module TensorFlow::Math
Public Class Methods
abs(x)
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# File lib/tensorflow/math.rb, line 4 def abs(x) RawOps.abs(x: x) end
acos(x)
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def accumulate_n end
# File lib/tensorflow/math.rb, line 11 def acos(x) RawOps.acos(x: x) end
acosh(x)
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# File lib/tensorflow/math.rb, line 15 def acosh(x) RawOps.acosh(x: x) end
add(x, y)
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# File lib/tensorflow/math.rb, line 19 def add(x, y) RawOps.add(x: x, y: y) end
add_n(inputs)
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# File lib/tensorflow/math.rb, line 23 def add_n(inputs) RawOps.add_n(inputs: inputs) end
angle(input)
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# File lib/tensorflow/math.rb, line 27 def angle(input) RawOps.angle(input: input) end
asin(x)
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def argmin end
# File lib/tensorflow/math.rb, line 37 def asin(x) RawOps.asin(x: x) end
asinh(x)
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# File lib/tensorflow/math.rb, line 41 def asinh(x) RawOps.asinh(x: x) end
atan(x)
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# File lib/tensorflow/math.rb, line 45 def atan(x) RawOps.atan(x: x) end
atan2(y, x)
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# File lib/tensorflow/math.rb, line 49 def atan2(y, x) RawOps.atan2(y: y, x: x) end
atanh(x)
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# File lib/tensorflow/math.rb, line 53 def atanh(x) RawOps.atanh(x: x) end
bessel_i0e(x)
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def bessel_i0 end
# File lib/tensorflow/math.rb, line 60 def bessel_i0e(x) RawOps.bessel_i0e(x: x) end
bessel_i1e(x)
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def bessel_i1 end
# File lib/tensorflow/math.rb, line 67 def bessel_i1e(x) RawOps.bessel_i1e(x: x) end
betainc(a, b, x)
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# File lib/tensorflow/math.rb, line 71 def betainc(a, b, x) RawOps.betainc(a: a, b: b, x: x) end
bincount(arr, size, weights)
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# File lib/tensorflow/math.rb, line 75 def bincount(arr, size, weights) RawOps.bincount(arr: arr, size: size, weights: weights) end
ceil(x)
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# File lib/tensorflow/math.rb, line 79 def ceil(x) RawOps.ceil(x: x) end
conj(input)
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def confusion_matrix end
# File lib/tensorflow/math.rb, line 86 def conj(input) RawOps.conj(input: input) end
cos(x)
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# File lib/tensorflow/math.rb, line 90 def cos(x) RawOps.cos(x: x) end
cosh(x)
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# File lib/tensorflow/math.rb, line 94 def cosh(x) RawOps.cosh(x: x) end
cumprod(x, axis, exclusive: nil, reverse: nil)
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def count_nonzero end
# File lib/tensorflow/math.rb, line 101 def cumprod(x, axis, exclusive: nil, reverse: nil) RawOps.cumprod(x: x, axis: axis, exclusive: exclusive, reverse: reverse) end
cumsum(x, axis, exclusive: nil, reverse: nil)
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# File lib/tensorflow/math.rb, line 105 def cumsum(x, axis, exclusive: nil, reverse: nil) RawOps.cumsum(x: x, axis: axis, exclusive: exclusive, reverse: reverse) end
digamma(x)
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def cumulative_logsumexp end
# File lib/tensorflow/math.rb, line 112 def digamma(x) RawOps.digamma(x: x) end
divide(x, y)
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# File lib/tensorflow/math.rb, line 116 def divide(x, y) RawOps.div(x: x, y: y) end
equal(x, y)
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def divide_no_nan end
# File lib/tensorflow/math.rb, line 123 def equal(x, y) RawOps.equal(x: x, y: y) end
erf(x)
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# File lib/tensorflow/math.rb, line 127 def erf(x) RawOps.erf(x: x) end
erfc(x)
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# File lib/tensorflow/math.rb, line 131 def erfc(x) RawOps.erfc(x: x) end
exp(x)
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# File lib/tensorflow/math.rb, line 135 def exp(x) RawOps.exp(x: x) end
expm1(x)
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# File lib/tensorflow/math.rb, line 139 def expm1(x) RawOps.expm1(x: x) end
floor(x)
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# File lib/tensorflow/math.rb, line 143 def floor(x) RawOps.floor(x: x) end
floordiv(x, y)
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# File lib/tensorflow/math.rb, line 147 def floordiv(x, y) RawOps.floor_div(x: x, y: y) end
floormod(x, y)
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# File lib/tensorflow/math.rb, line 151 def floormod(x, y) RawOps.floor_mod(x: x, y: y) end
greater(x, y)
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# File lib/tensorflow/math.rb, line 155 def greater(x, y) RawOps.greater(x: x, y: y) end
greater_equal(x, y)
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# File lib/tensorflow/math.rb, line 159 def greater_equal(x, y) RawOps.greater_equal(x: x, y: y) end
igamma(a, x)
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# File lib/tensorflow/math.rb, line 163 def igamma(a, x) RawOps.igamma(a: a, x: x) end
igammac(a, x)
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# File lib/tensorflow/math.rb, line 167 def igammac(a, x) RawOps.igammac(a: a, x: x) end
imag(input)
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# File lib/tensorflow/math.rb, line 171 def imag(input) RawOps.imag(input: input) end
in_top_k(predictions, targets, k: nil)
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# File lib/tensorflow/math.rb, line 175 def in_top_k(predictions, targets, k: nil) RawOps.in_top_k(predictions: predictions, targets: targets, k: k) end
invert_permutation(x)
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# File lib/tensorflow/math.rb, line 179 def invert_permutation(x) RawOps.invert_permutation(x: x) end
is_finite(x)
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# File lib/tensorflow/math.rb, line 183 def is_finite(x) RawOps.is_finite(x: x) end
is_inf(x)
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# File lib/tensorflow/math.rb, line 187 def is_inf(x) RawOps.is_inf(x: x) end
is_nan(x)
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# File lib/tensorflow/math.rb, line 191 def is_nan(x) RawOps.is_nan(x: x) end
less(x, y)
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def lbeta end
# File lib/tensorflow/math.rb, line 207 def less(x, y) RawOps.less(x: x, y: y) end
less_equal(x, y)
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# File lib/tensorflow/math.rb, line 211 def less_equal(x, y) RawOps.less_equal(x: x, y: y) end
lgamma(x)
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# File lib/tensorflow/math.rb, line 215 def lgamma(x) RawOps.lgamma(x: x) end
log(x)
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# File lib/tensorflow/math.rb, line 219 def log(x) RawOps.log(x: x) end
log1p(x)
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# File lib/tensorflow/math.rb, line 223 def log1p(x) RawOps.log1p(x: x) end
log_sigmoid(x)
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# File lib/tensorflow/math.rb, line 227 def log_sigmoid(x) x = TensorFlow.convert_to_tensor(x) negative(RawOps.softplus(features: -x)) end
log_softmax(logits)
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# File lib/tensorflow/math.rb, line 232 def log_softmax(logits) RawOps.log_softmax(logits: logits) end
logical_and(x, y)
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# File lib/tensorflow/math.rb, line 236 def logical_and(x, y) RawOps.logical_and(x: x, y: y) end
logical_not(x)
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# File lib/tensorflow/math.rb, line 240 def logical_not(x) RawOps.logical_not(x: x) end
logical_or(x, y)
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# File lib/tensorflow/math.rb, line 244 def logical_or(x, y) RawOps.logical_or(x: x, y: y) end
logical_xor(x, y)
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# File lib/tensorflow/math.rb, line 248 def logical_xor(x, y) logical_and(logical_or(x, y), logical_not(logical_and(x, y))) end
maximum(x, y)
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# File lib/tensorflow/math.rb, line 252 def maximum(x, y) RawOps.maximum(x: x, y: y) end
minimum(x, y)
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# File lib/tensorflow/math.rb, line 256 def minimum(x, y) RawOps.minimum(x: x, y: y) end
mod(x, y)
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# File lib/tensorflow/math.rb, line 260 def mod(x, y) RawOps.mod(x: x, y: y) end
multiply(x, y)
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# File lib/tensorflow/math.rb, line 264 def multiply(x, y) RawOps.mul(x: x, y: y) end
multiply_no_nan(x, y)
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# File lib/tensorflow/math.rb, line 268 def multiply_no_nan(x, y) RawOps.mul_no_nan(x: x, y: y) end
negative(x)
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# File lib/tensorflow/math.rb, line 272 def negative(x) RawOps.neg(x: x) end
not_equal(x, y)
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def nextafter end
# File lib/tensorflow/math.rb, line 279 def not_equal(x, y) RawOps.not_equal(x: x, y: y) end
polygamma(a, x)
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# File lib/tensorflow/math.rb, line 283 def polygamma(a, x) RawOps.polygamma(a: a, x: x) end
pow(x, y)
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def polyval end
# File lib/tensorflow/math.rb, line 290 def pow(x, y) RawOps.pow(x: x, y: y) end
real(input)
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# File lib/tensorflow/math.rb, line 294 def real(input) RawOps.real(input: input) end
reciprocal(x)
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# File lib/tensorflow/math.rb, line 298 def reciprocal(x) RawOps.reciprocal(x: x) end
reduce_any(input_tensor, axis: nil, keepdims: false)
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def reduce_all end
# File lib/tensorflow/math.rb, line 308 def reduce_any(input_tensor, axis: nil, keepdims: false) input_tensor = TensorFlow.convert_to_tensor(input_tensor) axis ||= reduction_dims(input_tensor) RawOps.any(input: input_tensor, reduction_indices: axis, keep_dims: keepdims) end
reduce_max(input_tensor, axis: nil, keepdims: false)
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def reduce_logsumexp end
# File lib/tensorflow/math.rb, line 320 def reduce_max(input_tensor, axis: nil, keepdims: false) input_tensor = TensorFlow.convert_to_tensor(input_tensor) axis ||= reduction_dims(input_tensor) RawOps.max(input: input_tensor, reduction_indices: axis, keep_dims: keepdims) end
reduce_mean(input_tensor, axis: nil, keepdims: false)
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# File lib/tensorflow/math.rb, line 326 def reduce_mean(input_tensor, axis: nil, keepdims: false) input_tensor = TensorFlow.convert_to_tensor(input_tensor) axis ||= reduction_dims(input_tensor) RawOps.mean(input: input_tensor, reduction_indices: axis, keep_dims: keepdims) end
reduce_min(input_tensor, axis: nil, keepdims: false)
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# File lib/tensorflow/math.rb, line 332 def reduce_min(input_tensor, axis: nil, keepdims: false) input_tensor = TensorFlow.convert_to_tensor(input_tensor) axis ||= reduction_dims(input_tensor) RawOps.min(input: input_tensor, reduction_indices: axis, keep_dims: keepdims) end
reduce_prod(input_tensor, axis: nil, keepdims: false)
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# File lib/tensorflow/math.rb, line 338 def reduce_prod(input_tensor, axis: nil, keepdims: false) input_tensor = TensorFlow.convert_to_tensor(input_tensor) axis ||= reduction_dims(input_tensor) RawOps.prod(input: input_tensor, reduction_indices: axis, keep_dims: keepdims) end
reduce_std(input_tensor, axis: nil, keepdims: false)
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# File lib/tensorflow/math.rb, line 344 def reduce_std(input_tensor, axis: nil, keepdims: false) variance = reduce_variance(input_tensor, axis: axis, keepdims: keepdims) sqrt(variance) end
reduce_sum(input_tensor, axis: nil, keepdims: false)
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# File lib/tensorflow/math.rb, line 349 def reduce_sum(input_tensor, axis: nil, keepdims: false) input_tensor = TensorFlow.convert_to_tensor(input_tensor) axis ||= reduction_dims(input_tensor) RawOps.sum(input: input_tensor, reduction_indices: axis, keep_dims: keepdims) end
reduce_variance(input_tensor, axis: nil, keepdims: false)
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# File lib/tensorflow/math.rb, line 355 def reduce_variance(input_tensor, axis: nil, keepdims: false) means = reduce_mean(input_tensor, axis: axis, keepdims: true) squared_deviations = RawOps.square(x: input_tensor - means) reduce_mean(squared_deviations, axis: axis, keepdims: keepdims) end
rint(x)
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# File lib/tensorflow/math.rb, line 361 def rint(x) RawOps.rint(x: x) end
round(x)
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# File lib/tensorflow/math.rb, line 365 def round(x) RawOps.round(x: x) end
rsqrt(x)
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# File lib/tensorflow/math.rb, line 369 def rsqrt(x) RawOps.rsqrt(x: x) end
segment_max(data, segment_ids)
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def scalar_mul end
# File lib/tensorflow/math.rb, line 376 def segment_max(data, segment_ids) RawOps.segment_max(data: data, segment_ids: segment_ids) end
segment_mean(data, segment_ids)
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# File lib/tensorflow/math.rb, line 380 def segment_mean(data, segment_ids) RawOps.segment_mean(data: data, segment_ids: segment_ids) end
segment_min(data, segment_ids)
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# File lib/tensorflow/math.rb, line 384 def segment_min(data, segment_ids) RawOps.segment_min(data: data, segment_ids: segment_ids) end
segment_prod(data, segment_ids)
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# File lib/tensorflow/math.rb, line 388 def segment_prod(data, segment_ids) RawOps.segment_prod(data: data, segment_ids: segment_ids) end
segment_sum(data, segment_ids)
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# File lib/tensorflow/math.rb, line 392 def segment_sum(data, segment_ids) RawOps.segment_sum(data: data, segment_ids: segment_ids) end
sigmoid(x)
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# File lib/tensorflow/math.rb, line 396 def sigmoid(x) RawOps.sigmoid(x: x) end
sign(x)
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# File lib/tensorflow/math.rb, line 400 def sign(x) RawOps.sign(x: x) end
sin(x)
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# File lib/tensorflow/math.rb, line 404 def sin(x) RawOps.sin(x: x) end
sinh(x)
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# File lib/tensorflow/math.rb, line 408 def sinh(x) RawOps.sinh(x: x) end
softmax(logits)
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# File lib/tensorflow/math.rb, line 412 def softmax(logits) RawOps.softmax(logits: logits) end
softplus(features)
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# File lib/tensorflow/math.rb, line 416 def softplus(features) RawOps.softplus(features: features) end
softsign(features)
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# File lib/tensorflow/math.rb, line 420 def softsign(features) RawOps.softsign(features: features) end
sqrt(x)
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# File lib/tensorflow/math.rb, line 424 def sqrt(x) RawOps.sqrt(x: x) end
square(x)
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# File lib/tensorflow/math.rb, line 428 def square(x) RawOps.square(x: x) end
squared_difference(x, y)
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# File lib/tensorflow/math.rb, line 432 def squared_difference(x, y) RawOps.squared_difference(x: x, y: y) end
subtract(x, y)
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# File lib/tensorflow/math.rb, line 436 def subtract(x, y) RawOps.sub(x: x, y: y) end
tan(x)
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# File lib/tensorflow/math.rb, line 440 def tan(x) RawOps.tan(x: x) end
tanh(x)
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# File lib/tensorflow/math.rb, line 444 def tanh(x) RawOps.tanh(x: x) end
top_k(input, k: nil, sorted: nil)
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# File lib/tensorflow/math.rb, line 448 def top_k(input, k: nil, sorted: nil) RawOps.top_k(input: input, k: k, sorted: sorted) end
unsorted_segment_max(data, segment_ids, num_segments)
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def truediv end
# File lib/tensorflow/math.rb, line 455 def unsorted_segment_max(data, segment_ids, num_segments) RawOps.unsorted_segment_max(data: data, segment_ids: segment_ids, num_segments: num_segments) end
unsorted_segment_min(data, segment_ids, num_segments)
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def unsorted_segment_mean end
# File lib/tensorflow/math.rb, line 462 def unsorted_segment_min(data, segment_ids, num_segments) RawOps.unsorted_segment_min(data: data, segment_ids: segment_ids, num_segments: num_segments) end
unsorted_segment_prod(data, segment_ids, num_segments)
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# File lib/tensorflow/math.rb, line 466 def unsorted_segment_prod(data, segment_ids, num_segments) RawOps.unsorted_segment_prod(data: data, segment_ids: segment_ids, num_segments: num_segments) end
unsorted_segment_sum(data, segment_ids, num_segments)
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def unsorted_segment_sqrt_n end
# File lib/tensorflow/math.rb, line 473 def unsorted_segment_sum(data, segment_ids, num_segments) RawOps.unsorted_segment_sum(data: data, segment_ids: segment_ids, num_segments: num_segments) end
xdivy(x, y)
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# File lib/tensorflow/math.rb, line 477 def xdivy(x, y) RawOps.xdivy(x: x, y: y) end
xlogy(x, y)
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# File lib/tensorflow/math.rb, line 481 def xlogy(x, y) RawOps.xlogy(x: x, y: y) end
zeta(x, q)
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def zero_fraction end
# File lib/tensorflow/math.rb, line 488 def zeta(x, q) RawOps.zeta(x: x, q: q) end
Private Class Methods
reduction_dims(input_tensor)
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# File lib/tensorflow/math.rb, line 494 def reduction_dims(input_tensor) rank = RawOps.rank(input: input_tensor).value TensorFlow.constant((0...rank).to_a, dtype: :int32) end