.igamma_grad_a(a, x, typeT: nil, name: "IgammaGradA") self.execute("IgammaGradA", [a, x], T: typeT, name: name) end
igammac(a, x, typeT: nil, name: "Igammac")
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# File lib/tensorflow/ops/raw_ops.rb, line 1738 def self.igammac(a, x, typeT: nil, name: "Igammac") self.execute("Igammac", [a, x], T: typeT, name: name) end
ignore_errors_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "IgnoreErrorsDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1742 def self.ignore_errors_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "IgnoreErrorsDataset") self.execute("IgnoreErrorsDataset", [input_dataset], output_types: output_types, output_shapes: output_shapes, name: name) end
imag(input, typeT: :complex64, tout: :float, name: "Imag")
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# File lib/tensorflow/ops/raw_ops.rb, line 1746 def self.imag(input, typeT: :complex64, tout: :float, name: "Imag") self.execute("Imag", [input], T: typeT, Tout: tout, name: name) end
image_summary(tag, tensor, max_images: 3, typeT: :float, bad_color: [], name: "ImageSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 1750 def self.image_summary(tag, tensor, max_images: 3, typeT: :float, bad_color: [], name: "ImageSummary") self.execute("ImageSummary", [tag, tensor], max_images: max_images, T: typeT, bad_color: bad_color, name: name) end
immutable_const(dtype: nil, shape: nil, memory_region_name: "", name: "ImmutableConst")
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# File lib/tensorflow/ops/raw_ops.rb, line 1754 def self.immutable_const(dtype: nil, shape: nil, memory_region_name: "", name: "ImmutableConst") self.execute("ImmutableConst", [], dtype: dtype, shape: shape, memory_region_name: memory_region_name, name: name) end
import_event(writer, event, name: "ImportEvent")
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# File lib/tensorflow/ops/raw_ops.rb, line 1758 def self.import_event(writer, event, name: "ImportEvent") self.execute("ImportEvent", [writer, event], name: name) end
in_top_k(predictions, targets, k: nil, typeT: :int32, name: "InTopK")
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# File lib/tensorflow/ops/raw_ops.rb, line 1762 def self.in_top_k(predictions, targets, k: nil, typeT: :int32, name: "InTopK") self.execute("InTopK", [predictions, targets], k: k, T: typeT, name: name) end
in_top_kv2(predictions, targets, k, typeT: :int32, name: "InTopKV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1766 def self.in_top_kv2(predictions, targets, k, typeT: :int32, name: "InTopKV2") self.execute("InTopKV2", [predictions, targets, k], T: typeT, name: name) end
infeed_dequeue(dtype: nil, shape: nil, name: "InfeedDequeue")
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# File lib/tensorflow/ops/raw_ops.rb, line 1770 def self.infeed_dequeue(dtype: nil, shape: nil, name: "InfeedDequeue") self.execute("InfeedDequeue", [], dtype: dtype, shape: shape, name: name) end
infeed_dequeue_tuple(dtypes: nil, shapes: nil, name: "InfeedDequeueTuple")
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# File lib/tensorflow/ops/raw_ops.rb, line 1774 def self.infeed_dequeue_tuple(dtypes: nil, shapes: nil, name: "InfeedDequeueTuple") self.execute("InfeedDequeueTuple", [], dtypes: dtypes, shapes: shapes, name: name) end
infeed_enqueue(input, dtype: nil, shape: [], layout: [], device_ordinal: -1, name: "InfeedEnqueue")
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# File lib/tensorflow/ops/raw_ops.rb, line 1778 def self.infeed_enqueue(input, dtype: nil, shape: [], layout: [], device_ordinal: -1, name: "InfeedEnqueue") self.execute("InfeedEnqueue", [input], dtype: dtype, shape: shape, layout: layout, device_ordinal: device_ordinal, name: name) end
infeed_enqueue_prelinearized_buffer(input, device_ordinal: -1, name: "InfeedEnqueuePrelinearizedBuffer")
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# File lib/tensorflow/ops/raw_ops.rb, line 1782 def self.infeed_enqueue_prelinearized_buffer(input, device_ordinal: -1, name: "InfeedEnqueuePrelinearizedBuffer") self.execute("InfeedEnqueuePrelinearizedBuffer", [input], device_ordinal: device_ordinal, name: name) end
infeed_enqueue_tuple(inputs, dtypes: nil, shapes: nil, layouts: [], device_ordinal: -1, name: "InfeedEnqueueTuple")
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# File lib/tensorflow/ops/raw_ops.rb, line 1786 def self.infeed_enqueue_tuple(inputs, dtypes: nil, shapes: nil, layouts: [], device_ordinal: -1, name: "InfeedEnqueueTuple") self.execute("InfeedEnqueueTuple", [inputs], dtypes: dtypes, shapes: shapes, layouts: layouts, device_ordinal: device_ordinal, name: name) end
initialize_table(table_handle, keys, values, tkey: nil, tval: nil, name: "InitializeTable")
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# File lib/tensorflow/ops/raw_ops.rb, line 1790 def self.initialize_table(table_handle, keys, values, tkey: nil, tval: nil, name: "InitializeTable") self.execute("InitializeTable", [table_handle, keys, values], Tkey: tkey, Tval: tval, name: name) end
initialize_table_from_text_file(table_handle, filename, key_index: nil, value_index: nil, vocab_size: -1, delimiter: " ", name: "InitializeTableFromTextFile")
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# File lib/tensorflow/ops/raw_ops.rb, line 1794 def self.initialize_table_from_text_file(table_handle, filename, key_index: nil, value_index: nil, vocab_size: -1, delimiter: " ", name: "InitializeTableFromTextFile") self.execute("InitializeTableFromTextFile", [table_handle, filename], key_index: key_index, value_index: value_index, vocab_size: vocab_size, delimiter: delimiter, name: name) end
initialize_table_from_text_file_v2(table_handle, filename, key_index: nil, value_index: nil, vocab_size: -1, delimiter: " ", name: "InitializeTableFromTextFileV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1798 def self.initialize_table_from_text_file_v2(table_handle, filename, key_index: nil, value_index: nil, vocab_size: -1, delimiter: " ", name: "InitializeTableFromTextFileV2") self.execute("InitializeTableFromTextFileV2", [table_handle, filename], key_index: key_index, value_index: value_index, vocab_size: vocab_size, delimiter: delimiter, name: name) end
initialize_table_v2(table_handle, keys, values, tkey: nil, tval: nil, name: "InitializeTableV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1802 def self.initialize_table_v2(table_handle, keys, values, tkey: nil, tval: nil, name: "InitializeTableV2") self.execute("InitializeTableV2", [table_handle, keys, values], Tkey: tkey, Tval: tval, name: name) end
inplace_add(x, i, v, typeT: nil, name: "InplaceAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 1806 def self.inplace_add(x, i, v, typeT: nil, name: "InplaceAdd") self.execute("InplaceAdd", [x, i, v], T: typeT, name: name) end
inplace_sub(x, i, v, typeT: nil, name: "InplaceSub")
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# File lib/tensorflow/ops/raw_ops.rb, line 1810 def self.inplace_sub(x, i, v, typeT: nil, name: "InplaceSub") self.execute("InplaceSub", [x, i, v], T: typeT, name: name) end
inplace_update(x, i, v, typeT: nil, name: "InplaceUpdate")
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# File lib/tensorflow/ops/raw_ops.rb, line 1814 def self.inplace_update(x, i, v, typeT: nil, name: "InplaceUpdate") self.execute("InplaceUpdate", [x, i, v], T: typeT, name: name) end
interleave_dataset(input_dataset, other_arguments, cycle_length, block_length, f: nil, targuments: nil, output_types: nil, output_shapes: nil, name: "InterleaveDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1818 def self.interleave_dataset(input_dataset, other_arguments, cycle_length, block_length, f: nil, targuments: nil, output_types: nil, output_shapes: nil, name: "InterleaveDataset") self.execute("InterleaveDataset", [input_dataset, other_arguments, cycle_length, block_length], f: f, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, name: name) end
inv(x, typeT: nil, name: "Inv")
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# File lib/tensorflow/ops/raw_ops.rb, line 1822 def self.inv(x, typeT: nil, name: "Inv") self.execute("Inv", [x], T: typeT, name: name) end
inv_grad(y, dy, typeT: nil, name: "InvGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 1826 def self.inv_grad(y, dy, typeT: nil, name: "InvGrad") self.execute("InvGrad", [y, dy], T: typeT, name: name) end
invert(x, typeT: nil, name: "Invert")
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# File lib/tensorflow/ops/raw_ops.rb, line 1830 def self.invert(x, typeT: nil, name: "Invert") self.execute("Invert", [x], T: typeT, name: name) end
invert_permutation(x, typeT: :int32, name: "InvertPermutation")
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# File lib/tensorflow/ops/raw_ops.rb, line 1834 def self.invert_permutation(x, typeT: :int32, name: "InvertPermutation") self.execute("InvertPermutation", [x], T: typeT, name: name) end
irfft(input, fft_length, treal: :float, tcomplex: :complex64, name: "IRFFT")
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# File lib/tensorflow/ops/raw_ops.rb, line 1698 def self.irfft(input, fft_length, treal: :float, tcomplex: :complex64, name: "IRFFT") self.execute("IRFFT", [input, fft_length], Treal: treal, Tcomplex: tcomplex, name: name) end
irfft2_d(input, fft_length, treal: :float, tcomplex: :complex64, name: "IRFFT2D")
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# File lib/tensorflow/ops/raw_ops.rb, line 1702 def self.irfft2_d(input, fft_length, treal: :float, tcomplex: :complex64, name: "IRFFT2D") self.execute("IRFFT2D", [input, fft_length], Treal: treal, Tcomplex: tcomplex, name: name) end
irfft3_d(input, fft_length, treal: :float, tcomplex: :complex64, name: "IRFFT3D")
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# File lib/tensorflow/ops/raw_ops.rb, line 1706 def self.irfft3_d(input, fft_length, treal: :float, tcomplex: :complex64, name: "IRFFT3D") self.execute("IRFFT3D", [input, fft_length], Treal: treal, Tcomplex: tcomplex, name: name) end
is_boosted_trees_ensemble_initialized(tree_ensemble_handle, name: "IsBoostedTreesEnsembleInitialized")
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# File lib/tensorflow/ops/raw_ops.rb, line 1838 def self.is_boosted_trees_ensemble_initialized(tree_ensemble_handle, name: "IsBoostedTreesEnsembleInitialized") self.execute("IsBoostedTreesEnsembleInitialized", [tree_ensemble_handle], name: name) end
is_boosted_trees_quantile_stream_resource_initialized(quantile_stream_resource_handle, name: "IsBoostedTreesQuantileStreamResourceInitialized")
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# File lib/tensorflow/ops/raw_ops.rb, line 1842 def self.is_boosted_trees_quantile_stream_resource_initialized(quantile_stream_resource_handle, name: "IsBoostedTreesQuantileStreamResourceInitialized") self.execute("IsBoostedTreesQuantileStreamResourceInitialized", [quantile_stream_resource_handle], name: name) end
is_finite(x, typeT: nil, name: "IsFinite")
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# File lib/tensorflow/ops/raw_ops.rb, line 1846 def self.is_finite(x, typeT: nil, name: "IsFinite") self.execute("IsFinite", [x], T: typeT, name: name) end
is_inf(x, typeT: nil, name: "IsInf")
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# File lib/tensorflow/ops/raw_ops.rb, line 1850 def self.is_inf(x, typeT: nil, name: "IsInf") self.execute("IsInf", [x], T: typeT, name: name) end
is_nan(x, typeT: nil, name: "IsNan")
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# File lib/tensorflow/ops/raw_ops.rb, line 1854 def self.is_nan(x, typeT: nil, name: "IsNan") self.execute("IsNan", [x], T: typeT, name: name) end
is_variable_initialized(ref, dtype: nil, name: "IsVariableInitialized")
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# File lib/tensorflow/ops/raw_ops.rb, line 1858 def self.is_variable_initialized(ref, dtype: nil, name: "IsVariableInitialized") self.execute("IsVariableInitialized", [ref], dtype: dtype, name: name) end
iterator(shared_name: "", container: "", output_types: nil, output_shapes: nil, name: "Iterator")
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# File lib/tensorflow/ops/raw_ops.rb, line 1862 def self.iterator(shared_name: "", container: "", output_types: nil, output_shapes: nil, name: "Iterator") self.execute("Iterator", [], shared_name: shared_name, container: container, output_types: output_types, output_shapes: output_shapes, name: name) end
iterator_from_string_handle(string_handle, output_types: [], output_shapes: [], name: "IteratorFromStringHandle")
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# File lib/tensorflow/ops/raw_ops.rb, line 1866 def self.iterator_from_string_handle(string_handle, output_types: [], output_shapes: [], name: "IteratorFromStringHandle") self.execute("IteratorFromStringHandle", [string_handle], output_types: output_types, output_shapes: output_shapes, name: name) end
iterator_from_string_handle_v2(string_handle, output_types: [], output_shapes: [], name: "IteratorFromStringHandleV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1870 def self.iterator_from_string_handle_v2(string_handle, output_types: [], output_shapes: [], name: "IteratorFromStringHandleV2") self.execute("IteratorFromStringHandleV2", [string_handle], output_types: output_types, output_shapes: output_shapes, name: name) end
iterator_get_device(resource, name: "IteratorGetDevice")
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# File lib/tensorflow/ops/raw_ops.rb, line 1874 def self.iterator_get_device(resource, name: "IteratorGetDevice") self.execute("IteratorGetDevice", [resource], name: name) end
iterator_get_next(iterator, output_types: nil, output_shapes: nil, name: "IteratorGetNext")
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# File lib/tensorflow/ops/raw_ops.rb, line 1878 def self.iterator_get_next(iterator, output_types: nil, output_shapes: nil, name: "IteratorGetNext") self.execute("IteratorGetNext", [iterator], output_types: output_types, output_shapes: output_shapes, name: name) end
iterator_get_next_as_optional(iterator, output_types: nil, output_shapes: nil, name: "IteratorGetNextAsOptional")
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# File lib/tensorflow/ops/raw_ops.rb, line 1882 def self.iterator_get_next_as_optional(iterator, output_types: nil, output_shapes: nil, name: "IteratorGetNextAsOptional") self.execute("IteratorGetNextAsOptional", [iterator], output_types: output_types, output_shapes: output_shapes, name: name) end
iterator_get_next_sync(iterator, output_types: nil, output_shapes: nil, name: "IteratorGetNextSync")
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# File lib/tensorflow/ops/raw_ops.rb, line 1886 def self.iterator_get_next_sync(iterator, output_types: nil, output_shapes: nil, name: "IteratorGetNextSync") self.execute("IteratorGetNextSync", [iterator], output_types: output_types, output_shapes: output_shapes, name: name) end
iterator_to_string_handle(resource_handle, name: "IteratorToStringHandle")
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# File lib/tensorflow/ops/raw_ops.rb, line 1890 def self.iterator_to_string_handle(resource_handle, name: "IteratorToStringHandle") self.execute("IteratorToStringHandle", [resource_handle], name: name) end
iterator_v2(shared_name: "", container: "", output_types: nil, output_shapes: nil, name: "IteratorV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 1894 def self.iterator_v2(shared_name: "", container: "", output_types: nil, output_shapes: nil, name: "IteratorV2") self.execute("IteratorV2", [], shared_name: shared_name, container: container, output_types: output_types, output_shapes: output_shapes, name: name) end
kmc2_chain_initialization(distances, seed, name: "KMC2ChainInitialization")
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# File lib/tensorflow/ops/raw_ops.rb, line 1898 def self.kmc2_chain_initialization(distances, seed, name: "KMC2ChainInitialization") self.execute("KMC2ChainInitialization", [distances, seed], name: name) end
kmeans_plus_plus_initialization(points, num_to_sample, seed, num_retries_per_sample, name: "KmeansPlusPlusInitialization")
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# File lib/tensorflow/ops/raw_ops.rb, line 1902 def self.kmeans_plus_plus_initialization(points, num_to_sample, seed, num_retries_per_sample, name: "KmeansPlusPlusInitialization") self.execute("KmeansPlusPlusInitialization", [points, num_to_sample, seed, num_retries_per_sample], name: name) end
l2_loss(t, typeT: nil, name: "L2Loss")
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# File lib/tensorflow/ops/raw_ops.rb, line 1906 def self.l2_loss(t, typeT: nil, name: "L2Loss") self.execute("L2Loss", [t], T: typeT, name: name) end
latency_stats_dataset(input_dataset, tag, output_types: nil, output_shapes: nil, name: "LatencyStatsDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1934 def self.latency_stats_dataset(input_dataset, tag, output_types: nil, output_shapes: nil, name: "LatencyStatsDataset") self.execute("LatencyStatsDataset", [input_dataset, tag], output_types: output_types, output_shapes: output_shapes, name: name) end
leaky_relu(features, alpha: 0.20000000298023224, typeT: :float, name: "LeakyRelu")
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# File lib/tensorflow/ops/raw_ops.rb, line 1938 def self.leaky_relu(features, alpha: 0.20000000298023224, typeT: :float, name: "LeakyRelu") self.execute("LeakyRelu", [features], alpha: alpha, T: typeT, name: name) end
leaky_relu_grad(gradients, features, alpha: 0.20000000298023224, typeT: :float, name: "LeakyReluGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 1942 def self.leaky_relu_grad(gradients, features, alpha: 0.20000000298023224, typeT: :float, name: "LeakyReluGrad") self.execute("LeakyReluGrad", [gradients, features], alpha: alpha, T: typeT, name: name) end
learned_unigram_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, seed: 0, seed2: 0, name: "LearnedUnigramCandidateSampler")
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# File lib/tensorflow/ops/raw_ops.rb, line 1946 def self.learned_unigram_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, seed: 0, seed2: 0, name: "LearnedUnigramCandidateSampler") self.execute("LearnedUnigramCandidateSampler", [true_classes], num_true: num_true, num_sampled: num_sampled, unique: unique, range_max: range_max, seed: seed, seed2: seed2, name: name) end
left_shift(x, y, typeT: nil, name: "LeftShift")
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# File lib/tensorflow/ops/raw_ops.rb, line 1950 def self.left_shift(x, y, typeT: nil, name: "LeftShift") self.execute("LeftShift", [x, y], T: typeT, name: name) end
less(x, y, typeT: nil, name: "Less")
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# File lib/tensorflow/ops/raw_ops.rb, line 1954 def self.less(x, y, typeT: nil, name: "Less") self.execute("Less", [x, y], T: typeT, name: name) end
less_equal(x, y, typeT: nil, name: "LessEqual")
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# File lib/tensorflow/ops/raw_ops.rb, line 1958 def self.less_equal(x, y, typeT: nil, name: "LessEqual") self.execute("LessEqual", [x, y], T: typeT, name: name) end
lgamma(x, typeT: nil, name: "Lgamma")
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# File lib/tensorflow/ops/raw_ops.rb, line 1962 def self.lgamma(x, typeT: nil, name: "Lgamma") self.execute("Lgamma", [x], T: typeT, name: name) end
lin_space(start, stop, num, typeT: nil, tidx: :int32, name: "LinSpace")
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# File lib/tensorflow/ops/raw_ops.rb, line 1966 def self.lin_space(start, stop, num, typeT: nil, tidx: :int32, name: "LinSpace") self.execute("LinSpace", [start, stop, num], T: typeT, Tidx: tidx, name: name) end
list_diff(x, y, typeT: nil, out_idx: :int32, name: "ListDiff")
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# File lib/tensorflow/ops/raw_ops.rb, line 1970 def self.list_diff(x, y, typeT: nil, out_idx: :int32, name: "ListDiff") self.execute("ListDiff", [x, y], T: typeT, out_idx: out_idx, name: name) end
lmdb_dataset(filenames, output_types: nil, output_shapes: nil, name: "LMDBDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 1910 def self.lmdb_dataset(filenames, output_types: nil, output_shapes: nil, name: "LMDBDataset") self.execute("LMDBDataset", [filenames], output_types: output_types, output_shapes: output_shapes, name: name) end
lmdb_reader(container: "", shared_name: "", name: "LMDBReader")
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# File lib/tensorflow/ops/raw_ops.rb, line 1914 def self.lmdb_reader(container: "", shared_name: "", name: "LMDBReader") self.execute("LMDBReader", [], container: container, shared_name: shared_name, name: name) end
load_and_remap_matrix(ckpt_path, old_tensor_name, row_remapping, col_remapping, initializing_values, num_rows: nil, num_cols: nil, max_rows_in_memory: -1, name: "LoadAndRemapMatrix")
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# File lib/tensorflow/ops/raw_ops.rb, line 1974 def self.load_and_remap_matrix(ckpt_path, old_tensor_name, row_remapping, col_remapping, initializing_values, num_rows: nil, num_cols: nil, max_rows_in_memory: -1, name: "LoadAndRemapMatrix") self.execute("LoadAndRemapMatrix", [ckpt_path, old_tensor_name, row_remapping, col_remapping, initializing_values], num_rows: num_rows, num_cols: num_cols, max_rows_in_memory: max_rows_in_memory, name: name) end
load_tpu_embedding_adadelta_parameters(parameters, accumulators, updates, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingAdadeltaParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 1986 def self.load_tpu_embedding_adadelta_parameters(parameters, accumulators, updates, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingAdadeltaParameters") self.execute("LoadTPUEmbeddingAdadeltaParameters", [parameters, accumulators, updates], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_adadelta_parameters_grad_accum_debug(parameters, accumulators, updates, gradient_accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingAdadeltaParametersGradAccumDebug")
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# File lib/tensorflow/ops/raw_ops.rb, line 1990 def self.load_tpu_embedding_adadelta_parameters_grad_accum_debug(parameters, accumulators, updates, gradient_accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingAdadeltaParametersGradAccumDebug") self.execute("LoadTPUEmbeddingAdadeltaParametersGradAccumDebug", [parameters, accumulators, updates, gradient_accumulators], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_adagrad_parameters(parameters, accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingAdagradParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 1994 def self.load_tpu_embedding_adagrad_parameters(parameters, accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingAdagradParameters") self.execute("LoadTPUEmbeddingAdagradParameters", [parameters, accumulators], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_adagrad_parameters_grad_accum_debug(parameters, accumulators, gradient_accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingAdagradParametersGradAccumDebug")
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# File lib/tensorflow/ops/raw_ops.rb, line 1998 def self.load_tpu_embedding_adagrad_parameters_grad_accum_debug(parameters, accumulators, gradient_accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingAdagradParametersGradAccumDebug") self.execute("LoadTPUEmbeddingAdagradParametersGradAccumDebug", [parameters, accumulators, gradient_accumulators], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_adam_parameters(parameters, momenta, velocities, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingADAMParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 1978 def self.load_tpu_embedding_adam_parameters(parameters, momenta, velocities, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingADAMParameters") self.execute("LoadTPUEmbeddingADAMParameters", [parameters, momenta, velocities], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_adam_parameters_grad_accum_debug(parameters, momenta, velocities, gradient_accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingADAMParametersGradAccumDebug")
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# File lib/tensorflow/ops/raw_ops.rb, line 1982 def self.load_tpu_embedding_adam_parameters_grad_accum_debug(parameters, momenta, velocities, gradient_accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingADAMParametersGradAccumDebug") self.execute("LoadTPUEmbeddingADAMParametersGradAccumDebug", [parameters, momenta, velocities, gradient_accumulators], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_centered_rms_prop_parameters(parameters, ms, mom, mg, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingCenteredRMSPropParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 2002 def self.load_tpu_embedding_centered_rms_prop_parameters(parameters, ms, mom, mg, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingCenteredRMSPropParameters") self.execute("LoadTPUEmbeddingCenteredRMSPropParameters", [parameters, ms, mom, mg], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_ftrl_parameters(parameters, accumulators, linears, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingFTRLParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 2006 def self.load_tpu_embedding_ftrl_parameters(parameters, accumulators, linears, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingFTRLParameters") self.execute("LoadTPUEmbeddingFTRLParameters", [parameters, accumulators, linears], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_ftrl_parameters_grad_accum_debug(parameters, accumulators, linears, gradient_accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingFTRLParametersGradAccumDebug")
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# File lib/tensorflow/ops/raw_ops.rb, line 2010 def self.load_tpu_embedding_ftrl_parameters_grad_accum_debug(parameters, accumulators, linears, gradient_accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingFTRLParametersGradAccumDebug") self.execute("LoadTPUEmbeddingFTRLParametersGradAccumDebug", [parameters, accumulators, linears, gradient_accumulators], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_mdl_adagrad_light_parameters(parameters, accumulators, weights, benefits, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingMDLAdagradLightParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 2014 def self.load_tpu_embedding_mdl_adagrad_light_parameters(parameters, accumulators, weights, benefits, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingMDLAdagradLightParameters") self.execute("LoadTPUEmbeddingMDLAdagradLightParameters", [parameters, accumulators, weights, benefits], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_momentum_parameters(parameters, momenta, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingMomentumParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 2018 def self.load_tpu_embedding_momentum_parameters(parameters, momenta, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingMomentumParameters") self.execute("LoadTPUEmbeddingMomentumParameters", [parameters, momenta], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_momentum_parameters_grad_accum_debug(parameters, momenta, gradient_accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingMomentumParametersGradAccumDebug")
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# File lib/tensorflow/ops/raw_ops.rb, line 2022 def self.load_tpu_embedding_momentum_parameters_grad_accum_debug(parameters, momenta, gradient_accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingMomentumParametersGradAccumDebug") self.execute("LoadTPUEmbeddingMomentumParametersGradAccumDebug", [parameters, momenta, gradient_accumulators], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_proximal_adagrad_parameters(parameters, accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingProximalAdagradParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 2026 def self.load_tpu_embedding_proximal_adagrad_parameters(parameters, accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingProximalAdagradParameters") self.execute("LoadTPUEmbeddingProximalAdagradParameters", [parameters, accumulators], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug(parameters, accumulators, gradient_accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug")
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# File lib/tensorflow/ops/raw_ops.rb, line 2030 def self.load_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug(parameters, accumulators, gradient_accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug") self.execute("LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug", [parameters, accumulators, gradient_accumulators], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_rms_prop_parameters(parameters, ms, mom, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingRMSPropParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 2034 def self.load_tpu_embedding_rms_prop_parameters(parameters, ms, mom, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingRMSPropParameters") self.execute("LoadTPUEmbeddingRMSPropParameters", [parameters, ms, mom], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_rms_prop_parameters_grad_accum_debug(parameters, ms, mom, gradient_accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingRMSPropParametersGradAccumDebug")
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# File lib/tensorflow/ops/raw_ops.rb, line 2038 def self.load_tpu_embedding_rms_prop_parameters_grad_accum_debug(parameters, ms, mom, gradient_accumulators, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingRMSPropParametersGradAccumDebug") self.execute("LoadTPUEmbeddingRMSPropParametersGradAccumDebug", [parameters, ms, mom, gradient_accumulators], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
load_tpu_embedding_stochastic_gradient_descent_parameters(parameters, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingStochasticGradientDescentParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 2042 def self.load_tpu_embedding_stochastic_gradient_descent_parameters(parameters, table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "LoadTPUEmbeddingStochasticGradientDescentParameters") self.execute("LoadTPUEmbeddingStochasticGradientDescentParameters", [parameters], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
log(x, typeT: nil, name: "Log")
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# File lib/tensorflow/ops/raw_ops.rb, line 2046 def self.log(x, typeT: nil, name: "Log") self.execute("Log", [x], T: typeT, name: name) end
log1p(x, typeT: nil, name: "Log1p")
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# File lib/tensorflow/ops/raw_ops.rb, line 2050 def self.log1p(x, typeT: nil, name: "Log1p") self.execute("Log1p", [x], T: typeT, name: name) end
log_matrix_determinant(input, typeT: nil, name: "LogMatrixDeterminant")
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# File lib/tensorflow/ops/raw_ops.rb, line 2054 def self.log_matrix_determinant(input, typeT: nil, name: "LogMatrixDeterminant") self.execute("LogMatrixDeterminant", [input], T: typeT, name: name) end
log_softmax(logits, typeT: nil, name: "LogSoftmax")
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# File lib/tensorflow/ops/raw_ops.rb, line 2058 def self.log_softmax(logits, typeT: nil, name: "LogSoftmax") self.execute("LogSoftmax", [logits], T: typeT, name: name) end
log_uniform_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, seed: 0, seed2: 0, name: "LogUniformCandidateSampler")
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# File lib/tensorflow/ops/raw_ops.rb, line 2062 def self.log_uniform_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, seed: 0, seed2: 0, name: "LogUniformCandidateSampler") self.execute("LogUniformCandidateSampler", [true_classes], num_true: num_true, num_sampled: num_sampled, unique: unique, range_max: range_max, seed: seed, seed2: seed2, name: name) end
logical_and(x, y, name: "LogicalAnd")
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# File lib/tensorflow/ops/raw_ops.rb, line 2066 def self.logical_and(x, y, name: "LogicalAnd") self.execute("LogicalAnd", [x, y], name: name) end
logical_not(x, name: "LogicalNot")
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# File lib/tensorflow/ops/raw_ops.rb, line 2070 def self.logical_not(x, name: "LogicalNot") self.execute("LogicalNot", [x], name: name) end
logical_or(x, y, name: "LogicalOr")
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# File lib/tensorflow/ops/raw_ops.rb, line 2074 def self.logical_or(x, y, name: "LogicalOr") self.execute("LogicalOr", [x, y], name: name) end
lookup_table_export(table_handle, tkeys: nil, tvalues: nil, name: "LookupTableExport")
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# File lib/tensorflow/ops/raw_ops.rb, line 2078 def self.lookup_table_export(table_handle, tkeys: nil, tvalues: nil, name: "LookupTableExport") self.execute("LookupTableExport", [table_handle], Tkeys: tkeys, Tvalues: tvalues, name: name) end
lookup_table_export_v2(table_handle, tkeys: nil, tvalues: nil, name: "LookupTableExportV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2082 def self.lookup_table_export_v2(table_handle, tkeys: nil, tvalues: nil, name: "LookupTableExportV2") self.execute("LookupTableExportV2", [table_handle], Tkeys: tkeys, Tvalues: tvalues, name: name) end
lookup_table_find(table_handle, keys, default_value, tin: nil, tout: nil, name: "LookupTableFind")
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# File lib/tensorflow/ops/raw_ops.rb, line 2086 def self.lookup_table_find(table_handle, keys, default_value, tin: nil, tout: nil, name: "LookupTableFind") self.execute("LookupTableFind", [table_handle, keys, default_value], Tin: tin, Tout: tout, name: name) end
lookup_table_find_v2(table_handle, keys, default_value, tin: nil, tout: nil, name: "LookupTableFindV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2090 def self.lookup_table_find_v2(table_handle, keys, default_value, tin: nil, tout: nil, name: "LookupTableFindV2") self.execute("LookupTableFindV2", [table_handle, keys, default_value], Tin: tin, Tout: tout, name: name) end
lookup_table_import(table_handle, keys, values, tin: nil, tout: nil, name: "LookupTableImport")
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# File lib/tensorflow/ops/raw_ops.rb, line 2094 def self.lookup_table_import(table_handle, keys, values, tin: nil, tout: nil, name: "LookupTableImport") self.execute("LookupTableImport", [table_handle, keys, values], Tin: tin, Tout: tout, name: name) end
lookup_table_import_v2(table_handle, keys, values, tin: nil, tout: nil, name: "LookupTableImportV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2098 def self.lookup_table_import_v2(table_handle, keys, values, tin: nil, tout: nil, name: "LookupTableImportV2") self.execute("LookupTableImportV2", [table_handle, keys, values], Tin: tin, Tout: tout, name: name) end
lookup_table_insert(table_handle, keys, values, tin: nil, tout: nil, name: "LookupTableInsert")
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# File lib/tensorflow/ops/raw_ops.rb, line 2102 def self.lookup_table_insert(table_handle, keys, values, tin: nil, tout: nil, name: "LookupTableInsert") self.execute("LookupTableInsert", [table_handle, keys, values], Tin: tin, Tout: tout, name: name) end
lookup_table_insert_v2(table_handle, keys, values, tin: nil, tout: nil, name: "LookupTableInsertV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2106 def self.lookup_table_insert_v2(table_handle, keys, values, tin: nil, tout: nil, name: "LookupTableInsertV2") self.execute("LookupTableInsertV2", [table_handle, keys, values], Tin: tin, Tout: tout, name: name) end
lookup_table_remove_v2(table_handle, keys, tin: nil, name: "LookupTableRemoveV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2110 def self.lookup_table_remove_v2(table_handle, keys, tin: nil, name: "LookupTableRemoveV2") self.execute("LookupTableRemoveV2", [table_handle, keys], Tin: tin, name: name) end
lookup_table_size(table_handle, name: "LookupTableSize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2114 def self.lookup_table_size(table_handle, name: "LookupTableSize") self.execute("LookupTableSize", [table_handle], name: name) end
lookup_table_size_v2(table_handle, name: "LookupTableSizeV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2118 def self.lookup_table_size_v2(table_handle, name: "LookupTableSizeV2") self.execute("LookupTableSizeV2", [table_handle], name: name) end
loop_cond(input, name: "LoopCond")
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# File lib/tensorflow/ops/raw_ops.rb, line 2122 def self.loop_cond(input, name: "LoopCond") self.execute("LoopCond", [input], name: name) end
lower_bound(sorted_inputs, values, typeT: nil, out_type: :int32, name: "LowerBound")
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# File lib/tensorflow/ops/raw_ops.rb, line 2126 def self.lower_bound(sorted_inputs, values, typeT: nil, out_type: :int32, name: "LowerBound") self.execute("LowerBound", [sorted_inputs, values], T: typeT, out_type: out_type, name: name) end
lrn(input, depth_radius: 5, bias: 1.0, alpha: 1.0, beta: 0.5, typeT: :float, name: "LRN")
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# File lib/tensorflow/ops/raw_ops.rb, line 1918 def self.lrn(input, depth_radius: 5, bias: 1.0, alpha: 1.0, beta: 0.5, typeT: :float, name: "LRN") self.execute("LRN", [input], depth_radius: depth_radius, bias: bias, alpha: alpha, beta: beta, T: typeT, name: name) end
lrn_grad(input_grads, input_image, output_image, depth_radius: 5, bias: 1.0, alpha: 1.0, beta: 0.5, typeT: :float, name: "LRNGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 1922 def self.lrn_grad(input_grads, input_image, output_image, depth_radius: 5, bias: 1.0, alpha: 1.0, beta: 0.5, typeT: :float, name: "LRNGrad") self.execute("LRNGrad", [input_grads, input_image, output_image], depth_radius: depth_radius, bias: bias, alpha: alpha, beta: beta, T: typeT, name: name) end
lstm_block_cell(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias: 1.0, cell_clip: 3.0, use_peephole: false, typeT: nil, name: "LSTMBlockCell")
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# File lib/tensorflow/ops/raw_ops.rb, line 1926 def self.lstm_block_cell(x, cs_prev, h_prev, w, wci, wcf, wco, b, forget_bias: 1.0, cell_clip: 3.0, use_peephole: false, typeT: nil, name: "LSTMBlockCell") self.execute("LSTMBlockCell", [x, cs_prev, h_prev, w, wci, wcf, wco, b], forget_bias: forget_bias, cell_clip: cell_clip, use_peephole: use_peephole, T: typeT, name: name) end
lstm_block_cell_grad(x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole: nil, typeT: nil, name: "LSTMBlockCellGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 1930 def self.lstm_block_cell_grad(x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad, use_peephole: nil, typeT: nil, name: "LSTMBlockCellGrad") self.execute("LSTMBlockCellGrad", [x, cs_prev, h_prev, w, wci, wcf, wco, b, i, cs, f, o, ci, co, cs_grad, h_grad], use_peephole: use_peephole, T: typeT, name: name) end
lu(input, typeT: nil, output_idx_type: :int32, name: "Lu")
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# File lib/tensorflow/ops/raw_ops.rb, line 2130 def self.lu(input, typeT: nil, output_idx_type: :int32, name: "Lu") self.execute("Lu", [input], T: typeT, output_idx_type: output_idx_type, name: name) end
make_iterator(dataset, iterator, name: "MakeIterator")
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# File lib/tensorflow/ops/raw_ops.rb, line 2134 def self.make_iterator(dataset, iterator, name: "MakeIterator") self.execute("MakeIterator", [dataset, iterator], name: name) end
map_and_batch_dataset(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f: nil, targuments: nil, output_types: nil, output_shapes: nil, preserve_cardinality: false, name: "MapAndBatchDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 2138 def self.map_and_batch_dataset(input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder, f: nil, targuments: nil, output_types: nil, output_shapes: nil, preserve_cardinality: false, name: "MapAndBatchDataset") self.execute("MapAndBatchDataset", [input_dataset, other_arguments, batch_size, num_parallel_calls, drop_remainder], f: f, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, preserve_cardinality: preserve_cardinality, name: name) end
map_clear(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "MapClear")
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# File lib/tensorflow/ops/raw_ops.rb, line 2142 def self.map_clear(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "MapClear") self.execute("MapClear", [], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
map_dataset(input_dataset, other_arguments, f: nil, targuments: nil, output_types: nil, output_shapes: nil, use_inter_op_parallelism: true, preserve_cardinality: false, name: "MapDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 2146 def self.map_dataset(input_dataset, other_arguments, f: nil, targuments: nil, output_types: nil, output_shapes: nil, use_inter_op_parallelism: true, preserve_cardinality: false, name: "MapDataset") self.execute("MapDataset", [input_dataset, other_arguments], f: f, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, use_inter_op_parallelism: use_inter_op_parallelism, preserve_cardinality: preserve_cardinality, name: name) end
map_defun(arguments, captured_inputs, targuments: nil, tcaptured: [], output_types: nil, output_shapes: nil, f: nil, max_intra_op_parallelism: 1, name: "MapDefun")
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# File lib/tensorflow/ops/raw_ops.rb, line 2150 def self.map_defun(arguments, captured_inputs, targuments: nil, tcaptured: [], output_types: nil, output_shapes: nil, f: nil, max_intra_op_parallelism: 1, name: "MapDefun") self.execute("MapDefun", [arguments, captured_inputs], Targuments: targuments, Tcaptured: tcaptured, output_types: output_types, output_shapes: output_shapes, f: f, max_intra_op_parallelism: max_intra_op_parallelism, name: name) end
map_incomplete_size(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "MapIncompleteSize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2154 def self.map_incomplete_size(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "MapIncompleteSize") self.execute("MapIncompleteSize", [], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
map_peek(key, indices, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "MapPeek")
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# File lib/tensorflow/ops/raw_ops.rb, line 2158 def self.map_peek(key, indices, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "MapPeek") self.execute("MapPeek", [key, indices], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
map_size(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "MapSize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2162 def self.map_size(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "MapSize") self.execute("MapSize", [], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
map_stage(key, indices, values, capacity: 0, memory_limit: 0, dtypes: nil, fake_dtypes: nil, container: "", shared_name: "", name: "MapStage")
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# File lib/tensorflow/ops/raw_ops.rb, line 2166 def self.map_stage(key, indices, values, capacity: 0, memory_limit: 0, dtypes: nil, fake_dtypes: nil, container: "", shared_name: "", name: "MapStage") self.execute("MapStage", [key, indices, values], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, fake_dtypes: fake_dtypes, container: container, shared_name: shared_name, name: name) end
map_unstage(key, indices, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "MapUnstage")
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# File lib/tensorflow/ops/raw_ops.rb, line 2170 def self.map_unstage(key, indices, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "MapUnstage") self.execute("MapUnstage", [key, indices], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
map_unstage_no_key(indices, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "MapUnstageNoKey")
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# File lib/tensorflow/ops/raw_ops.rb, line 2174 def self.map_unstage_no_key(indices, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "MapUnstageNoKey") self.execute("MapUnstageNoKey", [indices], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
mat_mul(a, b, transpose_a: false, transpose_b: false, typeT: nil, name: "MatMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 2178 def self.mat_mul(a, b, transpose_a: false, transpose_b: false, typeT: nil, name: "MatMul") self.execute("MatMul", [a, b], transpose_a: transpose_a, transpose_b: transpose_b, T: typeT, name: name) end
matching_files(pattern, name: "MatchingFiles")
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# File lib/tensorflow/ops/raw_ops.rb, line 2182 def self.matching_files(pattern, name: "MatchingFiles") self.execute("MatchingFiles", [pattern], name: name) end
matching_files_dataset(patterns, name: "MatchingFilesDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 2186 def self.matching_files_dataset(patterns, name: "MatchingFilesDataset") self.execute("MatchingFilesDataset", [patterns], name: name) end
matrix_band_part(input, num_lower, num_upper, typeT: nil, tindex: :int64, name: "MatrixBandPart")
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# File lib/tensorflow/ops/raw_ops.rb, line 2190 def self.matrix_band_part(input, num_lower, num_upper, typeT: nil, tindex: :int64, name: "MatrixBandPart") self.execute("MatrixBandPart", [input, num_lower, num_upper], T: typeT, Tindex: tindex, name: name) end
matrix_determinant(input, typeT: nil, name: "MatrixDeterminant")
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# File lib/tensorflow/ops/raw_ops.rb, line 2194 def self.matrix_determinant(input, typeT: nil, name: "MatrixDeterminant") self.execute("MatrixDeterminant", [input], T: typeT, name: name) end
matrix_diag(diagonal, typeT: nil, name: "MatrixDiag")
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# File lib/tensorflow/ops/raw_ops.rb, line 2198 def self.matrix_diag(diagonal, typeT: nil, name: "MatrixDiag") self.execute("MatrixDiag", [diagonal], T: typeT, name: name) end
matrix_diag_part(input, typeT: nil, name: "MatrixDiagPart")
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# File lib/tensorflow/ops/raw_ops.rb, line 2202 def self.matrix_diag_part(input, typeT: nil, name: "MatrixDiagPart") self.execute("MatrixDiagPart", [input], T: typeT, name: name) end
matrix_diag_part_v2(input, k, padding_value, typeT: nil, name: "MatrixDiagPartV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2206 def self.matrix_diag_part_v2(input, k, padding_value, typeT: nil, name: "MatrixDiagPartV2") self.execute("MatrixDiagPartV2", [input, k, padding_value], T: typeT, name: name) end
matrix_diag_v2(diagonal, k, num_rows, num_cols, padding_value, typeT: nil, name: "MatrixDiagV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2210 def self.matrix_diag_v2(diagonal, k, num_rows, num_cols, padding_value, typeT: nil, name: "MatrixDiagV2") self.execute("MatrixDiagV2", [diagonal, k, num_rows, num_cols, padding_value], T: typeT, name: name) end
matrix_exponential(input, typeT: nil, name: "MatrixExponential")
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# File lib/tensorflow/ops/raw_ops.rb, line 2214 def self.matrix_exponential(input, typeT: nil, name: "MatrixExponential") self.execute("MatrixExponential", [input], T: typeT, name: name) end
matrix_inverse(input, adjoint: false, typeT: nil, name: "MatrixInverse")
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# File lib/tensorflow/ops/raw_ops.rb, line 2218 def self.matrix_inverse(input, adjoint: false, typeT: nil, name: "MatrixInverse") self.execute("MatrixInverse", [input], adjoint: adjoint, T: typeT, name: name) end
matrix_logarithm(input, typeT: nil, name: "MatrixLogarithm")
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# File lib/tensorflow/ops/raw_ops.rb, line 2222 def self.matrix_logarithm(input, typeT: nil, name: "MatrixLogarithm") self.execute("MatrixLogarithm", [input], T: typeT, name: name) end
matrix_set_diag(input, diagonal, typeT: nil, name: "MatrixSetDiag")
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# File lib/tensorflow/ops/raw_ops.rb, line 2226 def self.matrix_set_diag(input, diagonal, typeT: nil, name: "MatrixSetDiag") self.execute("MatrixSetDiag", [input, diagonal], T: typeT, name: name) end
matrix_set_diag_v2(input, diagonal, k, typeT: nil, name: "MatrixSetDiagV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2230 def self.matrix_set_diag_v2(input, diagonal, k, typeT: nil, name: "MatrixSetDiagV2") self.execute("MatrixSetDiagV2", [input, diagonal, k], T: typeT, name: name) end
matrix_solve(matrix, rhs, adjoint: false, typeT: nil, name: "MatrixSolve")
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# File lib/tensorflow/ops/raw_ops.rb, line 2234 def self.matrix_solve(matrix, rhs, adjoint: false, typeT: nil, name: "MatrixSolve") self.execute("MatrixSolve", [matrix, rhs], adjoint: adjoint, T: typeT, name: name) end
matrix_solve_ls(matrix, rhs, l2_regularizer, typeT: nil, fast: true, name: "MatrixSolveLs")
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# File lib/tensorflow/ops/raw_ops.rb, line 2238 def self.matrix_solve_ls(matrix, rhs, l2_regularizer, typeT: nil, fast: true, name: "MatrixSolveLs") self.execute("MatrixSolveLs", [matrix, rhs, l2_regularizer], T: typeT, fast: fast, name: name) end
matrix_square_root(input, typeT: nil, name: "MatrixSquareRoot")
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# File lib/tensorflow/ops/raw_ops.rb, line 2242 def self.matrix_square_root(input, typeT: nil, name: "MatrixSquareRoot") self.execute("MatrixSquareRoot", [input], T: typeT, name: name) end
matrix_triangular_solve(matrix, rhs, lower: true, adjoint: false, typeT: nil, name: "MatrixTriangularSolve")
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# File lib/tensorflow/ops/raw_ops.rb, line 2246 def self.matrix_triangular_solve(matrix, rhs, lower: true, adjoint: false, typeT: nil, name: "MatrixTriangularSolve") self.execute("MatrixTriangularSolve", [matrix, rhs], lower: lower, adjoint: adjoint, T: typeT, name: name) end
max(input, reduction_indices, keep_dims: false, typeT: nil, tidx: :int32, name: "Max")
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# File lib/tensorflow/ops/raw_ops.rb, line 2250 def self.max(input, reduction_indices, keep_dims: false, typeT: nil, tidx: :int32, name: "Max") self.execute("Max", [input, reduction_indices], keep_dims: keep_dims, T: typeT, Tidx: tidx, name: name) end
max_intra_op_parallelism_dataset(input_dataset, max_intra_op_parallelism, output_types: nil, output_shapes: nil, name: "MaxIntraOpParallelismDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 2254 def self.max_intra_op_parallelism_dataset(input_dataset, max_intra_op_parallelism, output_types: nil, output_shapes: nil, name: "MaxIntraOpParallelismDataset") self.execute("MaxIntraOpParallelismDataset", [input_dataset, max_intra_op_parallelism], output_types: output_types, output_shapes: output_shapes, name: name) end
max_pool(input, typeT: :float, ksize: nil, strides: nil, padding: nil, data_format: "NHWC", name: "MaxPool")
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# File lib/tensorflow/ops/raw_ops.rb, line 2258 def self.max_pool(input, typeT: :float, ksize: nil, strides: nil, padding: nil, data_format: "NHWC", name: "MaxPool") self.execute("MaxPool", [input], T: typeT, ksize: ksize, strides: strides, padding: padding, data_format: data_format, name: name) end
max_pool3_d(input, ksize: nil, strides: nil, padding: nil, data_format: "NDHWC", typeT: nil, name: "MaxPool3D")
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# File lib/tensorflow/ops/raw_ops.rb, line 2262 def self.max_pool3_d(input, ksize: nil, strides: nil, padding: nil, data_format: "NDHWC", typeT: nil, name: "MaxPool3D") self.execute("MaxPool3D", [input], ksize: ksize, strides: strides, padding: padding, data_format: data_format, T: typeT, name: name) end
max_pool3_d_grad(orig_input, orig_output, grad, ksize: nil, strides: nil, padding: nil, data_format: "NDHWC", typeT: :float, tinput: :float, name: "MaxPool3DGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 2266 def self.max_pool3_d_grad(orig_input, orig_output, grad, ksize: nil, strides: nil, padding: nil, data_format: "NDHWC", typeT: :float, tinput: :float, name: "MaxPool3DGrad") self.execute("MaxPool3DGrad", [orig_input, orig_output, grad], ksize: ksize, strides: strides, padding: padding, data_format: data_format, T: typeT, TInput: tinput, name: name) end
max_pool3_d_grad_grad(orig_input, orig_output, grad, ksize: nil, strides: nil, padding: nil, data_format: "NDHWC", typeT: nil, name: "MaxPool3DGradGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 2270 def self.max_pool3_d_grad_grad(orig_input, orig_output, grad, ksize: nil, strides: nil, padding: nil, data_format: "NDHWC", typeT: nil, name: "MaxPool3DGradGrad") self.execute("MaxPool3DGradGrad", [orig_input, orig_output, grad], ksize: ksize, strides: strides, padding: padding, data_format: data_format, T: typeT, name: name) end
max_pool_grad(orig_input, orig_output, grad, ksize: nil, strides: nil, padding: nil, data_format: "NHWC", typeT: :float, name: "MaxPoolGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 2274 def self.max_pool_grad(orig_input, orig_output, grad, ksize: nil, strides: nil, padding: nil, data_format: "NHWC", typeT: :float, name: "MaxPoolGrad") self.execute("MaxPoolGrad", [orig_input, orig_output, grad], ksize: ksize, strides: strides, padding: padding, data_format: data_format, T: typeT, name: name) end
max_pool_grad_grad(orig_input, orig_output, grad, ksize: nil, strides: nil, padding: nil, data_format: "NHWC", typeT: nil, name: "MaxPoolGradGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 2278 def self.max_pool_grad_grad(orig_input, orig_output, grad, ksize: nil, strides: nil, padding: nil, data_format: "NHWC", typeT: nil, name: "MaxPoolGradGrad") self.execute("MaxPoolGradGrad", [orig_input, orig_output, grad], ksize: ksize, strides: strides, padding: padding, data_format: data_format, T: typeT, name: name) end
max_pool_grad_grad_v2(orig_input, orig_output, grad, ksize, strides, padding: nil, data_format: "NHWC", typeT: nil, name: "MaxPoolGradGradV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2282 def self.max_pool_grad_grad_v2(orig_input, orig_output, grad, ksize, strides, padding: nil, data_format: "NHWC", typeT: nil, name: "MaxPoolGradGradV2") self.execute("MaxPoolGradGradV2", [orig_input, orig_output, grad, ksize, strides], padding: padding, data_format: data_format, T: typeT, name: name) end
max_pool_grad_grad_with_argmax(input, grad, argmax, ksize: nil, strides: nil, padding: nil, include_batch_in_index: false, targmax: nil, typeT: nil, name: "MaxPoolGradGradWithArgmax")
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# File lib/tensorflow/ops/raw_ops.rb, line 2286 def self.max_pool_grad_grad_with_argmax(input, grad, argmax, ksize: nil, strides: nil, padding: nil, include_batch_in_index: false, targmax: nil, typeT: nil, name: "MaxPoolGradGradWithArgmax") self.execute("MaxPoolGradGradWithArgmax", [input, grad, argmax], ksize: ksize, strides: strides, padding: padding, include_batch_in_index: include_batch_in_index, Targmax: targmax, T: typeT, name: name) end
max_pool_grad_v2(orig_input, orig_output, grad, ksize, strides, padding: nil, data_format: "NHWC", typeT: :float, name: "MaxPoolGradV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2290 def self.max_pool_grad_v2(orig_input, orig_output, grad, ksize, strides, padding: nil, data_format: "NHWC", typeT: :float, name: "MaxPoolGradV2") self.execute("MaxPoolGradV2", [orig_input, orig_output, grad, ksize, strides], padding: padding, data_format: data_format, T: typeT, name: name) end
max_pool_grad_with_argmax(input, grad, argmax, ksize: nil, strides: nil, padding: nil, include_batch_in_index: false, targmax: nil, typeT: nil, name: "MaxPoolGradWithArgmax")
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# File lib/tensorflow/ops/raw_ops.rb, line 2294 def self.max_pool_grad_with_argmax(input, grad, argmax, ksize: nil, strides: nil, padding: nil, include_batch_in_index: false, targmax: nil, typeT: nil, name: "MaxPoolGradWithArgmax") self.execute("MaxPoolGradWithArgmax", [input, grad, argmax], ksize: ksize, strides: strides, padding: padding, include_batch_in_index: include_batch_in_index, Targmax: targmax, T: typeT, name: name) end
max_pool_v2(input, ksize, strides, typeT: :float, padding: nil, data_format: "NHWC", name: "MaxPoolV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2298 def self.max_pool_v2(input, ksize, strides, typeT: :float, padding: nil, data_format: "NHWC", name: "MaxPoolV2") self.execute("MaxPoolV2", [input, ksize, strides], T: typeT, padding: padding, data_format: data_format, name: name) end
max_pool_with_argmax(input, ksize: nil, strides: nil, targmax: :int64, padding: nil, include_batch_in_index: false, typeT: nil, name: "MaxPoolWithArgmax")
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# File lib/tensorflow/ops/raw_ops.rb, line 2302 def self.max_pool_with_argmax(input, ksize: nil, strides: nil, targmax: :int64, padding: nil, include_batch_in_index: false, typeT: nil, name: "MaxPoolWithArgmax") self.execute("MaxPoolWithArgmax", [input], ksize: ksize, strides: strides, Targmax: targmax, padding: padding, include_batch_in_index: include_batch_in_index, T: typeT, name: name) end
maximum(x, y, typeT: nil, name: "Maximum")
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# File lib/tensorflow/ops/raw_ops.rb, line 2306 def self.maximum(x, y, typeT: nil, name: "Maximum") self.execute("Maximum", [x, y], T: typeT, name: name) end
mean(input, reduction_indices, keep_dims: false, typeT: nil, tidx: :int32, name: "Mean")
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# File lib/tensorflow/ops/raw_ops.rb, line 2310 def self.mean(input, reduction_indices, keep_dims: false, typeT: nil, tidx: :int32, name: "Mean") self.execute("Mean", [input, reduction_indices], keep_dims: keep_dims, T: typeT, Tidx: tidx, name: name) end
merge(inputs, typeT: nil, n: nil, name: "Merge")
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# File lib/tensorflow/ops/raw_ops.rb, line 2314 def self.merge(inputs, typeT: nil, n: nil, name: "Merge") self.execute("Merge", [inputs], T: typeT, N: n, name: name) end
merge_summary(inputs, n: nil, name: "MergeSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 2318 def self.merge_summary(inputs, n: nil, name: "MergeSummary") self.execute("MergeSummary", [inputs], N: n, name: name) end
merge_v2_checkpoints(checkpoint_prefixes, destination_prefix, delete_old_dirs: true, name: "MergeV2Checkpoints")
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# File lib/tensorflow/ops/raw_ops.rb, line 2322 def self.merge_v2_checkpoints(checkpoint_prefixes, destination_prefix, delete_old_dirs: true, name: "MergeV2Checkpoints") self.execute("MergeV2Checkpoints", [checkpoint_prefixes, destination_prefix], delete_old_dirs: delete_old_dirs, name: name) end
mfcc(spectrogram, sample_rate, upper_frequency_limit: 4000.0, lower_frequency_limit: 20.0, filterbank_channel_count: 40, dct_coefficient_count: 13, name: "Mfcc")
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# File lib/tensorflow/ops/raw_ops.rb, line 2326 def self.mfcc(spectrogram, sample_rate, upper_frequency_limit: 4000.0, lower_frequency_limit: 20.0, filterbank_channel_count: 40, dct_coefficient_count: 13, name: "Mfcc") self.execute("Mfcc", [spectrogram, sample_rate], upper_frequency_limit: upper_frequency_limit, lower_frequency_limit: lower_frequency_limit, filterbank_channel_count: filterbank_channel_count, dct_coefficient_count: dct_coefficient_count, name: name) end
min(input, reduction_indices, keep_dims: false, typeT: nil, tidx: :int32, name: "Min")
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# File lib/tensorflow/ops/raw_ops.rb, line 2330 def self.min(input, reduction_indices, keep_dims: false, typeT: nil, tidx: :int32, name: "Min") self.execute("Min", [input, reduction_indices], keep_dims: keep_dims, T: typeT, Tidx: tidx, name: name) end
minimum(x, y, typeT: nil, name: "Minimum")
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# File lib/tensorflow/ops/raw_ops.rb, line 2334 def self.minimum(x, y, typeT: nil, name: "Minimum") self.execute("Minimum", [x, y], T: typeT, name: name) end
mirror_pad(input, paddings, typeT: nil, tpaddings: :int32, mode: nil, name: "MirrorPad")
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# File lib/tensorflow/ops/raw_ops.rb, line 2338 def self.mirror_pad(input, paddings, typeT: nil, tpaddings: :int32, mode: nil, name: "MirrorPad") self.execute("MirrorPad", [input, paddings], T: typeT, Tpaddings: tpaddings, mode: mode, name: name) end
mirror_pad_grad(input, paddings, typeT: nil, tpaddings: :int32, mode: nil, name: "MirrorPadGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 2342 def self.mirror_pad_grad(input, paddings, typeT: nil, tpaddings: :int32, mode: nil, name: "MirrorPadGrad") self.execute("MirrorPadGrad", [input, paddings], T: typeT, Tpaddings: tpaddings, mode: mode, name: name) end
mlir_passthrough_op(inputs, mlir_module: "", tinputs: nil, toutputs: nil, name: "MlirPassthroughOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 2346 def self.mlir_passthrough_op(inputs, mlir_module: "", tinputs: nil, toutputs: nil, name: "MlirPassthroughOp") self.execute("MlirPassthroughOp", [inputs], mlir_module: mlir_module, Tinputs: tinputs, Toutputs: toutputs, name: name) end
mod(x, y, typeT: nil, name: "Mod")
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# File lib/tensorflow/ops/raw_ops.rb, line 2350 def self.mod(x, y, typeT: nil, name: "Mod") self.execute("Mod", [x, y], T: typeT, name: name) end
model_dataset(input_dataset, algorithm: 0, cpu_budget: 0, output_types: nil, output_shapes: nil, name: "ModelDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 2354 def self.model_dataset(input_dataset, algorithm: 0, cpu_budget: 0, output_types: nil, output_shapes: nil, name: "ModelDataset") self.execute("ModelDataset", [input_dataset], algorithm: algorithm, cpu_budget: cpu_budget, output_types: output_types, output_shapes: output_shapes, name: name) end
mul(x, y, typeT: nil, name: "Mul")
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# File lib/tensorflow/ops/raw_ops.rb, line 2358 def self.mul(x, y, typeT: nil, name: "Mul") self.execute("Mul", [x, y], T: typeT, name: name) end
mul_no_nan(x, y, typeT: nil, name: "MulNoNan")
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# File lib/tensorflow/ops/raw_ops.rb, line 2362 def self.mul_no_nan(x, y, typeT: nil, name: "MulNoNan") self.execute("MulNoNan", [x, y], T: typeT, name: name) end
multi_device_iterator(devices: nil, shared_name: "", container: "", output_types: nil, output_shapes: nil, name: "MultiDeviceIterator")
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# File lib/tensorflow/ops/raw_ops.rb, line 2366 def self.multi_device_iterator(devices: nil, shared_name: "", container: "", output_types: nil, output_shapes: nil, name: "MultiDeviceIterator") self.execute("MultiDeviceIterator", [], devices: devices, shared_name: shared_name, container: container, output_types: output_types, output_shapes: output_shapes, name: name) end
multi_device_iterator_from_string_handle(string_handle, output_types: [], output_shapes: [], name: "MultiDeviceIteratorFromStringHandle")
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# File lib/tensorflow/ops/raw_ops.rb, line 2370 def self.multi_device_iterator_from_string_handle(string_handle, output_types: [], output_shapes: [], name: "MultiDeviceIteratorFromStringHandle") self.execute("MultiDeviceIteratorFromStringHandle", [string_handle], output_types: output_types, output_shapes: output_shapes, name: name) end
multi_device_iterator_get_next_from_shard(multi_device_iterator, shard_num, incarnation_id, output_types: nil, output_shapes: nil, name: "MultiDeviceIteratorGetNextFromShard")
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# File lib/tensorflow/ops/raw_ops.rb, line 2374 def self.multi_device_iterator_get_next_from_shard(multi_device_iterator, shard_num, incarnation_id, output_types: nil, output_shapes: nil, name: "MultiDeviceIteratorGetNextFromShard") self.execute("MultiDeviceIteratorGetNextFromShard", [multi_device_iterator, shard_num, incarnation_id], output_types: output_types, output_shapes: output_shapes, name: name) end
multi_device_iterator_init(dataset, multi_device_iterator, max_buffer_size, name: "MultiDeviceIteratorInit")
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# File lib/tensorflow/ops/raw_ops.rb, line 2378 def self.multi_device_iterator_init(dataset, multi_device_iterator, max_buffer_size, name: "MultiDeviceIteratorInit") self.execute("MultiDeviceIteratorInit", [dataset, multi_device_iterator, max_buffer_size], name: name) end
multi_device_iterator_to_string_handle(multi_device_iterator, name: "MultiDeviceIteratorToStringHandle")
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# File lib/tensorflow/ops/raw_ops.rb, line 2382 def self.multi_device_iterator_to_string_handle(multi_device_iterator, name: "MultiDeviceIteratorToStringHandle") self.execute("MultiDeviceIteratorToStringHandle", [multi_device_iterator], name: name) end
multinomial(logits, num_samples, seed: 0, seed2: 0, typeT: nil, output_dtype: :int64, name: "Multinomial")
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# File lib/tensorflow/ops/raw_ops.rb, line 2386 def self.multinomial(logits, num_samples, seed: 0, seed2: 0, typeT: nil, output_dtype: :int64, name: "Multinomial") self.execute("Multinomial", [logits, num_samples], seed: seed, seed2: seed2, T: typeT, output_dtype: output_dtype, name: name) end
mutable_dense_hash_table(empty_key, container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, value_shape: [], initial_num_buckets: 131072, max_load_factor: 0.800000011920929, name: "MutableDenseHashTable")
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# File lib/tensorflow/ops/raw_ops.rb, line 2390 def self.mutable_dense_hash_table(empty_key, container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, value_shape: [], initial_num_buckets: 131072, max_load_factor: 0.800000011920929, name: "MutableDenseHashTable") self.execute("MutableDenseHashTable", [empty_key], container: container, shared_name: shared_name, use_node_name_sharing: use_node_name_sharing, key_dtype: key_dtype, value_dtype: value_dtype, value_shape: value_shape, initial_num_buckets: initial_num_buckets, max_load_factor: max_load_factor, name: name) end
mutable_dense_hash_table_v2(empty_key, deleted_key, container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, value_shape: [], initial_num_buckets: 131072, max_load_factor: 0.800000011920929, name: "MutableDenseHashTableV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2394 def self.mutable_dense_hash_table_v2(empty_key, deleted_key, container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, value_shape: [], initial_num_buckets: 131072, max_load_factor: 0.800000011920929, name: "MutableDenseHashTableV2") self.execute("MutableDenseHashTableV2", [empty_key, deleted_key], container: container, shared_name: shared_name, use_node_name_sharing: use_node_name_sharing, key_dtype: key_dtype, value_dtype: value_dtype, value_shape: value_shape, initial_num_buckets: initial_num_buckets, max_load_factor: max_load_factor, name: name) end
mutable_hash_table(container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, name: "MutableHashTable")
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# File lib/tensorflow/ops/raw_ops.rb, line 2398 def self.mutable_hash_table(container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, name: "MutableHashTable") self.execute("MutableHashTable", [], container: container, shared_name: shared_name, use_node_name_sharing: use_node_name_sharing, key_dtype: key_dtype, value_dtype: value_dtype, name: name) end
mutable_hash_table_of_tensors(container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, value_shape: [], name: "MutableHashTableOfTensors")
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# File lib/tensorflow/ops/raw_ops.rb, line 2402 def self.mutable_hash_table_of_tensors(container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, value_shape: [], name: "MutableHashTableOfTensors") self.execute("MutableHashTableOfTensors", [], container: container, shared_name: shared_name, use_node_name_sharing: use_node_name_sharing, key_dtype: key_dtype, value_dtype: value_dtype, value_shape: value_shape, name: name) end
mutable_hash_table_of_tensors_v2(container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, value_shape: [], name: "MutableHashTableOfTensorsV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2406 def self.mutable_hash_table_of_tensors_v2(container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, value_shape: [], name: "MutableHashTableOfTensorsV2") self.execute("MutableHashTableOfTensorsV2", [], container: container, shared_name: shared_name, use_node_name_sharing: use_node_name_sharing, key_dtype: key_dtype, value_dtype: value_dtype, value_shape: value_shape, name: name) end
mutable_hash_table_v2(container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, name: "MutableHashTableV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2410 def self.mutable_hash_table_v2(container: "", shared_name: "", use_node_name_sharing: false, key_dtype: nil, value_dtype: nil, name: "MutableHashTableV2") self.execute("MutableHashTableV2", [], container: container, shared_name: shared_name, use_node_name_sharing: use_node_name_sharing, key_dtype: key_dtype, value_dtype: value_dtype, name: name) end
mutex_lock(mutex, name: "MutexLock")
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# File lib/tensorflow/ops/raw_ops.rb, line 2414 def self.mutex_lock(mutex, name: "MutexLock") self.execute("MutexLock", [mutex], name: name) end
mutex_v2(container: "", shared_name: "", name: "MutexV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2418 def self.mutex_v2(container: "", shared_name: "", name: "MutexV2") self.execute("MutexV2", [], container: container, shared_name: shared_name, name: name) end
nccl_all_reduce(input, reduction: nil, typeT: nil, num_devices: nil, shared_name: "", name: "NcclAllReduce")
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# File lib/tensorflow/ops/raw_ops.rb, line 2422 def self.nccl_all_reduce(input, reduction: nil, typeT: nil, num_devices: nil, shared_name: "", name: "NcclAllReduce") self.execute("NcclAllReduce", [input], reduction: reduction, T: typeT, num_devices: num_devices, shared_name: shared_name, name: name) end
nccl_broadcast(input, typeT: nil, shape: nil, name: "NcclBroadcast")
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# File lib/tensorflow/ops/raw_ops.rb, line 2426 def self.nccl_broadcast(input, typeT: nil, shape: nil, name: "NcclBroadcast") self.execute("NcclBroadcast", [input], T: typeT, shape: shape, name: name) end
nccl_reduce(input, reduction: nil, typeT: nil, num_devices: nil, name: "NcclReduce")
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# File lib/tensorflow/ops/raw_ops.rb, line 2430 def self.nccl_reduce(input, reduction: nil, typeT: nil, num_devices: nil, name: "NcclReduce") self.execute("NcclReduce", [input], reduction: reduction, T: typeT, num_devices: num_devices, name: name) end
ndtri(x, typeT: nil, name: "Ndtri")
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# File lib/tensorflow/ops/raw_ops.rb, line 2434 def self.ndtri(x, typeT: nil, name: "Ndtri") self.execute("Ndtri", [x], T: typeT, name: name) end
nearest_neighbors(points, centers, k, name: "NearestNeighbors")
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# File lib/tensorflow/ops/raw_ops.rb, line 2438 def self.nearest_neighbors(points, centers, k, name: "NearestNeighbors") self.execute("NearestNeighbors", [points, centers, k], name: name) end
neg(x, typeT: nil, name: "Neg")
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# File lib/tensorflow/ops/raw_ops.rb, line 2442 def self.neg(x, typeT: nil, name: "Neg") self.execute("Neg", [x], T: typeT, name: name) end
neg_train(w_in, w_out, examples, labels, lr, vocab_count: nil, num_negative_samples: nil, name: "NegTrain")
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# File lib/tensorflow/ops/raw_ops.rb, line 2446 def self.neg_train(w_in, w_out, examples, labels, lr, vocab_count: nil, num_negative_samples: nil, name: "NegTrain") self.execute("NegTrain", [w_in, w_out, examples, labels, lr], vocab_count: vocab_count, num_negative_samples: num_negative_samples, name: name) end
next_after(x1, x2, typeT: :float, name: "NextAfter")
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# File lib/tensorflow/ops/raw_ops.rb, line 2450 def self.next_after(x1, x2, typeT: :float, name: "NextAfter") self.execute("NextAfter", [x1, x2], T: typeT, name: name) end
next_iteration(data, typeT: nil, name: "NextIteration")
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# File lib/tensorflow/ops/raw_ops.rb, line 2454 def self.next_iteration(data, typeT: nil, name: "NextIteration") self.execute("NextIteration", [data], T: typeT, name: name) end
no_op(name: "NoOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 2458 def self.no_op(name: "NoOp") self.execute("NoOp", [], name: name) end
non_deterministic_ints(shape, dtype: :int64, shape_dtype: :int64, name: "NonDeterministicInts")
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# File lib/tensorflow/ops/raw_ops.rb, line 2462 def self.non_deterministic_ints(shape, dtype: :int64, shape_dtype: :int64, name: "NonDeterministicInts") self.execute("NonDeterministicInts", [shape], dtype: dtype, shape_dtype: shape_dtype, name: name) end
non_max_suppression(boxes, scores, max_output_size, iou_threshold: 0.5, name: "NonMaxSuppression")
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# File lib/tensorflow/ops/raw_ops.rb, line 2466 def self.non_max_suppression(boxes, scores, max_output_size, iou_threshold: 0.5, name: "NonMaxSuppression") self.execute("NonMaxSuppression", [boxes, scores, max_output_size], iou_threshold: iou_threshold, name: name) end
non_max_suppression_v2(boxes, scores, max_output_size, iou_threshold, typeT: :float, t_threshold: :float, name: "NonMaxSuppressionV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2470 def self.non_max_suppression_v2(boxes, scores, max_output_size, iou_threshold, typeT: :float, t_threshold: :float, name: "NonMaxSuppressionV2") self.execute("NonMaxSuppressionV2", [boxes, scores, max_output_size, iou_threshold], T: typeT, T_threshold: t_threshold, name: name) end
non_max_suppression_v3(boxes, scores, max_output_size, iou_threshold, score_threshold, typeT: :float, t_threshold: :float, name: "NonMaxSuppressionV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 2474 def self.non_max_suppression_v3(boxes, scores, max_output_size, iou_threshold, score_threshold, typeT: :float, t_threshold: :float, name: "NonMaxSuppressionV3") self.execute("NonMaxSuppressionV3", [boxes, scores, max_output_size, iou_threshold, score_threshold], T: typeT, T_threshold: t_threshold, name: name) end
non_max_suppression_v4(boxes, scores, max_output_size, iou_threshold, score_threshold, typeT: :float, t_threshold: :float, pad_to_max_output_size: false, name: "NonMaxSuppressionV4")
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# File lib/tensorflow/ops/raw_ops.rb, line 2478 def self.non_max_suppression_v4(boxes, scores, max_output_size, iou_threshold, score_threshold, typeT: :float, t_threshold: :float, pad_to_max_output_size: false, name: "NonMaxSuppressionV4") self.execute("NonMaxSuppressionV4", [boxes, scores, max_output_size, iou_threshold, score_threshold], T: typeT, T_threshold: t_threshold, pad_to_max_output_size: pad_to_max_output_size, name: name) end
non_max_suppression_v5(boxes, scores, max_output_size, iou_threshold, score_threshold, soft_nms_sigma, typeT: :float, pad_to_max_output_size: false, name: "NonMaxSuppressionV5")
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# File lib/tensorflow/ops/raw_ops.rb, line 2482 def self.non_max_suppression_v5(boxes, scores, max_output_size, iou_threshold, score_threshold, soft_nms_sigma, typeT: :float, pad_to_max_output_size: false, name: "NonMaxSuppressionV5") self.execute("NonMaxSuppressionV5", [boxes, scores, max_output_size, iou_threshold, score_threshold, soft_nms_sigma], T: typeT, pad_to_max_output_size: pad_to_max_output_size, name: name) end
non_max_suppression_with_overlaps(overlaps, scores, max_output_size, overlap_threshold, score_threshold, name: "NonMaxSuppressionWithOverlaps")
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# File lib/tensorflow/ops/raw_ops.rb, line 2486 def self.non_max_suppression_with_overlaps(overlaps, scores, max_output_size, overlap_threshold, score_threshold, name: "NonMaxSuppressionWithOverlaps") self.execute("NonMaxSuppressionWithOverlaps", [overlaps, scores, max_output_size, overlap_threshold, score_threshold], name: name) end
non_serializable_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "NonSerializableDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 2490 def self.non_serializable_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "NonSerializableDataset") self.execute("NonSerializableDataset", [input_dataset], output_types: output_types, output_shapes: output_shapes, name: name) end
not_equal(x, y, typeT: nil, incompatible_shape_error: true, name: "NotEqual")
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# File lib/tensorflow/ops/raw_ops.rb, line 2494 def self.not_equal(x, y, typeT: nil, incompatible_shape_error: true, name: "NotEqual") self.execute("NotEqual", [x, y], T: typeT, incompatible_shape_error: incompatible_shape_error, name: name) end
nth_element(input, n, reverse: false, typeT: nil, name: "NthElement")
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# File lib/tensorflow/ops/raw_ops.rb, line 2498 def self.nth_element(input, n, reverse: false, typeT: nil, name: "NthElement") self.execute("NthElement", [input, n], reverse: reverse, T: typeT, name: name) end
one_hot(indices, depth, on_value, off_value, axis: -1, typeT: nil, ti: :int64, name: "OneHot")
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# File lib/tensorflow/ops/raw_ops.rb, line 2502 def self.one_hot(indices, depth, on_value, off_value, axis: -1, typeT: nil, ti: :int64, name: "OneHot") self.execute("OneHot", [indices, depth, on_value, off_value], axis: axis, T: typeT, TI: ti, name: name) end
one_shot_iterator(dataset_factory: nil, output_types: nil, output_shapes: nil, container: "", shared_name: "", name: "OneShotIterator")
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# File lib/tensorflow/ops/raw_ops.rb, line 2506 def self.one_shot_iterator(dataset_factory: nil, output_types: nil, output_shapes: nil, container: "", shared_name: "", name: "OneShotIterator") self.execute("OneShotIterator", [], dataset_factory: dataset_factory, output_types: output_types, output_shapes: output_shapes, container: container, shared_name: shared_name, name: name) end
ones_like(x, typeT: nil, name: "OnesLike")
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# File lib/tensorflow/ops/raw_ops.rb, line 2510 def self.ones_like(x, typeT: nil, name: "OnesLike") self.execute("OnesLike", [x], T: typeT, name: name) end
optimize_dataset(input_dataset, optimizations, output_types: nil, output_shapes: nil, optimization_configs: [], name: "OptimizeDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 2514 def self.optimize_dataset(input_dataset, optimizations, output_types: nil, output_shapes: nil, optimization_configs: [], name: "OptimizeDataset") self.execute("OptimizeDataset", [input_dataset, optimizations], output_types: output_types, output_shapes: output_shapes, optimization_configs: optimization_configs, name: name) end
optional_from_value(components, toutput_types: nil, name: "OptionalFromValue")
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# File lib/tensorflow/ops/raw_ops.rb, line 2518 def self.optional_from_value(components, toutput_types: nil, name: "OptionalFromValue") self.execute("OptionalFromValue", [components], Toutput_types: toutput_types, name: name) end
optional_get_value(optional, output_types: nil, output_shapes: nil, name: "OptionalGetValue")
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# File lib/tensorflow/ops/raw_ops.rb, line 2522 def self.optional_get_value(optional, output_types: nil, output_shapes: nil, name: "OptionalGetValue") self.execute("OptionalGetValue", [optional], output_types: output_types, output_shapes: output_shapes, name: name) end
optional_has_value(optional, name: "OptionalHasValue")
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# File lib/tensorflow/ops/raw_ops.rb, line 2526 def self.optional_has_value(optional, name: "OptionalHasValue") self.execute("OptionalHasValue", [optional], name: name) end
optional_none(name: "OptionalNone")
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# File lib/tensorflow/ops/raw_ops.rb, line 2530 def self.optional_none(name: "OptionalNone") self.execute("OptionalNone", [], name: name) end
ordered_map_clear(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "OrderedMapClear")
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# File lib/tensorflow/ops/raw_ops.rb, line 2534 def self.ordered_map_clear(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "OrderedMapClear") self.execute("OrderedMapClear", [], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
ordered_map_incomplete_size(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "OrderedMapIncompleteSize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2538 def self.ordered_map_incomplete_size(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "OrderedMapIncompleteSize") self.execute("OrderedMapIncompleteSize", [], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
ordered_map_peek(key, indices, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "OrderedMapPeek")
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# File lib/tensorflow/ops/raw_ops.rb, line 2542 def self.ordered_map_peek(key, indices, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "OrderedMapPeek") self.execute("OrderedMapPeek", [key, indices], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
ordered_map_size(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "OrderedMapSize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2546 def self.ordered_map_size(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "OrderedMapSize") self.execute("OrderedMapSize", [], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
ordered_map_stage(key, indices, values, capacity: 0, memory_limit: 0, dtypes: nil, fake_dtypes: nil, container: "", shared_name: "", name: "OrderedMapStage")
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# File lib/tensorflow/ops/raw_ops.rb, line 2550 def self.ordered_map_stage(key, indices, values, capacity: 0, memory_limit: 0, dtypes: nil, fake_dtypes: nil, container: "", shared_name: "", name: "OrderedMapStage") self.execute("OrderedMapStage", [key, indices, values], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, fake_dtypes: fake_dtypes, container: container, shared_name: shared_name, name: name) end
ordered_map_unstage(key, indices, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "OrderedMapUnstage")
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# File lib/tensorflow/ops/raw_ops.rb, line 2554 def self.ordered_map_unstage(key, indices, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "OrderedMapUnstage") self.execute("OrderedMapUnstage", [key, indices], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
ordered_map_unstage_no_key(indices, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "OrderedMapUnstageNoKey")
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# File lib/tensorflow/ops/raw_ops.rb, line 2558 def self.ordered_map_unstage_no_key(indices, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "OrderedMapUnstageNoKey") self.execute("OrderedMapUnstageNoKey", [indices], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
outfeed_dequeue(dtype: nil, shape: nil, device_ordinal: -1, name: "OutfeedDequeue")
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# File lib/tensorflow/ops/raw_ops.rb, line 2562 def self.outfeed_dequeue(dtype: nil, shape: nil, device_ordinal: -1, name: "OutfeedDequeue") self.execute("OutfeedDequeue", [], dtype: dtype, shape: shape, device_ordinal: device_ordinal, name: name) end
outfeed_dequeue_tuple(dtypes: nil, shapes: nil, device_ordinal: -1, name: "OutfeedDequeueTuple")
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# File lib/tensorflow/ops/raw_ops.rb, line 2566 def self.outfeed_dequeue_tuple(dtypes: nil, shapes: nil, device_ordinal: -1, name: "OutfeedDequeueTuple") self.execute("OutfeedDequeueTuple", [], dtypes: dtypes, shapes: shapes, device_ordinal: device_ordinal, name: name) end
outfeed_enqueue(input, dtype: nil, name: "OutfeedEnqueue")
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# File lib/tensorflow/ops/raw_ops.rb, line 2570 def self.outfeed_enqueue(input, dtype: nil, name: "OutfeedEnqueue") self.execute("OutfeedEnqueue", [input], dtype: dtype, name: name) end
outfeed_enqueue_tuple(inputs, dtypes: nil, name: "OutfeedEnqueueTuple")
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# File lib/tensorflow/ops/raw_ops.rb, line 2574 def self.outfeed_enqueue_tuple(inputs, dtypes: nil, name: "OutfeedEnqueueTuple") self.execute("OutfeedEnqueueTuple", [inputs], dtypes: dtypes, name: name) end
pack(values, n: nil, typeT: nil, axis: 0, name: "Pack")
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# File lib/tensorflow/ops/raw_ops.rb, line 2578 def self.pack(values, n: nil, typeT: nil, axis: 0, name: "Pack") self.execute("Pack", [values], N: n, T: typeT, axis: axis, name: name) end
pad(input, paddings, typeT: nil, tpaddings: :int32, name: "Pad")
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# File lib/tensorflow/ops/raw_ops.rb, line 2582 def self.pad(input, paddings, typeT: nil, tpaddings: :int32, name: "Pad") self.execute("Pad", [input, paddings], T: typeT, Tpaddings: tpaddings, name: name) end
pad_v2(input, paddings, constant_values, typeT: nil, tpaddings: :int32, name: "PadV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2586 def self.pad_v2(input, paddings, constant_values, typeT: nil, tpaddings: :int32, name: "PadV2") self.execute("PadV2", [input, paddings, constant_values], T: typeT, Tpaddings: tpaddings, name: name) end
padded_batch_dataset(input_dataset, batch_size, padded_shapes, padding_values, toutput_types: nil, output_shapes: nil, n: nil, name: "PaddedBatchDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 2590 def self.padded_batch_dataset(input_dataset, batch_size, padded_shapes, padding_values, toutput_types: nil, output_shapes: nil, n: nil, name: "PaddedBatchDataset") self.execute("PaddedBatchDataset", [input_dataset, batch_size, padded_shapes, padding_values], Toutput_types: toutput_types, output_shapes: output_shapes, N: n, name: name) end
padded_batch_dataset_v2(input_dataset, batch_size, padded_shapes, padding_values, drop_remainder, parallel_copy: false, toutput_types: nil, output_shapes: nil, n: nil, name: "PaddedBatchDatasetV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2594 def self.padded_batch_dataset_v2(input_dataset, batch_size, padded_shapes, padding_values, drop_remainder, parallel_copy: false, toutput_types: nil, output_shapes: nil, n: nil, name: "PaddedBatchDatasetV2") self.execute("PaddedBatchDatasetV2", [input_dataset, batch_size, padded_shapes, padding_values, drop_remainder], parallel_copy: parallel_copy, Toutput_types: toutput_types, output_shapes: output_shapes, N: n, name: name) end
padding_fifo_queue(component_types: nil, shapes: [], capacity: -1, container: "", shared_name: "", name: "PaddingFIFOQueue")
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# File lib/tensorflow/ops/raw_ops.rb, line 2598 def self.padding_fifo_queue(component_types: nil, shapes: [], capacity: -1, container: "", shared_name: "", name: "PaddingFIFOQueue") self.execute("PaddingFIFOQueue", [], component_types: component_types, shapes: shapes, capacity: capacity, container: container, shared_name: shared_name, name: name) end
padding_fifo_queue_v2(component_types: nil, shapes: [], capacity: -1, container: "", shared_name: "", name: "PaddingFIFOQueueV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2602 def self.padding_fifo_queue_v2(component_types: nil, shapes: [], capacity: -1, container: "", shared_name: "", name: "PaddingFIFOQueueV2") self.execute("PaddingFIFOQueueV2", [], component_types: component_types, shapes: shapes, capacity: capacity, container: container, shared_name: shared_name, name: name) end
parallel_concat(values, n: nil, typeT: nil, shape: nil, name: "ParallelConcat")
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# File lib/tensorflow/ops/raw_ops.rb, line 2606 def self.parallel_concat(values, n: nil, typeT: nil, shape: nil, name: "ParallelConcat") self.execute("ParallelConcat", [values], N: n, T: typeT, shape: shape, name: name) end
parallel_dynamic_stitch(indices, data, n: nil, typeT: nil, name: "ParallelDynamicStitch")
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# File lib/tensorflow/ops/raw_ops.rb, line 2610 def self.parallel_dynamic_stitch(indices, data, n: nil, typeT: nil, name: "ParallelDynamicStitch") self.execute("ParallelDynamicStitch", [indices, data], N: n, T: typeT, name: name) end
parallel_interleave_dataset(input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements, f: nil, targuments: nil, output_types: nil, output_shapes: nil, name: "ParallelInterleaveDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 2614 def self.parallel_interleave_dataset(input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements, f: nil, targuments: nil, output_types: nil, output_shapes: nil, name: "ParallelInterleaveDataset") self.execute("ParallelInterleaveDataset", [input_dataset, other_arguments, cycle_length, block_length, sloppy, buffer_output_elements, prefetch_input_elements], f: f, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, name: name) end
parallel_interleave_dataset_v2(input_dataset, other_arguments, cycle_length, block_length, num_parallel_calls, f: nil, targuments: nil, output_types: nil, output_shapes: nil, sloppy: false, name: "ParallelInterleaveDatasetV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2618 def self.parallel_interleave_dataset_v2(input_dataset, other_arguments, cycle_length, block_length, num_parallel_calls, f: nil, targuments: nil, output_types: nil, output_shapes: nil, sloppy: false, name: "ParallelInterleaveDatasetV2") self.execute("ParallelInterleaveDatasetV2", [input_dataset, other_arguments, cycle_length, block_length, num_parallel_calls], f: f, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, sloppy: sloppy, name: name) end
parallel_map_dataset(input_dataset, other_arguments, num_parallel_calls, f: nil, targuments: nil, output_types: nil, output_shapes: nil, use_inter_op_parallelism: true, sloppy: false, preserve_cardinality: false, name: "ParallelMapDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 2622 def self.parallel_map_dataset(input_dataset, other_arguments, num_parallel_calls, f: nil, targuments: nil, output_types: nil, output_shapes: nil, use_inter_op_parallelism: true, sloppy: false, preserve_cardinality: false, name: "ParallelMapDataset") self.execute("ParallelMapDataset", [input_dataset, other_arguments, num_parallel_calls], f: f, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, use_inter_op_parallelism: use_inter_op_parallelism, sloppy: sloppy, preserve_cardinality: preserve_cardinality, name: name) end
parameterized_truncated_normal(shape, means, stdevs, minvals, maxvals, seed: 0, seed2: 0, dtype: nil, typeT: nil, name: "ParameterizedTruncatedNormal")
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# File lib/tensorflow/ops/raw_ops.rb, line 2626 def self.parameterized_truncated_normal(shape, means, stdevs, minvals, maxvals, seed: 0, seed2: 0, dtype: nil, typeT: nil, name: "ParameterizedTruncatedNormal") self.execute("ParameterizedTruncatedNormal", [shape, means, stdevs, minvals, maxvals], seed: seed, seed2: seed2, dtype: dtype, T: typeT, name: name) end
parse_example(serialized, names, sparse_keys, dense_keys, dense_defaults, nsparse: nil, ndense: nil, sparse_types: nil, tdense: nil, dense_shapes: nil, name: "ParseExample")
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# File lib/tensorflow/ops/raw_ops.rb, line 2630 def self.parse_example(serialized, names, sparse_keys, dense_keys, dense_defaults, nsparse: nil, ndense: nil, sparse_types: nil, tdense: nil, dense_shapes: nil, name: "ParseExample") self.execute("ParseExample", [serialized, names, sparse_keys, dense_keys, dense_defaults], Nsparse: nsparse, Ndense: ndense, sparse_types: sparse_types, Tdense: tdense, dense_shapes: dense_shapes, name: name) end
parse_example_dataset(input_dataset, num_parallel_calls, dense_defaults, sparse_keys: nil, dense_keys: nil, sparse_types: nil, tdense: nil, dense_shapes: nil, output_types: nil, output_shapes: nil, sloppy: false, ragged_keys: [], ragged_value_types: [], ragged_split_types: [], name: "ParseExampleDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 2634 def self.parse_example_dataset(input_dataset, num_parallel_calls, dense_defaults, sparse_keys: nil, dense_keys: nil, sparse_types: nil, tdense: nil, dense_shapes: nil, output_types: nil, output_shapes: nil, sloppy: false, ragged_keys: [], ragged_value_types: [], ragged_split_types: [], name: "ParseExampleDataset") self.execute("ParseExampleDataset", [input_dataset, num_parallel_calls, dense_defaults], sparse_keys: sparse_keys, dense_keys: dense_keys, sparse_types: sparse_types, Tdense: tdense, dense_shapes: dense_shapes, output_types: output_types, output_shapes: output_shapes, sloppy: sloppy, ragged_keys: ragged_keys, ragged_value_types: ragged_value_types, ragged_split_types: ragged_split_types, name: name) end
parse_example_v2(serialized, names, sparse_keys, dense_keys, ragged_keys, dense_defaults, tdense: nil, num_sparse: nil, sparse_types: nil, ragged_value_types: nil, ragged_split_types: nil, dense_shapes: nil, name: "ParseExampleV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2638 def self.parse_example_v2(serialized, names, sparse_keys, dense_keys, ragged_keys, dense_defaults, tdense: nil, num_sparse: nil, sparse_types: nil, ragged_value_types: nil, ragged_split_types: nil, dense_shapes: nil, name: "ParseExampleV2") self.execute("ParseExampleV2", [serialized, names, sparse_keys, dense_keys, ragged_keys, dense_defaults], Tdense: tdense, num_sparse: num_sparse, sparse_types: sparse_types, ragged_value_types: ragged_value_types, ragged_split_types: ragged_split_types, dense_shapes: dense_shapes, name: name) end
parse_sequence_example(serialized, debug_name, context_dense_defaults, feature_list_dense_missing_assumed_empty: nil, context_sparse_keys: nil, context_dense_keys: nil, feature_list_sparse_keys: nil, feature_list_dense_keys: nil, ncontext_sparse: 0, ncontext_dense: 0, nfeature_list_sparse: 0, nfeature_list_dense: 0, context_sparse_types: [], tcontext_dense: [], feature_list_dense_types: [], context_dense_shapes: [], feature_list_sparse_types: [], feature_list_dense_shapes: [], name: "ParseSequenceExample")
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# File lib/tensorflow/ops/raw_ops.rb, line 2642 def self.parse_sequence_example(serialized, debug_name, context_dense_defaults, feature_list_dense_missing_assumed_empty: nil, context_sparse_keys: nil, context_dense_keys: nil, feature_list_sparse_keys: nil, feature_list_dense_keys: nil, ncontext_sparse: 0, ncontext_dense: 0, nfeature_list_sparse: 0, nfeature_list_dense: 0, context_sparse_types: [], tcontext_dense: [], feature_list_dense_types: [], context_dense_shapes: [], feature_list_sparse_types: [], feature_list_dense_shapes: [], name: "ParseSequenceExample") self.execute("ParseSequenceExample", [serialized, debug_name, context_dense_defaults], feature_list_dense_missing_assumed_empty: feature_list_dense_missing_assumed_empty, context_sparse_keys: context_sparse_keys, context_dense_keys: context_dense_keys, feature_list_sparse_keys: feature_list_sparse_keys, feature_list_dense_keys: feature_list_dense_keys, Ncontext_sparse: ncontext_sparse, Ncontext_dense: ncontext_dense, Nfeature_list_sparse: nfeature_list_sparse, Nfeature_list_dense: nfeature_list_dense, context_sparse_types: context_sparse_types, Tcontext_dense: tcontext_dense, feature_list_dense_types: feature_list_dense_types, context_dense_shapes: context_dense_shapes, feature_list_sparse_types: feature_list_sparse_types, feature_list_dense_shapes: feature_list_dense_shapes, name: name) end
parse_sequence_example_v2(serialized, debug_name, context_sparse_keys, context_dense_keys, context_ragged_keys, feature_list_sparse_keys, feature_list_dense_keys, feature_list_ragged_keys, feature_list_dense_missing_assumed_empty, context_dense_defaults, ncontext_sparse: 0, tcontext_dense: [], context_sparse_types: [], context_ragged_value_types: [], context_ragged_split_types: [], context_dense_shapes: [], nfeature_list_sparse: 0, nfeature_list_dense: 0, feature_list_dense_types: [], feature_list_sparse_types: [], feature_list_ragged_value_types: [], feature_list_ragged_split_types: [], feature_list_dense_shapes: [], name: "ParseSequenceExampleV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2646 def self.parse_sequence_example_v2(serialized, debug_name, context_sparse_keys, context_dense_keys, context_ragged_keys, feature_list_sparse_keys, feature_list_dense_keys, feature_list_ragged_keys, feature_list_dense_missing_assumed_empty, context_dense_defaults, ncontext_sparse: 0, tcontext_dense: [], context_sparse_types: [], context_ragged_value_types: [], context_ragged_split_types: [], context_dense_shapes: [], nfeature_list_sparse: 0, nfeature_list_dense: 0, feature_list_dense_types: [], feature_list_sparse_types: [], feature_list_ragged_value_types: [], feature_list_ragged_split_types: [], feature_list_dense_shapes: [], name: "ParseSequenceExampleV2") self.execute("ParseSequenceExampleV2", [serialized, debug_name, context_sparse_keys, context_dense_keys, context_ragged_keys, feature_list_sparse_keys, feature_list_dense_keys, feature_list_ragged_keys, feature_list_dense_missing_assumed_empty, context_dense_defaults], Ncontext_sparse: ncontext_sparse, Tcontext_dense: tcontext_dense, context_sparse_types: context_sparse_types, context_ragged_value_types: context_ragged_value_types, context_ragged_split_types: context_ragged_split_types, context_dense_shapes: context_dense_shapes, Nfeature_list_sparse: nfeature_list_sparse, Nfeature_list_dense: nfeature_list_dense, feature_list_dense_types: feature_list_dense_types, feature_list_sparse_types: feature_list_sparse_types, feature_list_ragged_value_types: feature_list_ragged_value_types, feature_list_ragged_split_types: feature_list_ragged_split_types, feature_list_dense_shapes: feature_list_dense_shapes, name: name) end
parse_single_example(serialized, dense_defaults, num_sparse: nil, sparse_keys: nil, dense_keys: nil, sparse_types: nil, tdense: nil, dense_shapes: nil, name: "ParseSingleExample")
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# File lib/tensorflow/ops/raw_ops.rb, line 2650 def self.parse_single_example(serialized, dense_defaults, num_sparse: nil, sparse_keys: nil, dense_keys: nil, sparse_types: nil, tdense: nil, dense_shapes: nil, name: "ParseSingleExample") self.execute("ParseSingleExample", [serialized, dense_defaults], num_sparse: num_sparse, sparse_keys: sparse_keys, dense_keys: dense_keys, sparse_types: sparse_types, Tdense: tdense, dense_shapes: dense_shapes, name: name) end
parse_single_sequence_example(serialized, feature_list_dense_missing_assumed_empty, context_sparse_keys, context_dense_keys, feature_list_sparse_keys, feature_list_dense_keys, context_dense_defaults, debug_name, ncontext_sparse: 0, ncontext_dense: 0, nfeature_list_sparse: 0, nfeature_list_dense: 0, context_sparse_types: [], tcontext_dense: [], feature_list_dense_types: [], context_dense_shapes: [], feature_list_sparse_types: [], feature_list_dense_shapes: [], name: "ParseSingleSequenceExample")
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# File lib/tensorflow/ops/raw_ops.rb, line 2654 def self.parse_single_sequence_example(serialized, feature_list_dense_missing_assumed_empty, context_sparse_keys, context_dense_keys, feature_list_sparse_keys, feature_list_dense_keys, context_dense_defaults, debug_name, ncontext_sparse: 0, ncontext_dense: 0, nfeature_list_sparse: 0, nfeature_list_dense: 0, context_sparse_types: [], tcontext_dense: [], feature_list_dense_types: [], context_dense_shapes: [], feature_list_sparse_types: [], feature_list_dense_shapes: [], name: "ParseSingleSequenceExample") self.execute("ParseSingleSequenceExample", [serialized, feature_list_dense_missing_assumed_empty, context_sparse_keys, context_dense_keys, feature_list_sparse_keys, feature_list_dense_keys, context_dense_defaults, debug_name], Ncontext_sparse: ncontext_sparse, Ncontext_dense: ncontext_dense, Nfeature_list_sparse: nfeature_list_sparse, Nfeature_list_dense: nfeature_list_dense, context_sparse_types: context_sparse_types, Tcontext_dense: tcontext_dense, feature_list_dense_types: feature_list_dense_types, context_dense_shapes: context_dense_shapes, feature_list_sparse_types: feature_list_sparse_types, feature_list_dense_shapes: feature_list_dense_shapes, name: name) end
parse_tensor(serialized, out_type: nil, name: "ParseTensor")
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# File lib/tensorflow/ops/raw_ops.rb, line 2658 def self.parse_tensor(serialized, out_type: nil, name: "ParseTensor") self.execute("ParseTensor", [serialized], out_type: out_type, name: name) end
partitioned_call(args, tin: nil, tout: nil, f: nil, config: "", config_proto: "", executor_type: "", name: "PartitionedCall")
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# File lib/tensorflow/ops/raw_ops.rb, line 2662 def self.partitioned_call(args, tin: nil, tout: nil, f: nil, config: "", config_proto: "", executor_type: "", name: "PartitionedCall") self.execute("PartitionedCall", [args], Tin: tin, Tout: tout, f: f, config: config, config_proto: config_proto, executor_type: executor_type, name: name) end
placeholder(dtype: nil, shape: [], name: "Placeholder")
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# File lib/tensorflow/ops/raw_ops.rb, line 2666 def self.placeholder(dtype: nil, shape: [], name: "Placeholder") self.execute("Placeholder", [], dtype: dtype, shape: shape, name: name) end
placeholder_v2(dtype: nil, shape: nil, name: "PlaceholderV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2670 def self.placeholder_v2(dtype: nil, shape: nil, name: "PlaceholderV2") self.execute("PlaceholderV2", [], dtype: dtype, shape: shape, name: name) end
placeholder_with_default(input, dtype: nil, shape: nil, name: "PlaceholderWithDefault")
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# File lib/tensorflow/ops/raw_ops.rb, line 2674 def self.placeholder_with_default(input, dtype: nil, shape: nil, name: "PlaceholderWithDefault") self.execute("PlaceholderWithDefault", [input], dtype: dtype, shape: shape, name: name) end
polygamma(a, x, typeT: nil, name: "Polygamma")
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# File lib/tensorflow/ops/raw_ops.rb, line 2678 def self.polygamma(a, x, typeT: nil, name: "Polygamma") self.execute("Polygamma", [a, x], T: typeT, name: name) end
population_count(x, typeT: nil, name: "PopulationCount")
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# File lib/tensorflow/ops/raw_ops.rb, line 2682 def self.population_count(x, typeT: nil, name: "PopulationCount") self.execute("PopulationCount", [x], T: typeT, name: name) end
pow(x, y, typeT: nil, name: "Pow")
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# File lib/tensorflow/ops/raw_ops.rb, line 2686 def self.pow(x, y, typeT: nil, name: "Pow") self.execute("Pow", [x, y], T: typeT, name: name) end
prefetch_dataset(input_dataset, buffer_size, output_types: nil, output_shapes: nil, slack_period: 0, legacy_autotune: true, name: "PrefetchDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 2690 def self.prefetch_dataset(input_dataset, buffer_size, output_types: nil, output_shapes: nil, slack_period: 0, legacy_autotune: true, name: "PrefetchDataset") self.execute("PrefetchDataset", [input_dataset, buffer_size], output_types: output_types, output_shapes: output_shapes, slack_period: slack_period, legacy_autotune: legacy_autotune, name: name) end
prelinearize(input, dtype: nil, shape: [], layout: [], name: "Prelinearize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2694 def self.prelinearize(input, dtype: nil, shape: [], layout: [], name: "Prelinearize") self.execute("Prelinearize", [input], dtype: dtype, shape: shape, layout: layout, name: name) end
prelinearize_tuple(inputs, dtypes: nil, shapes: nil, layouts: [], name: "PrelinearizeTuple")
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# File lib/tensorflow/ops/raw_ops.rb, line 2698 def self.prelinearize_tuple(inputs, dtypes: nil, shapes: nil, layouts: [], name: "PrelinearizeTuple") self.execute("PrelinearizeTuple", [inputs], dtypes: dtypes, shapes: shapes, layouts: layouts, name: name) end
prevent_gradient(input, typeT: nil, message: "", name: "PreventGradient")
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# File lib/tensorflow/ops/raw_ops.rb, line 2702 def self.prevent_gradient(input, typeT: nil, message: "", name: "PreventGradient") self.execute("PreventGradient", [input], T: typeT, message: message, name: name) end
print(input, data, typeT: nil, u: nil, message: "", first_n: -1, summarize: 3, name: "Print")
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# File lib/tensorflow/ops/raw_ops.rb, line 2706 def self.print(input, data, typeT: nil, u: nil, message: "", first_n: -1, summarize: 3, name: "Print") self.execute("Print", [input, data], T: typeT, U: u, message: message, first_n: first_n, summarize: summarize, name: name) end
print_v2(input, output_stream: "stderr", stop: " ", name: "PrintV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2710 def self.print_v2(input, output_stream: "stderr", stop: " ", name: "PrintV2") self.execute("PrintV2", [input], output_stream: output_stream, end: stop, name: name) end
priority_queue(component_types: [], shapes: nil, capacity: -1, container: "", shared_name: "", name: "PriorityQueue")
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# File lib/tensorflow/ops/raw_ops.rb, line 2715 def self.priority_queue(component_types: [], shapes: nil, capacity: -1, container: "", shared_name: "", name: "PriorityQueue") self.execute("PriorityQueue", [], component_types: component_types, shapes: shapes, capacity: capacity, container: container, shared_name: shared_name, name: name) end
priority_queue_v2(component_types: [], shapes: nil, capacity: -1, container: "", shared_name: "", name: "PriorityQueueV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2719 def self.priority_queue_v2(component_types: [], shapes: nil, capacity: -1, container: "", shared_name: "", name: "PriorityQueueV2") self.execute("PriorityQueueV2", [], component_types: component_types, shapes: shapes, capacity: capacity, container: container, shared_name: shared_name, name: name) end
private_thread_pool_dataset(input_dataset, num_threads, output_types: nil, output_shapes: nil, name: "PrivateThreadPoolDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 2723 def self.private_thread_pool_dataset(input_dataset, num_threads, output_types: nil, output_shapes: nil, name: "PrivateThreadPoolDataset") self.execute("PrivateThreadPoolDataset", [input_dataset, num_threads], output_types: output_types, output_shapes: output_shapes, name: name) end
prod(input, reduction_indices, keep_dims: false, typeT: nil, tidx: :int32, name: "Prod")
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# File lib/tensorflow/ops/raw_ops.rb, line 2727 def self.prod(input, reduction_indices, keep_dims: false, typeT: nil, tidx: :int32, name: "Prod") self.execute("Prod", [input, reduction_indices], keep_dims: keep_dims, T: typeT, Tidx: tidx, name: name) end
py_func(input, token: "", tin: nil, tout: nil, name: "PyFunc")
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# File lib/tensorflow/ops/raw_ops.rb, line 2731 def self.py_func(input, token: "", tin: nil, tout: nil, name: "PyFunc") self.execute("PyFunc", [input], token: token, Tin: tin, Tout: tout, name: name) end
py_func_stateless(input, token: "", tin: nil, tout: nil, name: "PyFuncStateless")
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# File lib/tensorflow/ops/raw_ops.rb, line 2735 def self.py_func_stateless(input, token: "", tin: nil, tout: nil, name: "PyFuncStateless") self.execute("PyFuncStateless", [input], token: token, Tin: tin, Tout: tout, name: name) end
qr(input, full_matrices: false, typeT: nil, name: "Qr")
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# File lib/tensorflow/ops/raw_ops.rb, line 2739 def self.qr(input, full_matrices: false, typeT: nil, name: "Qr") self.execute("Qr", [input], full_matrices: full_matrices, T: typeT, name: name) end
quantize_and_dequantize(input, signed_input: true, num_bits: 8, range_given: false, input_min: 0.0, input_max: 0.0, typeT: nil, name: "QuantizeAndDequantize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2743 def self.quantize_and_dequantize(input, signed_input: true, num_bits: 8, range_given: false, input_min: 0.0, input_max: 0.0, typeT: nil, name: "QuantizeAndDequantize") self.execute("QuantizeAndDequantize", [input], signed_input: signed_input, num_bits: num_bits, range_given: range_given, input_min: input_min, input_max: input_max, T: typeT, name: name) end
quantize_and_dequantize_v2(input, input_min, input_max, signed_input: true, num_bits: 8, range_given: false, typeT: nil, round_mode: "HALF_TO_EVEN", narrow_range: false, axis: -1, name: "QuantizeAndDequantizeV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2747 def self.quantize_and_dequantize_v2(input, input_min, input_max, signed_input: true, num_bits: 8, range_given: false, typeT: nil, round_mode: "HALF_TO_EVEN", narrow_range: false, axis: -1, name: "QuantizeAndDequantizeV2") self.execute("QuantizeAndDequantizeV2", [input, input_min, input_max], signed_input: signed_input, num_bits: num_bits, range_given: range_given, T: typeT, round_mode: round_mode, narrow_range: narrow_range, axis: axis, name: name) end
quantize_and_dequantize_v3(input, input_min, input_max, num_bits, signed_input: true, range_given: true, typeT: nil, narrow_range: false, axis: -1, name: "QuantizeAndDequantizeV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 2751 def self.quantize_and_dequantize_v3(input, input_min, input_max, num_bits, signed_input: true, range_given: true, typeT: nil, narrow_range: false, axis: -1, name: "QuantizeAndDequantizeV3") self.execute("QuantizeAndDequantizeV3", [input, input_min, input_max, num_bits], signed_input: signed_input, range_given: range_given, T: typeT, narrow_range: narrow_range, axis: axis, name: name) end
quantize_down_and_shrink_range(input, input_min, input_max, tinput: nil, out_type: nil, name: "QuantizeDownAndShrinkRange")
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# File lib/tensorflow/ops/raw_ops.rb, line 2755 def self.quantize_down_and_shrink_range(input, input_min, input_max, tinput: nil, out_type: nil, name: "QuantizeDownAndShrinkRange") self.execute("QuantizeDownAndShrinkRange", [input, input_min, input_max], Tinput: tinput, out_type: out_type, name: name) end
quantize_v2(input, min_range, max_range, typeT: nil, mode: "MIN_COMBINED", round_mode: "HALF_AWAY_FROM_ZERO", narrow_range: false, axis: -1, ensure_minimum_range: 0.009999999776482582, name: "QuantizeV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2759 def self.quantize_v2(input, min_range, max_range, typeT: nil, mode: "MIN_COMBINED", round_mode: "HALF_AWAY_FROM_ZERO", narrow_range: false, axis: -1, ensure_minimum_range: 0.009999999776482582, name: "QuantizeV2") self.execute("QuantizeV2", [input, min_range, max_range], T: typeT, mode: mode, round_mode: round_mode, narrow_range: narrow_range, axis: axis, ensure_minimum_range: ensure_minimum_range, name: name) end
quantized_add(x, y, min_x, max_x, min_y, max_y, t1: nil, t2: nil, toutput: :qint32, name: "QuantizedAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 2763 def self.quantized_add(x, y, min_x, max_x, min_y, max_y, t1: nil, t2: nil, toutput: :qint32, name: "QuantizedAdd") self.execute("QuantizedAdd", [x, y, min_x, max_x, min_y, max_y], T1: t1, T2: t2, Toutput: toutput, name: name) end
quantized_avg_pool(input, min_input, max_input, typeT: nil, ksize: nil, strides: nil, padding: nil, name: "QuantizedAvgPool")
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# File lib/tensorflow/ops/raw_ops.rb, line 2767 def self.quantized_avg_pool(input, min_input, max_input, typeT: nil, ksize: nil, strides: nil, padding: nil, name: "QuantizedAvgPool") self.execute("QuantizedAvgPool", [input, min_input, max_input], T: typeT, ksize: ksize, strides: strides, padding: padding, name: name) end
quantized_batch_norm_with_global_normalization(t, t_min, t_max, m, m_min, m_max, v, v_min, v_max, beta, beta_min, beta_max, gamma, gamma_min, gamma_max, tinput: nil, out_type: nil, variance_epsilon: nil, scale_after_normalization: nil, name: "QuantizedBatchNormWithGlobalNormalization")
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# File lib/tensorflow/ops/raw_ops.rb, line 2771 def self.quantized_batch_norm_with_global_normalization(t, t_min, t_max, m, m_min, m_max, v, v_min, v_max, beta, beta_min, beta_max, gamma, gamma_min, gamma_max, tinput: nil, out_type: nil, variance_epsilon: nil, scale_after_normalization: nil, name: "QuantizedBatchNormWithGlobalNormalization") self.execute("QuantizedBatchNormWithGlobalNormalization", [t, t_min, t_max, m, m_min, m_max, v, v_min, v_max, beta, beta_min, beta_max, gamma, gamma_min, gamma_max], Tinput: tinput, out_type: out_type, variance_epsilon: variance_epsilon, scale_after_normalization: scale_after_normalization, name: name) end
quantized_bias_add(input, bias, min_input, max_input, min_bias, max_bias, t1: nil, t2: nil, out_type: nil, name: "QuantizedBiasAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 2775 def self.quantized_bias_add(input, bias, min_input, max_input, min_bias, max_bias, t1: nil, t2: nil, out_type: nil, name: "QuantizedBiasAdd") self.execute("QuantizedBiasAdd", [input, bias, min_input, max_input, min_bias, max_bias], T1: t1, T2: t2, out_type: out_type, name: name) end
quantized_concat(concat_dim, values, input_mins, input_maxes, n: nil, typeT: nil, name: "QuantizedConcat")
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# File lib/tensorflow/ops/raw_ops.rb, line 2779 def self.quantized_concat(concat_dim, values, input_mins, input_maxes, n: nil, typeT: nil, name: "QuantizedConcat") self.execute("QuantizedConcat", [concat_dim, values, input_mins, input_maxes], N: n, T: typeT, name: name) end
quantized_conv2_d(input, filter, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], name: "QuantizedConv2D")
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# File lib/tensorflow/ops/raw_ops.rb, line 2783 def self.quantized_conv2_d(input, filter, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], name: "QuantizedConv2D") self.execute("QuantizedConv2D", [input, filter, min_input, max_input, min_filter, max_filter], Tinput: tinput, Tfilter: tfilter, out_type: out_type, strides: strides, padding: padding, dilations: dilations, name: name) end
quantized_conv2_d_and_relu(input, filter, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DAndRelu")
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# File lib/tensorflow/ops/raw_ops.rb, line 2787 def self.quantized_conv2_d_and_relu(input, filter, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DAndRelu") self.execute("QuantizedConv2DAndRelu", [input, filter, min_input, max_input, min_filter, max_filter], Tinput: tinput, Tfilter: tfilter, out_type: out_type, strides: strides, padding: padding, dilations: dilations, padding_list: padding_list, name: name) end
quantized_conv2_d_and_relu_and_requantize(input, filter, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, tinput: nil, tfilter: nil, out_type: :quint8, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DAndReluAndRequantize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2791 def self.quantized_conv2_d_and_relu_and_requantize(input, filter, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, tinput: nil, tfilter: nil, out_type: :quint8, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DAndReluAndRequantize") self.execute("QuantizedConv2DAndReluAndRequantize", [input, filter, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output], Tinput: tinput, Tfilter: tfilter, out_type: out_type, strides: strides, padding: padding, dilations: dilations, padding_list: padding_list, name: name) end
quantized_conv2_d_and_requantize(input, filter, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, tinput: nil, tfilter: nil, out_type: :qint8, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DAndRequantize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2795 def self.quantized_conv2_d_and_requantize(input, filter, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, tinput: nil, tfilter: nil, out_type: :qint8, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DAndRequantize") self.execute("QuantizedConv2DAndRequantize", [input, filter, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output], Tinput: tinput, Tfilter: tfilter, out_type: out_type, strides: strides, padding: padding, dilations: dilations, padding_list: padding_list, name: name) end
quantized_conv2_d_per_channel(input, filter, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], name: "QuantizedConv2DPerChannel")
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# File lib/tensorflow/ops/raw_ops.rb, line 2799 def self.quantized_conv2_d_per_channel(input, filter, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], name: "QuantizedConv2DPerChannel") self.execute("QuantizedConv2DPerChannel", [input, filter, min_input, max_input, min_filter, max_filter], Tinput: tinput, Tfilter: tfilter, out_type: out_type, strides: strides, padding: padding, dilations: dilations, name: name) end
quantized_conv2_d_with_bias(input, filter, bias, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DWithBias")
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# File lib/tensorflow/ops/raw_ops.rb, line 2803 def self.quantized_conv2_d_with_bias(input, filter, bias, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DWithBias") self.execute("QuantizedConv2DWithBias", [input, filter, bias, min_input, max_input, min_filter, max_filter], Tinput: tinput, Tfilter: tfilter, out_type: out_type, strides: strides, padding: padding, dilations: dilations, padding_list: padding_list, name: name) end
quantized_conv2_d_with_bias_and_relu(input, filter, bias, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DWithBiasAndRelu")
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# File lib/tensorflow/ops/raw_ops.rb, line 2807 def self.quantized_conv2_d_with_bias_and_relu(input, filter, bias, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DWithBiasAndRelu") self.execute("QuantizedConv2DWithBiasAndRelu", [input, filter, bias, min_input, max_input, min_filter, max_filter], Tinput: tinput, Tfilter: tfilter, out_type: out_type, strides: strides, padding: padding, dilations: dilations, padding_list: padding_list, name: name) end
quantized_conv2_d_with_bias_and_relu_and_requantize(input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, tinput: nil, tfilter: nil, tbias: nil, out_type: :quint8, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DWithBiasAndReluAndRequantize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2811 def self.quantized_conv2_d_with_bias_and_relu_and_requantize(input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, tinput: nil, tfilter: nil, tbias: nil, out_type: :quint8, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DWithBiasAndReluAndRequantize") self.execute("QuantizedConv2DWithBiasAndReluAndRequantize", [input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output], Tinput: tinput, Tfilter: tfilter, Tbias: tbias, out_type: out_type, strides: strides, padding: padding, dilations: dilations, padding_list: padding_list, name: name) end
quantized_conv2_d_with_bias_and_requantize(input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, tinput: nil, tfilter: nil, tbias: nil, out_type: :qint8, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DWithBiasAndRequantize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2815 def self.quantized_conv2_d_with_bias_and_requantize(input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, tinput: nil, tfilter: nil, tbias: nil, out_type: :qint8, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DWithBiasAndRequantize") self.execute("QuantizedConv2DWithBiasAndRequantize", [input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output], Tinput: tinput, Tfilter: tfilter, Tbias: tbias, out_type: out_type, strides: strides, padding: padding, dilations: dilations, padding_list: padding_list, name: name) end
quantized_conv2_d_with_bias_signed_sum_and_relu_and_requantize(input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, summand, min_summand, max_summand, tinput: nil, tfilter: nil, tbias: nil, tsummand: nil, out_type: :quint8, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DWithBiasSignedSumAndReluAndRequantize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2819 def self.quantized_conv2_d_with_bias_signed_sum_and_relu_and_requantize(input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, summand, min_summand, max_summand, tinput: nil, tfilter: nil, tbias: nil, tsummand: nil, out_type: :quint8, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DWithBiasSignedSumAndReluAndRequantize") self.execute("QuantizedConv2DWithBiasSignedSumAndReluAndRequantize", [input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, summand, min_summand, max_summand], Tinput: tinput, Tfilter: tfilter, Tbias: tbias, Tsummand: tsummand, out_type: out_type, strides: strides, padding: padding, dilations: dilations, padding_list: padding_list, name: name) end
quantized_conv2_d_with_bias_sum_and_relu(input, filter, bias, min_input, max_input, min_filter, max_filter, summand, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DWithBiasSumAndRelu")
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# File lib/tensorflow/ops/raw_ops.rb, line 2823 def self.quantized_conv2_d_with_bias_sum_and_relu(input, filter, bias, min_input, max_input, min_filter, max_filter, summand, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DWithBiasSumAndRelu") self.execute("QuantizedConv2DWithBiasSumAndRelu", [input, filter, bias, min_input, max_input, min_filter, max_filter, summand], Tinput: tinput, Tfilter: tfilter, out_type: out_type, strides: strides, padding: padding, dilations: dilations, padding_list: padding_list, name: name) end
quantized_conv2_d_with_bias_sum_and_relu_and_requantize(input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, summand, min_summand, max_summand, tinput: nil, tfilter: nil, tbias: nil, tsummand: nil, out_type: :quint8, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DWithBiasSumAndReluAndRequantize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2827 def self.quantized_conv2_d_with_bias_sum_and_relu_and_requantize(input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, summand, min_summand, max_summand, tinput: nil, tfilter: nil, tbias: nil, tsummand: nil, out_type: :quint8, strides: nil, padding: nil, dilations: [], padding_list: [], name: "QuantizedConv2DWithBiasSumAndReluAndRequantize") self.execute("QuantizedConv2DWithBiasSumAndReluAndRequantize", [input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, summand, min_summand, max_summand], Tinput: tinput, Tfilter: tfilter, Tbias: tbias, Tsummand: tsummand, out_type: out_type, strides: strides, padding: padding, dilations: dilations, padding_list: padding_list, name: name) end
quantized_depthwise_conv2_d(input, filter, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], name: "QuantizedDepthwiseConv2D")
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# File lib/tensorflow/ops/raw_ops.rb, line 2831 def self.quantized_depthwise_conv2_d(input, filter, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], name: "QuantizedDepthwiseConv2D") self.execute("QuantizedDepthwiseConv2D", [input, filter, min_input, max_input, min_filter, max_filter], Tinput: tinput, Tfilter: tfilter, out_type: out_type, strides: strides, padding: padding, dilations: dilations, name: name) end
quantized_depthwise_conv2_d_with_bias(input, filter, bias, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], name: "QuantizedDepthwiseConv2DWithBias")
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# File lib/tensorflow/ops/raw_ops.rb, line 2835 def self.quantized_depthwise_conv2_d_with_bias(input, filter, bias, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], name: "QuantizedDepthwiseConv2DWithBias") self.execute("QuantizedDepthwiseConv2DWithBias", [input, filter, bias, min_input, max_input, min_filter, max_filter], Tinput: tinput, Tfilter: tfilter, out_type: out_type, strides: strides, padding: padding, dilations: dilations, name: name) end
quantized_depthwise_conv2_d_with_bias_and_relu(input, filter, bias, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], name: "QuantizedDepthwiseConv2DWithBiasAndRelu")
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# File lib/tensorflow/ops/raw_ops.rb, line 2839 def self.quantized_depthwise_conv2_d_with_bias_and_relu(input, filter, bias, min_input, max_input, min_filter, max_filter, tinput: nil, tfilter: nil, out_type: :qint32, strides: nil, padding: nil, dilations: [], name: "QuantizedDepthwiseConv2DWithBiasAndRelu") self.execute("QuantizedDepthwiseConv2DWithBiasAndRelu", [input, filter, bias, min_input, max_input, min_filter, max_filter], Tinput: tinput, Tfilter: tfilter, out_type: out_type, strides: strides, padding: padding, dilations: dilations, name: name) end
quantized_depthwise_conv2_d_with_bias_and_relu_and_requantize(input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, tinput: nil, tfilter: nil, tbias: nil, out_type: :quint8, strides: nil, padding: nil, dilations: [], name: "QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2843 def self.quantized_depthwise_conv2_d_with_bias_and_relu_and_requantize(input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output, tinput: nil, tfilter: nil, tbias: nil, out_type: :quint8, strides: nil, padding: nil, dilations: [], name: "QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize") self.execute("QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize", [input, filter, bias, min_input, max_input, min_filter, max_filter, min_freezed_output, max_freezed_output], Tinput: tinput, Tfilter: tfilter, Tbias: tbias, out_type: out_type, strides: strides, padding: padding, dilations: dilations, name: name) end
quantized_instance_norm(x, x_min, x_max, typeT: nil, output_range_given: false, given_y_min: 0.0, given_y_max: 0.0, variance_epsilon: 9.999999747378752e-06, min_separation: 0.0010000000474974513, name: "QuantizedInstanceNorm")
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# File lib/tensorflow/ops/raw_ops.rb, line 2847 def self.quantized_instance_norm(x, x_min, x_max, typeT: nil, output_range_given: false, given_y_min: 0.0, given_y_max: 0.0, variance_epsilon: 9.999999747378752e-06, min_separation: 0.0010000000474974513, name: "QuantizedInstanceNorm") self.execute("QuantizedInstanceNorm", [x, x_min, x_max], T: typeT, output_range_given: output_range_given, given_y_min: given_y_min, given_y_max: given_y_max, variance_epsilon: variance_epsilon, min_separation: min_separation, name: name) end
quantized_mat_mul(a, b, min_a, max_a, min_b, max_b, t1: nil, t2: nil, toutput: :qint32, transpose_a: false, transpose_b: false, tactivation: :quint8, name: "QuantizedMatMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 2851 def self.quantized_mat_mul(a, b, min_a, max_a, min_b, max_b, t1: nil, t2: nil, toutput: :qint32, transpose_a: false, transpose_b: false, tactivation: :quint8, name: "QuantizedMatMul") self.execute("QuantizedMatMul", [a, b, min_a, max_a, min_b, max_b], T1: t1, T2: t2, Toutput: toutput, transpose_a: transpose_a, transpose_b: transpose_b, Tactivation: tactivation, name: name) end
quantized_mat_mul_with_bias(a, b, bias, min_a, max_a, min_b, max_b, t1: nil, t2: nil, tbias: nil, toutput: :qint32, transpose_a: false, transpose_b: false, input_quant_mode: "MIN_FIRST", name: "QuantizedMatMulWithBias")
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# File lib/tensorflow/ops/raw_ops.rb, line 2855 def self.quantized_mat_mul_with_bias(a, b, bias, min_a, max_a, min_b, max_b, t1: nil, t2: nil, tbias: nil, toutput: :qint32, transpose_a: false, transpose_b: false, input_quant_mode: "MIN_FIRST", name: "QuantizedMatMulWithBias") self.execute("QuantizedMatMulWithBias", [a, b, bias, min_a, max_a, min_b, max_b], T1: t1, T2: t2, Tbias: tbias, Toutput: toutput, transpose_a: transpose_a, transpose_b: transpose_b, input_quant_mode: input_quant_mode, name: name) end
quantized_mat_mul_with_bias_and_relu(a, b, bias, min_a, max_a, min_b, max_b, t1: nil, t2: nil, toutput: :qint32, transpose_a: false, transpose_b: false, input_quant_mode: "MIN_FIRST", name: "QuantizedMatMulWithBiasAndRelu")
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# File lib/tensorflow/ops/raw_ops.rb, line 2859 def self.quantized_mat_mul_with_bias_and_relu(a, b, bias, min_a, max_a, min_b, max_b, t1: nil, t2: nil, toutput: :qint32, transpose_a: false, transpose_b: false, input_quant_mode: "MIN_FIRST", name: "QuantizedMatMulWithBiasAndRelu") self.execute("QuantizedMatMulWithBiasAndRelu", [a, b, bias, min_a, max_a, min_b, max_b], T1: t1, T2: t2, Toutput: toutput, transpose_a: transpose_a, transpose_b: transpose_b, input_quant_mode: input_quant_mode, name: name) end
quantized_mat_mul_with_bias_and_relu_and_requantize(a, b, bias, min_a, max_a, min_b, max_b, min_freezed_output, max_freezed_output, t1: nil, t2: nil, tbias: nil, toutput: :quint8, transpose_a: false, transpose_b: false, input_quant_mode: "MIN_FIRST", name: "QuantizedMatMulWithBiasAndReluAndRequantize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2863 def self.quantized_mat_mul_with_bias_and_relu_and_requantize(a, b, bias, min_a, max_a, min_b, max_b, min_freezed_output, max_freezed_output, t1: nil, t2: nil, tbias: nil, toutput: :quint8, transpose_a: false, transpose_b: false, input_quant_mode: "MIN_FIRST", name: "QuantizedMatMulWithBiasAndReluAndRequantize") self.execute("QuantizedMatMulWithBiasAndReluAndRequantize", [a, b, bias, min_a, max_a, min_b, max_b, min_freezed_output, max_freezed_output], T1: t1, T2: t2, Tbias: tbias, Toutput: toutput, transpose_a: transpose_a, transpose_b: transpose_b, input_quant_mode: input_quant_mode, name: name) end
quantized_max_pool(input, min_input, max_input, typeT: nil, ksize: nil, strides: nil, padding: nil, name: "QuantizedMaxPool")
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# File lib/tensorflow/ops/raw_ops.rb, line 2867 def self.quantized_max_pool(input, min_input, max_input, typeT: nil, ksize: nil, strides: nil, padding: nil, name: "QuantizedMaxPool") self.execute("QuantizedMaxPool", [input, min_input, max_input], T: typeT, ksize: ksize, strides: strides, padding: padding, name: name) end
quantized_mul(x, y, min_x, max_x, min_y, max_y, t1: nil, t2: nil, toutput: :qint32, name: "QuantizedMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 2871 def self.quantized_mul(x, y, min_x, max_x, min_y, max_y, t1: nil, t2: nil, toutput: :qint32, name: "QuantizedMul") self.execute("QuantizedMul", [x, y, min_x, max_x, min_y, max_y], T1: t1, T2: t2, Toutput: toutput, name: name) end
quantized_relu(features, min_features, max_features, tinput: nil, out_type: :quint8, name: "QuantizedRelu")
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# File lib/tensorflow/ops/raw_ops.rb, line 2875 def self.quantized_relu(features, min_features, max_features, tinput: nil, out_type: :quint8, name: "QuantizedRelu") self.execute("QuantizedRelu", [features, min_features, max_features], Tinput: tinput, out_type: out_type, name: name) end
quantized_relu6(features, min_features, max_features, tinput: nil, out_type: :quint8, name: "QuantizedRelu6")
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# File lib/tensorflow/ops/raw_ops.rb, line 2879 def self.quantized_relu6(features, min_features, max_features, tinput: nil, out_type: :quint8, name: "QuantizedRelu6") self.execute("QuantizedRelu6", [features, min_features, max_features], Tinput: tinput, out_type: out_type, name: name) end
quantized_relu_x(features, max_value, min_features, max_features, tinput: nil, out_type: :quint8, name: "QuantizedReluX")
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# File lib/tensorflow/ops/raw_ops.rb, line 2883 def self.quantized_relu_x(features, max_value, min_features, max_features, tinput: nil, out_type: :quint8, name: "QuantizedReluX") self.execute("QuantizedReluX", [features, max_value, min_features, max_features], Tinput: tinput, out_type: out_type, name: name) end
quantized_reshape(tensor, shape, input_min, input_max, typeT: nil, tshape: :int32, name: "QuantizedReshape")
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# File lib/tensorflow/ops/raw_ops.rb, line 2887 def self.quantized_reshape(tensor, shape, input_min, input_max, typeT: nil, tshape: :int32, name: "QuantizedReshape") self.execute("QuantizedReshape", [tensor, shape, input_min, input_max], T: typeT, Tshape: tshape, name: name) end
quantized_resize_bilinear(images, size, min, max, typeT: nil, align_corners: false, half_pixel_centers: false, name: "QuantizedResizeBilinear")
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# File lib/tensorflow/ops/raw_ops.rb, line 2891 def self.quantized_resize_bilinear(images, size, min, max, typeT: nil, align_corners: false, half_pixel_centers: false, name: "QuantizedResizeBilinear") self.execute("QuantizedResizeBilinear", [images, size, min, max], T: typeT, align_corners: align_corners, half_pixel_centers: half_pixel_centers, name: name) end
queue_close(handle, cancel_pending_enqueues: false, name: "QueueClose")
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# File lib/tensorflow/ops/raw_ops.rb, line 2895 def self.queue_close(handle, cancel_pending_enqueues: false, name: "QueueClose") self.execute("QueueClose", [handle], cancel_pending_enqueues: cancel_pending_enqueues, name: name) end
queue_close_v2(handle, cancel_pending_enqueues: false, name: "QueueCloseV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2899 def self.queue_close_v2(handle, cancel_pending_enqueues: false, name: "QueueCloseV2") self.execute("QueueCloseV2", [handle], cancel_pending_enqueues: cancel_pending_enqueues, name: name) end
queue_dequeue(handle, component_types: nil, timeout_ms: -1, name: "QueueDequeue")
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# File lib/tensorflow/ops/raw_ops.rb, line 2903 def self.queue_dequeue(handle, component_types: nil, timeout_ms: -1, name: "QueueDequeue") self.execute("QueueDequeue", [handle], component_types: component_types, timeout_ms: timeout_ms, name: name) end
queue_dequeue_many(handle, n, component_types: nil, timeout_ms: -1, name: "QueueDequeueMany")
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# File lib/tensorflow/ops/raw_ops.rb, line 2907 def self.queue_dequeue_many(handle, n, component_types: nil, timeout_ms: -1, name: "QueueDequeueMany") self.execute("QueueDequeueMany", [handle, n], component_types: component_types, timeout_ms: timeout_ms, name: name) end
queue_dequeue_many_v2(handle, n, component_types: nil, timeout_ms: -1, name: "QueueDequeueManyV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2911 def self.queue_dequeue_many_v2(handle, n, component_types: nil, timeout_ms: -1, name: "QueueDequeueManyV2") self.execute("QueueDequeueManyV2", [handle, n], component_types: component_types, timeout_ms: timeout_ms, name: name) end
queue_dequeue_up_to(handle, n, component_types: nil, timeout_ms: -1, name: "QueueDequeueUpTo")
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# File lib/tensorflow/ops/raw_ops.rb, line 2915 def self.queue_dequeue_up_to(handle, n, component_types: nil, timeout_ms: -1, name: "QueueDequeueUpTo") self.execute("QueueDequeueUpTo", [handle, n], component_types: component_types, timeout_ms: timeout_ms, name: name) end
queue_dequeue_up_to_v2(handle, n, component_types: nil, timeout_ms: -1, name: "QueueDequeueUpToV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2919 def self.queue_dequeue_up_to_v2(handle, n, component_types: nil, timeout_ms: -1, name: "QueueDequeueUpToV2") self.execute("QueueDequeueUpToV2", [handle, n], component_types: component_types, timeout_ms: timeout_ms, name: name) end
queue_dequeue_v2(handle, component_types: nil, timeout_ms: -1, name: "QueueDequeueV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2923 def self.queue_dequeue_v2(handle, component_types: nil, timeout_ms: -1, name: "QueueDequeueV2") self.execute("QueueDequeueV2", [handle], component_types: component_types, timeout_ms: timeout_ms, name: name) end
queue_enqueue(handle, components, tcomponents: nil, timeout_ms: -1, name: "QueueEnqueue")
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# File lib/tensorflow/ops/raw_ops.rb, line 2927 def self.queue_enqueue(handle, components, tcomponents: nil, timeout_ms: -1, name: "QueueEnqueue") self.execute("QueueEnqueue", [handle, components], Tcomponents: tcomponents, timeout_ms: timeout_ms, name: name) end
queue_enqueue_many(handle, components, tcomponents: nil, timeout_ms: -1, name: "QueueEnqueueMany")
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# File lib/tensorflow/ops/raw_ops.rb, line 2931 def self.queue_enqueue_many(handle, components, tcomponents: nil, timeout_ms: -1, name: "QueueEnqueueMany") self.execute("QueueEnqueueMany", [handle, components], Tcomponents: tcomponents, timeout_ms: timeout_ms, name: name) end
queue_enqueue_many_v2(handle, components, tcomponents: nil, timeout_ms: -1, name: "QueueEnqueueManyV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2935 def self.queue_enqueue_many_v2(handle, components, tcomponents: nil, timeout_ms: -1, name: "QueueEnqueueManyV2") self.execute("QueueEnqueueManyV2", [handle, components], Tcomponents: tcomponents, timeout_ms: timeout_ms, name: name) end
queue_enqueue_v2(handle, components, tcomponents: nil, timeout_ms: -1, name: "QueueEnqueueV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2939 def self.queue_enqueue_v2(handle, components, tcomponents: nil, timeout_ms: -1, name: "QueueEnqueueV2") self.execute("QueueEnqueueV2", [handle, components], Tcomponents: tcomponents, timeout_ms: timeout_ms, name: name) end
queue_is_closed(handle, name: "QueueIsClosed")
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# File lib/tensorflow/ops/raw_ops.rb, line 2943 def self.queue_is_closed(handle, name: "QueueIsClosed") self.execute("QueueIsClosed", [handle], name: name) end
queue_is_closed_v2(handle, name: "QueueIsClosedV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2947 def self.queue_is_closed_v2(handle, name: "QueueIsClosedV2") self.execute("QueueIsClosedV2", [handle], name: name) end
queue_size(handle, name: "QueueSize")
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# File lib/tensorflow/ops/raw_ops.rb, line 2951 def self.queue_size(handle, name: "QueueSize") self.execute("QueueSize", [handle], name: name) end
queue_size_v2(handle, name: "QueueSizeV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 2955 def self.queue_size_v2(handle, name: "QueueSizeV2") self.execute("QueueSizeV2", [handle], name: name) end
ragged_gather(params_nested_splits, params_dense_values, indices, tvalues: nil, tindices: nil, tsplits: :int64, params_ragged_rank: nil, output_ragged_rank: nil, name: "RaggedGather")
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# File lib/tensorflow/ops/raw_ops.rb, line 2975 def self.ragged_gather(params_nested_splits, params_dense_values, indices, tvalues: nil, tindices: nil, tsplits: :int64, params_ragged_rank: nil, output_ragged_rank: nil, name: "RaggedGather") self.execute("RaggedGather", [params_nested_splits, params_dense_values, indices], Tvalues: tvalues, Tindices: tindices, Tsplits: tsplits, PARAMS_RAGGED_RANK: params_ragged_rank, OUTPUT_RAGGED_RANK: output_ragged_rank, name: name) end
ragged_range(starts, limits, deltas, typeT: :int32, tsplits: :int64, name: "RaggedRange")
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# File lib/tensorflow/ops/raw_ops.rb, line 2979 def self.ragged_range(starts, limits, deltas, typeT: :int32, tsplits: :int64, name: "RaggedRange") self.execute("RaggedRange", [starts, limits, deltas], T: typeT, Tsplits: tsplits, name: name) end
ragged_tensor_from_variant(encoded_ragged, input_ragged_rank: nil, output_ragged_rank: nil, tvalues: nil, tsplits: :int64, name: "RaggedTensorFromVariant")
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# File lib/tensorflow/ops/raw_ops.rb, line 2983 def self.ragged_tensor_from_variant(encoded_ragged, input_ragged_rank: nil, output_ragged_rank: nil, tvalues: nil, tsplits: :int64, name: "RaggedTensorFromVariant") self.execute("RaggedTensorFromVariant", [encoded_ragged], input_ragged_rank: input_ragged_rank, output_ragged_rank: output_ragged_rank, Tvalues: tvalues, Tsplits: tsplits, name: name) end
ragged_tensor_to_sparse(rt_nested_splits, rt_dense_values, ragged_rank: nil, typeT: nil, tsplits: :int64, name: "RaggedTensorToSparse")
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# File lib/tensorflow/ops/raw_ops.rb, line 2987 def self.ragged_tensor_to_sparse(rt_nested_splits, rt_dense_values, ragged_rank: nil, typeT: nil, tsplits: :int64, name: "RaggedTensorToSparse") self.execute("RaggedTensorToSparse", [rt_nested_splits, rt_dense_values], RAGGED_RANK: ragged_rank, T: typeT, Tsplits: tsplits, name: name) end
ragged_tensor_to_tensor(shape, values, default_value, row_partition_tensors, typeT: nil, tindex: nil, tshape: nil, num_row_partition_tensors: nil, row_partition_types: nil, name: "RaggedTensorToTensor")
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# File lib/tensorflow/ops/raw_ops.rb, line 2991 def self.ragged_tensor_to_tensor(shape, values, default_value, row_partition_tensors, typeT: nil, tindex: nil, tshape: nil, num_row_partition_tensors: nil, row_partition_types: nil, name: "RaggedTensorToTensor") self.execute("RaggedTensorToTensor", [shape, values, default_value, row_partition_tensors], T: typeT, Tindex: tindex, Tshape: tshape, num_row_partition_tensors: num_row_partition_tensors, row_partition_types: row_partition_types, name: name) end
ragged_tensor_to_variant(rt_nested_splits, rt_dense_values, ragged_rank: nil, tvalues: nil, tsplits: :int64, batched_input: nil, name: "RaggedTensorToVariant")
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# File lib/tensorflow/ops/raw_ops.rb, line 2995 def self.ragged_tensor_to_variant(rt_nested_splits, rt_dense_values, ragged_rank: nil, tvalues: nil, tsplits: :int64, batched_input: nil, name: "RaggedTensorToVariant") self.execute("RaggedTensorToVariant", [rt_nested_splits, rt_dense_values], RAGGED_RANK: ragged_rank, Tvalues: tvalues, Tsplits: tsplits, batched_input: batched_input, name: name) end
random_crop(image, size, typeT: nil, seed: 0, seed2: 0, name: "RandomCrop")
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# File lib/tensorflow/ops/raw_ops.rb, line 2999 def self.random_crop(image, size, typeT: nil, seed: 0, seed2: 0, name: "RandomCrop") self.execute("RandomCrop", [image, size], T: typeT, seed: seed, seed2: seed2, name: name) end
random_dataset(seed, seed2, output_types: nil, output_shapes: nil, name: "RandomDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3003 def self.random_dataset(seed, seed2, output_types: nil, output_shapes: nil, name: "RandomDataset") self.execute("RandomDataset", [seed, seed2], output_types: output_types, output_shapes: output_shapes, name: name) end
random_gamma(shape, alpha, seed: 0, seed2: 0, s: nil, typeT: nil, name: "RandomGamma")
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# File lib/tensorflow/ops/raw_ops.rb, line 3007 def self.random_gamma(shape, alpha, seed: 0, seed2: 0, s: nil, typeT: nil, name: "RandomGamma") self.execute("RandomGamma", [shape, alpha], seed: seed, seed2: seed2, S: s, T: typeT, name: name) end
random_gamma_grad(alpha, sample, typeT: nil, name: "RandomGammaGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3011 def self.random_gamma_grad(alpha, sample, typeT: nil, name: "RandomGammaGrad") self.execute("RandomGammaGrad", [alpha, sample], T: typeT, name: name) end
random_poisson(shape, rate, seed: 0, seed2: 0, s: nil, dtype: nil, name: "RandomPoisson")
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# File lib/tensorflow/ops/raw_ops.rb, line 3015 def self.random_poisson(shape, rate, seed: 0, seed2: 0, s: nil, dtype: nil, name: "RandomPoisson") self.execute("RandomPoisson", [shape, rate], seed: seed, seed2: seed2, S: s, dtype: dtype, name: name) end
random_poisson_v2(shape, rate, seed: 0, seed2: 0, s: nil, r: :double, dtype: :int64, name: "RandomPoissonV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3019 def self.random_poisson_v2(shape, rate, seed: 0, seed2: 0, s: nil, r: :double, dtype: :int64, name: "RandomPoissonV2") self.execute("RandomPoissonV2", [shape, rate], seed: seed, seed2: seed2, S: s, R: r, dtype: dtype, name: name) end
random_shuffle(value, seed: 0, seed2: 0, typeT: nil, name: "RandomShuffle")
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# File lib/tensorflow/ops/raw_ops.rb, line 3023 def self.random_shuffle(value, seed: 0, seed2: 0, typeT: nil, name: "RandomShuffle") self.execute("RandomShuffle", [value], seed: seed, seed2: seed2, T: typeT, name: name) end
random_shuffle_queue(component_types: nil, shapes: [], capacity: -1, min_after_dequeue: 0, seed: 0, seed2: 0, container: "", shared_name: "", name: "RandomShuffleQueue")
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# File lib/tensorflow/ops/raw_ops.rb, line 3027 def self.random_shuffle_queue(component_types: nil, shapes: [], capacity: -1, min_after_dequeue: 0, seed: 0, seed2: 0, container: "", shared_name: "", name: "RandomShuffleQueue") self.execute("RandomShuffleQueue", [], component_types: component_types, shapes: shapes, capacity: capacity, min_after_dequeue: min_after_dequeue, seed: seed, seed2: seed2, container: container, shared_name: shared_name, name: name) end
random_shuffle_queue_v2(component_types: nil, shapes: [], capacity: -1, min_after_dequeue: 0, seed: 0, seed2: 0, container: "", shared_name: "", name: "RandomShuffleQueueV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3031 def self.random_shuffle_queue_v2(component_types: nil, shapes: [], capacity: -1, min_after_dequeue: 0, seed: 0, seed2: 0, container: "", shared_name: "", name: "RandomShuffleQueueV2") self.execute("RandomShuffleQueueV2", [], component_types: component_types, shapes: shapes, capacity: capacity, min_after_dequeue: min_after_dequeue, seed: seed, seed2: seed2, container: container, shared_name: shared_name, name: name) end
random_standard_normal(shape, seed: 0, seed2: 0, dtype: nil, typeT: nil, name: "RandomStandardNormal")
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# File lib/tensorflow/ops/raw_ops.rb, line 3035 def self.random_standard_normal(shape, seed: 0, seed2: 0, dtype: nil, typeT: nil, name: "RandomStandardNormal") self.execute("RandomStandardNormal", [shape], seed: seed, seed2: seed2, dtype: dtype, T: typeT, name: name) end
random_uniform(shape, seed: 0, seed2: 0, dtype: nil, typeT: nil, name: "RandomUniform")
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# File lib/tensorflow/ops/raw_ops.rb, line 3039 def self.random_uniform(shape, seed: 0, seed2: 0, dtype: nil, typeT: nil, name: "RandomUniform") self.execute("RandomUniform", [shape], seed: seed, seed2: seed2, dtype: dtype, T: typeT, name: name) end
random_uniform_int(shape, minval, maxval, seed: 0, seed2: 0, tout: nil, typeT: nil, name: "RandomUniformInt")
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# File lib/tensorflow/ops/raw_ops.rb, line 3043 def self.random_uniform_int(shape, minval, maxval, seed: 0, seed2: 0, tout: nil, typeT: nil, name: "RandomUniformInt") self.execute("RandomUniformInt", [shape, minval, maxval], seed: seed, seed2: seed2, Tout: tout, T: typeT, name: name) end
range(start, limit, delta, tidx: :int32, name: "Range")
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# File lib/tensorflow/ops/raw_ops.rb, line 3047 def self.range(start, limit, delta, tidx: :int32, name: "Range") self.execute("Range", [start, limit, delta], Tidx: tidx, name: name) end
range_dataset(start, stop, step, output_types: nil, output_shapes: nil, name: "RangeDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3051 def self.range_dataset(start, stop, step, output_types: nil, output_shapes: nil, name: "RangeDataset") self.execute("RangeDataset", [start, stop, step], output_types: output_types, output_shapes: output_shapes, name: name) end
rank(input, typeT: nil, name: "Rank")
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# File lib/tensorflow/ops/raw_ops.rb, line 3055 def self.rank(input, typeT: nil, name: "Rank") self.execute("Rank", [input], T: typeT, name: name) end
read_file(filename, name: "ReadFile")
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# File lib/tensorflow/ops/raw_ops.rb, line 3059 def self.read_file(filename, name: "ReadFile") self.execute("ReadFile", [filename], name: name) end
read_variable_op(resource, dtype: nil, name: "ReadVariableOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 3063 def self.read_variable_op(resource, dtype: nil, name: "ReadVariableOp") self.execute("ReadVariableOp", [resource], dtype: dtype, name: name) end
reader_num_records_produced(reader_handle, name: "ReaderNumRecordsProduced")
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# File lib/tensorflow/ops/raw_ops.rb, line 3067 def self.reader_num_records_produced(reader_handle, name: "ReaderNumRecordsProduced") self.execute("ReaderNumRecordsProduced", [reader_handle], name: name) end
reader_num_records_produced_v2(reader_handle, name: "ReaderNumRecordsProducedV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3071 def self.reader_num_records_produced_v2(reader_handle, name: "ReaderNumRecordsProducedV2") self.execute("ReaderNumRecordsProducedV2", [reader_handle], name: name) end
reader_num_work_units_completed(reader_handle, name: "ReaderNumWorkUnitsCompleted")
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# File lib/tensorflow/ops/raw_ops.rb, line 3075 def self.reader_num_work_units_completed(reader_handle, name: "ReaderNumWorkUnitsCompleted") self.execute("ReaderNumWorkUnitsCompleted", [reader_handle], name: name) end
reader_num_work_units_completed_v2(reader_handle, name: "ReaderNumWorkUnitsCompletedV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3079 def self.reader_num_work_units_completed_v2(reader_handle, name: "ReaderNumWorkUnitsCompletedV2") self.execute("ReaderNumWorkUnitsCompletedV2", [reader_handle], name: name) end
reader_read(reader_handle, queue_handle, name: "ReaderRead")
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# File lib/tensorflow/ops/raw_ops.rb, line 3083 def self.reader_read(reader_handle, queue_handle, name: "ReaderRead") self.execute("ReaderRead", [reader_handle, queue_handle], name: name) end
reader_read_up_to(reader_handle, queue_handle, num_records, name: "ReaderReadUpTo")
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# File lib/tensorflow/ops/raw_ops.rb, line 3087 def self.reader_read_up_to(reader_handle, queue_handle, num_records, name: "ReaderReadUpTo") self.execute("ReaderReadUpTo", [reader_handle, queue_handle, num_records], name: name) end
reader_read_up_to_v2(reader_handle, queue_handle, num_records, name: "ReaderReadUpToV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3091 def self.reader_read_up_to_v2(reader_handle, queue_handle, num_records, name: "ReaderReadUpToV2") self.execute("ReaderReadUpToV2", [reader_handle, queue_handle, num_records], name: name) end
reader_read_v2(reader_handle, queue_handle, name: "ReaderReadV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3095 def self.reader_read_v2(reader_handle, queue_handle, name: "ReaderReadV2") self.execute("ReaderReadV2", [reader_handle, queue_handle], name: name) end
reader_reset(reader_handle, name: "ReaderReset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3099 def self.reader_reset(reader_handle, name: "ReaderReset") self.execute("ReaderReset", [reader_handle], name: name) end
reader_reset_v2(reader_handle, name: "ReaderResetV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3103 def self.reader_reset_v2(reader_handle, name: "ReaderResetV2") self.execute("ReaderResetV2", [reader_handle], name: name) end
reader_restore_state(reader_handle, state, name: "ReaderRestoreState")
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# File lib/tensorflow/ops/raw_ops.rb, line 3107 def self.reader_restore_state(reader_handle, state, name: "ReaderRestoreState") self.execute("ReaderRestoreState", [reader_handle, state], name: name) end
reader_restore_state_v2(reader_handle, state, name: "ReaderRestoreStateV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3111 def self.reader_restore_state_v2(reader_handle, state, name: "ReaderRestoreStateV2") self.execute("ReaderRestoreStateV2", [reader_handle, state], name: name) end
reader_serialize_state(reader_handle, name: "ReaderSerializeState")
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# File lib/tensorflow/ops/raw_ops.rb, line 3115 def self.reader_serialize_state(reader_handle, name: "ReaderSerializeState") self.execute("ReaderSerializeState", [reader_handle], name: name) end
reader_serialize_state_v2(reader_handle, name: "ReaderSerializeStateV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3119 def self.reader_serialize_state_v2(reader_handle, name: "ReaderSerializeStateV2") self.execute("ReaderSerializeStateV2", [reader_handle], name: name) end
real(input, typeT: :complex64, tout: :float, name: "Real")
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# File lib/tensorflow/ops/raw_ops.rb, line 3123 def self.real(input, typeT: :complex64, tout: :float, name: "Real") self.execute("Real", [input], T: typeT, Tout: tout, name: name) end
real_div(x, y, typeT: nil, name: "RealDiv")
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# File lib/tensorflow/ops/raw_ops.rb, line 3127 def self.real_div(x, y, typeT: nil, name: "RealDiv") self.execute("RealDiv", [x, y], T: typeT, name: name) end
rebatch_dataset(input_dataset, num_replicas, output_types: nil, output_shapes: nil, use_fallback: true, name: "RebatchDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3131 def self.rebatch_dataset(input_dataset, num_replicas, output_types: nil, output_shapes: nil, use_fallback: true, name: "RebatchDataset") self.execute("RebatchDataset", [input_dataset, num_replicas], output_types: output_types, output_shapes: output_shapes, use_fallback: use_fallback, name: name) end
reciprocal(x, typeT: nil, name: "Reciprocal")
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# File lib/tensorflow/ops/raw_ops.rb, line 3135 def self.reciprocal(x, typeT: nil, name: "Reciprocal") self.execute("Reciprocal", [x], T: typeT, name: name) end
reciprocal_grad(y, dy, typeT: nil, name: "ReciprocalGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3139 def self.reciprocal_grad(y, dy, typeT: nil, name: "ReciprocalGrad") self.execute("ReciprocalGrad", [y, dy], T: typeT, name: name) end
record_input(file_pattern: "", file_random_seed: 301, file_shuffle_shift_ratio: 0.0, file_buffer_size: 10000, file_parallelism: 16, batch_size: 32, compression_type: "", name: "RecordInput")
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# File lib/tensorflow/ops/raw_ops.rb, line 3143 def self.record_input(file_pattern: "", file_random_seed: 301, file_shuffle_shift_ratio: 0.0, file_buffer_size: 10000, file_parallelism: 16, batch_size: 32, compression_type: "", name: "RecordInput") self.execute("RecordInput", [], file_pattern: file_pattern, file_random_seed: file_random_seed, file_shuffle_shift_ratio: file_shuffle_shift_ratio, file_buffer_size: file_buffer_size, file_parallelism: file_parallelism, batch_size: batch_size, compression_type: compression_type, name: name) end
recv(tensor_type: nil, tensor_name: "", send_device: "", send_device_incarnation: nil, recv_device: "", client_terminated: false, name: "Recv")
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# File lib/tensorflow/ops/raw_ops.rb, line 3147 def self.recv(tensor_type: nil, tensor_name: "", send_device: "", send_device_incarnation: nil, recv_device: "", client_terminated: false, name: "Recv") self.execute("Recv", [], tensor_type: tensor_type, tensor_name: tensor_name, send_device: send_device, send_device_incarnation: send_device_incarnation, recv_device: recv_device, client_terminated: client_terminated, name: name) end
recv_tpu_embedding_activations(num_outputs: nil, config: "", name: "RecvTPUEmbeddingActivations")
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# File lib/tensorflow/ops/raw_ops.rb, line 3151 def self.recv_tpu_embedding_activations(num_outputs: nil, config: "", name: "RecvTPUEmbeddingActivations") self.execute("RecvTPUEmbeddingActivations", [], num_outputs: num_outputs, config: config, name: name) end
reduce_dataset(input_dataset, initial_state, other_arguments, f: nil, tstate: nil, targuments: nil, output_types: nil, output_shapes: nil, use_inter_op_parallelism: true, name: "ReduceDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3155 def self.reduce_dataset(input_dataset, initial_state, other_arguments, f: nil, tstate: nil, targuments: nil, output_types: nil, output_shapes: nil, use_inter_op_parallelism: true, name: "ReduceDataset") self.execute("ReduceDataset", [input_dataset, initial_state, other_arguments], f: f, Tstate: tstate, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, use_inter_op_parallelism: use_inter_op_parallelism, name: name) end
reduce_join(inputs, reduction_indices, keep_dims: false, separator: "", name: "ReduceJoin")
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# File lib/tensorflow/ops/raw_ops.rb, line 3159 def self.reduce_join(inputs, reduction_indices, keep_dims: false, separator: "", name: "ReduceJoin") self.execute("ReduceJoin", [inputs, reduction_indices], keep_dims: keep_dims, separator: separator, name: name) end
ref_enter(data, typeT: nil, frame_name: "", is_constant: false, parallel_iterations: 10, name: "RefEnter")
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# File lib/tensorflow/ops/raw_ops.rb, line 3163 def self.ref_enter(data, typeT: nil, frame_name: "", is_constant: false, parallel_iterations: 10, name: "RefEnter") self.execute("RefEnter", [data], T: typeT, frame_name: frame_name, is_constant: is_constant, parallel_iterations: parallel_iterations, name: name) end
ref_exit(data, typeT: nil, name: "RefExit")
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# File lib/tensorflow/ops/raw_ops.rb, line 3167 def self.ref_exit(data, typeT: nil, name: "RefExit") self.execute("RefExit", [data], T: typeT, name: name) end
ref_identity(input, typeT: nil, name: "RefIdentity")
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# File lib/tensorflow/ops/raw_ops.rb, line 3171 def self.ref_identity(input, typeT: nil, name: "RefIdentity") self.execute("RefIdentity", [input], T: typeT, name: name) end
ref_merge(inputs, typeT: nil, n: nil, name: "RefMerge")
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# File lib/tensorflow/ops/raw_ops.rb, line 3175 def self.ref_merge(inputs, typeT: nil, n: nil, name: "RefMerge") self.execute("RefMerge", [inputs], T: typeT, N: n, name: name) end
ref_next_iteration(data, typeT: nil, name: "RefNextIteration")
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# File lib/tensorflow/ops/raw_ops.rb, line 3179 def self.ref_next_iteration(data, typeT: nil, name: "RefNextIteration") self.execute("RefNextIteration", [data], T: typeT, name: name) end
ref_select(index, inputs, typeT: nil, n: nil, name: "RefSelect")
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# File lib/tensorflow/ops/raw_ops.rb, line 3183 def self.ref_select(index, inputs, typeT: nil, n: nil, name: "RefSelect") self.execute("RefSelect", [index, inputs], T: typeT, N: n, name: name) end
ref_switch(data, pred, typeT: nil, name: "RefSwitch")
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# File lib/tensorflow/ops/raw_ops.rb, line 3187 def self.ref_switch(data, pred, typeT: nil, name: "RefSwitch") self.execute("RefSwitch", [data, pred], T: typeT, name: name) end
regex_full_match(input, pattern, name: "RegexFullMatch")
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# File lib/tensorflow/ops/raw_ops.rb, line 3191 def self.regex_full_match(input, pattern, name: "RegexFullMatch") self.execute("RegexFullMatch", [input, pattern], name: name) end
regex_replace(input, pattern, rewrite, replace_global: true, name: "RegexReplace")
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# File lib/tensorflow/ops/raw_ops.rb, line 3195 def self.regex_replace(input, pattern, rewrite, replace_global: true, name: "RegexReplace") self.execute("RegexReplace", [input, pattern, rewrite], replace_global: replace_global, name: name) end
relu(features, typeT: nil, name: "Relu")
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# File lib/tensorflow/ops/raw_ops.rb, line 3199 def self.relu(features, typeT: nil, name: "Relu") self.execute("Relu", [features], T: typeT, name: name) end
relu6(features, typeT: nil, name: "Relu6")
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# File lib/tensorflow/ops/raw_ops.rb, line 3203 def self.relu6(features, typeT: nil, name: "Relu6") self.execute("Relu6", [features], T: typeT, name: name) end
relu6_grad(gradients, features, typeT: nil, name: "Relu6Grad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3207 def self.relu6_grad(gradients, features, typeT: nil, name: "Relu6Grad") self.execute("Relu6Grad", [gradients, features], T: typeT, name: name) end
relu_grad(gradients, features, typeT: nil, name: "ReluGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3211 def self.relu_grad(gradients, features, typeT: nil, name: "ReluGrad") self.execute("ReluGrad", [gradients, features], T: typeT, name: name) end
remote_call(target, args, tin: nil, tout: nil, f: nil, name: "RemoteCall")
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# File lib/tensorflow/ops/raw_ops.rb, line 3215 def self.remote_call(target, args, tin: nil, tout: nil, f: nil, name: "RemoteCall") self.execute("RemoteCall", [target, args], Tin: tin, Tout: tout, f: f, name: name) end
remote_fused_graph_execute(inputs, tinputs: nil, toutputs: nil, serialized_remote_fused_graph_execute_info: "", name: "RemoteFusedGraphExecute")
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# File lib/tensorflow/ops/raw_ops.rb, line 3219 def self.remote_fused_graph_execute(inputs, tinputs: nil, toutputs: nil, serialized_remote_fused_graph_execute_info: "", name: "RemoteFusedGraphExecute") self.execute("RemoteFusedGraphExecute", [inputs], Tinputs: tinputs, Toutputs: toutputs, serialized_remote_fused_graph_execute_info: serialized_remote_fused_graph_execute_info, name: name) end
repeat_dataset(input_dataset, count, output_types: nil, output_shapes: nil, name: "RepeatDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3223 def self.repeat_dataset(input_dataset, count, output_types: nil, output_shapes: nil, name: "RepeatDataset") self.execute("RepeatDataset", [input_dataset, count], output_types: output_types, output_shapes: output_shapes, name: name) end
requantization_range(input, input_min, input_max, tinput: nil, name: "RequantizationRange")
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# File lib/tensorflow/ops/raw_ops.rb, line 3227 def self.requantization_range(input, input_min, input_max, tinput: nil, name: "RequantizationRange") self.execute("RequantizationRange", [input, input_min, input_max], Tinput: tinput, name: name) end
requantization_range_per_channel(input, input_min, input_max, typeT: :qint32, clip_value_max: nil, name: "RequantizationRangePerChannel")
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# File lib/tensorflow/ops/raw_ops.rb, line 3231 def self.requantization_range_per_channel(input, input_min, input_max, typeT: :qint32, clip_value_max: nil, name: "RequantizationRangePerChannel") self.execute("RequantizationRangePerChannel", [input, input_min, input_max], T: typeT, clip_value_max: clip_value_max, name: name) end
requantize(input, input_min, input_max, requested_output_min, requested_output_max, tinput: nil, out_type: nil, name: "Requantize")
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# File lib/tensorflow/ops/raw_ops.rb, line 3235 def self.requantize(input, input_min, input_max, requested_output_min, requested_output_max, tinput: nil, out_type: nil, name: "Requantize") self.execute("Requantize", [input, input_min, input_max, requested_output_min, requested_output_max], Tinput: tinput, out_type: out_type, name: name) end
requantize_per_channel(input, input_min, input_max, requested_output_min, requested_output_max, typeT: :qint32, out_type: :quint8, name: "RequantizePerChannel")
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# File lib/tensorflow/ops/raw_ops.rb, line 3239 def self.requantize_per_channel(input, input_min, input_max, requested_output_min, requested_output_max, typeT: :qint32, out_type: :quint8, name: "RequantizePerChannel") self.execute("RequantizePerChannel", [input, input_min, input_max, requested_output_min, requested_output_max], T: typeT, out_type: out_type, name: name) end
reshape(tensor, shape, typeT: nil, tshape: :int32, name: "Reshape")
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# File lib/tensorflow/ops/raw_ops.rb, line 3243 def self.reshape(tensor, shape, typeT: nil, tshape: :int32, name: "Reshape") self.execute("Reshape", [tensor, shape], T: typeT, Tshape: tshape, name: name) end
resize_area(images, size, typeT: nil, align_corners: false, name: "ResizeArea")
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# File lib/tensorflow/ops/raw_ops.rb, line 3247 def self.resize_area(images, size, typeT: nil, align_corners: false, name: "ResizeArea") self.execute("ResizeArea", [images, size], T: typeT, align_corners: align_corners, name: name) end
resize_bicubic(images, size, typeT: nil, align_corners: false, half_pixel_centers: false, name: "ResizeBicubic")
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# File lib/tensorflow/ops/raw_ops.rb, line 3251 def self.resize_bicubic(images, size, typeT: nil, align_corners: false, half_pixel_centers: false, name: "ResizeBicubic") self.execute("ResizeBicubic", [images, size], T: typeT, align_corners: align_corners, half_pixel_centers: half_pixel_centers, name: name) end
resize_bicubic_grad(grads, original_image, typeT: nil, align_corners: false, half_pixel_centers: false, name: "ResizeBicubicGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3255 def self.resize_bicubic_grad(grads, original_image, typeT: nil, align_corners: false, half_pixel_centers: false, name: "ResizeBicubicGrad") self.execute("ResizeBicubicGrad", [grads, original_image], T: typeT, align_corners: align_corners, half_pixel_centers: half_pixel_centers, name: name) end
resize_bilinear(images, size, typeT: nil, align_corners: false, half_pixel_centers: false, name: "ResizeBilinear")
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# File lib/tensorflow/ops/raw_ops.rb, line 3259 def self.resize_bilinear(images, size, typeT: nil, align_corners: false, half_pixel_centers: false, name: "ResizeBilinear") self.execute("ResizeBilinear", [images, size], T: typeT, align_corners: align_corners, half_pixel_centers: half_pixel_centers, name: name) end
resize_bilinear_grad(grads, original_image, typeT: nil, align_corners: false, half_pixel_centers: false, name: "ResizeBilinearGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3263 def self.resize_bilinear_grad(grads, original_image, typeT: nil, align_corners: false, half_pixel_centers: false, name: "ResizeBilinearGrad") self.execute("ResizeBilinearGrad", [grads, original_image], T: typeT, align_corners: align_corners, half_pixel_centers: half_pixel_centers, name: name) end
resize_nearest_neighbor(images, size, typeT: nil, align_corners: false, half_pixel_centers: false, name: "ResizeNearestNeighbor")
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# File lib/tensorflow/ops/raw_ops.rb, line 3267 def self.resize_nearest_neighbor(images, size, typeT: nil, align_corners: false, half_pixel_centers: false, name: "ResizeNearestNeighbor") self.execute("ResizeNearestNeighbor", [images, size], T: typeT, align_corners: align_corners, half_pixel_centers: half_pixel_centers, name: name) end
resize_nearest_neighbor_grad(grads, size, typeT: nil, align_corners: false, half_pixel_centers: false, name: "ResizeNearestNeighborGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3271 def self.resize_nearest_neighbor_grad(grads, size, typeT: nil, align_corners: false, half_pixel_centers: false, name: "ResizeNearestNeighborGrad") self.execute("ResizeNearestNeighborGrad", [grads, size], T: typeT, align_corners: align_corners, half_pixel_centers: half_pixel_centers, name: name) end
resource_accumulator_apply_gradient(handle, local_step, gradient, dtype: nil, name: "ResourceAccumulatorApplyGradient")
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# File lib/tensorflow/ops/raw_ops.rb, line 3275 def self.resource_accumulator_apply_gradient(handle, local_step, gradient, dtype: nil, name: "ResourceAccumulatorApplyGradient") self.execute("ResourceAccumulatorApplyGradient", [handle, local_step, gradient], dtype: dtype, name: name) end
resource_accumulator_num_accumulated(handle, name: "ResourceAccumulatorNumAccumulated")
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# File lib/tensorflow/ops/raw_ops.rb, line 3279 def self.resource_accumulator_num_accumulated(handle, name: "ResourceAccumulatorNumAccumulated") self.execute("ResourceAccumulatorNumAccumulated", [handle], name: name) end
resource_accumulator_set_global_step(handle, new_global_step, name: "ResourceAccumulatorSetGlobalStep")
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# File lib/tensorflow/ops/raw_ops.rb, line 3283 def self.resource_accumulator_set_global_step(handle, new_global_step, name: "ResourceAccumulatorSetGlobalStep") self.execute("ResourceAccumulatorSetGlobalStep", [handle, new_global_step], name: name) end
resource_accumulator_take_gradient(handle, num_required, dtype: nil, name: "ResourceAccumulatorTakeGradient")
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# File lib/tensorflow/ops/raw_ops.rb, line 3287 def self.resource_accumulator_take_gradient(handle, num_required, dtype: nil, name: "ResourceAccumulatorTakeGradient") self.execute("ResourceAccumulatorTakeGradient", [handle, num_required], dtype: dtype, name: name) end
resource_apply_ada_max(var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, typeT: nil, use_locking: false, name: "ResourceApplyAdaMax")
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# File lib/tensorflow/ops/raw_ops.rb, line 3291 def self.resource_apply_ada_max(var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad, typeT: nil, use_locking: false, name: "ResourceApplyAdaMax") self.execute("ResourceApplyAdaMax", [var, m, v, beta1_power, lr, beta1, beta2, epsilon, grad], T: typeT, use_locking: use_locking, name: name) end
resource_apply_adadelta(var, accum, accum_update, lr, rho, epsilon, grad, typeT: nil, use_locking: false, name: "ResourceApplyAdadelta")
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# File lib/tensorflow/ops/raw_ops.rb, line 3295 def self.resource_apply_adadelta(var, accum, accum_update, lr, rho, epsilon, grad, typeT: nil, use_locking: false, name: "ResourceApplyAdadelta") self.execute("ResourceApplyAdadelta", [var, accum, accum_update, lr, rho, epsilon, grad], T: typeT, use_locking: use_locking, name: name) end
resource_apply_adagrad(var, accum, lr, grad, typeT: nil, use_locking: false, update_slots: true, name: "ResourceApplyAdagrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3299 def self.resource_apply_adagrad(var, accum, lr, grad, typeT: nil, use_locking: false, update_slots: true, name: "ResourceApplyAdagrad") self.execute("ResourceApplyAdagrad", [var, accum, lr, grad], T: typeT, use_locking: use_locking, update_slots: update_slots, name: name) end
resource_apply_adagrad_da(var, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step, typeT: nil, use_locking: false, name: "ResourceApplyAdagradDA")
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# File lib/tensorflow/ops/raw_ops.rb, line 3303 def self.resource_apply_adagrad_da(var, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step, typeT: nil, use_locking: false, name: "ResourceApplyAdagradDA") self.execute("ResourceApplyAdagradDA", [var, gradient_accumulator, gradient_squared_accumulator, grad, lr, l1, l2, global_step], T: typeT, use_locking: use_locking, name: name) end
resource_apply_adagrad_v2(var, accum, lr, epsilon, grad, typeT: nil, use_locking: false, update_slots: true, name: "ResourceApplyAdagradV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3307 def self.resource_apply_adagrad_v2(var, accum, lr, epsilon, grad, typeT: nil, use_locking: false, update_slots: true, name: "ResourceApplyAdagradV2") self.execute("ResourceApplyAdagradV2", [var, accum, lr, epsilon, grad], T: typeT, use_locking: use_locking, update_slots: update_slots, name: name) end
resource_apply_adam(var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, typeT: nil, use_locking: false, use_nesterov: false, name: "ResourceApplyAdam")
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# File lib/tensorflow/ops/raw_ops.rb, line 3311 def self.resource_apply_adam(var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, typeT: nil, use_locking: false, use_nesterov: false, name: "ResourceApplyAdam") self.execute("ResourceApplyAdam", [var, m, v, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad], T: typeT, use_locking: use_locking, use_nesterov: use_nesterov, name: name) end
resource_apply_adam_with_amsgrad(var, m, v, vhat, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, typeT: nil, use_locking: false, name: "ResourceApplyAdamWithAmsgrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3315 def self.resource_apply_adam_with_amsgrad(var, m, v, vhat, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad, typeT: nil, use_locking: false, name: "ResourceApplyAdamWithAmsgrad") self.execute("ResourceApplyAdamWithAmsgrad", [var, m, v, vhat, beta1_power, beta2_power, lr, beta1, beta2, epsilon, grad], T: typeT, use_locking: use_locking, name: name) end
resource_apply_add_sign(var, m, lr, alpha, sign_decay, beta, grad, typeT: nil, use_locking: false, name: "ResourceApplyAddSign")
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# File lib/tensorflow/ops/raw_ops.rb, line 3319 def self.resource_apply_add_sign(var, m, lr, alpha, sign_decay, beta, grad, typeT: nil, use_locking: false, name: "ResourceApplyAddSign") self.execute("ResourceApplyAddSign", [var, m, lr, alpha, sign_decay, beta, grad], T: typeT, use_locking: use_locking, name: name) end
resource_apply_centered_rms_prop(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, typeT: nil, use_locking: false, name: "ResourceApplyCenteredRMSProp")
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# File lib/tensorflow/ops/raw_ops.rb, line 3323 def self.resource_apply_centered_rms_prop(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, typeT: nil, use_locking: false, name: "ResourceApplyCenteredRMSProp") self.execute("ResourceApplyCenteredRMSProp", [var, mg, ms, mom, lr, rho, momentum, epsilon, grad], T: typeT, use_locking: use_locking, name: name) end
resource_apply_ftrl(var, accum, linear, grad, lr, l1, l2, lr_power, typeT: nil, use_locking: false, name: "ResourceApplyFtrl")
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# File lib/tensorflow/ops/raw_ops.rb, line 3327 def self.resource_apply_ftrl(var, accum, linear, grad, lr, l1, l2, lr_power, typeT: nil, use_locking: false, name: "ResourceApplyFtrl") self.execute("ResourceApplyFtrl", [var, accum, linear, grad, lr, l1, l2, lr_power], T: typeT, use_locking: use_locking, name: name) end
resource_apply_ftrl_v2(var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, typeT: nil, use_locking: false, name: "ResourceApplyFtrlV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3331 def self.resource_apply_ftrl_v2(var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power, typeT: nil, use_locking: false, name: "ResourceApplyFtrlV2") self.execute("ResourceApplyFtrlV2", [var, accum, linear, grad, lr, l1, l2, l2_shrinkage, lr_power], T: typeT, use_locking: use_locking, name: name) end
resource_apply_gradient_descent(var, alpha, delta, typeT: nil, use_locking: false, name: "ResourceApplyGradientDescent")
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# File lib/tensorflow/ops/raw_ops.rb, line 3335 def self.resource_apply_gradient_descent(var, alpha, delta, typeT: nil, use_locking: false, name: "ResourceApplyGradientDescent") self.execute("ResourceApplyGradientDescent", [var, alpha, delta], T: typeT, use_locking: use_locking, name: name) end
resource_apply_keras_momentum(var, accum, lr, grad, momentum, typeT: nil, use_locking: false, use_nesterov: false, name: "ResourceApplyKerasMomentum")
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# File lib/tensorflow/ops/raw_ops.rb, line 3339 def self.resource_apply_keras_momentum(var, accum, lr, grad, momentum, typeT: nil, use_locking: false, use_nesterov: false, name: "ResourceApplyKerasMomentum") self.execute("ResourceApplyKerasMomentum", [var, accum, lr, grad, momentum], T: typeT, use_locking: use_locking, use_nesterov: use_nesterov, name: name) end
resource_apply_momentum(var, accum, lr, grad, momentum, typeT: nil, use_locking: false, use_nesterov: false, name: "ResourceApplyMomentum")
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# File lib/tensorflow/ops/raw_ops.rb, line 3343 def self.resource_apply_momentum(var, accum, lr, grad, momentum, typeT: nil, use_locking: false, use_nesterov: false, name: "ResourceApplyMomentum") self.execute("ResourceApplyMomentum", [var, accum, lr, grad, momentum], T: typeT, use_locking: use_locking, use_nesterov: use_nesterov, name: name) end
resource_apply_power_sign(var, m, lr, logbase, sign_decay, beta, grad, typeT: nil, use_locking: false, name: "ResourceApplyPowerSign")
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# File lib/tensorflow/ops/raw_ops.rb, line 3347 def self.resource_apply_power_sign(var, m, lr, logbase, sign_decay, beta, grad, typeT: nil, use_locking: false, name: "ResourceApplyPowerSign") self.execute("ResourceApplyPowerSign", [var, m, lr, logbase, sign_decay, beta, grad], T: typeT, use_locking: use_locking, name: name) end
resource_apply_proximal_adagrad(var, accum, lr, l1, l2, grad, typeT: nil, use_locking: false, name: "ResourceApplyProximalAdagrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3351 def self.resource_apply_proximal_adagrad(var, accum, lr, l1, l2, grad, typeT: nil, use_locking: false, name: "ResourceApplyProximalAdagrad") self.execute("ResourceApplyProximalAdagrad", [var, accum, lr, l1, l2, grad], T: typeT, use_locking: use_locking, name: name) end
resource_apply_proximal_gradient_descent(var, alpha, l1, l2, delta, typeT: nil, use_locking: false, name: "ResourceApplyProximalGradientDescent")
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# File lib/tensorflow/ops/raw_ops.rb, line 3355 def self.resource_apply_proximal_gradient_descent(var, alpha, l1, l2, delta, typeT: nil, use_locking: false, name: "ResourceApplyProximalGradientDescent") self.execute("ResourceApplyProximalGradientDescent", [var, alpha, l1, l2, delta], T: typeT, use_locking: use_locking, name: name) end
resource_apply_rms_prop(var, ms, mom, lr, rho, momentum, epsilon, grad, typeT: nil, use_locking: false, name: "ResourceApplyRMSProp")
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# File lib/tensorflow/ops/raw_ops.rb, line 3359 def self.resource_apply_rms_prop(var, ms, mom, lr, rho, momentum, epsilon, grad, typeT: nil, use_locking: false, name: "ResourceApplyRMSProp") self.execute("ResourceApplyRMSProp", [var, ms, mom, lr, rho, momentum, epsilon, grad], T: typeT, use_locking: use_locking, name: name) end
resource_conditional_accumulator(dtype: nil, shape: nil, container: "", shared_name: "", reduction_type: "MEAN", name: "ResourceConditionalAccumulator")
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# File lib/tensorflow/ops/raw_ops.rb, line 3363 def self.resource_conditional_accumulator(dtype: nil, shape: nil, container: "", shared_name: "", reduction_type: "MEAN", name: "ResourceConditionalAccumulator") self.execute("ResourceConditionalAccumulator", [], dtype: dtype, shape: shape, container: container, shared_name: shared_name, reduction_type: reduction_type, name: name) end
resource_count_up_to(resource, limit: nil, typeT: nil, name: "ResourceCountUpTo")
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# File lib/tensorflow/ops/raw_ops.rb, line 3367 def self.resource_count_up_to(resource, limit: nil, typeT: nil, name: "ResourceCountUpTo") self.execute("ResourceCountUpTo", [resource], limit: limit, T: typeT, name: name) end
resource_gather(resource, indices, batch_dims: 0, validate_indices: true, dtype: nil, tindices: nil, name: "ResourceGather")
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# File lib/tensorflow/ops/raw_ops.rb, line 3371 def self.resource_gather(resource, indices, batch_dims: 0, validate_indices: true, dtype: nil, tindices: nil, name: "ResourceGather") self.execute("ResourceGather", [resource, indices], batch_dims: batch_dims, validate_indices: validate_indices, dtype: dtype, Tindices: tindices, name: name) end
resource_gather_nd(resource, indices, dtype: nil, tindices: nil, name: "ResourceGatherNd")
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# File lib/tensorflow/ops/raw_ops.rb, line 3375 def self.resource_gather_nd(resource, indices, dtype: nil, tindices: nil, name: "ResourceGatherNd") self.execute("ResourceGatherNd", [resource, indices], dtype: dtype, Tindices: tindices, name: name) end
resource_scatter_add(resource, indices, updates, dtype: nil, tindices: nil, name: "ResourceScatterAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 3379 def self.resource_scatter_add(resource, indices, updates, dtype: nil, tindices: nil, name: "ResourceScatterAdd") self.execute("ResourceScatterAdd", [resource, indices, updates], dtype: dtype, Tindices: tindices, name: name) end
resource_scatter_div(resource, indices, updates, dtype: nil, tindices: nil, name: "ResourceScatterDiv")
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# File lib/tensorflow/ops/raw_ops.rb, line 3383 def self.resource_scatter_div(resource, indices, updates, dtype: nil, tindices: nil, name: "ResourceScatterDiv") self.execute("ResourceScatterDiv", [resource, indices, updates], dtype: dtype, Tindices: tindices, name: name) end
resource_scatter_max(resource, indices, updates, dtype: nil, tindices: nil, name: "ResourceScatterMax")
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# File lib/tensorflow/ops/raw_ops.rb, line 3387 def self.resource_scatter_max(resource, indices, updates, dtype: nil, tindices: nil, name: "ResourceScatterMax") self.execute("ResourceScatterMax", [resource, indices, updates], dtype: dtype, Tindices: tindices, name: name) end
resource_scatter_min(resource, indices, updates, dtype: nil, tindices: nil, name: "ResourceScatterMin")
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# File lib/tensorflow/ops/raw_ops.rb, line 3391 def self.resource_scatter_min(resource, indices, updates, dtype: nil, tindices: nil, name: "ResourceScatterMin") self.execute("ResourceScatterMin", [resource, indices, updates], dtype: dtype, Tindices: tindices, name: name) end
resource_scatter_mul(resource, indices, updates, dtype: nil, tindices: nil, name: "ResourceScatterMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 3395 def self.resource_scatter_mul(resource, indices, updates, dtype: nil, tindices: nil, name: "ResourceScatterMul") self.execute("ResourceScatterMul", [resource, indices, updates], dtype: dtype, Tindices: tindices, name: name) end
resource_scatter_nd_add(ref, indices, updates, typeT: nil, tindices: nil, use_locking: true, name: "ResourceScatterNdAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 3399 def self.resource_scatter_nd_add(ref, indices, updates, typeT: nil, tindices: nil, use_locking: true, name: "ResourceScatterNdAdd") self.execute("ResourceScatterNdAdd", [ref, indices, updates], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
resource_scatter_nd_sub(ref, indices, updates, typeT: nil, tindices: nil, use_locking: true, name: "ResourceScatterNdSub")
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# File lib/tensorflow/ops/raw_ops.rb, line 3403 def self.resource_scatter_nd_sub(ref, indices, updates, typeT: nil, tindices: nil, use_locking: true, name: "ResourceScatterNdSub") self.execute("ResourceScatterNdSub", [ref, indices, updates], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
resource_scatter_nd_update(ref, indices, updates, typeT: nil, tindices: nil, use_locking: true, name: "ResourceScatterNdUpdate")
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# File lib/tensorflow/ops/raw_ops.rb, line 3407 def self.resource_scatter_nd_update(ref, indices, updates, typeT: nil, tindices: nil, use_locking: true, name: "ResourceScatterNdUpdate") self.execute("ResourceScatterNdUpdate", [ref, indices, updates], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
resource_scatter_sub(resource, indices, updates, dtype: nil, tindices: nil, name: "ResourceScatterSub")
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# File lib/tensorflow/ops/raw_ops.rb, line 3411 def self.resource_scatter_sub(resource, indices, updates, dtype: nil, tindices: nil, name: "ResourceScatterSub") self.execute("ResourceScatterSub", [resource, indices, updates], dtype: dtype, Tindices: tindices, name: name) end
resource_scatter_update(resource, indices, updates, dtype: nil, tindices: nil, name: "ResourceScatterUpdate")
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# File lib/tensorflow/ops/raw_ops.rb, line 3415 def self.resource_scatter_update(resource, indices, updates, dtype: nil, tindices: nil, name: "ResourceScatterUpdate") self.execute("ResourceScatterUpdate", [resource, indices, updates], dtype: dtype, Tindices: tindices, name: name) end
resource_sparse_apply_adadelta(var, accum, accum_update, lr, rho, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyAdadelta")
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# File lib/tensorflow/ops/raw_ops.rb, line 3419 def self.resource_sparse_apply_adadelta(var, accum, accum_update, lr, rho, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyAdadelta") self.execute("ResourceSparseApplyAdadelta", [var, accum, accum_update, lr, rho, epsilon, grad, indices], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
resource_sparse_apply_adagrad(var, accum, lr, grad, indices, typeT: nil, tindices: nil, use_locking: false, update_slots: true, name: "ResourceSparseApplyAdagrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3423 def self.resource_sparse_apply_adagrad(var, accum, lr, grad, indices, typeT: nil, tindices: nil, use_locking: false, update_slots: true, name: "ResourceSparseApplyAdagrad") self.execute("ResourceSparseApplyAdagrad", [var, accum, lr, grad, indices], T: typeT, Tindices: tindices, use_locking: use_locking, update_slots: update_slots, name: name) end
resource_sparse_apply_adagrad_da(var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyAdagradDA")
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# File lib/tensorflow/ops/raw_ops.rb, line 3427 def self.resource_sparse_apply_adagrad_da(var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyAdagradDA") self.execute("ResourceSparseApplyAdagradDA", [var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
resource_sparse_apply_adagrad_v2(var, accum, lr, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, update_slots: true, name: "ResourceSparseApplyAdagradV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3431 def self.resource_sparse_apply_adagrad_v2(var, accum, lr, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, update_slots: true, name: "ResourceSparseApplyAdagradV2") self.execute("ResourceSparseApplyAdagradV2", [var, accum, lr, epsilon, grad, indices], T: typeT, Tindices: tindices, use_locking: use_locking, update_slots: update_slots, name: name) end
resource_sparse_apply_centered_rms_prop(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyCenteredRMSProp")
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# File lib/tensorflow/ops/raw_ops.rb, line 3435 def self.resource_sparse_apply_centered_rms_prop(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyCenteredRMSProp") self.execute("ResourceSparseApplyCenteredRMSProp", [var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
resource_sparse_apply_ftrl(var, accum, linear, grad, indices, lr, l1, l2, lr_power, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyFtrl")
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# File lib/tensorflow/ops/raw_ops.rb, line 3439 def self.resource_sparse_apply_ftrl(var, accum, linear, grad, indices, lr, l1, l2, lr_power, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyFtrl") self.execute("ResourceSparseApplyFtrl", [var, accum, linear, grad, indices, lr, l1, l2, lr_power], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
resource_sparse_apply_ftrl_v2(var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyFtrlV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3443 def self.resource_sparse_apply_ftrl_v2(var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyFtrlV2") self.execute("ResourceSparseApplyFtrlV2", [var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
resource_sparse_apply_keras_momentum(var, accum, lr, grad, indices, momentum, typeT: nil, tindices: nil, use_locking: false, use_nesterov: false, name: "ResourceSparseApplyKerasMomentum")
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# File lib/tensorflow/ops/raw_ops.rb, line 3447 def self.resource_sparse_apply_keras_momentum(var, accum, lr, grad, indices, momentum, typeT: nil, tindices: nil, use_locking: false, use_nesterov: false, name: "ResourceSparseApplyKerasMomentum") self.execute("ResourceSparseApplyKerasMomentum", [var, accum, lr, grad, indices, momentum], T: typeT, Tindices: tindices, use_locking: use_locking, use_nesterov: use_nesterov, name: name) end
resource_sparse_apply_momentum(var, accum, lr, grad, indices, momentum, typeT: nil, tindices: nil, use_locking: false, use_nesterov: false, name: "ResourceSparseApplyMomentum")
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# File lib/tensorflow/ops/raw_ops.rb, line 3451 def self.resource_sparse_apply_momentum(var, accum, lr, grad, indices, momentum, typeT: nil, tindices: nil, use_locking: false, use_nesterov: false, name: "ResourceSparseApplyMomentum") self.execute("ResourceSparseApplyMomentum", [var, accum, lr, grad, indices, momentum], T: typeT, Tindices: tindices, use_locking: use_locking, use_nesterov: use_nesterov, name: name) end
resource_sparse_apply_proximal_adagrad(var, accum, lr, l1, l2, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyProximalAdagrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3455 def self.resource_sparse_apply_proximal_adagrad(var, accum, lr, l1, l2, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyProximalAdagrad") self.execute("ResourceSparseApplyProximalAdagrad", [var, accum, lr, l1, l2, grad, indices], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
resource_sparse_apply_proximal_gradient_descent(var, alpha, l1, l2, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyProximalGradientDescent")
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# File lib/tensorflow/ops/raw_ops.rb, line 3459 def self.resource_sparse_apply_proximal_gradient_descent(var, alpha, l1, l2, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyProximalGradientDescent") self.execute("ResourceSparseApplyProximalGradientDescent", [var, alpha, l1, l2, grad, indices], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
resource_sparse_apply_rms_prop(var, ms, mom, lr, rho, momentum, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyRMSProp")
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# File lib/tensorflow/ops/raw_ops.rb, line 3463 def self.resource_sparse_apply_rms_prop(var, ms, mom, lr, rho, momentum, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "ResourceSparseApplyRMSProp") self.execute("ResourceSparseApplyRMSProp", [var, ms, mom, lr, rho, momentum, epsilon, grad, indices], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
resource_strided_slice_assign(ref, start, stop, strides, value, typeT: nil, index: nil, begin_mask: 0, end_mask: 0, ellipsis_mask: 0, new_axis_mask: 0, shrink_axis_mask: 0, name: "ResourceStridedSliceAssign")
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# File lib/tensorflow/ops/raw_ops.rb, line 3467 def self.resource_strided_slice_assign(ref, start, stop, strides, value, typeT: nil, index: nil, begin_mask: 0, end_mask: 0, ellipsis_mask: 0, new_axis_mask: 0, shrink_axis_mask: 0, name: "ResourceStridedSliceAssign") self.execute("ResourceStridedSliceAssign", [ref, start, stop, strides, value], T: typeT, Index: index, begin_mask: begin_mask, end_mask: end_mask, ellipsis_mask: ellipsis_mask, new_axis_mask: new_axis_mask, shrink_axis_mask: shrink_axis_mask, name: name) end
restore(file_pattern, tensor_name, dt: nil, preferred_shard: -1, name: "Restore")
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# File lib/tensorflow/ops/raw_ops.rb, line 3471 def self.restore(file_pattern, tensor_name, dt: nil, preferred_shard: -1, name: "Restore") self.execute("Restore", [file_pattern, tensor_name], dt: dt, preferred_shard: preferred_shard, name: name) end
restore_slice(file_pattern, tensor_name, shape_and_slice, dt: nil, preferred_shard: -1, name: "RestoreSlice")
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# File lib/tensorflow/ops/raw_ops.rb, line 3475 def self.restore_slice(file_pattern, tensor_name, shape_and_slice, dt: nil, preferred_shard: -1, name: "RestoreSlice") self.execute("RestoreSlice", [file_pattern, tensor_name, shape_and_slice], dt: dt, preferred_shard: preferred_shard, name: name) end
restore_v2(prefix, tensor_names, shape_and_slices, dtypes: nil, name: "RestoreV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3479 def self.restore_v2(prefix, tensor_names, shape_and_slices, dtypes: nil, name: "RestoreV2") self.execute("RestoreV2", [prefix, tensor_names, shape_and_slices], dtypes: dtypes, name: name) end
retrieve_tpu_embedding_adadelta_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingAdadeltaParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 3491 def self.retrieve_tpu_embedding_adadelta_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingAdadeltaParameters") self.execute("RetrieveTPUEmbeddingAdadeltaParameters", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_adadelta_parameters_grad_accum_debug(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug")
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# File lib/tensorflow/ops/raw_ops.rb, line 3495 def self.retrieve_tpu_embedding_adadelta_parameters_grad_accum_debug(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug") self.execute("RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_adagrad_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingAdagradParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 3499 def self.retrieve_tpu_embedding_adagrad_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingAdagradParameters") self.execute("RetrieveTPUEmbeddingAdagradParameters", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_adagrad_parameters_grad_accum_debug(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingAdagradParametersGradAccumDebug")
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# File lib/tensorflow/ops/raw_ops.rb, line 3503 def self.retrieve_tpu_embedding_adagrad_parameters_grad_accum_debug(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingAdagradParametersGradAccumDebug") self.execute("RetrieveTPUEmbeddingAdagradParametersGradAccumDebug", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_adam_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingADAMParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 3483 def self.retrieve_tpu_embedding_adam_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingADAMParameters") self.execute("RetrieveTPUEmbeddingADAMParameters", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_adam_parameters_grad_accum_debug(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingADAMParametersGradAccumDebug")
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# File lib/tensorflow/ops/raw_ops.rb, line 3487 def self.retrieve_tpu_embedding_adam_parameters_grad_accum_debug(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingADAMParametersGradAccumDebug") self.execute("RetrieveTPUEmbeddingADAMParametersGradAccumDebug", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_centered_rms_prop_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingCenteredRMSPropParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 3507 def self.retrieve_tpu_embedding_centered_rms_prop_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingCenteredRMSPropParameters") self.execute("RetrieveTPUEmbeddingCenteredRMSPropParameters", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_ftrl_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingFTRLParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 3511 def self.retrieve_tpu_embedding_ftrl_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingFTRLParameters") self.execute("RetrieveTPUEmbeddingFTRLParameters", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_ftrl_parameters_grad_accum_debug(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingFTRLParametersGradAccumDebug")
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# File lib/tensorflow/ops/raw_ops.rb, line 3515 def self.retrieve_tpu_embedding_ftrl_parameters_grad_accum_debug(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingFTRLParametersGradAccumDebug") self.execute("RetrieveTPUEmbeddingFTRLParametersGradAccumDebug", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_mdl_adagrad_light_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingMDLAdagradLightParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 3519 def self.retrieve_tpu_embedding_mdl_adagrad_light_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingMDLAdagradLightParameters") self.execute("RetrieveTPUEmbeddingMDLAdagradLightParameters", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_momentum_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingMomentumParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 3523 def self.retrieve_tpu_embedding_momentum_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingMomentumParameters") self.execute("RetrieveTPUEmbeddingMomentumParameters", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_momentum_parameters_grad_accum_debug(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingMomentumParametersGradAccumDebug")
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# File lib/tensorflow/ops/raw_ops.rb, line 3527 def self.retrieve_tpu_embedding_momentum_parameters_grad_accum_debug(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingMomentumParametersGradAccumDebug") self.execute("RetrieveTPUEmbeddingMomentumParametersGradAccumDebug", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_proximal_adagrad_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingProximalAdagradParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 3531 def self.retrieve_tpu_embedding_proximal_adagrad_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingProximalAdagradParameters") self.execute("RetrieveTPUEmbeddingProximalAdagradParameters", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug")
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# File lib/tensorflow/ops/raw_ops.rb, line 3535 def self.retrieve_tpu_embedding_proximal_adagrad_parameters_grad_accum_debug(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug") self.execute("RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_rms_prop_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingRMSPropParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 3539 def self.retrieve_tpu_embedding_rms_prop_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingRMSPropParameters") self.execute("RetrieveTPUEmbeddingRMSPropParameters", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_rms_prop_parameters_grad_accum_debug(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug")
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# File lib/tensorflow/ops/raw_ops.rb, line 3543 def self.retrieve_tpu_embedding_rms_prop_parameters_grad_accum_debug(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug") self.execute("RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
retrieve_tpu_embedding_stochastic_gradient_descent_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingStochasticGradientDescentParameters")
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# File lib/tensorflow/ops/raw_ops.rb, line 3547 def self.retrieve_tpu_embedding_stochastic_gradient_descent_parameters(table_id: -1, table_name: "", num_shards: nil, shard_id: nil, config: "", name: "RetrieveTPUEmbeddingStochasticGradientDescentParameters") self.execute("RetrieveTPUEmbeddingStochasticGradientDescentParameters", [], table_id: table_id, table_name: table_name, num_shards: num_shards, shard_id: shard_id, config: config, name: name) end
reverse(tensor, dims, typeT: nil, name: "Reverse")
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# File lib/tensorflow/ops/raw_ops.rb, line 3551 def self.reverse(tensor, dims, typeT: nil, name: "Reverse") self.execute("Reverse", [tensor, dims], T: typeT, name: name) end
reverse_sequence(input, seq_lengths, seq_dim: nil, batch_dim: 0, typeT: nil, tlen: :int64, name: "ReverseSequence")
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# File lib/tensorflow/ops/raw_ops.rb, line 3555 def self.reverse_sequence(input, seq_lengths, seq_dim: nil, batch_dim: 0, typeT: nil, tlen: :int64, name: "ReverseSequence") self.execute("ReverseSequence", [input, seq_lengths], seq_dim: seq_dim, batch_dim: batch_dim, T: typeT, Tlen: tlen, name: name) end
reverse_v2(tensor, axis, tidx: :int32, typeT: nil, name: "ReverseV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3559 def self.reverse_v2(tensor, axis, tidx: :int32, typeT: nil, name: "ReverseV2") self.execute("ReverseV2", [tensor, axis], Tidx: tidx, T: typeT, name: name) end
rfft(input, fft_length, treal: :float, tcomplex: :complex64, name: "RFFT")
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# File lib/tensorflow/ops/raw_ops.rb, line 2959 def self.rfft(input, fft_length, treal: :float, tcomplex: :complex64, name: "RFFT") self.execute("RFFT", [input, fft_length], Treal: treal, Tcomplex: tcomplex, name: name) end
rfft2_d(input, fft_length, treal: :float, tcomplex: :complex64, name: "RFFT2D")
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# File lib/tensorflow/ops/raw_ops.rb, line 2963 def self.rfft2_d(input, fft_length, treal: :float, tcomplex: :complex64, name: "RFFT2D") self.execute("RFFT2D", [input, fft_length], Treal: treal, Tcomplex: tcomplex, name: name) end
rfft3_d(input, fft_length, treal: :float, tcomplex: :complex64, name: "RFFT3D")
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# File lib/tensorflow/ops/raw_ops.rb, line 2967 def self.rfft3_d(input, fft_length, treal: :float, tcomplex: :complex64, name: "RFFT3D") self.execute("RFFT3D", [input, fft_length], Treal: treal, Tcomplex: tcomplex, name: name) end
rgb_to_hsv(images, typeT: :float, name: "RGBToHSV")
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# File lib/tensorflow/ops/raw_ops.rb, line 2971 def self.rgb_to_hsv(images, typeT: :float, name: "RGBToHSV") self.execute("RGBToHSV", [images], T: typeT, name: name) end
right_shift(x, y, typeT: nil, name: "RightShift")
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# File lib/tensorflow/ops/raw_ops.rb, line 3563 def self.right_shift(x, y, typeT: nil, name: "RightShift") self.execute("RightShift", [x, y], T: typeT, name: name) end
rint(x, typeT: nil, name: "Rint")
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# File lib/tensorflow/ops/raw_ops.rb, line 3567 def self.rint(x, typeT: nil, name: "Rint") self.execute("Rint", [x], T: typeT, name: name) end
rng_skip(resource, algorithm, delta, name: "RngSkip")
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# File lib/tensorflow/ops/raw_ops.rb, line 3571 def self.rng_skip(resource, algorithm, delta, name: "RngSkip") self.execute("RngSkip", [resource, algorithm, delta], name: name) end
roll(input, shift, axis, typeT: nil, tshift: nil, taxis: nil, name: "Roll")
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# File lib/tensorflow/ops/raw_ops.rb, line 3575 def self.roll(input, shift, axis, typeT: nil, tshift: nil, taxis: nil, name: "Roll") self.execute("Roll", [input, shift, axis], T: typeT, Tshift: tshift, Taxis: taxis, name: name) end
round(x, typeT: nil, name: "Round")
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# File lib/tensorflow/ops/raw_ops.rb, line 3579 def self.round(x, typeT: nil, name: "Round") self.execute("Round", [x], T: typeT, name: name) end
rpc(address, method, request, protocol: "", fail_fast: true, timeout_in_ms: 0, name: "Rpc")
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# File lib/tensorflow/ops/raw_ops.rb, line 3583 def self.rpc(address, method, request, protocol: "", fail_fast: true, timeout_in_ms: 0, name: "Rpc") self.execute("Rpc", [address, method, request], protocol: protocol, fail_fast: fail_fast, timeout_in_ms: timeout_in_ms, name: name) end
rsqrt(x, typeT: nil, name: "Rsqrt")
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# File lib/tensorflow/ops/raw_ops.rb, line 3587 def self.rsqrt(x, typeT: nil, name: "Rsqrt") self.execute("Rsqrt", [x], T: typeT, name: name) end
rsqrt_grad(y, dy, typeT: nil, name: "RsqrtGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3591 def self.rsqrt_grad(y, dy, typeT: nil, name: "RsqrtGrad") self.execute("RsqrtGrad", [y, dy], T: typeT, name: name) end
sample_distorted_bounding_box(image_size, bounding_boxes, typeT: nil, seed: 0, seed2: 0, min_object_covered: 0.10000000149011612, aspect_ratio_range: [], area_range: [], max_attempts: 100, use_image_if_no_bounding_boxes: false, name: "SampleDistortedBoundingBox")
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# File lib/tensorflow/ops/raw_ops.rb, line 3595 def self.sample_distorted_bounding_box(image_size, bounding_boxes, typeT: nil, seed: 0, seed2: 0, min_object_covered: 0.10000000149011612, aspect_ratio_range: [], area_range: [], max_attempts: 100, use_image_if_no_bounding_boxes: false, name: "SampleDistortedBoundingBox") self.execute("SampleDistortedBoundingBox", [image_size, bounding_boxes], T: typeT, seed: seed, seed2: seed2, min_object_covered: min_object_covered, aspect_ratio_range: aspect_ratio_range, area_range: area_range, max_attempts: max_attempts, use_image_if_no_bounding_boxes: use_image_if_no_bounding_boxes, name: name) end
sample_distorted_bounding_box_v2(image_size, bounding_boxes, min_object_covered, typeT: nil, seed: 0, seed2: 0, aspect_ratio_range: [], area_range: [], max_attempts: 100, use_image_if_no_bounding_boxes: false, name: "SampleDistortedBoundingBoxV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3599 def self.sample_distorted_bounding_box_v2(image_size, bounding_boxes, min_object_covered, typeT: nil, seed: 0, seed2: 0, aspect_ratio_range: [], area_range: [], max_attempts: 100, use_image_if_no_bounding_boxes: false, name: "SampleDistortedBoundingBoxV2") self.execute("SampleDistortedBoundingBoxV2", [image_size, bounding_boxes, min_object_covered], T: typeT, seed: seed, seed2: seed2, aspect_ratio_range: aspect_ratio_range, area_range: area_range, max_attempts: max_attempts, use_image_if_no_bounding_boxes: use_image_if_no_bounding_boxes, name: name) end
sampling_dataset(input_dataset, rate, seed, seed2, output_types: nil, output_shapes: nil, name: "SamplingDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3603 def self.sampling_dataset(input_dataset, rate, seed, seed2, output_types: nil, output_shapes: nil, name: "SamplingDataset") self.execute("SamplingDataset", [input_dataset, rate, seed, seed2], output_types: output_types, output_shapes: output_shapes, name: name) end
save(filename, tensor_names, data, typeT: nil, name: "Save")
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# File lib/tensorflow/ops/raw_ops.rb, line 3607 def self.save(filename, tensor_names, data, typeT: nil, name: "Save") self.execute("Save", [filename, tensor_names, data], T: typeT, name: name) end
save_slices(filename, tensor_names, shapes_and_slices, data, typeT: nil, name: "SaveSlices")
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# File lib/tensorflow/ops/raw_ops.rb, line 3611 def self.save_slices(filename, tensor_names, shapes_and_slices, data, typeT: nil, name: "SaveSlices") self.execute("SaveSlices", [filename, tensor_names, shapes_and_slices, data], T: typeT, name: name) end
save_v2(prefix, tensor_names, shape_and_slices, tensors, dtypes: nil, name: "SaveV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3615 def self.save_v2(prefix, tensor_names, shape_and_slices, tensors, dtypes: nil, name: "SaveV2") self.execute("SaveV2", [prefix, tensor_names, shape_and_slices, tensors], dtypes: dtypes, name: name) end
scalar_summary(tags, values, typeT: nil, name: "ScalarSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 3619 def self.scalar_summary(tags, values, typeT: nil, name: "ScalarSummary") self.execute("ScalarSummary", [tags, values], T: typeT, name: name) end
scale_and_translate(images, size, scale, translation, typeT: nil, kernel_type: "lanczos3", antialias: true, name: "ScaleAndTranslate")
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# File lib/tensorflow/ops/raw_ops.rb, line 3623 def self.scale_and_translate(images, size, scale, translation, typeT: nil, kernel_type: "lanczos3", antialias: true, name: "ScaleAndTranslate") self.execute("ScaleAndTranslate", [images, size, scale, translation], T: typeT, kernel_type: kernel_type, antialias: antialias, name: name) end
scale_and_translate_grad(grads, original_image, scale, translation, typeT: nil, kernel_type: "lanczos3", antialias: true, name: "ScaleAndTranslateGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3627 def self.scale_and_translate_grad(grads, original_image, scale, translation, typeT: nil, kernel_type: "lanczos3", antialias: true, name: "ScaleAndTranslateGrad") self.execute("ScaleAndTranslateGrad", [grads, original_image, scale, translation], T: typeT, kernel_type: kernel_type, antialias: antialias, name: name) end
scan_dataset(input_dataset, initial_state, other_arguments, f: nil, tstate: nil, targuments: nil, output_types: nil, output_shapes: nil, preserve_cardinality: false, use_default_device: true, name: "ScanDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3631 def self.scan_dataset(input_dataset, initial_state, other_arguments, f: nil, tstate: nil, targuments: nil, output_types: nil, output_shapes: nil, preserve_cardinality: false, use_default_device: true, name: "ScanDataset") self.execute("ScanDataset", [input_dataset, initial_state, other_arguments], f: f, Tstate: tstate, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, preserve_cardinality: preserve_cardinality, use_default_device: use_default_device, name: name) end
scatter_add(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 3635 def self.scatter_add(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterAdd") self.execute("ScatterAdd", [ref, indices, updates], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
scatter_div(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterDiv")
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# File lib/tensorflow/ops/raw_ops.rb, line 3639 def self.scatter_div(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterDiv") self.execute("ScatterDiv", [ref, indices, updates], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
scatter_max(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterMax")
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# File lib/tensorflow/ops/raw_ops.rb, line 3643 def self.scatter_max(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterMax") self.execute("ScatterMax", [ref, indices, updates], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
scatter_min(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterMin")
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# File lib/tensorflow/ops/raw_ops.rb, line 3647 def self.scatter_min(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterMin") self.execute("ScatterMin", [ref, indices, updates], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
scatter_mul(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 3651 def self.scatter_mul(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterMul") self.execute("ScatterMul", [ref, indices, updates], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
scatter_nd(indices, updates, shape, typeT: nil, tindices: nil, name: "ScatterNd")
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# File lib/tensorflow/ops/raw_ops.rb, line 3655 def self.scatter_nd(indices, updates, shape, typeT: nil, tindices: nil, name: "ScatterNd") self.execute("ScatterNd", [indices, updates, shape], T: typeT, Tindices: tindices, name: name) end
scatter_nd_add(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterNdAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 3659 def self.scatter_nd_add(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterNdAdd") self.execute("ScatterNdAdd", [ref, indices, updates], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
scatter_nd_non_aliasing_add(input, indices, updates, typeT: nil, tindices: nil, name: "ScatterNdNonAliasingAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 3663 def self.scatter_nd_non_aliasing_add(input, indices, updates, typeT: nil, tindices: nil, name: "ScatterNdNonAliasingAdd") self.execute("ScatterNdNonAliasingAdd", [input, indices, updates], T: typeT, Tindices: tindices, name: name) end
scatter_nd_sub(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterNdSub")
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# File lib/tensorflow/ops/raw_ops.rb, line 3667 def self.scatter_nd_sub(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterNdSub") self.execute("ScatterNdSub", [ref, indices, updates], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
scatter_nd_update(ref, indices, updates, typeT: nil, tindices: nil, use_locking: true, name: "ScatterNdUpdate")
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# File lib/tensorflow/ops/raw_ops.rb, line 3671 def self.scatter_nd_update(ref, indices, updates, typeT: nil, tindices: nil, use_locking: true, name: "ScatterNdUpdate") self.execute("ScatterNdUpdate", [ref, indices, updates], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
scatter_sub(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterSub")
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# File lib/tensorflow/ops/raw_ops.rb, line 3675 def self.scatter_sub(ref, indices, updates, typeT: nil, tindices: nil, use_locking: false, name: "ScatterSub") self.execute("ScatterSub", [ref, indices, updates], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
scatter_update(ref, indices, updates, typeT: nil, tindices: nil, use_locking: true, name: "ScatterUpdate")
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# File lib/tensorflow/ops/raw_ops.rb, line 3679 def self.scatter_update(ref, indices, updates, typeT: nil, tindices: nil, use_locking: true, name: "ScatterUpdate") self.execute("ScatterUpdate", [ref, indices, updates], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
sdca_fprint(input, name: "SdcaFprint")
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# File lib/tensorflow/ops/raw_ops.rb, line 3683 def self.sdca_fprint(input, name: "SdcaFprint") self.execute("SdcaFprint", [input], name: name) end
sdca_optimizer(sparse_example_indices, sparse_feature_indices, sparse_feature_values, dense_features, example_weights, example_labels, sparse_indices, sparse_weights, dense_weights, example_state_data, loss_type: nil, adaptative: false, num_sparse_features: nil, num_sparse_features_with_values: nil, num_dense_features: nil, l1: nil, l2: nil, num_loss_partitions: nil, num_inner_iterations: nil, name: "SdcaOptimizer")
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# File lib/tensorflow/ops/raw_ops.rb, line 3687 def self.sdca_optimizer(sparse_example_indices, sparse_feature_indices, sparse_feature_values, dense_features, example_weights, example_labels, sparse_indices, sparse_weights, dense_weights, example_state_data, loss_type: nil, adaptative: false, num_sparse_features: nil, num_sparse_features_with_values: nil, num_dense_features: nil, l1: nil, l2: nil, num_loss_partitions: nil, num_inner_iterations: nil, name: "SdcaOptimizer") self.execute("SdcaOptimizer", [sparse_example_indices, sparse_feature_indices, sparse_feature_values, dense_features, example_weights, example_labels, sparse_indices, sparse_weights, dense_weights, example_state_data], loss_type: loss_type, adaptative: adaptative, num_sparse_features: num_sparse_features, num_sparse_features_with_values: num_sparse_features_with_values, num_dense_features: num_dense_features, l1: l1, l2: l2, num_loss_partitions: num_loss_partitions, num_inner_iterations: num_inner_iterations, name: name) end
sdca_optimizer_v2(sparse_example_indices, sparse_feature_indices, sparse_feature_values, dense_features, example_weights, example_labels, sparse_indices, sparse_weights, dense_weights, example_state_data, loss_type: nil, adaptive: false, num_sparse_features: nil, num_sparse_features_with_values: nil, num_dense_features: nil, l1: nil, l2: nil, num_loss_partitions: nil, num_inner_iterations: nil, name: "SdcaOptimizerV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3691 def self.sdca_optimizer_v2(sparse_example_indices, sparse_feature_indices, sparse_feature_values, dense_features, example_weights, example_labels, sparse_indices, sparse_weights, dense_weights, example_state_data, loss_type: nil, adaptive: false, num_sparse_features: nil, num_sparse_features_with_values: nil, num_dense_features: nil, l1: nil, l2: nil, num_loss_partitions: nil, num_inner_iterations: nil, name: "SdcaOptimizerV2") self.execute("SdcaOptimizerV2", [sparse_example_indices, sparse_feature_indices, sparse_feature_values, dense_features, example_weights, example_labels, sparse_indices, sparse_weights, dense_weights, example_state_data], loss_type: loss_type, adaptive: adaptive, num_sparse_features: num_sparse_features, num_sparse_features_with_values: num_sparse_features_with_values, num_dense_features: num_dense_features, l1: l1, l2: l2, num_loss_partitions: num_loss_partitions, num_inner_iterations: num_inner_iterations, name: name) end
sdca_shrink_l1(weights, num_features: nil, l1: nil, l2: nil, name: "SdcaShrinkL1")
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# File lib/tensorflow/ops/raw_ops.rb, line 3695 def self.sdca_shrink_l1(weights, num_features: nil, l1: nil, l2: nil, name: "SdcaShrinkL1") self.execute("SdcaShrinkL1", [weights], num_features: num_features, l1: l1, l2: l2, name: name) end
segment_max(data, segment_ids, typeT: nil, tindices: nil, name: "SegmentMax")
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# File lib/tensorflow/ops/raw_ops.rb, line 3699 def self.segment_max(data, segment_ids, typeT: nil, tindices: nil, name: "SegmentMax") self.execute("SegmentMax", [data, segment_ids], T: typeT, Tindices: tindices, name: name) end
segment_mean(data, segment_ids, typeT: nil, tindices: nil, name: "SegmentMean")
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# File lib/tensorflow/ops/raw_ops.rb, line 3703 def self.segment_mean(data, segment_ids, typeT: nil, tindices: nil, name: "SegmentMean") self.execute("SegmentMean", [data, segment_ids], T: typeT, Tindices: tindices, name: name) end
segment_min(data, segment_ids, typeT: nil, tindices: nil, name: "SegmentMin")
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# File lib/tensorflow/ops/raw_ops.rb, line 3707 def self.segment_min(data, segment_ids, typeT: nil, tindices: nil, name: "SegmentMin") self.execute("SegmentMin", [data, segment_ids], T: typeT, Tindices: tindices, name: name) end
segment_prod(data, segment_ids, typeT: nil, tindices: nil, name: "SegmentProd")
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# File lib/tensorflow/ops/raw_ops.rb, line 3711 def self.segment_prod(data, segment_ids, typeT: nil, tindices: nil, name: "SegmentProd") self.execute("SegmentProd", [data, segment_ids], T: typeT, Tindices: tindices, name: name) end
segment_sum(data, segment_ids, typeT: nil, tindices: nil, name: "SegmentSum")
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# File lib/tensorflow/ops/raw_ops.rb, line 3715 def self.segment_sum(data, segment_ids, typeT: nil, tindices: nil, name: "SegmentSum") self.execute("SegmentSum", [data, segment_ids], T: typeT, Tindices: tindices, name: name) end
select(condition, t, e, typeT: nil, name: "Select")
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# File lib/tensorflow/ops/raw_ops.rb, line 3719 def self.select(condition, t, e, typeT: nil, name: "Select") self.execute("Select", [condition, t, e], T: typeT, name: name) end
select_v2(condition, t, e, typeT: nil, name: "SelectV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3723 def self.select_v2(condition, t, e, typeT: nil, name: "SelectV2") self.execute("SelectV2", [condition, t, e], T: typeT, name: name) end
self_adjoint_eig(input, typeT: nil, name: "SelfAdjointEig")
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# File lib/tensorflow/ops/raw_ops.rb, line 3727 def self.self_adjoint_eig(input, typeT: nil, name: "SelfAdjointEig") self.execute("SelfAdjointEig", [input], T: typeT, name: name) end
self_adjoint_eig_v2(input, compute_v: true, typeT: nil, name: "SelfAdjointEigV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3731 def self.self_adjoint_eig_v2(input, compute_v: true, typeT: nil, name: "SelfAdjointEigV2") self.execute("SelfAdjointEigV2", [input], compute_v: compute_v, T: typeT, name: name) end
selu(features, typeT: nil, name: "Selu")
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# File lib/tensorflow/ops/raw_ops.rb, line 3735 def self.selu(features, typeT: nil, name: "Selu") self.execute("Selu", [features], T: typeT, name: name) end
selu_grad(gradients, outputs, typeT: nil, name: "SeluGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3739 def self.selu_grad(gradients, outputs, typeT: nil, name: "SeluGrad") self.execute("SeluGrad", [gradients, outputs], T: typeT, name: name) end
send(tensor, typeT: nil, tensor_name: "", send_device: "", send_device_incarnation: nil, recv_device: "", client_terminated: false, name: "Send")
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# File lib/tensorflow/ops/raw_ops.rb, line 3743 def self.send(tensor, typeT: nil, tensor_name: "", send_device: "", send_device_incarnation: nil, recv_device: "", client_terminated: false, name: "Send") self.execute("Send", [tensor], T: typeT, tensor_name: tensor_name, send_device: send_device, send_device_incarnation: send_device_incarnation, recv_device: recv_device, client_terminated: client_terminated, name: name) end
send_tpu_embedding_gradients(inputs, learning_rates, n: nil, nn: 0, config: "", name: "SendTPUEmbeddingGradients")
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# File lib/tensorflow/ops/raw_ops.rb, line 3747 def self.send_tpu_embedding_gradients(inputs, learning_rates, n: nil, nn: 0, config: "", name: "SendTPUEmbeddingGradients") self.execute("SendTPUEmbeddingGradients", [inputs, learning_rates], N: n, NN: nn, config: config, name: name) end
serialize_iterator(resource_handle, name: "SerializeIterator")
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# File lib/tensorflow/ops/raw_ops.rb, line 3751 def self.serialize_iterator(resource_handle, name: "SerializeIterator") self.execute("SerializeIterator", [resource_handle], name: name) end
serialize_many_sparse(sparse_indices, sparse_values, sparse_shape, typeT: nil, out_type: :string, name: "SerializeManySparse")
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# File lib/tensorflow/ops/raw_ops.rb, line 3755 def self.serialize_many_sparse(sparse_indices, sparse_values, sparse_shape, typeT: nil, out_type: :string, name: "SerializeManySparse") self.execute("SerializeManySparse", [sparse_indices, sparse_values, sparse_shape], T: typeT, out_type: out_type, name: name) end
serialize_sparse(sparse_indices, sparse_values, sparse_shape, typeT: nil, out_type: :string, name: "SerializeSparse")
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# File lib/tensorflow/ops/raw_ops.rb, line 3759 def self.serialize_sparse(sparse_indices, sparse_values, sparse_shape, typeT: nil, out_type: :string, name: "SerializeSparse") self.execute("SerializeSparse", [sparse_indices, sparse_values, sparse_shape], T: typeT, out_type: out_type, name: name) end
serialize_tensor(tensor, typeT: nil, name: "SerializeTensor")
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# File lib/tensorflow/ops/raw_ops.rb, line 3763 def self.serialize_tensor(tensor, typeT: nil, name: "SerializeTensor") self.execute("SerializeTensor", [tensor], T: typeT, name: name) end
set_size(set_indices, set_values, set_shape, validate_indices: true, typeT: nil, name: "SetSize")
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# File lib/tensorflow/ops/raw_ops.rb, line 3767 def self.set_size(set_indices, set_values, set_shape, validate_indices: true, typeT: nil, name: "SetSize") self.execute("SetSize", [set_indices, set_values, set_shape], validate_indices: validate_indices, T: typeT, name: name) end
set_stats_aggregator_dataset(input_dataset, stats_aggregator, tag, counter_prefix, output_types: nil, output_shapes: nil, name: "SetStatsAggregatorDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3771 def self.set_stats_aggregator_dataset(input_dataset, stats_aggregator, tag, counter_prefix, output_types: nil, output_shapes: nil, name: "SetStatsAggregatorDataset") self.execute("SetStatsAggregatorDataset", [input_dataset, stats_aggregator, tag, counter_prefix], output_types: output_types, output_shapes: output_shapes, name: name) end
shape(input, typeT: nil, out_type: :int32, name: "Shape")
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# File lib/tensorflow/ops/raw_ops.rb, line 3775 def self.shape(input, typeT: nil, out_type: :int32, name: "Shape") self.execute("Shape", [input], T: typeT, out_type: out_type, name: name) end
shape_n(input, n: nil, typeT: nil, out_type: :int32, name: "ShapeN")
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# File lib/tensorflow/ops/raw_ops.rb, line 3779 def self.shape_n(input, n: nil, typeT: nil, out_type: :int32, name: "ShapeN") self.execute("ShapeN", [input], N: n, T: typeT, out_type: out_type, name: name) end
shard_dataset(input_dataset, num_shards, index, require_non_empty: false, output_types: nil, output_shapes: nil, name: "ShardDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3783 def self.shard_dataset(input_dataset, num_shards, index, require_non_empty: false, output_types: nil, output_shapes: nil, name: "ShardDataset") self.execute("ShardDataset", [input_dataset, num_shards, index], require_non_empty: require_non_empty, output_types: output_types, output_shapes: output_shapes, name: name) end
sharded_filename(basename, shard, num_shards, name: "ShardedFilename")
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# File lib/tensorflow/ops/raw_ops.rb, line 3787 def self.sharded_filename(basename, shard, num_shards, name: "ShardedFilename") self.execute("ShardedFilename", [basename, shard, num_shards], name: name) end
sharded_filespec(basename, num_shards, name: "ShardedFilespec")
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# File lib/tensorflow/ops/raw_ops.rb, line 3791 def self.sharded_filespec(basename, num_shards, name: "ShardedFilespec") self.execute("ShardedFilespec", [basename, num_shards], name: name) end
shuffle_and_repeat_dataset(input_dataset, buffer_size, seed, seed2, count, output_types: nil, output_shapes: nil, name: "ShuffleAndRepeatDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3795 def self.shuffle_and_repeat_dataset(input_dataset, buffer_size, seed, seed2, count, output_types: nil, output_shapes: nil, name: "ShuffleAndRepeatDataset") self.execute("ShuffleAndRepeatDataset", [input_dataset, buffer_size, seed, seed2, count], output_types: output_types, output_shapes: output_shapes, name: name) end
shuffle_dataset(input_dataset, buffer_size, seed, seed2, reshuffle_each_iteration: true, output_types: nil, output_shapes: nil, name: "ShuffleDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3799 def self.shuffle_dataset(input_dataset, buffer_size, seed, seed2, reshuffle_each_iteration: true, output_types: nil, output_shapes: nil, name: "ShuffleDataset") self.execute("ShuffleDataset", [input_dataset, buffer_size, seed, seed2], reshuffle_each_iteration: reshuffle_each_iteration, output_types: output_types, output_shapes: output_shapes, name: name) end
shuffle_dataset_v2(input_dataset, buffer_size, seed_generator, output_types: nil, output_shapes: nil, name: "ShuffleDatasetV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3803 def self.shuffle_dataset_v2(input_dataset, buffer_size, seed_generator, output_types: nil, output_shapes: nil, name: "ShuffleDatasetV2") self.execute("ShuffleDatasetV2", [input_dataset, buffer_size, seed_generator], output_types: output_types, output_shapes: output_shapes, name: name) end
shutdown_distributed_tpu(name: "ShutdownDistributedTPU")
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# File lib/tensorflow/ops/raw_ops.rb, line 3807 def self.shutdown_distributed_tpu(name: "ShutdownDistributedTPU") self.execute("ShutdownDistributedTPU", [], name: name) end
sigmoid(x, typeT: nil, name: "Sigmoid")
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# File lib/tensorflow/ops/raw_ops.rb, line 3811 def self.sigmoid(x, typeT: nil, name: "Sigmoid") self.execute("Sigmoid", [x], T: typeT, name: name) end
sigmoid_grad(y, dy, typeT: nil, name: "SigmoidGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3815 def self.sigmoid_grad(y, dy, typeT: nil, name: "SigmoidGrad") self.execute("SigmoidGrad", [y, dy], T: typeT, name: name) end
sign(x, typeT: nil, name: "Sign")
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# File lib/tensorflow/ops/raw_ops.rb, line 3819 def self.sign(x, typeT: nil, name: "Sign") self.execute("Sign", [x], T: typeT, name: name) end
sin(x, typeT: nil, name: "Sin")
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# File lib/tensorflow/ops/raw_ops.rb, line 3823 def self.sin(x, typeT: nil, name: "Sin") self.execute("Sin", [x], T: typeT, name: name) end
sinh(x, typeT: nil, name: "Sinh")
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# File lib/tensorflow/ops/raw_ops.rb, line 3827 def self.sinh(x, typeT: nil, name: "Sinh") self.execute("Sinh", [x], T: typeT, name: name) end
size(input, typeT: nil, out_type: :int32, name: "Size")
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# File lib/tensorflow/ops/raw_ops.rb, line 3831 def self.size(input, typeT: nil, out_type: :int32, name: "Size") self.execute("Size", [input], T: typeT, out_type: out_type, name: name) end
skip_dataset(input_dataset, count, output_types: nil, output_shapes: nil, name: "SkipDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3835 def self.skip_dataset(input_dataset, count, output_types: nil, output_shapes: nil, name: "SkipDataset") self.execute("SkipDataset", [input_dataset, count], output_types: output_types, output_shapes: output_shapes, name: name) end
skipgram(filename: "", batch_size: nil, window_size: 5, min_count: 5, subsample: 0.0010000000474974513, name: "Skipgram")
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# File lib/tensorflow/ops/raw_ops.rb, line 3839 def self.skipgram(filename: "", batch_size: nil, window_size: 5, min_count: 5, subsample: 0.0010000000474974513, name: "Skipgram") self.execute("Skipgram", [], filename: filename, batch_size: batch_size, window_size: window_size, min_count: min_count, subsample: subsample, name: name) end
sleep_dataset(input_dataset, sleep_microseconds, output_types: nil, output_shapes: nil, name: "SleepDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3843 def self.sleep_dataset(input_dataset, sleep_microseconds, output_types: nil, output_shapes: nil, name: "SleepDataset") self.execute("SleepDataset", [input_dataset, sleep_microseconds], output_types: output_types, output_shapes: output_shapes, name: name) end
slice(input, start, size, typeT: nil, index: nil, name: "Slice")
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# File lib/tensorflow/ops/raw_ops.rb, line 3847 def self.slice(input, start, size, typeT: nil, index: nil, name: "Slice") self.execute("Slice", [input, start, size], T: typeT, Index: index, name: name) end
sliding_window_dataset(input_dataset, window_size, window_shift, window_stride, output_types: nil, output_shapes: nil, name: "SlidingWindowDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3851 def self.sliding_window_dataset(input_dataset, window_size, window_shift, window_stride, output_types: nil, output_shapes: nil, name: "SlidingWindowDataset") self.execute("SlidingWindowDataset", [input_dataset, window_size, window_shift, window_stride], output_types: output_types, output_shapes: output_shapes, name: name) end
snapshot(input, typeT: nil, name: "Snapshot")
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# File lib/tensorflow/ops/raw_ops.rb, line 3855 def self.snapshot(input, typeT: nil, name: "Snapshot") self.execute("Snapshot", [input], T: typeT, name: name) end
snapshot_dataset(input_dataset, path, output_types: nil, output_shapes: nil, compression: "", reader_path_prefix: "", writer_path_prefix: "", shard_size_bytes: 10737418240, pending_snapshot_expiry_seconds: 86400, num_reader_threads: 1, reader_buffer_size: 1, num_writer_threads: 1, writer_buffer_size: 1, shuffle_on_read: false, seed: 0, seed2: 0, name: "SnapshotDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 3859 def self.snapshot_dataset(input_dataset, path, output_types: nil, output_shapes: nil, compression: "", reader_path_prefix: "", writer_path_prefix: "", shard_size_bytes: 10737418240, pending_snapshot_expiry_seconds: 86400, num_reader_threads: 1, reader_buffer_size: 1, num_writer_threads: 1, writer_buffer_size: 1, shuffle_on_read: false, seed: 0, seed2: 0, name: "SnapshotDataset") self.execute("SnapshotDataset", [input_dataset, path], output_types: output_types, output_shapes: output_shapes, compression: compression, reader_path_prefix: reader_path_prefix, writer_path_prefix: writer_path_prefix, shard_size_bytes: shard_size_bytes, pending_snapshot_expiry_seconds: pending_snapshot_expiry_seconds, num_reader_threads: num_reader_threads, reader_buffer_size: reader_buffer_size, num_writer_threads: num_writer_threads, writer_buffer_size: writer_buffer_size, shuffle_on_read: shuffle_on_read, seed: seed, seed2: seed2, name: name) end
softmax(logits, typeT: nil, name: "Softmax")
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# File lib/tensorflow/ops/raw_ops.rb, line 3863 def self.softmax(logits, typeT: nil, name: "Softmax") self.execute("Softmax", [logits], T: typeT, name: name) end
softmax_cross_entropy_with_logits(features, labels, typeT: nil, name: "SoftmaxCrossEntropyWithLogits")
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# File lib/tensorflow/ops/raw_ops.rb, line 3867 def self.softmax_cross_entropy_with_logits(features, labels, typeT: nil, name: "SoftmaxCrossEntropyWithLogits") self.execute("SoftmaxCrossEntropyWithLogits", [features, labels], T: typeT, name: name) end
softplus(features, typeT: nil, name: "Softplus")
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# File lib/tensorflow/ops/raw_ops.rb, line 3871 def self.softplus(features, typeT: nil, name: "Softplus") self.execute("Softplus", [features], T: typeT, name: name) end
softplus_grad(gradients, features, typeT: nil, name: "SoftplusGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3875 def self.softplus_grad(gradients, features, typeT: nil, name: "SoftplusGrad") self.execute("SoftplusGrad", [gradients, features], T: typeT, name: name) end
softsign(features, typeT: nil, name: "Softsign")
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# File lib/tensorflow/ops/raw_ops.rb, line 3879 def self.softsign(features, typeT: nil, name: "Softsign") self.execute("Softsign", [features], T: typeT, name: name) end
softsign_grad(gradients, features, typeT: nil, name: "SoftsignGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3883 def self.softsign_grad(gradients, features, typeT: nil, name: "SoftsignGrad") self.execute("SoftsignGrad", [gradients, features], T: typeT, name: name) end
space_to_batch(input, paddings, typeT: nil, tpaddings: :int32, block_size: nil, name: "SpaceToBatch")
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# File lib/tensorflow/ops/raw_ops.rb, line 3887 def self.space_to_batch(input, paddings, typeT: nil, tpaddings: :int32, block_size: nil, name: "SpaceToBatch") self.execute("SpaceToBatch", [input, paddings], T: typeT, Tpaddings: tpaddings, block_size: block_size, name: name) end
space_to_batch_nd(input, block_shape, paddings, typeT: nil, tblock_shape: :int32, tpaddings: :int32, name: "SpaceToBatchND")
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# File lib/tensorflow/ops/raw_ops.rb, line 3891 def self.space_to_batch_nd(input, block_shape, paddings, typeT: nil, tblock_shape: :int32, tpaddings: :int32, name: "SpaceToBatchND") self.execute("SpaceToBatchND", [input, block_shape, paddings], T: typeT, Tblock_shape: tblock_shape, Tpaddings: tpaddings, name: name) end
space_to_depth(input, typeT: nil, block_size: nil, data_format: "NHWC", name: "SpaceToDepth")
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# File lib/tensorflow/ops/raw_ops.rb, line 3895 def self.space_to_depth(input, typeT: nil, block_size: nil, data_format: "NHWC", name: "SpaceToDepth") self.execute("SpaceToDepth", [input], T: typeT, block_size: block_size, data_format: data_format, name: name) end
sparse_accumulator_apply_gradient(handle, local_step, gradient_indices, gradient_values, gradient_shape, dtype: nil, has_known_shape: nil, name: "SparseAccumulatorApplyGradient")
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# File lib/tensorflow/ops/raw_ops.rb, line 3899 def self.sparse_accumulator_apply_gradient(handle, local_step, gradient_indices, gradient_values, gradient_shape, dtype: nil, has_known_shape: nil, name: "SparseAccumulatorApplyGradient") self.execute("SparseAccumulatorApplyGradient", [handle, local_step, gradient_indices, gradient_values, gradient_shape], dtype: dtype, has_known_shape: has_known_shape, name: name) end
sparse_accumulator_take_gradient(handle, num_required, dtype: nil, name: "SparseAccumulatorTakeGradient")
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# File lib/tensorflow/ops/raw_ops.rb, line 3903 def self.sparse_accumulator_take_gradient(handle, num_required, dtype: nil, name: "SparseAccumulatorTakeGradient") self.execute("SparseAccumulatorTakeGradient", [handle, num_required], dtype: dtype, name: name) end
sparse_add(a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh, typeT: nil, treal: nil, name: "SparseAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 3907 def self.sparse_add(a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh, typeT: nil, treal: nil, name: "SparseAdd") self.execute("SparseAdd", [a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh], T: typeT, Treal: treal, name: name) end
sparse_add_grad(backprop_val_grad, a_indices, b_indices, sum_indices, typeT: nil, name: "SparseAddGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3911 def self.sparse_add_grad(backprop_val_grad, a_indices, b_indices, sum_indices, typeT: nil, name: "SparseAddGrad") self.execute("SparseAddGrad", [backprop_val_grad, a_indices, b_indices, sum_indices], T: typeT, name: name) end
sparse_apply_adadelta(var, accum, accum_update, lr, rho, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyAdadelta")
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# File lib/tensorflow/ops/raw_ops.rb, line 3915 def self.sparse_apply_adadelta(var, accum, accum_update, lr, rho, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyAdadelta") self.execute("SparseApplyAdadelta", [var, accum, accum_update, lr, rho, epsilon, grad, indices], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
sparse_apply_adagrad(var, accum, lr, grad, indices, typeT: nil, tindices: nil, use_locking: false, update_slots: true, name: "SparseApplyAdagrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3919 def self.sparse_apply_adagrad(var, accum, lr, grad, indices, typeT: nil, tindices: nil, use_locking: false, update_slots: true, name: "SparseApplyAdagrad") self.execute("SparseApplyAdagrad", [var, accum, lr, grad, indices], T: typeT, Tindices: tindices, use_locking: use_locking, update_slots: update_slots, name: name) end
sparse_apply_adagrad_da(var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyAdagradDA")
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# File lib/tensorflow/ops/raw_ops.rb, line 3923 def self.sparse_apply_adagrad_da(var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyAdagradDA") self.execute("SparseApplyAdagradDA", [var, gradient_accumulator, gradient_squared_accumulator, grad, indices, lr, l1, l2, global_step], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
sparse_apply_adagrad_v2(var, accum, lr, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, update_slots: true, name: "SparseApplyAdagradV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3927 def self.sparse_apply_adagrad_v2(var, accum, lr, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, update_slots: true, name: "SparseApplyAdagradV2") self.execute("SparseApplyAdagradV2", [var, accum, lr, epsilon, grad, indices], T: typeT, Tindices: tindices, use_locking: use_locking, update_slots: update_slots, name: name) end
sparse_apply_centered_rms_prop(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyCenteredRMSProp")
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# File lib/tensorflow/ops/raw_ops.rb, line 3931 def self.sparse_apply_centered_rms_prop(var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyCenteredRMSProp") self.execute("SparseApplyCenteredRMSProp", [var, mg, ms, mom, lr, rho, momentum, epsilon, grad, indices], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
sparse_apply_ftrl(var, accum, linear, grad, indices, lr, l1, l2, lr_power, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyFtrl")
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# File lib/tensorflow/ops/raw_ops.rb, line 3935 def self.sparse_apply_ftrl(var, accum, linear, grad, indices, lr, l1, l2, lr_power, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyFtrl") self.execute("SparseApplyFtrl", [var, accum, linear, grad, indices, lr, l1, l2, lr_power], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
sparse_apply_ftrl_v2(var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyFtrlV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 3939 def self.sparse_apply_ftrl_v2(var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyFtrlV2") self.execute("SparseApplyFtrlV2", [var, accum, linear, grad, indices, lr, l1, l2, l2_shrinkage, lr_power], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
sparse_apply_momentum(var, accum, lr, grad, indices, momentum, typeT: nil, tindices: nil, use_locking: false, use_nesterov: false, name: "SparseApplyMomentum")
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# File lib/tensorflow/ops/raw_ops.rb, line 3943 def self.sparse_apply_momentum(var, accum, lr, grad, indices, momentum, typeT: nil, tindices: nil, use_locking: false, use_nesterov: false, name: "SparseApplyMomentum") self.execute("SparseApplyMomentum", [var, accum, lr, grad, indices, momentum], T: typeT, Tindices: tindices, use_locking: use_locking, use_nesterov: use_nesterov, name: name) end
sparse_apply_proximal_adagrad(var, accum, lr, l1, l2, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyProximalAdagrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3947 def self.sparse_apply_proximal_adagrad(var, accum, lr, l1, l2, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyProximalAdagrad") self.execute("SparseApplyProximalAdagrad", [var, accum, lr, l1, l2, grad, indices], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
sparse_apply_proximal_gradient_descent(var, alpha, l1, l2, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyProximalGradientDescent")
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# File lib/tensorflow/ops/raw_ops.rb, line 3951 def self.sparse_apply_proximal_gradient_descent(var, alpha, l1, l2, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyProximalGradientDescent") self.execute("SparseApplyProximalGradientDescent", [var, alpha, l1, l2, grad, indices], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
sparse_apply_rms_prop(var, ms, mom, lr, rho, momentum, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyRMSProp")
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# File lib/tensorflow/ops/raw_ops.rb, line 3955 def self.sparse_apply_rms_prop(var, ms, mom, lr, rho, momentum, epsilon, grad, indices, typeT: nil, tindices: nil, use_locking: false, name: "SparseApplyRMSProp") self.execute("SparseApplyRMSProp", [var, ms, mom, lr, rho, momentum, epsilon, grad, indices], T: typeT, Tindices: tindices, use_locking: use_locking, name: name) end
sparse_concat(indices, values, shapes, concat_dim: nil, n: nil, typeT: nil, name: "SparseConcat")
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# File lib/tensorflow/ops/raw_ops.rb, line 3959 def self.sparse_concat(indices, values, shapes, concat_dim: nil, n: nil, typeT: nil, name: "SparseConcat") self.execute("SparseConcat", [indices, values, shapes], concat_dim: concat_dim, N: n, T: typeT, name: name) end
sparse_conditional_accumulator(dtype: nil, shape: nil, container: "", shared_name: "", reduction_type: "MEAN", name: "SparseConditionalAccumulator")
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# File lib/tensorflow/ops/raw_ops.rb, line 3963 def self.sparse_conditional_accumulator(dtype: nil, shape: nil, container: "", shared_name: "", reduction_type: "MEAN", name: "SparseConditionalAccumulator") self.execute("SparseConditionalAccumulator", [], dtype: dtype, shape: shape, container: container, shared_name: shared_name, reduction_type: reduction_type, name: name) end
sparse_cross(indices, values, shapes, dense_inputs, n: nil, hashed_output: nil, num_buckets: nil, hash_key: nil, sparse_types: nil, dense_types: nil, out_type: nil, internal_type: nil, name: "SparseCross")
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# File lib/tensorflow/ops/raw_ops.rb, line 3967 def self.sparse_cross(indices, values, shapes, dense_inputs, n: nil, hashed_output: nil, num_buckets: nil, hash_key: nil, sparse_types: nil, dense_types: nil, out_type: nil, internal_type: nil, name: "SparseCross") self.execute("SparseCross", [indices, values, shapes, dense_inputs], N: n, hashed_output: hashed_output, num_buckets: num_buckets, hash_key: hash_key, sparse_types: sparse_types, dense_types: dense_types, out_type: out_type, internal_type: internal_type, name: name) end
sparse_dense_cwise_add(sp_indices, sp_values, sp_shape, dense, typeT: nil, name: "SparseDenseCwiseAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 3971 def self.sparse_dense_cwise_add(sp_indices, sp_values, sp_shape, dense, typeT: nil, name: "SparseDenseCwiseAdd") self.execute("SparseDenseCwiseAdd", [sp_indices, sp_values, sp_shape, dense], T: typeT, name: name) end
sparse_dense_cwise_div(sp_indices, sp_values, sp_shape, dense, typeT: nil, name: "SparseDenseCwiseDiv")
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# File lib/tensorflow/ops/raw_ops.rb, line 3975 def self.sparse_dense_cwise_div(sp_indices, sp_values, sp_shape, dense, typeT: nil, name: "SparseDenseCwiseDiv") self.execute("SparseDenseCwiseDiv", [sp_indices, sp_values, sp_shape, dense], T: typeT, name: name) end
sparse_dense_cwise_mul(sp_indices, sp_values, sp_shape, dense, typeT: nil, name: "SparseDenseCwiseMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 3979 def self.sparse_dense_cwise_mul(sp_indices, sp_values, sp_shape, dense, typeT: nil, name: "SparseDenseCwiseMul") self.execute("SparseDenseCwiseMul", [sp_indices, sp_values, sp_shape, dense], T: typeT, name: name) end
sparse_fill_empty_rows(indices, values, dense_shape, default_value, typeT: nil, name: "SparseFillEmptyRows")
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# File lib/tensorflow/ops/raw_ops.rb, line 3983 def self.sparse_fill_empty_rows(indices, values, dense_shape, default_value, typeT: nil, name: "SparseFillEmptyRows") self.execute("SparseFillEmptyRows", [indices, values, dense_shape, default_value], T: typeT, name: name) end
sparse_fill_empty_rows_grad(reverse_index_map, grad_values, typeT: nil, name: "SparseFillEmptyRowsGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 3987 def self.sparse_fill_empty_rows_grad(reverse_index_map, grad_values, typeT: nil, name: "SparseFillEmptyRowsGrad") self.execute("SparseFillEmptyRowsGrad", [reverse_index_map, grad_values], T: typeT, name: name) end
sparse_mat_mul(a, b, transpose_a: false, transpose_b: false, a_is_sparse: false, b_is_sparse: false, ta: :float, tb: :float, name: "SparseMatMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 3991 def self.sparse_mat_mul(a, b, transpose_a: false, transpose_b: false, a_is_sparse: false, b_is_sparse: false, ta: :float, tb: :float, name: "SparseMatMul") self.execute("SparseMatMul", [a, b], transpose_a: transpose_a, transpose_b: transpose_b, a_is_sparse: a_is_sparse, b_is_sparse: b_is_sparse, Ta: ta, Tb: tb, name: name) end
sparse_matrix_add(a, b, alpha, beta, typeT: nil, name: "SparseMatrixAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 3995 def self.sparse_matrix_add(a, b, alpha, beta, typeT: nil, name: "SparseMatrixAdd") self.execute("SparseMatrixAdd", [a, b, alpha, beta], T: typeT, name: name) end
sparse_matrix_mat_mul(a, b, typeT: nil, transpose_a: false, transpose_b: false, adjoint_a: false, adjoint_b: false, transpose_output: false, conjugate_output: false, name: "SparseMatrixMatMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 3999 def self.sparse_matrix_mat_mul(a, b, typeT: nil, transpose_a: false, transpose_b: false, adjoint_a: false, adjoint_b: false, transpose_output: false, conjugate_output: false, name: "SparseMatrixMatMul") self.execute("SparseMatrixMatMul", [a, b], T: typeT, transpose_a: transpose_a, transpose_b: transpose_b, adjoint_a: adjoint_a, adjoint_b: adjoint_b, transpose_output: transpose_output, conjugate_output: conjugate_output, name: name) end
sparse_matrix_mul(a, b, typeT: nil, name: "SparseMatrixMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 4003 def self.sparse_matrix_mul(a, b, typeT: nil, name: "SparseMatrixMul") self.execute("SparseMatrixMul", [a, b], T: typeT, name: name) end
sparse_matrix_nnz(sparse_matrix, name: "SparseMatrixNNZ")
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# File lib/tensorflow/ops/raw_ops.rb, line 4007 def self.sparse_matrix_nnz(sparse_matrix, name: "SparseMatrixNNZ") self.execute("SparseMatrixNNZ", [sparse_matrix], name: name) end
sparse_matrix_ordering_amd(input, name: "SparseMatrixOrderingAMD")
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# File lib/tensorflow/ops/raw_ops.rb, line 4011 def self.sparse_matrix_ordering_amd(input, name: "SparseMatrixOrderingAMD") self.execute("SparseMatrixOrderingAMD", [input], name: name) end
sparse_matrix_softmax(logits, type: nil, name: "SparseMatrixSoftmax")
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# File lib/tensorflow/ops/raw_ops.rb, line 4015 def self.sparse_matrix_softmax(logits, type: nil, name: "SparseMatrixSoftmax") self.execute("SparseMatrixSoftmax", [logits], type: type, name: name) end
sparse_matrix_softmax_grad(softmax, grad_softmax, type: nil, name: "SparseMatrixSoftmaxGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 4019 def self.sparse_matrix_softmax_grad(softmax, grad_softmax, type: nil, name: "SparseMatrixSoftmaxGrad") self.execute("SparseMatrixSoftmaxGrad", [softmax, grad_softmax], type: type, name: name) end
sparse_matrix_sparse_cholesky(input, permutation, type: nil, name: "SparseMatrixSparseCholesky")
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# File lib/tensorflow/ops/raw_ops.rb, line 4023 def self.sparse_matrix_sparse_cholesky(input, permutation, type: nil, name: "SparseMatrixSparseCholesky") self.execute("SparseMatrixSparseCholesky", [input, permutation], type: type, name: name) end
sparse_matrix_sparse_mat_mul(a, b, type: nil, transpose_a: false, transpose_b: false, adjoint_a: false, adjoint_b: false, name: "SparseMatrixSparseMatMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 4027 def self.sparse_matrix_sparse_mat_mul(a, b, type: nil, transpose_a: false, transpose_b: false, adjoint_a: false, adjoint_b: false, name: "SparseMatrixSparseMatMul") self.execute("SparseMatrixSparseMatMul", [a, b], type: type, transpose_a: transpose_a, transpose_b: transpose_b, adjoint_a: adjoint_a, adjoint_b: adjoint_b, name: name) end
sparse_matrix_transpose(input, conjugate: false, type: nil, name: "SparseMatrixTranspose")
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# File lib/tensorflow/ops/raw_ops.rb, line 4031 def self.sparse_matrix_transpose(input, conjugate: false, type: nil, name: "SparseMatrixTranspose") self.execute("SparseMatrixTranspose", [input], conjugate: conjugate, type: type, name: name) end
sparse_matrix_zeros(dense_shape, type: nil, name: "SparseMatrixZeros")
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# File lib/tensorflow/ops/raw_ops.rb, line 4035 def self.sparse_matrix_zeros(dense_shape, type: nil, name: "SparseMatrixZeros") self.execute("SparseMatrixZeros", [dense_shape], type: type, name: name) end
sparse_reduce_max(input_indices, input_values, input_shape, reduction_axes, keep_dims: false, typeT: nil, name: "SparseReduceMax")
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# File lib/tensorflow/ops/raw_ops.rb, line 4039 def self.sparse_reduce_max(input_indices, input_values, input_shape, reduction_axes, keep_dims: false, typeT: nil, name: "SparseReduceMax") self.execute("SparseReduceMax", [input_indices, input_values, input_shape, reduction_axes], keep_dims: keep_dims, T: typeT, name: name) end
sparse_reduce_max_sparse(input_indices, input_values, input_shape, reduction_axes, keep_dims: false, typeT: nil, name: "SparseReduceMaxSparse")
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# File lib/tensorflow/ops/raw_ops.rb, line 4043 def self.sparse_reduce_max_sparse(input_indices, input_values, input_shape, reduction_axes, keep_dims: false, typeT: nil, name: "SparseReduceMaxSparse") self.execute("SparseReduceMaxSparse", [input_indices, input_values, input_shape, reduction_axes], keep_dims: keep_dims, T: typeT, name: name) end
sparse_reduce_sum(input_indices, input_values, input_shape, reduction_axes, keep_dims: false, typeT: nil, name: "SparseReduceSum")
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# File lib/tensorflow/ops/raw_ops.rb, line 4047 def self.sparse_reduce_sum(input_indices, input_values, input_shape, reduction_axes, keep_dims: false, typeT: nil, name: "SparseReduceSum") self.execute("SparseReduceSum", [input_indices, input_values, input_shape, reduction_axes], keep_dims: keep_dims, T: typeT, name: name) end
sparse_reduce_sum_sparse(input_indices, input_values, input_shape, reduction_axes, keep_dims: false, typeT: nil, name: "SparseReduceSumSparse")
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# File lib/tensorflow/ops/raw_ops.rb, line 4051 def self.sparse_reduce_sum_sparse(input_indices, input_values, input_shape, reduction_axes, keep_dims: false, typeT: nil, name: "SparseReduceSumSparse") self.execute("SparseReduceSumSparse", [input_indices, input_values, input_shape, reduction_axes], keep_dims: keep_dims, T: typeT, name: name) end
sparse_reorder(input_indices, input_values, input_shape, typeT: nil, name: "SparseReorder")
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# File lib/tensorflow/ops/raw_ops.rb, line 4055 def self.sparse_reorder(input_indices, input_values, input_shape, typeT: nil, name: "SparseReorder") self.execute("SparseReorder", [input_indices, input_values, input_shape], T: typeT, name: name) end
sparse_reshape(input_indices, input_shape, new_shape, name: "SparseReshape")
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# File lib/tensorflow/ops/raw_ops.rb, line 4059 def self.sparse_reshape(input_indices, input_shape, new_shape, name: "SparseReshape") self.execute("SparseReshape", [input_indices, input_shape, new_shape], name: name) end
sparse_segment_mean(data, indices, segment_ids, typeT: nil, tidx: :int32, name: "SparseSegmentMean")
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# File lib/tensorflow/ops/raw_ops.rb, line 4063 def self.sparse_segment_mean(data, indices, segment_ids, typeT: nil, tidx: :int32, name: "SparseSegmentMean") self.execute("SparseSegmentMean", [data, indices, segment_ids], T: typeT, Tidx: tidx, name: name) end
sparse_segment_mean_grad(grad, indices, segment_ids, output_dim0, typeT: nil, tidx: :int32, name: "SparseSegmentMeanGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 4067 def self.sparse_segment_mean_grad(grad, indices, segment_ids, output_dim0, typeT: nil, tidx: :int32, name: "SparseSegmentMeanGrad") self.execute("SparseSegmentMeanGrad", [grad, indices, segment_ids, output_dim0], T: typeT, Tidx: tidx, name: name) end
sparse_segment_mean_with_num_segments(data, indices, segment_ids, num_segments, typeT: nil, tidx: :int32, tnumsegments: :int32, name: "SparseSegmentMeanWithNumSegments")
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# File lib/tensorflow/ops/raw_ops.rb, line 4071 def self.sparse_segment_mean_with_num_segments(data, indices, segment_ids, num_segments, typeT: nil, tidx: :int32, tnumsegments: :int32, name: "SparseSegmentMeanWithNumSegments") self.execute("SparseSegmentMeanWithNumSegments", [data, indices, segment_ids, num_segments], T: typeT, Tidx: tidx, Tnumsegments: tnumsegments, name: name) end
sparse_segment_sqrt_n(data, indices, segment_ids, typeT: nil, tidx: :int32, name: "SparseSegmentSqrtN")
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# File lib/tensorflow/ops/raw_ops.rb, line 4075 def self.sparse_segment_sqrt_n(data, indices, segment_ids, typeT: nil, tidx: :int32, name: "SparseSegmentSqrtN") self.execute("SparseSegmentSqrtN", [data, indices, segment_ids], T: typeT, Tidx: tidx, name: name) end
sparse_segment_sqrt_n_grad(grad, indices, segment_ids, output_dim0, typeT: nil, tidx: :int32, name: "SparseSegmentSqrtNGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 4079 def self.sparse_segment_sqrt_n_grad(grad, indices, segment_ids, output_dim0, typeT: nil, tidx: :int32, name: "SparseSegmentSqrtNGrad") self.execute("SparseSegmentSqrtNGrad", [grad, indices, segment_ids, output_dim0], T: typeT, Tidx: tidx, name: name) end
sparse_segment_sqrt_n_with_num_segments(data, indices, segment_ids, num_segments, typeT: nil, tidx: :int32, tnumsegments: :int32, name: "SparseSegmentSqrtNWithNumSegments")
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# File lib/tensorflow/ops/raw_ops.rb, line 4083 def self.sparse_segment_sqrt_n_with_num_segments(data, indices, segment_ids, num_segments, typeT: nil, tidx: :int32, tnumsegments: :int32, name: "SparseSegmentSqrtNWithNumSegments") self.execute("SparseSegmentSqrtNWithNumSegments", [data, indices, segment_ids, num_segments], T: typeT, Tidx: tidx, Tnumsegments: tnumsegments, name: name) end
sparse_segment_sum(data, indices, segment_ids, typeT: nil, tidx: :int32, name: "SparseSegmentSum")
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# File lib/tensorflow/ops/raw_ops.rb, line 4087 def self.sparse_segment_sum(data, indices, segment_ids, typeT: nil, tidx: :int32, name: "SparseSegmentSum") self.execute("SparseSegmentSum", [data, indices, segment_ids], T: typeT, Tidx: tidx, name: name) end
sparse_segment_sum_with_num_segments(data, indices, segment_ids, num_segments, typeT: nil, tidx: :int32, tnumsegments: :int32, name: "SparseSegmentSumWithNumSegments")
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# File lib/tensorflow/ops/raw_ops.rb, line 4091 def self.sparse_segment_sum_with_num_segments(data, indices, segment_ids, num_segments, typeT: nil, tidx: :int32, tnumsegments: :int32, name: "SparseSegmentSumWithNumSegments") self.execute("SparseSegmentSumWithNumSegments", [data, indices, segment_ids, num_segments], T: typeT, Tidx: tidx, Tnumsegments: tnumsegments, name: name) end
sparse_slice(indices, values, shape, start, size, typeT: nil, name: "SparseSlice")
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# File lib/tensorflow/ops/raw_ops.rb, line 4095 def self.sparse_slice(indices, values, shape, start, size, typeT: nil, name: "SparseSlice") self.execute("SparseSlice", [indices, values, shape, start, size], T: typeT, name: name) end
sparse_slice_grad(backprop_val_grad, input_indices, input_start, output_indices, typeT: nil, name: "SparseSliceGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 4099 def self.sparse_slice_grad(backprop_val_grad, input_indices, input_start, output_indices, typeT: nil, name: "SparseSliceGrad") self.execute("SparseSliceGrad", [backprop_val_grad, input_indices, input_start, output_indices], T: typeT, name: name) end
sparse_softmax(sp_indices, sp_values, sp_shape, typeT: nil, name: "SparseSoftmax")
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# File lib/tensorflow/ops/raw_ops.rb, line 4103 def self.sparse_softmax(sp_indices, sp_values, sp_shape, typeT: nil, name: "SparseSoftmax") self.execute("SparseSoftmax", [sp_indices, sp_values, sp_shape], T: typeT, name: name) end
sparse_softmax_cross_entropy_with_logits(features, labels, typeT: nil, tlabels: :int64, name: "SparseSoftmaxCrossEntropyWithLogits")
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# File lib/tensorflow/ops/raw_ops.rb, line 4107 def self.sparse_softmax_cross_entropy_with_logits(features, labels, typeT: nil, tlabels: :int64, name: "SparseSoftmaxCrossEntropyWithLogits") self.execute("SparseSoftmaxCrossEntropyWithLogits", [features, labels], T: typeT, Tlabels: tlabels, name: name) end
sparse_sparse_maximum(a_indices, a_values, a_shape, b_indices, b_values, b_shape, typeT: nil, name: "SparseSparseMaximum")
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# File lib/tensorflow/ops/raw_ops.rb, line 4111 def self.sparse_sparse_maximum(a_indices, a_values, a_shape, b_indices, b_values, b_shape, typeT: nil, name: "SparseSparseMaximum") self.execute("SparseSparseMaximum", [a_indices, a_values, a_shape, b_indices, b_values, b_shape], T: typeT, name: name) end
sparse_sparse_minimum(a_indices, a_values, a_shape, b_indices, b_values, b_shape, typeT: nil, name: "SparseSparseMinimum")
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# File lib/tensorflow/ops/raw_ops.rb, line 4115 def self.sparse_sparse_minimum(a_indices, a_values, a_shape, b_indices, b_values, b_shape, typeT: nil, name: "SparseSparseMinimum") self.execute("SparseSparseMinimum", [a_indices, a_values, a_shape, b_indices, b_values, b_shape], T: typeT, name: name) end
sparse_split(split_dim, indices, values, shape, num_split: nil, typeT: nil, name: "SparseSplit")
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# File lib/tensorflow/ops/raw_ops.rb, line 4119 def self.sparse_split(split_dim, indices, values, shape, num_split: nil, typeT: nil, name: "SparseSplit") self.execute("SparseSplit", [split_dim, indices, values, shape], num_split: num_split, T: typeT, name: name) end
sparse_tensor_dense_add(a_indices, a_values, a_shape, b, typeT: nil, tindices: nil, name: "SparseTensorDenseAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 4123 def self.sparse_tensor_dense_add(a_indices, a_values, a_shape, b, typeT: nil, tindices: nil, name: "SparseTensorDenseAdd") self.execute("SparseTensorDenseAdd", [a_indices, a_values, a_shape, b], T: typeT, Tindices: tindices, name: name) end
sparse_tensor_dense_mat_mul(a_indices, a_values, a_shape, b, typeT: nil, tindices: :int64, adjoint_a: false, adjoint_b: false, name: "SparseTensorDenseMatMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 4127 def self.sparse_tensor_dense_mat_mul(a_indices, a_values, a_shape, b, typeT: nil, tindices: :int64, adjoint_a: false, adjoint_b: false, name: "SparseTensorDenseMatMul") self.execute("SparseTensorDenseMatMul", [a_indices, a_values, a_shape, b], T: typeT, Tindices: tindices, adjoint_a: adjoint_a, adjoint_b: adjoint_b, name: name) end
sparse_tensor_slice_dataset(indices, values, dense_shape, tvalues: nil, name: "SparseTensorSliceDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 4131 def self.sparse_tensor_slice_dataset(indices, values, dense_shape, tvalues: nil, name: "SparseTensorSliceDataset") self.execute("SparseTensorSliceDataset", [indices, values, dense_shape], Tvalues: tvalues, name: name) end
sparse_tensor_to_csr_sparse_matrix(indices, values, dense_shape, typeT: nil, name: "SparseTensorToCSRSparseMatrix")
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# File lib/tensorflow/ops/raw_ops.rb, line 4135 def self.sparse_tensor_to_csr_sparse_matrix(indices, values, dense_shape, typeT: nil, name: "SparseTensorToCSRSparseMatrix") self.execute("SparseTensorToCSRSparseMatrix", [indices, values, dense_shape], T: typeT, name: name) end
sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value, validate_indices: true, typeT: nil, tindices: nil, name: "SparseToDense")
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# File lib/tensorflow/ops/raw_ops.rb, line 4139 def self.sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value, validate_indices: true, typeT: nil, tindices: nil, name: "SparseToDense") self.execute("SparseToDense", [sparse_indices, output_shape, sparse_values, default_value], validate_indices: validate_indices, T: typeT, Tindices: tindices, name: name) end
sparse_to_sparse_set_operation(set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, set_operation: "", validate_indices: true, typeT: nil, name: "SparseToSparseSetOperation")
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# File lib/tensorflow/ops/raw_ops.rb, line 4143 def self.sparse_to_sparse_set_operation(set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape, set_operation: "", validate_indices: true, typeT: nil, name: "SparseToSparseSetOperation") self.execute("SparseToSparseSetOperation", [set1_indices, set1_values, set1_shape, set2_indices, set2_values, set2_shape], set_operation: set_operation, validate_indices: validate_indices, T: typeT, name: name) end
split(split_dim, value, num_split: nil, typeT: nil, name: "Split")
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# File lib/tensorflow/ops/raw_ops.rb, line 4147 def self.split(split_dim, value, num_split: nil, typeT: nil, name: "Split") self.execute("Split", [split_dim, value], num_split: num_split, T: typeT, name: name) end
split_v(value, size_splits, split_dim, num_split: nil, typeT: nil, tlen: :int64, name: "SplitV")
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# File lib/tensorflow/ops/raw_ops.rb, line 4151 def self.split_v(value, size_splits, split_dim, num_split: nil, typeT: nil, tlen: :int64, name: "SplitV") self.execute("SplitV", [value, size_splits, split_dim], num_split: num_split, T: typeT, Tlen: tlen, name: name) end
sql_dataset(driver_name, data_source_name, query, output_types: nil, output_shapes: nil, name: "SqlDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 4155 def self.sql_dataset(driver_name, data_source_name, query, output_types: nil, output_shapes: nil, name: "SqlDataset") self.execute("SqlDataset", [driver_name, data_source_name, query], output_types: output_types, output_shapes: output_shapes, name: name) end
sqrt(x, typeT: nil, name: "Sqrt")
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# File lib/tensorflow/ops/raw_ops.rb, line 4159 def self.sqrt(x, typeT: nil, name: "Sqrt") self.execute("Sqrt", [x], T: typeT, name: name) end
sqrt_grad(y, dy, typeT: nil, name: "SqrtGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 4163 def self.sqrt_grad(y, dy, typeT: nil, name: "SqrtGrad") self.execute("SqrtGrad", [y, dy], T: typeT, name: name) end
square(x, typeT: nil, name: "Square")
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# File lib/tensorflow/ops/raw_ops.rb, line 4167 def self.square(x, typeT: nil, name: "Square") self.execute("Square", [x], T: typeT, name: name) end
squared_difference(x, y, typeT: nil, name: "SquaredDifference")
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# File lib/tensorflow/ops/raw_ops.rb, line 4171 def self.squared_difference(x, y, typeT: nil, name: "SquaredDifference") self.execute("SquaredDifference", [x, y], T: typeT, name: name) end
squeeze(input, typeT: nil, squeeze_dims: [], name: "Squeeze")
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# File lib/tensorflow/ops/raw_ops.rb, line 4175 def self.squeeze(input, typeT: nil, squeeze_dims: [], name: "Squeeze") self.execute("Squeeze", [input], T: typeT, squeeze_dims: squeeze_dims, name: name) end
stack(elem_type: nil, stack_name: "", name: "Stack")
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# File lib/tensorflow/ops/raw_ops.rb, line 4179 def self.stack(elem_type: nil, stack_name: "", name: "Stack") self.execute("Stack", [], elem_type: elem_type, stack_name: stack_name, name: name) end
stack_close(handle, name: "StackClose")
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# File lib/tensorflow/ops/raw_ops.rb, line 4183 def self.stack_close(handle, name: "StackClose") self.execute("StackClose", [handle], name: name) end
stack_close_v2(handle, name: "StackCloseV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4187 def self.stack_close_v2(handle, name: "StackCloseV2") self.execute("StackCloseV2", [handle], name: name) end
stack_pop(handle, elem_type: nil, name: "StackPop")
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# File lib/tensorflow/ops/raw_ops.rb, line 4191 def self.stack_pop(handle, elem_type: nil, name: "StackPop") self.execute("StackPop", [handle], elem_type: elem_type, name: name) end
stack_pop_v2(handle, elem_type: nil, name: "StackPopV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4195 def self.stack_pop_v2(handle, elem_type: nil, name: "StackPopV2") self.execute("StackPopV2", [handle], elem_type: elem_type, name: name) end
stack_push(handle, elem, typeT: nil, swap_memory: false, name: "StackPush")
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# File lib/tensorflow/ops/raw_ops.rb, line 4199 def self.stack_push(handle, elem, typeT: nil, swap_memory: false, name: "StackPush") self.execute("StackPush", [handle, elem], T: typeT, swap_memory: swap_memory, name: name) end
stack_push_v2(handle, elem, typeT: nil, swap_memory: false, name: "StackPushV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4203 def self.stack_push_v2(handle, elem, typeT: nil, swap_memory: false, name: "StackPushV2") self.execute("StackPushV2", [handle, elem], T: typeT, swap_memory: swap_memory, name: name) end
stack_v2(max_size, elem_type: nil, stack_name: "", name: "StackV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4207 def self.stack_v2(max_size, elem_type: nil, stack_name: "", name: "StackV2") self.execute("StackV2", [max_size], elem_type: elem_type, stack_name: stack_name, name: name) end
stage(values, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "Stage")
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# File lib/tensorflow/ops/raw_ops.rb, line 4211 def self.stage(values, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "Stage") self.execute("Stage", [values], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
stage_clear(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "StageClear")
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# File lib/tensorflow/ops/raw_ops.rb, line 4215 def self.stage_clear(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "StageClear") self.execute("StageClear", [], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
stage_peek(index, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "StagePeek")
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# File lib/tensorflow/ops/raw_ops.rb, line 4219 def self.stage_peek(index, capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "StagePeek") self.execute("StagePeek", [index], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
stage_size(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "StageSize")
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# File lib/tensorflow/ops/raw_ops.rb, line 4223 def self.stage_size(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "StageSize") self.execute("StageSize", [], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
stateful_partitioned_call(args, tin: nil, tout: nil, f: nil, config: "", config_proto: "", executor_type: "", name: "StatefulPartitionedCall")
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# File lib/tensorflow/ops/raw_ops.rb, line 4227 def self.stateful_partitioned_call(args, tin: nil, tout: nil, f: nil, config: "", config_proto: "", executor_type: "", name: "StatefulPartitionedCall") self.execute("StatefulPartitionedCall", [args], Tin: tin, Tout: tout, f: f, config: config, config_proto: config_proto, executor_type: executor_type, name: name) end
stateful_random_binomial(resource, algorithm, shape, counts, probs, s: nil, typeT: :double, dtype: :int64, name: "StatefulRandomBinomial")
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# File lib/tensorflow/ops/raw_ops.rb, line 4231 def self.stateful_random_binomial(resource, algorithm, shape, counts, probs, s: nil, typeT: :double, dtype: :int64, name: "StatefulRandomBinomial") self.execute("StatefulRandomBinomial", [resource, algorithm, shape, counts, probs], S: s, T: typeT, dtype: dtype, name: name) end
stateful_standard_normal(resource, shape, dtype: :float, shape_dtype: :int64, name: "StatefulStandardNormal")
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# File lib/tensorflow/ops/raw_ops.rb, line 4235 def self.stateful_standard_normal(resource, shape, dtype: :float, shape_dtype: :int64, name: "StatefulStandardNormal") self.execute("StatefulStandardNormal", [resource, shape], dtype: dtype, shape_dtype: shape_dtype, name: name) end
stateful_standard_normal_v2(resource, algorithm, shape, dtype: :float, shape_dtype: :int64, name: "StatefulStandardNormalV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4239 def self.stateful_standard_normal_v2(resource, algorithm, shape, dtype: :float, shape_dtype: :int64, name: "StatefulStandardNormalV2") self.execute("StatefulStandardNormalV2", [resource, algorithm, shape], dtype: dtype, shape_dtype: shape_dtype, name: name) end
stateful_truncated_normal(resource, algorithm, shape, dtype: :float, shape_dtype: :int64, name: "StatefulTruncatedNormal")
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# File lib/tensorflow/ops/raw_ops.rb, line 4243 def self.stateful_truncated_normal(resource, algorithm, shape, dtype: :float, shape_dtype: :int64, name: "StatefulTruncatedNormal") self.execute("StatefulTruncatedNormal", [resource, algorithm, shape], dtype: dtype, shape_dtype: shape_dtype, name: name) end
stateful_uniform(resource, algorithm, shape, dtype: :float, shape_dtype: :int64, name: "StatefulUniform")
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# File lib/tensorflow/ops/raw_ops.rb, line 4247 def self.stateful_uniform(resource, algorithm, shape, dtype: :float, shape_dtype: :int64, name: "StatefulUniform") self.execute("StatefulUniform", [resource, algorithm, shape], dtype: dtype, shape_dtype: shape_dtype, name: name) end
stateful_uniform_full_int(resource, algorithm, shape, dtype: :uint64, shape_dtype: :int64, name: "StatefulUniformFullInt")
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# File lib/tensorflow/ops/raw_ops.rb, line 4251 def self.stateful_uniform_full_int(resource, algorithm, shape, dtype: :uint64, shape_dtype: :int64, name: "StatefulUniformFullInt") self.execute("StatefulUniformFullInt", [resource, algorithm, shape], dtype: dtype, shape_dtype: shape_dtype, name: name) end
stateful_uniform_int(resource, algorithm, shape, minval, maxval, dtype: :int64, shape_dtype: :int64, name: "StatefulUniformInt")
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# File lib/tensorflow/ops/raw_ops.rb, line 4255 def self.stateful_uniform_int(resource, algorithm, shape, minval, maxval, dtype: :int64, shape_dtype: :int64, name: "StatefulUniformInt") self.execute("StatefulUniformInt", [resource, algorithm, shape, minval, maxval], dtype: dtype, shape_dtype: shape_dtype, name: name) end
stateless_if(cond, input, tcond: nil, tin: nil, tout: nil, then_branch: nil, else_branch: nil, output_shapes: [], name: "StatelessIf")
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# File lib/tensorflow/ops/raw_ops.rb, line 4259 def self.stateless_if(cond, input, tcond: nil, tin: nil, tout: nil, then_branch: nil, else_branch: nil, output_shapes: [], name: "StatelessIf") self.execute("StatelessIf", [cond, input], Tcond: tcond, Tin: tin, Tout: tout, then_branch: then_branch, else_branch: else_branch, output_shapes: output_shapes, name: name) end
stateless_multinomial(logits, num_samples, seed, typeT: nil, tseed: :int64, output_dtype: :int64, name: "StatelessMultinomial")
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# File lib/tensorflow/ops/raw_ops.rb, line 4263 def self.stateless_multinomial(logits, num_samples, seed, typeT: nil, tseed: :int64, output_dtype: :int64, name: "StatelessMultinomial") self.execute("StatelessMultinomial", [logits, num_samples, seed], T: typeT, Tseed: tseed, output_dtype: output_dtype, name: name) end
stateless_random_normal(shape, seed, dtype: :float, typeT: :int32, tseed: :int64, name: "StatelessRandomNormal")
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# File lib/tensorflow/ops/raw_ops.rb, line 4267 def self.stateless_random_normal(shape, seed, dtype: :float, typeT: :int32, tseed: :int64, name: "StatelessRandomNormal") self.execute("StatelessRandomNormal", [shape, seed], dtype: dtype, T: typeT, Tseed: tseed, name: name) end
stateless_random_uniform(shape, seed, dtype: :float, typeT: :int32, tseed: :int64, name: "StatelessRandomUniform")
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# File lib/tensorflow/ops/raw_ops.rb, line 4271 def self.stateless_random_uniform(shape, seed, dtype: :float, typeT: :int32, tseed: :int64, name: "StatelessRandomUniform") self.execute("StatelessRandomUniform", [shape, seed], dtype: dtype, T: typeT, Tseed: tseed, name: name) end
stateless_random_uniform_int(shape, seed, minval, maxval, dtype: nil, typeT: nil, tseed: :int64, name: "StatelessRandomUniformInt")
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# File lib/tensorflow/ops/raw_ops.rb, line 4275 def self.stateless_random_uniform_int(shape, seed, minval, maxval, dtype: nil, typeT: nil, tseed: :int64, name: "StatelessRandomUniformInt") self.execute("StatelessRandomUniformInt", [shape, seed, minval, maxval], dtype: dtype, T: typeT, Tseed: tseed, name: name) end
stateless_truncated_normal(shape, seed, dtype: :float, typeT: :int32, tseed: :int64, name: "StatelessTruncatedNormal")
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# File lib/tensorflow/ops/raw_ops.rb, line 4279 def self.stateless_truncated_normal(shape, seed, dtype: :float, typeT: :int32, tseed: :int64, name: "StatelessTruncatedNormal") self.execute("StatelessTruncatedNormal", [shape, seed], dtype: dtype, T: typeT, Tseed: tseed, name: name) end
stateless_while(input, typeT: nil, cond: nil, body: nil, output_shapes: [], parallel_iterations: 10, name: "StatelessWhile")
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# File lib/tensorflow/ops/raw_ops.rb, line 4283 def self.stateless_while(input, typeT: nil, cond: nil, body: nil, output_shapes: [], parallel_iterations: 10, name: "StatelessWhile") self.execute("StatelessWhile", [input], T: typeT, cond: cond, body: body, output_shapes: output_shapes, parallel_iterations: parallel_iterations, name: name) end
static_regex_full_match(input, pattern: "", name: "StaticRegexFullMatch")
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# File lib/tensorflow/ops/raw_ops.rb, line 4287 def self.static_regex_full_match(input, pattern: "", name: "StaticRegexFullMatch") self.execute("StaticRegexFullMatch", [input], pattern: pattern, name: name) end
static_regex_replace(input, pattern: "", rewrite: "", replace_global: true, name: "StaticRegexReplace")
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# File lib/tensorflow/ops/raw_ops.rb, line 4291 def self.static_regex_replace(input, pattern: "", rewrite: "", replace_global: true, name: "StaticRegexReplace") self.execute("StaticRegexReplace", [input], pattern: pattern, rewrite: rewrite, replace_global: replace_global, name: name) end
stats_aggregator_handle(container: "", shared_name: "", name: "StatsAggregatorHandle")
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# File lib/tensorflow/ops/raw_ops.rb, line 4295 def self.stats_aggregator_handle(container: "", shared_name: "", name: "StatsAggregatorHandle") self.execute("StatsAggregatorHandle", [], container: container, shared_name: shared_name, name: name) end
stats_aggregator_handle_v2(container: "", shared_name: "", name: "StatsAggregatorHandleV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4299 def self.stats_aggregator_handle_v2(container: "", shared_name: "", name: "StatsAggregatorHandleV2") self.execute("StatsAggregatorHandleV2", [], container: container, shared_name: shared_name, name: name) end
stats_aggregator_set_summary_writer(stats_aggregator, summary, name: "StatsAggregatorSetSummaryWriter")
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# File lib/tensorflow/ops/raw_ops.rb, line 4303 def self.stats_aggregator_set_summary_writer(stats_aggregator, summary, name: "StatsAggregatorSetSummaryWriter") self.execute("StatsAggregatorSetSummaryWriter", [stats_aggregator, summary], name: name) end
stats_aggregator_summary(iterator, name: "StatsAggregatorSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 4307 def self.stats_aggregator_summary(iterator, name: "StatsAggregatorSummary") self.execute("StatsAggregatorSummary", [iterator], name: name) end
stop_gradient(input, typeT: nil, name: "StopGradient")
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# File lib/tensorflow/ops/raw_ops.rb, line 4311 def self.stop_gradient(input, typeT: nil, name: "StopGradient") self.execute("StopGradient", [input], T: typeT, name: name) end
strided_slice(input, start, stop, strides, typeT: nil, index: nil, begin_mask: 0, end_mask: 0, ellipsis_mask: 0, new_axis_mask: 0, shrink_axis_mask: 0, name: "StridedSlice")
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# File lib/tensorflow/ops/raw_ops.rb, line 4315 def self.strided_slice(input, start, stop, strides, typeT: nil, index: nil, begin_mask: 0, end_mask: 0, ellipsis_mask: 0, new_axis_mask: 0, shrink_axis_mask: 0, name: "StridedSlice") self.execute("StridedSlice", [input, start, stop, strides], T: typeT, Index: index, begin_mask: begin_mask, end_mask: end_mask, ellipsis_mask: ellipsis_mask, new_axis_mask: new_axis_mask, shrink_axis_mask: shrink_axis_mask, name: name) end
strided_slice_assign(ref, start, stop, strides, value, typeT: nil, index: nil, begin_mask: 0, end_mask: 0, ellipsis_mask: 0, new_axis_mask: 0, shrink_axis_mask: 0, name: "StridedSliceAssign")
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# File lib/tensorflow/ops/raw_ops.rb, line 4319 def self.strided_slice_assign(ref, start, stop, strides, value, typeT: nil, index: nil, begin_mask: 0, end_mask: 0, ellipsis_mask: 0, new_axis_mask: 0, shrink_axis_mask: 0, name: "StridedSliceAssign") self.execute("StridedSliceAssign", [ref, start, stop, strides, value], T: typeT, Index: index, begin_mask: begin_mask, end_mask: end_mask, ellipsis_mask: ellipsis_mask, new_axis_mask: new_axis_mask, shrink_axis_mask: shrink_axis_mask, name: name) end
strided_slice_grad(shape, start, stop, strides, dy, typeT: nil, index: nil, begin_mask: 0, end_mask: 0, ellipsis_mask: 0, new_axis_mask: 0, shrink_axis_mask: 0, name: "StridedSliceGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 4323 def self.strided_slice_grad(shape, start, stop, strides, dy, typeT: nil, index: nil, begin_mask: 0, end_mask: 0, ellipsis_mask: 0, new_axis_mask: 0, shrink_axis_mask: 0, name: "StridedSliceGrad") self.execute("StridedSliceGrad", [shape, start, stop, strides, dy], T: typeT, Index: index, begin_mask: begin_mask, end_mask: end_mask, ellipsis_mask: ellipsis_mask, new_axis_mask: new_axis_mask, shrink_axis_mask: shrink_axis_mask, name: name) end
string_format(inputs, typeT: nil, template: "%s", placeholder: "%s", summarize: 3, name: "StringFormat")
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# File lib/tensorflow/ops/raw_ops.rb, line 4327 def self.string_format(inputs, typeT: nil, template: "%s", placeholder: "%s", summarize: 3, name: "StringFormat") self.execute("StringFormat", [inputs], T: typeT, template: template, placeholder: placeholder, summarize: summarize, name: name) end
string_join(inputs, n: nil, separator: "", name: "StringJoin")
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# File lib/tensorflow/ops/raw_ops.rb, line 4331 def self.string_join(inputs, n: nil, separator: "", name: "StringJoin") self.execute("StringJoin", [inputs], N: n, separator: separator, name: name) end
string_length(input, unit: "BYTE", name: "StringLength")
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# File lib/tensorflow/ops/raw_ops.rb, line 4335 def self.string_length(input, unit: "BYTE", name: "StringLength") self.execute("StringLength", [input], unit: unit, name: name) end
string_lower(input, encoding: "", name: "StringLower")
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# File lib/tensorflow/ops/raw_ops.rb, line 4339 def self.string_lower(input, encoding: "", name: "StringLower") self.execute("StringLower", [input], encoding: encoding, name: name) end
string_n_grams(data, data_splits, separator: "", ngram_widths: nil, left_pad: "", right_pad: "", pad_width: nil, preserve_short_sequences: nil, tsplits: :int64, name: "StringNGrams")
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# File lib/tensorflow/ops/raw_ops.rb, line 4343 def self.string_n_grams(data, data_splits, separator: "", ngram_widths: nil, left_pad: "", right_pad: "", pad_width: nil, preserve_short_sequences: nil, tsplits: :int64, name: "StringNGrams") self.execute("StringNGrams", [data, data_splits], separator: separator, ngram_widths: ngram_widths, left_pad: left_pad, right_pad: right_pad, pad_width: pad_width, preserve_short_sequences: preserve_short_sequences, Tsplits: tsplits, name: name) end
string_split(input, delimiter, skip_empty: true, name: "StringSplit")
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# File lib/tensorflow/ops/raw_ops.rb, line 4347 def self.string_split(input, delimiter, skip_empty: true, name: "StringSplit") self.execute("StringSplit", [input, delimiter], skip_empty: skip_empty, name: name) end
string_split_v2(input, sep, maxsplit: -1, name: "StringSplitV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4351 def self.string_split_v2(input, sep, maxsplit: -1, name: "StringSplitV2") self.execute("StringSplitV2", [input, sep], maxsplit: maxsplit, name: name) end
string_strip(input, name: "StringStrip")
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# File lib/tensorflow/ops/raw_ops.rb, line 4355 def self.string_strip(input, name: "StringStrip") self.execute("StringStrip", [input], name: name) end
string_to_hash_bucket(string_tensor, num_buckets: nil, name: "StringToHashBucket")
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# File lib/tensorflow/ops/raw_ops.rb, line 4359 def self.string_to_hash_bucket(string_tensor, num_buckets: nil, name: "StringToHashBucket") self.execute("StringToHashBucket", [string_tensor], num_buckets: num_buckets, name: name) end
string_to_hash_bucket_fast(input, num_buckets: nil, name: "StringToHashBucketFast")
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# File lib/tensorflow/ops/raw_ops.rb, line 4363 def self.string_to_hash_bucket_fast(input, num_buckets: nil, name: "StringToHashBucketFast") self.execute("StringToHashBucketFast", [input], num_buckets: num_buckets, name: name) end
string_to_hash_bucket_strong(input, num_buckets: nil, key: nil, name: "StringToHashBucketStrong")
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# File lib/tensorflow/ops/raw_ops.rb, line 4367 def self.string_to_hash_bucket_strong(input, num_buckets: nil, key: nil, name: "StringToHashBucketStrong") self.execute("StringToHashBucketStrong", [input], num_buckets: num_buckets, key: key, name: name) end
string_to_number(string_tensor, out_type: :float, name: "StringToNumber")
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# File lib/tensorflow/ops/raw_ops.rb, line 4371 def self.string_to_number(string_tensor, out_type: :float, name: "StringToNumber") self.execute("StringToNumber", [string_tensor], out_type: out_type, name: name) end
string_upper(input, encoding: "", name: "StringUpper")
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# File lib/tensorflow/ops/raw_ops.rb, line 4375 def self.string_upper(input, encoding: "", name: "StringUpper") self.execute("StringUpper", [input], encoding: encoding, name: name) end
sub(x, y, typeT: nil, name: "Sub")
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# File lib/tensorflow/ops/raw_ops.rb, line 4379 def self.sub(x, y, typeT: nil, name: "Sub") self.execute("Sub", [x, y], T: typeT, name: name) end
substr(input, pos, len, typeT: nil, unit: "BYTE", name: "Substr")
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# File lib/tensorflow/ops/raw_ops.rb, line 4383 def self.substr(input, pos, len, typeT: nil, unit: "BYTE", name: "Substr") self.execute("Substr", [input, pos, len], T: typeT, unit: unit, name: name) end
sum(input, reduction_indices, keep_dims: false, typeT: nil, tidx: :int32, name: "Sum")
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# File lib/tensorflow/ops/raw_ops.rb, line 4387 def self.sum(input, reduction_indices, keep_dims: false, typeT: nil, tidx: :int32, name: "Sum") self.execute("Sum", [input, reduction_indices], keep_dims: keep_dims, T: typeT, Tidx: tidx, name: name) end
summary_writer(shared_name: "", container: "", name: "SummaryWriter")
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# File lib/tensorflow/ops/raw_ops.rb, line 4391 def self.summary_writer(shared_name: "", container: "", name: "SummaryWriter") self.execute("SummaryWriter", [], shared_name: shared_name, container: container, name: name) end
svd(input, compute_uv: true, full_matrices: false, typeT: nil, name: "Svd")
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# File lib/tensorflow/ops/raw_ops.rb, line 4395 def self.svd(input, compute_uv: true, full_matrices: false, typeT: nil, name: "Svd") self.execute("Svd", [input], compute_uv: compute_uv, full_matrices: full_matrices, T: typeT, name: name) end
switch(data, pred, typeT: nil, name: "Switch")
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# File lib/tensorflow/ops/raw_ops.rb, line 4399 def self.switch(data, pred, typeT: nil, name: "Switch") self.execute("Switch", [data, pred], T: typeT, name: name) end
symbolic_gradient(input, tin: nil, tout: nil, f: nil, name: "SymbolicGradient")
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# File lib/tensorflow/ops/raw_ops.rb, line 4403 def self.symbolic_gradient(input, tin: nil, tout: nil, f: nil, name: "SymbolicGradient") self.execute("SymbolicGradient", [input], Tin: tin, Tout: tout, f: f, name: name) end
take_dataset(input_dataset, count, output_types: nil, output_shapes: nil, name: "TakeDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 4447 def self.take_dataset(input_dataset, count, output_types: nil, output_shapes: nil, name: "TakeDataset") self.execute("TakeDataset", [input_dataset, count], output_types: output_types, output_shapes: output_shapes, name: name) end
take_many_sparse_from_tensors_map(sparse_handles, dtype: nil, container: "", shared_name: "", name: "TakeManySparseFromTensorsMap")
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# File lib/tensorflow/ops/raw_ops.rb, line 4451 def self.take_many_sparse_from_tensors_map(sparse_handles, dtype: nil, container: "", shared_name: "", name: "TakeManySparseFromTensorsMap") self.execute("TakeManySparseFromTensorsMap", [sparse_handles], dtype: dtype, container: container, shared_name: shared_name, name: name) end
take_while_dataset(input_dataset, other_arguments, predicate: nil, targuments: nil, output_types: nil, output_shapes: nil, name: "TakeWhileDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 4455 def self.take_while_dataset(input_dataset, other_arguments, predicate: nil, targuments: nil, output_types: nil, output_shapes: nil, name: "TakeWhileDataset") self.execute("TakeWhileDataset", [input_dataset, other_arguments], predicate: predicate, Targuments: targuments, output_types: output_types, output_shapes: output_shapes, name: name) end
tan(x, typeT: nil, name: "Tan")
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# File lib/tensorflow/ops/raw_ops.rb, line 4459 def self.tan(x, typeT: nil, name: "Tan") self.execute("Tan", [x], T: typeT, name: name) end
tanh(x, typeT: nil, name: "Tanh")
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# File lib/tensorflow/ops/raw_ops.rb, line 4463 def self.tanh(x, typeT: nil, name: "Tanh") self.execute("Tanh", [x], T: typeT, name: name) end
tanh_grad(y, dy, typeT: nil, name: "TanhGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 4467 def self.tanh_grad(y, dy, typeT: nil, name: "TanhGrad") self.execute("TanhGrad", [y, dy], T: typeT, name: name) end
temporary_variable(shape: nil, dtype: nil, var_name: "", name: "TemporaryVariable")
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# File lib/tensorflow/ops/raw_ops.rb, line 4471 def self.temporary_variable(shape: nil, dtype: nil, var_name: "", name: "TemporaryVariable") self.execute("TemporaryVariable", [], shape: shape, dtype: dtype, var_name: var_name, name: name) end
tensor_array(size, dtype: nil, dynamic_size: false, clear_after_read: true, tensor_array_name: "", element_shape: [], name: "TensorArray")
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# File lib/tensorflow/ops/raw_ops.rb, line 4475 def self.tensor_array(size, dtype: nil, dynamic_size: false, clear_after_read: true, tensor_array_name: "", element_shape: [], name: "TensorArray") self.execute("TensorArray", [size], dtype: dtype, dynamic_size: dynamic_size, clear_after_read: clear_after_read, tensor_array_name: tensor_array_name, element_shape: element_shape, name: name) end
tensor_array_close(handle, name: "TensorArrayClose")
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# File lib/tensorflow/ops/raw_ops.rb, line 4479 def self.tensor_array_close(handle, name: "TensorArrayClose") self.execute("TensorArrayClose", [handle], name: name) end
tensor_array_close_v2(handle, name: "TensorArrayCloseV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4483 def self.tensor_array_close_v2(handle, name: "TensorArrayCloseV2") self.execute("TensorArrayCloseV2", [handle], name: name) end
tensor_array_close_v3(handle, name: "TensorArrayCloseV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 4487 def self.tensor_array_close_v3(handle, name: "TensorArrayCloseV3") self.execute("TensorArrayCloseV3", [handle], name: name) end
tensor_array_concat(handle, flow_in, dtype: nil, element_shape_except0: [], name: "TensorArrayConcat")
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# File lib/tensorflow/ops/raw_ops.rb, line 4491 def self.tensor_array_concat(handle, flow_in, dtype: nil, element_shape_except0: [], name: "TensorArrayConcat") self.execute("TensorArrayConcat", [handle, flow_in], dtype: dtype, element_shape_except0: element_shape_except0, name: name) end
tensor_array_concat_v2(handle, flow_in, dtype: nil, element_shape_except0: [], name: "TensorArrayConcatV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4495 def self.tensor_array_concat_v2(handle, flow_in, dtype: nil, element_shape_except0: [], name: "TensorArrayConcatV2") self.execute("TensorArrayConcatV2", [handle, flow_in], dtype: dtype, element_shape_except0: element_shape_except0, name: name) end
tensor_array_concat_v3(handle, flow_in, dtype: nil, element_shape_except0: [], name: "TensorArrayConcatV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 4499 def self.tensor_array_concat_v3(handle, flow_in, dtype: nil, element_shape_except0: [], name: "TensorArrayConcatV3") self.execute("TensorArrayConcatV3", [handle, flow_in], dtype: dtype, element_shape_except0: element_shape_except0, name: name) end
tensor_array_gather(handle, indices, flow_in, dtype: nil, element_shape: [], name: "TensorArrayGather")
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# File lib/tensorflow/ops/raw_ops.rb, line 4503 def self.tensor_array_gather(handle, indices, flow_in, dtype: nil, element_shape: [], name: "TensorArrayGather") self.execute("TensorArrayGather", [handle, indices, flow_in], dtype: dtype, element_shape: element_shape, name: name) end
tensor_array_gather_v2(handle, indices, flow_in, dtype: nil, element_shape: [], name: "TensorArrayGatherV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4507 def self.tensor_array_gather_v2(handle, indices, flow_in, dtype: nil, element_shape: [], name: "TensorArrayGatherV2") self.execute("TensorArrayGatherV2", [handle, indices, flow_in], dtype: dtype, element_shape: element_shape, name: name) end
tensor_array_gather_v3(handle, indices, flow_in, dtype: nil, element_shape: [], name: "TensorArrayGatherV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 4511 def self.tensor_array_gather_v3(handle, indices, flow_in, dtype: nil, element_shape: [], name: "TensorArrayGatherV3") self.execute("TensorArrayGatherV3", [handle, indices, flow_in], dtype: dtype, element_shape: element_shape, name: name) end
tensor_array_grad(handle, flow_in, source: "", name: "TensorArrayGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 4515 def self.tensor_array_grad(handle, flow_in, source: "", name: "TensorArrayGrad") self.execute("TensorArrayGrad", [handle, flow_in], source: source, name: name) end
tensor_array_grad_v2(handle, flow_in, source: "", name: "TensorArrayGradV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4519 def self.tensor_array_grad_v2(handle, flow_in, source: "", name: "TensorArrayGradV2") self.execute("TensorArrayGradV2", [handle, flow_in], source: source, name: name) end
tensor_array_grad_v3(handle, flow_in, source: "", name: "TensorArrayGradV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 4523 def self.tensor_array_grad_v3(handle, flow_in, source: "", name: "TensorArrayGradV3") self.execute("TensorArrayGradV3", [handle, flow_in], source: source, name: name) end
tensor_array_grad_with_shape(handle, flow_in, shape_to_prepend, source: "", name: "TensorArrayGradWithShape")
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# File lib/tensorflow/ops/raw_ops.rb, line 4527 def self.tensor_array_grad_with_shape(handle, flow_in, shape_to_prepend, source: "", name: "TensorArrayGradWithShape") self.execute("TensorArrayGradWithShape", [handle, flow_in, shape_to_prepend], source: source, name: name) end
tensor_array_pack(handle, flow_in, dtype: nil, element_shape: [], name: "TensorArrayPack")
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# File lib/tensorflow/ops/raw_ops.rb, line 4531 def self.tensor_array_pack(handle, flow_in, dtype: nil, element_shape: [], name: "TensorArrayPack") self.execute("TensorArrayPack", [handle, flow_in], dtype: dtype, element_shape: element_shape, name: name) end
tensor_array_read(handle, index, flow_in, dtype: nil, name: "TensorArrayRead")
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# File lib/tensorflow/ops/raw_ops.rb, line 4535 def self.tensor_array_read(handle, index, flow_in, dtype: nil, name: "TensorArrayRead") self.execute("TensorArrayRead", [handle, index, flow_in], dtype: dtype, name: name) end
tensor_array_read_v2(handle, index, flow_in, dtype: nil, name: "TensorArrayReadV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4539 def self.tensor_array_read_v2(handle, index, flow_in, dtype: nil, name: "TensorArrayReadV2") self.execute("TensorArrayReadV2", [handle, index, flow_in], dtype: dtype, name: name) end
tensor_array_read_v3(handle, index, flow_in, dtype: nil, name: "TensorArrayReadV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 4543 def self.tensor_array_read_v3(handle, index, flow_in, dtype: nil, name: "TensorArrayReadV3") self.execute("TensorArrayReadV3", [handle, index, flow_in], dtype: dtype, name: name) end
tensor_array_scatter(handle, indices, value, flow_in, typeT: nil, name: "TensorArrayScatter")
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# File lib/tensorflow/ops/raw_ops.rb, line 4547 def self.tensor_array_scatter(handle, indices, value, flow_in, typeT: nil, name: "TensorArrayScatter") self.execute("TensorArrayScatter", [handle, indices, value, flow_in], T: typeT, name: name) end
tensor_array_scatter_v2(handle, indices, value, flow_in, typeT: nil, name: "TensorArrayScatterV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4551 def self.tensor_array_scatter_v2(handle, indices, value, flow_in, typeT: nil, name: "TensorArrayScatterV2") self.execute("TensorArrayScatterV2", [handle, indices, value, flow_in], T: typeT, name: name) end
tensor_array_scatter_v3(handle, indices, value, flow_in, typeT: nil, name: "TensorArrayScatterV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 4555 def self.tensor_array_scatter_v3(handle, indices, value, flow_in, typeT: nil, name: "TensorArrayScatterV3") self.execute("TensorArrayScatterV3", [handle, indices, value, flow_in], T: typeT, name: name) end
tensor_array_size(handle, flow_in, name: "TensorArraySize")
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# File lib/tensorflow/ops/raw_ops.rb, line 4559 def self.tensor_array_size(handle, flow_in, name: "TensorArraySize") self.execute("TensorArraySize", [handle, flow_in], name: name) end
tensor_array_size_v2(handle, flow_in, name: "TensorArraySizeV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4563 def self.tensor_array_size_v2(handle, flow_in, name: "TensorArraySizeV2") self.execute("TensorArraySizeV2", [handle, flow_in], name: name) end
tensor_array_size_v3(handle, flow_in, name: "TensorArraySizeV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 4567 def self.tensor_array_size_v3(handle, flow_in, name: "TensorArraySizeV3") self.execute("TensorArraySizeV3", [handle, flow_in], name: name) end
tensor_array_split(handle, value, lengths, flow_in, typeT: nil, name: "TensorArraySplit")
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# File lib/tensorflow/ops/raw_ops.rb, line 4571 def self.tensor_array_split(handle, value, lengths, flow_in, typeT: nil, name: "TensorArraySplit") self.execute("TensorArraySplit", [handle, value, lengths, flow_in], T: typeT, name: name) end
tensor_array_split_v2(handle, value, lengths, flow_in, typeT: nil, name: "TensorArraySplitV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4575 def self.tensor_array_split_v2(handle, value, lengths, flow_in, typeT: nil, name: "TensorArraySplitV2") self.execute("TensorArraySplitV2", [handle, value, lengths, flow_in], T: typeT, name: name) end
tensor_array_split_v3(handle, value, lengths, flow_in, typeT: nil, name: "TensorArraySplitV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 4579 def self.tensor_array_split_v3(handle, value, lengths, flow_in, typeT: nil, name: "TensorArraySplitV3") self.execute("TensorArraySplitV3", [handle, value, lengths, flow_in], T: typeT, name: name) end
tensor_array_unpack(handle, value, flow_in, typeT: nil, name: "TensorArrayUnpack")
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# File lib/tensorflow/ops/raw_ops.rb, line 4583 def self.tensor_array_unpack(handle, value, flow_in, typeT: nil, name: "TensorArrayUnpack") self.execute("TensorArrayUnpack", [handle, value, flow_in], T: typeT, name: name) end
tensor_array_v2(size, dtype: nil, element_shape: [], dynamic_size: false, clear_after_read: true, tensor_array_name: "", name: "TensorArrayV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4587 def self.tensor_array_v2(size, dtype: nil, element_shape: [], dynamic_size: false, clear_after_read: true, tensor_array_name: "", name: "TensorArrayV2") self.execute("TensorArrayV2", [size], dtype: dtype, element_shape: element_shape, dynamic_size: dynamic_size, clear_after_read: clear_after_read, tensor_array_name: tensor_array_name, name: name) end
tensor_array_v3(size, dtype: nil, element_shape: [], dynamic_size: false, clear_after_read: true, identical_element_shapes: false, tensor_array_name: "", name: "TensorArrayV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 4591 def self.tensor_array_v3(size, dtype: nil, element_shape: [], dynamic_size: false, clear_after_read: true, identical_element_shapes: false, tensor_array_name: "", name: "TensorArrayV3") self.execute("TensorArrayV3", [size], dtype: dtype, element_shape: element_shape, dynamic_size: dynamic_size, clear_after_read: clear_after_read, identical_element_shapes: identical_element_shapes, tensor_array_name: tensor_array_name, name: name) end
tensor_array_write(handle, index, value, flow_in, typeT: nil, name: "TensorArrayWrite")
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# File lib/tensorflow/ops/raw_ops.rb, line 4595 def self.tensor_array_write(handle, index, value, flow_in, typeT: nil, name: "TensorArrayWrite") self.execute("TensorArrayWrite", [handle, index, value, flow_in], T: typeT, name: name) end
tensor_array_write_v2(handle, index, value, flow_in, typeT: nil, name: "TensorArrayWriteV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4599 def self.tensor_array_write_v2(handle, index, value, flow_in, typeT: nil, name: "TensorArrayWriteV2") self.execute("TensorArrayWriteV2", [handle, index, value, flow_in], T: typeT, name: name) end
tensor_array_write_v3(handle, index, value, flow_in, typeT: nil, name: "TensorArrayWriteV3")
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# File lib/tensorflow/ops/raw_ops.rb, line 4603 def self.tensor_array_write_v3(handle, index, value, flow_in, typeT: nil, name: "TensorArrayWriteV3") self.execute("TensorArrayWriteV3", [handle, index, value, flow_in], T: typeT, name: name) end
tensor_dataset(components, toutput_types: nil, output_shapes: nil, name: "TensorDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 4607 def self.tensor_dataset(components, toutput_types: nil, output_shapes: nil, name: "TensorDataset") self.execute("TensorDataset", [components], Toutput_types: toutput_types, output_shapes: output_shapes, name: name) end
tensor_forest_create_tree_variable(tree_handle, tree_config, name: "TensorForestCreateTreeVariable")
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# File lib/tensorflow/ops/raw_ops.rb, line 4611 def self.tensor_forest_create_tree_variable(tree_handle, tree_config, name: "TensorForestCreateTreeVariable") self.execute("TensorForestCreateTreeVariable", [tree_handle, tree_config], name: name) end
tensor_forest_tree_deserialize(tree_handle, tree_config, name: "TensorForestTreeDeserialize")
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# File lib/tensorflow/ops/raw_ops.rb, line 4615 def self.tensor_forest_tree_deserialize(tree_handle, tree_config, name: "TensorForestTreeDeserialize") self.execute("TensorForestTreeDeserialize", [tree_handle, tree_config], name: name) end
tensor_forest_tree_is_initialized_op(tree_handle, name: "TensorForestTreeIsInitializedOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 4619 def self.tensor_forest_tree_is_initialized_op(tree_handle, name: "TensorForestTreeIsInitializedOp") self.execute("TensorForestTreeIsInitializedOp", [tree_handle], name: name) end
tensor_forest_tree_predict(tree_handle, dense_features, logits_dimension: nil, name: "TensorForestTreePredict")
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# File lib/tensorflow/ops/raw_ops.rb, line 4623 def self.tensor_forest_tree_predict(tree_handle, dense_features, logits_dimension: nil, name: "TensorForestTreePredict") self.execute("TensorForestTreePredict", [tree_handle, dense_features], logits_dimension: logits_dimension, name: name) end
tensor_forest_tree_resource_handle_op(container: "", shared_name: "", name: "TensorForestTreeResourceHandleOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 4627 def self.tensor_forest_tree_resource_handle_op(container: "", shared_name: "", name: "TensorForestTreeResourceHandleOp") self.execute("TensorForestTreeResourceHandleOp", [], container: container, shared_name: shared_name, name: name) end
tensor_forest_tree_serialize(tree_handle, name: "TensorForestTreeSerialize")
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# File lib/tensorflow/ops/raw_ops.rb, line 4631 def self.tensor_forest_tree_serialize(tree_handle, name: "TensorForestTreeSerialize") self.execute("TensorForestTreeSerialize", [tree_handle], name: name) end
tensor_forest_tree_size(tree_handle, name: "TensorForestTreeSize")
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# File lib/tensorflow/ops/raw_ops.rb, line 4635 def self.tensor_forest_tree_size(tree_handle, name: "TensorForestTreeSize") self.execute("TensorForestTreeSize", [tree_handle], name: name) end
tensor_list_concat(input_handle, element_dtype: nil, element_shape: [], name: "TensorListConcat")
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# File lib/tensorflow/ops/raw_ops.rb, line 4639 def self.tensor_list_concat(input_handle, element_dtype: nil, element_shape: [], name: "TensorListConcat") self.execute("TensorListConcat", [input_handle], element_dtype: element_dtype, element_shape: element_shape, name: name) end
tensor_list_concat_lists(input_a, input_b, element_dtype: nil, name: "TensorListConcatLists")
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# File lib/tensorflow/ops/raw_ops.rb, line 4643 def self.tensor_list_concat_lists(input_a, input_b, element_dtype: nil, name: "TensorListConcatLists") self.execute("TensorListConcatLists", [input_a, input_b], element_dtype: element_dtype, name: name) end
tensor_list_concat_v2(input_handle, element_shape, leading_dims, element_dtype: nil, shape_type: nil, name: "TensorListConcatV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4647 def self.tensor_list_concat_v2(input_handle, element_shape, leading_dims, element_dtype: nil, shape_type: nil, name: "TensorListConcatV2") self.execute("TensorListConcatV2", [input_handle, element_shape, leading_dims], element_dtype: element_dtype, shape_type: shape_type, name: name) end
tensor_list_element_shape(input_handle, shape_type: nil, name: "TensorListElementShape")
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# File lib/tensorflow/ops/raw_ops.rb, line 4651 def self.tensor_list_element_shape(input_handle, shape_type: nil, name: "TensorListElementShape") self.execute("TensorListElementShape", [input_handle], shape_type: shape_type, name: name) end
tensor_list_from_tensor(tensor, element_shape, element_dtype: nil, shape_type: nil, name: "TensorListFromTensor")
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# File lib/tensorflow/ops/raw_ops.rb, line 4655 def self.tensor_list_from_tensor(tensor, element_shape, element_dtype: nil, shape_type: nil, name: "TensorListFromTensor") self.execute("TensorListFromTensor", [tensor, element_shape], element_dtype: element_dtype, shape_type: shape_type, name: name) end
tensor_list_gather(input_handle, indices, element_shape, element_dtype: nil, name: "TensorListGather")
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# File lib/tensorflow/ops/raw_ops.rb, line 4659 def self.tensor_list_gather(input_handle, indices, element_shape, element_dtype: nil, name: "TensorListGather") self.execute("TensorListGather", [input_handle, indices, element_shape], element_dtype: element_dtype, name: name) end
tensor_list_get_item(input_handle, index, element_shape, element_dtype: nil, name: "TensorListGetItem")
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# File lib/tensorflow/ops/raw_ops.rb, line 4663 def self.tensor_list_get_item(input_handle, index, element_shape, element_dtype: nil, name: "TensorListGetItem") self.execute("TensorListGetItem", [input_handle, index, element_shape], element_dtype: element_dtype, name: name) end
tensor_list_length(input_handle, name: "TensorListLength")
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# File lib/tensorflow/ops/raw_ops.rb, line 4667 def self.tensor_list_length(input_handle, name: "TensorListLength") self.execute("TensorListLength", [input_handle], name: name) end
tensor_list_pop_back(input_handle, element_shape, element_dtype: nil, name: "TensorListPopBack")
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# File lib/tensorflow/ops/raw_ops.rb, line 4671 def self.tensor_list_pop_back(input_handle, element_shape, element_dtype: nil, name: "TensorListPopBack") self.execute("TensorListPopBack", [input_handle, element_shape], element_dtype: element_dtype, name: name) end
tensor_list_push_back(input_handle, tensor, element_dtype: nil, name: "TensorListPushBack")
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# File lib/tensorflow/ops/raw_ops.rb, line 4675 def self.tensor_list_push_back(input_handle, tensor, element_dtype: nil, name: "TensorListPushBack") self.execute("TensorListPushBack", [input_handle, tensor], element_dtype: element_dtype, name: name) end
tensor_list_push_back_batch(input_handles, tensor, element_dtype: nil, name: "TensorListPushBackBatch")
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# File lib/tensorflow/ops/raw_ops.rb, line 4679 def self.tensor_list_push_back_batch(input_handles, tensor, element_dtype: nil, name: "TensorListPushBackBatch") self.execute("TensorListPushBackBatch", [input_handles, tensor], element_dtype: element_dtype, name: name) end
tensor_list_reserve(element_shape, num_elements, element_dtype: nil, shape_type: nil, name: "TensorListReserve")
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# File lib/tensorflow/ops/raw_ops.rb, line 4683 def self.tensor_list_reserve(element_shape, num_elements, element_dtype: nil, shape_type: nil, name: "TensorListReserve") self.execute("TensorListReserve", [element_shape, num_elements], element_dtype: element_dtype, shape_type: shape_type, name: name) end
tensor_list_resize(input_handle, size, name: "TensorListResize")
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# File lib/tensorflow/ops/raw_ops.rb, line 4687 def self.tensor_list_resize(input_handle, size, name: "TensorListResize") self.execute("TensorListResize", [input_handle, size], name: name) end
tensor_list_scatter(tensor, indices, element_shape, element_dtype: nil, shape_type: nil, name: "TensorListScatter")
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# File lib/tensorflow/ops/raw_ops.rb, line 4691 def self.tensor_list_scatter(tensor, indices, element_shape, element_dtype: nil, shape_type: nil, name: "TensorListScatter") self.execute("TensorListScatter", [tensor, indices, element_shape], element_dtype: element_dtype, shape_type: shape_type, name: name) end
tensor_list_scatter_into_existing_list(input_handle, tensor, indices, element_dtype: nil, name: "TensorListScatterIntoExistingList")
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# File lib/tensorflow/ops/raw_ops.rb, line 4695 def self.tensor_list_scatter_into_existing_list(input_handle, tensor, indices, element_dtype: nil, name: "TensorListScatterIntoExistingList") self.execute("TensorListScatterIntoExistingList", [input_handle, tensor, indices], element_dtype: element_dtype, name: name) end
tensor_list_scatter_v2(tensor, indices, element_shape, num_elements, element_dtype: nil, shape_type: nil, name: "TensorListScatterV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4699 def self.tensor_list_scatter_v2(tensor, indices, element_shape, num_elements, element_dtype: nil, shape_type: nil, name: "TensorListScatterV2") self.execute("TensorListScatterV2", [tensor, indices, element_shape, num_elements], element_dtype: element_dtype, shape_type: shape_type, name: name) end
tensor_list_set_item(input_handle, index, item, element_dtype: nil, name: "TensorListSetItem")
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# File lib/tensorflow/ops/raw_ops.rb, line 4703 def self.tensor_list_set_item(input_handle, index, item, element_dtype: nil, name: "TensorListSetItem") self.execute("TensorListSetItem", [input_handle, index, item], element_dtype: element_dtype, name: name) end
tensor_list_split(tensor, element_shape, lengths, element_dtype: nil, shape_type: nil, name: "TensorListSplit")
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# File lib/tensorflow/ops/raw_ops.rb, line 4707 def self.tensor_list_split(tensor, element_shape, lengths, element_dtype: nil, shape_type: nil, name: "TensorListSplit") self.execute("TensorListSplit", [tensor, element_shape, lengths], element_dtype: element_dtype, shape_type: shape_type, name: name) end
tensor_list_stack(input_handle, element_shape, element_dtype: nil, num_elements: -1, name: "TensorListStack")
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# File lib/tensorflow/ops/raw_ops.rb, line 4711 def self.tensor_list_stack(input_handle, element_shape, element_dtype: nil, num_elements: -1, name: "TensorListStack") self.execute("TensorListStack", [input_handle, element_shape], element_dtype: element_dtype, num_elements: num_elements, name: name) end
tensor_scatter_add(tensor, indices, updates, typeT: nil, tindices: nil, name: "TensorScatterAdd")
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# File lib/tensorflow/ops/raw_ops.rb, line 4715 def self.tensor_scatter_add(tensor, indices, updates, typeT: nil, tindices: nil, name: "TensorScatterAdd") self.execute("TensorScatterAdd", [tensor, indices, updates], T: typeT, Tindices: tindices, name: name) end
tensor_scatter_sub(tensor, indices, updates, typeT: nil, tindices: nil, name: "TensorScatterSub")
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# File lib/tensorflow/ops/raw_ops.rb, line 4719 def self.tensor_scatter_sub(tensor, indices, updates, typeT: nil, tindices: nil, name: "TensorScatterSub") self.execute("TensorScatterSub", [tensor, indices, updates], T: typeT, Tindices: tindices, name: name) end
tensor_scatter_update(tensor, indices, updates, typeT: nil, tindices: nil, name: "TensorScatterUpdate")
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# File lib/tensorflow/ops/raw_ops.rb, line 4723 def self.tensor_scatter_update(tensor, indices, updates, typeT: nil, tindices: nil, name: "TensorScatterUpdate") self.execute("TensorScatterUpdate", [tensor, indices, updates], T: typeT, Tindices: tindices, name: name) end
tensor_slice_dataset(components, toutput_types: nil, output_shapes: nil, name: "TensorSliceDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 4727 def self.tensor_slice_dataset(components, toutput_types: nil, output_shapes: nil, name: "TensorSliceDataset") self.execute("TensorSliceDataset", [components], Toutput_types: toutput_types, output_shapes: output_shapes, name: name) end
tensor_strided_slice_update(input, start, stop, strides, value, typeT: nil, index: nil, begin_mask: 0, end_mask: 0, ellipsis_mask: 0, new_axis_mask: 0, shrink_axis_mask: 0, name: "TensorStridedSliceUpdate")
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# File lib/tensorflow/ops/raw_ops.rb, line 4731 def self.tensor_strided_slice_update(input, start, stop, strides, value, typeT: nil, index: nil, begin_mask: 0, end_mask: 0, ellipsis_mask: 0, new_axis_mask: 0, shrink_axis_mask: 0, name: "TensorStridedSliceUpdate") self.execute("TensorStridedSliceUpdate", [input, start, stop, strides, value], T: typeT, Index: index, begin_mask: begin_mask, end_mask: end_mask, ellipsis_mask: ellipsis_mask, new_axis_mask: new_axis_mask, shrink_axis_mask: shrink_axis_mask, name: name) end
tensor_summary(tensor, typeT: nil, description: "", labels: [], display_name: "", name: "TensorSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 4735 def self.tensor_summary(tensor, typeT: nil, description: "", labels: [], display_name: "", name: "TensorSummary") self.execute("TensorSummary", [tensor], T: typeT, description: description, labels: labels, display_name: display_name, name: name) end
tensor_summary_v2(tag, tensor, serialized_summary_metadata, typeT: nil, name: "TensorSummaryV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4739 def self.tensor_summary_v2(tag, tensor, serialized_summary_metadata, typeT: nil, name: "TensorSummaryV2") self.execute("TensorSummaryV2", [tag, tensor, serialized_summary_metadata], T: typeT, name: name) end
text_line_dataset(filenames, compression_type, buffer_size, name: "TextLineDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 4743 def self.text_line_dataset(filenames, compression_type, buffer_size, name: "TextLineDataset") self.execute("TextLineDataset", [filenames, compression_type, buffer_size], name: name) end
text_line_reader(skip_header_lines: 0, container: "", shared_name: "", name: "TextLineReader")
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# File lib/tensorflow/ops/raw_ops.rb, line 4747 def self.text_line_reader(skip_header_lines: 0, container: "", shared_name: "", name: "TextLineReader") self.execute("TextLineReader", [], skip_header_lines: skip_header_lines, container: container, shared_name: shared_name, name: name) end
text_line_reader_v2(skip_header_lines: 0, container: "", shared_name: "", name: "TextLineReaderV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4751 def self.text_line_reader_v2(skip_header_lines: 0, container: "", shared_name: "", name: "TextLineReaderV2") self.execute("TextLineReaderV2", [], skip_header_lines: skip_header_lines, container: container, shared_name: shared_name, name: name) end
tf_record_dataset(filenames, compression_type, buffer_size, name: "TFRecordDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 4407 def self.tf_record_dataset(filenames, compression_type, buffer_size, name: "TFRecordDataset") self.execute("TFRecordDataset", [filenames, compression_type, buffer_size], name: name) end
tf_record_reader(container: "", shared_name: "", compression_type: "", name: "TFRecordReader")
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# File lib/tensorflow/ops/raw_ops.rb, line 4411 def self.tf_record_reader(container: "", shared_name: "", compression_type: "", name: "TFRecordReader") self.execute("TFRecordReader", [], container: container, shared_name: shared_name, compression_type: compression_type, name: name) end
tf_record_reader_v2(container: "", shared_name: "", compression_type: "", name: "TFRecordReaderV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4415 def self.tf_record_reader_v2(container: "", shared_name: "", compression_type: "", name: "TFRecordReaderV2") self.execute("TFRecordReaderV2", [], container: container, shared_name: shared_name, compression_type: compression_type, name: name) end
thread_pool_dataset(input_dataset, thread_pool, output_types: nil, output_shapes: nil, name: "ThreadPoolDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 4755 def self.thread_pool_dataset(input_dataset, thread_pool, output_types: nil, output_shapes: nil, name: "ThreadPoolDataset") self.execute("ThreadPoolDataset", [input_dataset, thread_pool], output_types: output_types, output_shapes: output_shapes, name: name) end
thread_pool_handle(num_threads: nil, max_intra_op_parallelism: 1, display_name: "", container: "", shared_name: "", name: "ThreadPoolHandle")
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# File lib/tensorflow/ops/raw_ops.rb, line 4759 def self.thread_pool_handle(num_threads: nil, max_intra_op_parallelism: 1, display_name: "", container: "", shared_name: "", name: "ThreadPoolHandle") self.execute("ThreadPoolHandle", [], num_threads: num_threads, max_intra_op_parallelism: max_intra_op_parallelism, display_name: display_name, container: container, shared_name: shared_name, name: name) end
thread_unsafe_unigram_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, seed: 0, seed2: 0, name: "ThreadUnsafeUnigramCandidateSampler")
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# File lib/tensorflow/ops/raw_ops.rb, line 4763 def self.thread_unsafe_unigram_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, seed: 0, seed2: 0, name: "ThreadUnsafeUnigramCandidateSampler") self.execute("ThreadUnsafeUnigramCandidateSampler", [true_classes], num_true: num_true, num_sampled: num_sampled, unique: unique, range_max: range_max, seed: seed, seed2: seed2, name: name) end
tile(input, multiples, typeT: nil, tmultiples: :int32, name: "Tile")
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# File lib/tensorflow/ops/raw_ops.rb, line 4767 def self.tile(input, multiples, typeT: nil, tmultiples: :int32, name: "Tile") self.execute("Tile", [input, multiples], T: typeT, Tmultiples: tmultiples, name: name) end
tile_grad(input, multiples, typeT: nil, name: "TileGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 4771 def self.tile_grad(input, multiples, typeT: nil, name: "TileGrad") self.execute("TileGrad", [input, multiples], T: typeT, name: name) end
timestamp(name: "Timestamp")
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# File lib/tensorflow/ops/raw_ops.rb, line 4775 def self.timestamp(name: "Timestamp") self.execute("Timestamp", [], name: name) end
top_k(input, k: nil, sorted: true, typeT: nil, name: "TopK")
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# File lib/tensorflow/ops/raw_ops.rb, line 4779 def self.top_k(input, k: nil, sorted: true, typeT: nil, name: "TopK") self.execute("TopK", [input], k: k, sorted: sorted, T: typeT, name: name) end
top_kv2(input, k, sorted: true, typeT: nil, name: "TopKV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4783 def self.top_kv2(input, k, sorted: true, typeT: nil, name: "TopKV2") self.execute("TopKV2", [input, k], sorted: sorted, T: typeT, name: name) end
tpu_compilation_result(name: "TPUCompilationResult")
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# File lib/tensorflow/ops/raw_ops.rb, line 4419 def self.tpu_compilation_result(name: "TPUCompilationResult") self.execute("TPUCompilationResult", [], name: name) end
tpu_embedding_activations(embedding_variable, sliced_activations, table_id: nil, lookup_id: nil, name: "TPUEmbeddingActivations")
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# File lib/tensorflow/ops/raw_ops.rb, line 4423 def self.tpu_embedding_activations(embedding_variable, sliced_activations, table_id: nil, lookup_id: nil, name: "TPUEmbeddingActivations") self.execute("TPUEmbeddingActivations", [embedding_variable, sliced_activations], table_id: table_id, lookup_id: lookup_id, name: name) end
tpu_ordinal_selector(name: "TPUOrdinalSelector")
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# File lib/tensorflow/ops/raw_ops.rb, line 4427 def self.tpu_ordinal_selector(name: "TPUOrdinalSelector") self.execute("TPUOrdinalSelector", [], name: name) end
tpu_partitioned_call(args, device_ordinal, tin: nil, tout: nil, f: nil, autotuner_thresh: 0, name: "TPUPartitionedCall")
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# File lib/tensorflow/ops/raw_ops.rb, line 4431 def self.tpu_partitioned_call(args, device_ordinal, tin: nil, tout: nil, f: nil, autotuner_thresh: 0, name: "TPUPartitionedCall") self.execute("TPUPartitionedCall", [args, device_ordinal], Tin: tin, Tout: tout, f: f, autotuner_thresh: autotuner_thresh, name: name) end
tpu_replicate_metadata(num_replicas: nil, num_cores_per_replica: 1, topology: "", use_tpu: true, device_assignment: [], computation_shape: [], host_compute_core: [], padding_map: [], step_marker_location: "STEP_MARK_AT_ENTRY", allow_soft_placement: false, name: "TPUReplicateMetadata")
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# File lib/tensorflow/ops/raw_ops.rb, line 4435 def self.tpu_replicate_metadata(num_replicas: nil, num_cores_per_replica: 1, topology: "", use_tpu: true, device_assignment: [], computation_shape: [], host_compute_core: [], padding_map: [], step_marker_location: "STEP_MARK_AT_ENTRY", allow_soft_placement: false, name: "TPUReplicateMetadata") self.execute("TPUReplicateMetadata", [], num_replicas: num_replicas, num_cores_per_replica: num_cores_per_replica, topology: topology, use_tpu: use_tpu, device_assignment: device_assignment, computation_shape: computation_shape, host_compute_core: host_compute_core, padding_map: padding_map, step_marker_location: step_marker_location, allow_soft_placement: allow_soft_placement, name: name) end
tpu_replicated_input(inputs, n: nil, typeT: nil, is_mirrored_variable: false, index: -1, name: "TPUReplicatedInput")
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# File lib/tensorflow/ops/raw_ops.rb, line 4439 def self.tpu_replicated_input(inputs, n: nil, typeT: nil, is_mirrored_variable: false, index: -1, name: "TPUReplicatedInput") self.execute("TPUReplicatedInput", [inputs], N: n, T: typeT, is_mirrored_variable: is_mirrored_variable, index: index, name: name) end
tpu_replicated_output(input, num_replicas: nil, typeT: nil, name: "TPUReplicatedOutput")
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# File lib/tensorflow/ops/raw_ops.rb, line 4443 def self.tpu_replicated_output(input, num_replicas: nil, typeT: nil, name: "TPUReplicatedOutput") self.execute("TPUReplicatedOutput", [input], num_replicas: num_replicas, T: typeT, name: name) end
transpose(x, perm, typeT: nil, tperm: :int32, name: "Transpose")
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# File lib/tensorflow/ops/raw_ops.rb, line 4787 def self.transpose(x, perm, typeT: nil, tperm: :int32, name: "Transpose") self.execute("Transpose", [x, perm], T: typeT, Tperm: tperm, name: name) end
tridiagonal_mat_mul(superdiag, maindiag, subdiag, rhs, typeT: nil, name: "TridiagonalMatMul")
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# File lib/tensorflow/ops/raw_ops.rb, line 4791 def self.tridiagonal_mat_mul(superdiag, maindiag, subdiag, rhs, typeT: nil, name: "TridiagonalMatMul") self.execute("TridiagonalMatMul", [superdiag, maindiag, subdiag, rhs], T: typeT, name: name) end
tridiagonal_solve(diagonals, rhs, partial_pivoting: true, typeT: nil, name: "TridiagonalSolve")
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# File lib/tensorflow/ops/raw_ops.rb, line 4795 def self.tridiagonal_solve(diagonals, rhs, partial_pivoting: true, typeT: nil, name: "TridiagonalSolve") self.execute("TridiagonalSolve", [diagonals, rhs], partial_pivoting: partial_pivoting, T: typeT, name: name) end
truncate_div(x, y, typeT: nil, name: "TruncateDiv")
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# File lib/tensorflow/ops/raw_ops.rb, line 4799 def self.truncate_div(x, y, typeT: nil, name: "TruncateDiv") self.execute("TruncateDiv", [x, y], T: typeT, name: name) end
truncate_mod(x, y, typeT: nil, name: "TruncateMod")
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# File lib/tensorflow/ops/raw_ops.rb, line 4803 def self.truncate_mod(x, y, typeT: nil, name: "TruncateMod") self.execute("TruncateMod", [x, y], T: typeT, name: name) end
truncated_normal(shape, seed: 0, seed2: 0, dtype: nil, typeT: nil, name: "TruncatedNormal")
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# File lib/tensorflow/ops/raw_ops.rb, line 4807 def self.truncated_normal(shape, seed: 0, seed2: 0, dtype: nil, typeT: nil, name: "TruncatedNormal") self.execute("TruncatedNormal", [shape], seed: seed, seed2: seed2, dtype: dtype, T: typeT, name: name) end
try_rpc(address, method, request, protocol: "", fail_fast: true, timeout_in_ms: 0, name: "TryRpc")
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# File lib/tensorflow/ops/raw_ops.rb, line 4811 def self.try_rpc(address, method, request, protocol: "", fail_fast: true, timeout_in_ms: 0, name: "TryRpc") self.execute("TryRpc", [address, method, request], protocol: protocol, fail_fast: fail_fast, timeout_in_ms: timeout_in_ms, name: name) end
unbatch(batched_tensor, batch_index, id, timeout_micros: nil, container: "", shared_name: "", typeT: nil, name: "Unbatch")
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# File lib/tensorflow/ops/raw_ops.rb, line 4815 def self.unbatch(batched_tensor, batch_index, id, timeout_micros: nil, container: "", shared_name: "", typeT: nil, name: "Unbatch") self.execute("Unbatch", [batched_tensor, batch_index, id], timeout_micros: timeout_micros, container: container, shared_name: shared_name, T: typeT, name: name) end
unbatch_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "UnbatchDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 4819 def self.unbatch_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "UnbatchDataset") self.execute("UnbatchDataset", [input_dataset], output_types: output_types, output_shapes: output_shapes, name: name) end
unbatch_grad(original_input, batch_index, grad, id, container: "", shared_name: "", typeT: nil, name: "UnbatchGrad")
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# File lib/tensorflow/ops/raw_ops.rb, line 4823 def self.unbatch_grad(original_input, batch_index, grad, id, container: "", shared_name: "", typeT: nil, name: "UnbatchGrad") self.execute("UnbatchGrad", [original_input, batch_index, grad, id], container: container, shared_name: shared_name, T: typeT, name: name) end
unicode_decode(input, input_encoding: "", errors: "replace", replacement_char: 65533, replace_control_characters: false, tsplits: :int64, name: "UnicodeDecode")
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# File lib/tensorflow/ops/raw_ops.rb, line 4827 def self.unicode_decode(input, input_encoding: "", errors: "replace", replacement_char: 65533, replace_control_characters: false, tsplits: :int64, name: "UnicodeDecode") self.execute("UnicodeDecode", [input], input_encoding: input_encoding, errors: errors, replacement_char: replacement_char, replace_control_characters: replace_control_characters, Tsplits: tsplits, name: name) end
unicode_decode_with_offsets(input, input_encoding: "", errors: "replace", replacement_char: 65533, replace_control_characters: false, tsplits: :int64, name: "UnicodeDecodeWithOffsets")
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# File lib/tensorflow/ops/raw_ops.rb, line 4831 def self.unicode_decode_with_offsets(input, input_encoding: "", errors: "replace", replacement_char: 65533, replace_control_characters: false, tsplits: :int64, name: "UnicodeDecodeWithOffsets") self.execute("UnicodeDecodeWithOffsets", [input], input_encoding: input_encoding, errors: errors, replacement_char: replacement_char, replace_control_characters: replace_control_characters, Tsplits: tsplits, name: name) end
unicode_encode(input_values, input_splits, errors: "replace", output_encoding: nil, replacement_char: 65533, tsplits: :int64, name: "UnicodeEncode")
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# File lib/tensorflow/ops/raw_ops.rb, line 4835 def self.unicode_encode(input_values, input_splits, errors: "replace", output_encoding: nil, replacement_char: 65533, tsplits: :int64, name: "UnicodeEncode") self.execute("UnicodeEncode", [input_values, input_splits], errors: errors, output_encoding: output_encoding, replacement_char: replacement_char, Tsplits: tsplits, name: name) end
unicode_script(input, name: "UnicodeScript")
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# File lib/tensorflow/ops/raw_ops.rb, line 4839 def self.unicode_script(input, name: "UnicodeScript") self.execute("UnicodeScript", [input], name: name) end
unicode_transcode(input, input_encoding: "", output_encoding: nil, errors: "replace", replacement_char: 65533, replace_control_characters: false, name: "UnicodeTranscode")
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# File lib/tensorflow/ops/raw_ops.rb, line 4843 def self.unicode_transcode(input, input_encoding: "", output_encoding: nil, errors: "replace", replacement_char: 65533, replace_control_characters: false, name: "UnicodeTranscode") self.execute("UnicodeTranscode", [input], input_encoding: input_encoding, output_encoding: output_encoding, errors: errors, replacement_char: replacement_char, replace_control_characters: replace_control_characters, name: name) end
uniform_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, seed: 0, seed2: 0, name: "UniformCandidateSampler")
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# File lib/tensorflow/ops/raw_ops.rb, line 4847 def self.uniform_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, seed: 0, seed2: 0, name: "UniformCandidateSampler") self.execute("UniformCandidateSampler", [true_classes], num_true: num_true, num_sampled: num_sampled, unique: unique, range_max: range_max, seed: seed, seed2: seed2, name: name) end
unique(x, typeT: nil, out_idx: :int32, name: "Unique")
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# File lib/tensorflow/ops/raw_ops.rb, line 4851 def self.unique(x, typeT: nil, out_idx: :int32, name: "Unique") self.execute("Unique", [x], T: typeT, out_idx: out_idx, name: name) end
unique_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "UniqueDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 4855 def self.unique_dataset(input_dataset, output_types: nil, output_shapes: nil, name: "UniqueDataset") self.execute("UniqueDataset", [input_dataset], output_types: output_types, output_shapes: output_shapes, name: name) end
unique_v2(x, axis, typeT: nil, taxis: :int64, out_idx: :int32, name: "UniqueV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4859 def self.unique_v2(x, axis, typeT: nil, taxis: :int64, out_idx: :int32, name: "UniqueV2") self.execute("UniqueV2", [x, axis], T: typeT, Taxis: taxis, out_idx: out_idx, name: name) end
unique_with_counts(x, typeT: nil, out_idx: :int32, name: "UniqueWithCounts")
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# File lib/tensorflow/ops/raw_ops.rb, line 4863 def self.unique_with_counts(x, typeT: nil, out_idx: :int32, name: "UniqueWithCounts") self.execute("UniqueWithCounts", [x], T: typeT, out_idx: out_idx, name: name) end
unique_with_counts_v2(x, axis, typeT: nil, taxis: :int64, out_idx: :int32, name: "UniqueWithCountsV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4867 def self.unique_with_counts_v2(x, axis, typeT: nil, taxis: :int64, out_idx: :int32, name: "UniqueWithCountsV2") self.execute("UniqueWithCountsV2", [x, axis], T: typeT, Taxis: taxis, out_idx: out_idx, name: name) end
unpack(value, num: nil, typeT: nil, axis: 0, name: "Unpack")
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# File lib/tensorflow/ops/raw_ops.rb, line 4871 def self.unpack(value, num: nil, typeT: nil, axis: 0, name: "Unpack") self.execute("Unpack", [value], num: num, T: typeT, axis: axis, name: name) end
unravel_index(indices, dims, tidx: :int32, name: "UnravelIndex")
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# File lib/tensorflow/ops/raw_ops.rb, line 4875 def self.unravel_index(indices, dims, tidx: :int32, name: "UnravelIndex") self.execute("UnravelIndex", [indices, dims], Tidx: tidx, name: name) end
unsorted_segment_join(inputs, segment_ids, num_segments, separator: "", tindices: nil, tnumsegments: :int32, name: "UnsortedSegmentJoin")
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# File lib/tensorflow/ops/raw_ops.rb, line 4879 def self.unsorted_segment_join(inputs, segment_ids, num_segments, separator: "", tindices: nil, tnumsegments: :int32, name: "UnsortedSegmentJoin") self.execute("UnsortedSegmentJoin", [inputs, segment_ids, num_segments], separator: separator, Tindices: tindices, Tnumsegments: tnumsegments, name: name) end
unsorted_segment_max(data, segment_ids, num_segments, typeT: nil, tindices: nil, tnumsegments: :int32, name: "UnsortedSegmentMax")
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# File lib/tensorflow/ops/raw_ops.rb, line 4883 def self.unsorted_segment_max(data, segment_ids, num_segments, typeT: nil, tindices: nil, tnumsegments: :int32, name: "UnsortedSegmentMax") self.execute("UnsortedSegmentMax", [data, segment_ids, num_segments], T: typeT, Tindices: tindices, Tnumsegments: tnumsegments, name: name) end
unsorted_segment_min(data, segment_ids, num_segments, typeT: nil, tindices: nil, tnumsegments: :int32, name: "UnsortedSegmentMin")
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# File lib/tensorflow/ops/raw_ops.rb, line 4887 def self.unsorted_segment_min(data, segment_ids, num_segments, typeT: nil, tindices: nil, tnumsegments: :int32, name: "UnsortedSegmentMin") self.execute("UnsortedSegmentMin", [data, segment_ids, num_segments], T: typeT, Tindices: tindices, Tnumsegments: tnumsegments, name: name) end
unsorted_segment_prod(data, segment_ids, num_segments, typeT: nil, tindices: nil, tnumsegments: :int32, name: "UnsortedSegmentProd")
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# File lib/tensorflow/ops/raw_ops.rb, line 4891 def self.unsorted_segment_prod(data, segment_ids, num_segments, typeT: nil, tindices: nil, tnumsegments: :int32, name: "UnsortedSegmentProd") self.execute("UnsortedSegmentProd", [data, segment_ids, num_segments], T: typeT, Tindices: tindices, Tnumsegments: tnumsegments, name: name) end
unsorted_segment_sum(data, segment_ids, num_segments, typeT: nil, tindices: nil, tnumsegments: :int32, name: "UnsortedSegmentSum")
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# File lib/tensorflow/ops/raw_ops.rb, line 4895 def self.unsorted_segment_sum(data, segment_ids, num_segments, typeT: nil, tindices: nil, tnumsegments: :int32, name: "UnsortedSegmentSum") self.execute("UnsortedSegmentSum", [data, segment_ids, num_segments], T: typeT, Tindices: tindices, Tnumsegments: tnumsegments, name: name) end
unstage(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "Unstage")
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# File lib/tensorflow/ops/raw_ops.rb, line 4899 def self.unstage(capacity: 0, memory_limit: 0, dtypes: nil, container: "", shared_name: "", name: "Unstage") self.execute("Unstage", [], capacity: capacity, memory_limit: memory_limit, dtypes: dtypes, container: container, shared_name: shared_name, name: name) end
unwrap_dataset_variant(input_handle, name: "UnwrapDatasetVariant")
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# File lib/tensorflow/ops/raw_ops.rb, line 4903 def self.unwrap_dataset_variant(input_handle, name: "UnwrapDatasetVariant") self.execute("UnwrapDatasetVariant", [input_handle], name: name) end
upper_bound(sorted_inputs, values, typeT: nil, out_type: :int32, name: "UpperBound")
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# File lib/tensorflow/ops/raw_ops.rb, line 4907 def self.upper_bound(sorted_inputs, values, typeT: nil, out_type: :int32, name: "UpperBound") self.execute("UpperBound", [sorted_inputs, values], T: typeT, out_type: out_type, name: name) end
var_handle_op(container: "", shared_name: "", dtype: nil, shape: nil, name: "VarHandleOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 4911 def self.var_handle_op(container: "", shared_name: "", dtype: nil, shape: nil, name: "VarHandleOp") self.execute("VarHandleOp", [], container: container, shared_name: shared_name, dtype: dtype, shape: shape, name: name) end
var_is_initialized_op(resource, name: "VarIsInitializedOp")
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# File lib/tensorflow/ops/raw_ops.rb, line 4915 def self.var_is_initialized_op(resource, name: "VarIsInitializedOp") self.execute("VarIsInitializedOp", [resource], name: name) end
variable(shape: nil, dtype: nil, container: "", shared_name: "", name: "Variable")
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# File lib/tensorflow/ops/raw_ops.rb, line 4919 def self.variable(shape: nil, dtype: nil, container: "", shared_name: "", name: "Variable") self.execute("Variable", [], shape: shape, dtype: dtype, container: container, shared_name: shared_name, name: name) end
variable_shape(input, out_type: :int32, name: "VariableShape")
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# File lib/tensorflow/ops/raw_ops.rb, line 4923 def self.variable_shape(input, out_type: :int32, name: "VariableShape") self.execute("VariableShape", [input], out_type: out_type, name: name) end
variable_v2(shape: nil, dtype: nil, container: "", shared_name: "", name: "VariableV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4927 def self.variable_v2(shape: nil, dtype: nil, container: "", shared_name: "", name: "VariableV2") self.execute("VariableV2", [], shape: shape, dtype: dtype, container: container, shared_name: shared_name, name: name) end
where(input, typeT: :bool, name: "Where")
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# File lib/tensorflow/ops/raw_ops.rb, line 4931 def self.where(input, typeT: :bool, name: "Where") self.execute("Where", [input], T: typeT, name: name) end
while(input, typeT: nil, cond: nil, body: nil, output_shapes: [], parallel_iterations: 10, name: "While")
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# File lib/tensorflow/ops/raw_ops.rb, line 4935 def self.while(input, typeT: nil, cond: nil, body: nil, output_shapes: [], parallel_iterations: 10, name: "While") self.execute("While", [input], T: typeT, cond: cond, body: body, output_shapes: output_shapes, parallel_iterations: parallel_iterations, name: name) end
whole_file_reader(container: "", shared_name: "", name: "WholeFileReader")
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# File lib/tensorflow/ops/raw_ops.rb, line 4939 def self.whole_file_reader(container: "", shared_name: "", name: "WholeFileReader") self.execute("WholeFileReader", [], container: container, shared_name: shared_name, name: name) end
whole_file_reader_v2(container: "", shared_name: "", name: "WholeFileReaderV2")
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# File lib/tensorflow/ops/raw_ops.rb, line 4943 def self.whole_file_reader_v2(container: "", shared_name: "", name: "WholeFileReaderV2") self.execute("WholeFileReaderV2", [], container: container, shared_name: shared_name, name: name) end
window_dataset(input_dataset, size, shift, stride, drop_remainder, output_types: nil, output_shapes: nil, name: "WindowDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 4947 def self.window_dataset(input_dataset, size, shift, stride, drop_remainder, output_types: nil, output_shapes: nil, name: "WindowDataset") self.execute("WindowDataset", [input_dataset, size, shift, stride, drop_remainder], output_types: output_types, output_shapes: output_shapes, name: name) end
worker_heartbeat(request, name: "WorkerHeartbeat")
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# File lib/tensorflow/ops/raw_ops.rb, line 4951 def self.worker_heartbeat(request, name: "WorkerHeartbeat") self.execute("WorkerHeartbeat", [request], name: name) end
wrap_dataset_variant(input_handle, name: "WrapDatasetVariant")
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# File lib/tensorflow/ops/raw_ops.rb, line 4955 def self.wrap_dataset_variant(input_handle, name: "WrapDatasetVariant") self.execute("WrapDatasetVariant", [input_handle], name: name) end
write_audio_summary(writer, step, tag, tensor, sample_rate, max_outputs: 3, name: "WriteAudioSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 4959 def self.write_audio_summary(writer, step, tag, tensor, sample_rate, max_outputs: 3, name: "WriteAudioSummary") self.execute("WriteAudioSummary", [writer, step, tag, tensor, sample_rate], max_outputs: max_outputs, name: name) end
write_file(filename, contents, name: "WriteFile")
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# File lib/tensorflow/ops/raw_ops.rb, line 4963 def self.write_file(filename, contents, name: "WriteFile") self.execute("WriteFile", [filename, contents], name: name) end
write_graph_summary(writer, step, tensor, name: "WriteGraphSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 4967 def self.write_graph_summary(writer, step, tensor, name: "WriteGraphSummary") self.execute("WriteGraphSummary", [writer, step, tensor], name: name) end
write_histogram_summary(writer, step, tag, values, typeT: :float, name: "WriteHistogramSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 4971 def self.write_histogram_summary(writer, step, tag, values, typeT: :float, name: "WriteHistogramSummary") self.execute("WriteHistogramSummary", [writer, step, tag, values], T: typeT, name: name) end
write_image_summary(writer, step, tag, tensor, bad_color, max_images: 3, typeT: :float, name: "WriteImageSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 4975 def self.write_image_summary(writer, step, tag, tensor, bad_color, max_images: 3, typeT: :float, name: "WriteImageSummary") self.execute("WriteImageSummary", [writer, step, tag, tensor, bad_color], max_images: max_images, T: typeT, name: name) end
write_raw_proto_summary(writer, step, tensor, name: "WriteRawProtoSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 4979 def self.write_raw_proto_summary(writer, step, tensor, name: "WriteRawProtoSummary") self.execute("WriteRawProtoSummary", [writer, step, tensor], name: name) end
write_scalar_summary(writer, step, tag, value, typeT: nil, name: "WriteScalarSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 4983 def self.write_scalar_summary(writer, step, tag, value, typeT: nil, name: "WriteScalarSummary") self.execute("WriteScalarSummary", [writer, step, tag, value], T: typeT, name: name) end
write_summary(writer, step, tensor, tag, summary_metadata, typeT: nil, name: "WriteSummary")
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# File lib/tensorflow/ops/raw_ops.rb, line 4987 def self.write_summary(writer, step, tensor, tag, summary_metadata, typeT: nil, name: "WriteSummary") self.execute("WriteSummary", [writer, step, tensor, tag, summary_metadata], T: typeT, name: name) end
xdivy(x, y, typeT: nil, name: "Xdivy")
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# File lib/tensorflow/ops/raw_ops.rb, line 4991 def self.xdivy(x, y, typeT: nil, name: "Xdivy") self.execute("Xdivy", [x, y], T: typeT, name: name) end
xlogy(x, y, typeT: nil, name: "Xlogy")
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# File lib/tensorflow/ops/raw_ops.rb, line 4995 def self.xlogy(x, y, typeT: nil, name: "Xlogy") self.execute("Xlogy", [x, y], T: typeT, name: name) end
zeros_like(x, typeT: nil, name: "ZerosLike")
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# File lib/tensorflow/ops/raw_ops.rb, line 4999 def self.zeros_like(x, typeT: nil, name: "ZerosLike") self.execute("ZerosLike", [x], T: typeT, name: name) end
zeta(x, q, typeT: nil, name: "Zeta")
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# File lib/tensorflow/ops/raw_ops.rb, line 5003 def self.zeta(x, q, typeT: nil, name: "Zeta") self.execute("Zeta", [x, q], T: typeT, name: name) end
zip_dataset(input_datasets, output_types: nil, output_shapes: nil, n: nil, name: "ZipDataset")
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# File lib/tensorflow/ops/raw_ops.rb, line 5007 def self.zip_dataset(input_datasets, output_types: nil, output_shapes: nil, n: nil, name: "ZipDataset") self.execute("ZipDataset", [input_datasets], output_types: output_types, output_shapes: output_shapes, N: n, name: name) end