module Tensorflow::NN
Public Class Methods
all_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, seed: nil, seed2: nil)
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# File lib/tensorflow/ops/nn.rb, line 4 def all_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, seed: nil, seed2: nil) RawOps.all_candidate_sampler(true_classes, num_true: num_true, num_sampled: num_sampled, unique: unique, seed: seed, seed2: seed2) end
avg_pool(value, ksize: nil, strides: nil, padding: nil, data_format: nil)
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def atrous_conv2d_transpose end
# File lib/tensorflow/ops/nn.rb, line 14 def avg_pool(value, ksize: nil, strides: nil, padding: nil, data_format: nil) RawOps.avg_pool(value, ksize: ksize, strides: strides, padding: padding, data_format: data_format) end
avg_pool3d(input, ksize: nil, strides: nil, padding: nil, data_format: nil)
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def avg_pool2d end
# File lib/tensorflow/ops/nn.rb, line 24 def avg_pool3d(input, ksize: nil, strides: nil, padding: nil, data_format: nil) RawOps.avg_pool3_d(input, ksize: ksize, strides: strides, padding: padding, data_format: data_format) end
batch_norm_with_global_normalization(t, m, v, beta, gamma, variance_epsilon: nil, scale_after_normalization: nil)
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# File lib/tensorflow/ops/nn.rb, line 28 def batch_norm_with_global_normalization(t, m, v, beta, gamma, variance_epsilon: nil, scale_after_normalization: nil) RawOps.batch_norm_with_global_normalization(t, m, v, beta, gamma, variance_epsilon: variance_epsilon, scale_after_normalization: scale_after_normalization) end
bias_add(value, bias, data_format: nil)
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def batch_normalization end
# File lib/tensorflow/ops/nn.rb, line 35 def bias_add(value, bias, data_format: nil) RawOps.bias_add(value, bias, data_format: data_format) end
compute_accidental_hits(true_classes, sampled_candidates, num_true: nil, seed: nil, seed2: nil)
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def collapse_repeated end
# File lib/tensorflow/ops/nn.rb, line 42 def compute_accidental_hits(true_classes, sampled_candidates, num_true: nil, seed: nil, seed2: nil) RawOps.compute_accidental_hits(true_classes, sampled_candidates, num_true: num_true, seed: seed, seed2: seed2) end
conv2d(input, filter, strides: nil, use_cudnn_on_gpu: nil, padding: nil, explicit_paddings: nil, data_format: nil, dilations: nil)
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def conv1d_transpose end
# File lib/tensorflow/ops/nn.rb, line 55 def conv2d(input, filter, strides: nil, use_cudnn_on_gpu: nil, padding: nil, explicit_paddings: nil, data_format: nil, dilations: nil) RawOps.conv2d(input, filter, strides: strides, use_cudnn_on_gpu: use_cudnn_on_gpu, padding: padding, explicit_paddings: explicit_paddings, data_format: data_format, dilations: dilations) end
conv3d(input, filter, strides: nil, padding: nil, data_format: nil, dilations: nil)
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def conv2d_transpose end
# File lib/tensorflow/ops/nn.rb, line 62 def conv3d(input, filter, strides: nil, padding: nil, data_format: nil, dilations: nil) RawOps.conv3d(input, filter, strides: strides, padding: padding, data_format: data_format, dilations: dilations) end
ctc_beam_search_decoder(inputs, sequence_length, beam_width: nil, top_paths: nil, merge_repeated: nil)
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def crelu end
# File lib/tensorflow/ops/nn.rb, line 78 def ctc_beam_search_decoder(inputs, sequence_length, beam_width: nil, top_paths: nil, merge_repeated: nil) RawOps.ctc_beam_search_decoder(inputs, sequence_length, beam_width: beam_width, top_paths: top_paths, merge_repeated: merge_repeated) end
ctc_greedy_decoder(inputs, sequence_length, merge_repeated: nil)
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# File lib/tensorflow/ops/nn.rb, line 82 def ctc_greedy_decoder(inputs, sequence_length, merge_repeated: nil) RawOps.ctc_greedy_decoder(inputs, sequence_length, merge_repeated: merge_repeated) end
ctc_loss(inputs, labels_indices, labels_values, sequence_length, preprocess_collapse_repeated: nil, ctc_merge_repeated: nil, ignore_longer_outputs_than_inputs: nil)
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# File lib/tensorflow/ops/nn.rb, line 86 def ctc_loss(inputs, labels_indices, labels_values, sequence_length, preprocess_collapse_repeated: nil, ctc_merge_repeated: nil, ignore_longer_outputs_than_inputs: nil) RawOps.ctc_loss(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) end
depth_to_space(input, block_size: nil, data_format: nil)
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def ctc_unique_labels end
# File lib/tensorflow/ops/nn.rb, line 93 def depth_to_space(input, block_size: nil, data_format: nil) RawOps.depth_to_space(input, block_size: block_size, data_format: data_format) end
dilation2d(input, filter, strides: nil, rates: nil, padding: nil)
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def depthwise_conv2d_backprop_input end
# File lib/tensorflow/ops/nn.rb, line 106 def dilation2d(input, filter, strides: nil, rates: nil, padding: nil) RawOps.dilation2d(input, filter, strides: strides, rates: rates, padding: padding) end
elu(features)
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def dropout end
# File lib/tensorflow/ops/nn.rb, line 113 def elu(features) RawOps.elu(features) end
fixed_unigram_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, vocab_file: nil, distortion: nil, num_reserved_ids: nil, num_shards: nil, shard: nil, unigrams: nil, seed: nil, seed2: nil)
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def erosion2d end
# File lib/tensorflow/ops/nn.rb, line 126 def fixed_unigram_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, vocab_file: nil, distortion: nil, num_reserved_ids: nil, num_shards: nil, shard: nil, unigrams: nil, seed: nil, seed2: nil) RawOps.fixed_unigram_candidate_sampler(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) end
fractional_avg_pool(value, pooling_ratio: nil, pseudo_random: nil, overlapping: nil, deterministic: nil, seed: nil, seed2: nil)
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# File lib/tensorflow/ops/nn.rb, line 130 def fractional_avg_pool(value, pooling_ratio: nil, pseudo_random: nil, overlapping: nil, deterministic: nil, seed: nil, seed2: nil) RawOps.fractional_avg_pool(value, pooling_ratio: pooling_ratio, pseudo_random: pseudo_random, overlapping: overlapping, deterministic: deterministic, seed: seed, seed2: seed2) end
fractional_max_pool(value, pooling_ratio: nil, pseudo_random: nil, overlapping: nil, deterministic: nil, seed: nil, seed2: nil)
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# File lib/tensorflow/ops/nn.rb, line 134 def fractional_max_pool(value, pooling_ratio: nil, pseudo_random: nil, overlapping: nil, deterministic: nil, seed: nil, seed2: nil) RawOps.fractional_max_pool(value, pooling_ratio: pooling_ratio, pseudo_random: pseudo_random, overlapping: overlapping, deterministic: deterministic, seed: seed, seed2: seed2) end
in_top_k(predictions, targets, k: nil)
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# File lib/tensorflow/ops/nn.rb, line 138 def in_top_k(predictions, targets, k: nil) RawOps.in_top_k(predictions, targets, k: k) end
l2_loss(t)
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# File lib/tensorflow/ops/nn.rb, line 142 def l2_loss(t) RawOps.l2_loss(t: t) end
leaky_relu(features, alpha: nil)
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def l2_normalize end
# File lib/tensorflow/ops/nn.rb, line 149 def leaky_relu(features, alpha: nil) RawOps.leaky_relu(features, alpha: alpha) end
learned_unigram_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, seed: nil, seed2: nil)
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# File lib/tensorflow/ops/nn.rb, line 153 def learned_unigram_candidate_sampler(true_classes, num_true: nil, num_sampled: nil, unique: nil, range_max: nil, seed: nil, seed2: nil) RawOps.learned_unigram_candidate_sampler(true_classes, num_true: num_true, num_sampled: num_sampled, unique: unique, range_max: range_max, seed: seed, seed2: seed2) end
log_softmax(logits)
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def log_poisson_loss end
# File lib/tensorflow/ops/nn.rb, line 163 def log_softmax(logits) RawOps.log_softmax(logits) end
lrn(input, depth_radius: nil, bias: nil, alpha: nil, beta: nil)
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# File lib/tensorflow/ops/nn.rb, line 167 def lrn(input, depth_radius: nil, bias: nil, alpha: nil, beta: nil) RawOps.lrn(input, depth_radius: depth_radius, bias: bias, alpha: alpha, beta: beta) end
max_pool(input, ksize: nil, strides: nil, padding: nil, data_format: nil)
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# File lib/tensorflow/ops/nn.rb, line 171 def max_pool(input, ksize: nil, strides: nil, padding: nil, data_format: nil) RawOps.max_pool(input, ksize: ksize, strides: strides, padding: padding, data_format: data_format) end
max_pool3d(input, ksize: nil, strides: nil, padding: nil, data_format: nil)
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def max_pool2d end
# File lib/tensorflow/ops/nn.rb, line 181 def max_pool3d(input, ksize: nil, strides: nil, padding: nil, data_format: nil) RawOps.max_pool3d(input, ksize: ksize, strides: strides, padding: padding, data_format: data_format) end
max_pool_with_argmax(input, ksize: nil, strides: nil, padding: nil, include_batch_in_index: nil)
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# File lib/tensorflow/ops/nn.rb, line 185 def max_pool_with_argmax(input, ksize: nil, strides: nil, padding: nil, include_batch_in_index: nil) RawOps.max_pool_with_argmax(input, ksize: ksize, strides: strides, padding: padding, include_batch_in_index: include_batch_in_index) end
relu(features)
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def pool end
# File lib/tensorflow/ops/nn.rb, line 201 def relu(features) RawOps.relu(features) end
relu6(features)
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# File lib/tensorflow/ops/nn.rb, line 205 def relu6(features) RawOps.relu6(features) end
selu(features)
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def scale_regularization_loss end
# File lib/tensorflow/ops/nn.rb, line 218 def selu(features) RawOps.selu(features) end
sigmoid(x)
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def separable_conv2d end
# File lib/tensorflow/ops/nn.rb, line 225 def sigmoid(x) RawOps.sigmoid(x) end
softmax(logits)
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def sigmoid_cross_entropy_with_logits end
# File lib/tensorflow/ops/nn.rb, line 232 def softmax(logits) RawOps.softmax(logits) end
softmax_cross_entropy_with_logits(features, labels)
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# File lib/tensorflow/ops/nn.rb, line 236 def softmax_cross_entropy_with_logits(features, labels) RawOps.softmax_cross_entropy_with_logits(features, labels) end
softplus(features)
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# File lib/tensorflow/ops/nn.rb, line 240 def softplus(features) RawOps.softplus(features) end
softsign(features)
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# File lib/tensorflow/ops/nn.rb, line 244 def softsign(features) RawOps.softsign(features) end
space_to_batch(input, paddings, block_size: nil)
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# File lib/tensorflow/ops/nn.rb, line 248 def space_to_batch(input, paddings, block_size: nil) RawOps.space_to_batch(input, paddings, block_size: block_size) end
space_to_depth(input, block_size: nil, data_format: nil)
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# File lib/tensorflow/ops/nn.rb, line 252 def space_to_depth(input, block_size: nil, data_format: nil) RawOps.space_to_depth(input, block_size: block_size, data_format: data_format) end
sparse_softmax_cross_entropy_with_logits(features, labels)
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# File lib/tensorflow/ops/nn.rb, line 256 def sparse_softmax_cross_entropy_with_logits(features, labels) op = RawOps.sparse_softmax_cross_entropy_with_logits(features, labels) # Keep the first output, toss the 2nd (which is what the Python code does) op.outputs[0] end
tanh(x)
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def sufficient_statistics end
# File lib/tensorflow/ops/nn.rb, line 265 def tanh(x) RawOps.tanh(x) end
top_k(input, k: nil, sorted: nil)
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# File lib/tensorflow/ops/nn.rb, line 269 def top_k(input, k: nil, sorted: nil) RawOps.top_k(input, k: k, sorted: sorted) end