| /external/tensorflow/tensorflow/python/ops/parallel_for/ |
| D | math_test.py | 27 from tensorflow.python.ops import math_ops
40 math_ops.angle,
41 math_ops.imag,
42 math_ops.complex_abs,
43 math_ops.real,
44 math_ops.conj,
47 lambda x: math_ops.acosh(1 + math_ops.square(x)),
48 math_ops.abs,
49 math_ops.acos,
50 math_ops.asin,
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | cwise_ops_unary_test.py | 32 from tensorflow.python.ops import math_ops
96 if x.dtype in (np.complex64, np.complex128) and tf_func == math_ops.sign:
195 self._compareBoth(x, np.abs, math_ops.abs)
197 self._compareBoth(x, np.negative, math_ops.negative)
199 self._compareBoth(y, self._inv, math_ops.reciprocal)
200 self._compareBoth(x, np.square, math_ops.square)
201 self._compareBoth(z, np.sqrt, math_ops.sqrt)
202 self._compareBoth(z, self._rsqrt, math_ops.rsqrt)
203 self._compareBoth(x, np.exp, math_ops.exp)
204 self._compareBoth(x, np.expm1, math_ops.expm1)
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| D | basic_gpu_test.py | 33 from tensorflow.python.ops import math_ops
60 self._compareGPU(x, y, np.add, math_ops.add)
61 self._compareGPU(x, y, np.subtract, math_ops.subtract)
62 self._compareGPU(x, y, np.multiply, math_ops.multiply)
63 self._compareGPU(x, y + 0.1, np.true_divide, math_ops.truediv)
64 self._compareGPU(x, y + 0.1, np.floor_divide, math_ops.floordiv)
65 self._compareGPU(x, y, np.power, math_ops.pow)
70 self._compareGPU(x, y, np.add, math_ops.add)
71 self._compareGPU(x, y, np.subtract, math_ops.subtract)
72 self._compareGPU(x, y, np.multiply, math_ops.multiply)
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| /external/tensorflow/tensorflow/contrib/losses/python/metric_learning/ |
| D | metric_loss_ops.py | 27 from tensorflow.python.ops import math_ops
52 pairwise_distances_squared = math_ops.add(
53 math_ops.reduce_sum(math_ops.square(feature), axis=[1], keepdims=True),
54 math_ops.reduce_sum(
55 math_ops.square(array_ops.transpose(feature)),
57 keepdims=True)) - 2.0 * math_ops.matmul(feature,
61 pairwise_distances_squared = math_ops.maximum(pairwise_distances_squared, 0.0)
63 error_mask = math_ops.less_equal(pairwise_distances_squared, 0.0)
69 pairwise_distances = math_ops.sqrt(
71 math_ops.cast(error_mask, dtypes.float32) * 1e-16)
[all …]
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| /external/tensorflow/tensorflow/python/ops/ragged/ |
| D | ragged_dispatch.py | 30 from tensorflow.python.ops import math_ops
292 math_ops.abs,
293 math_ops.acos,
294 math_ops.acosh,
295 math_ops.angle,
296 math_ops.asin,
297 math_ops.asinh,
298 math_ops.atan,
299 math_ops.atanh,
300 math_ops.cast,
[all …]
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| D | ragged_dispatch_test.py | 33 from tensorflow.python.ops import math_ops
45 math_ops.abs,
46 math_ops.acos,
47 math_ops.acosh,
48 math_ops.angle,
49 math_ops.asin,
50 math_ops.asinh,
51 math_ops.atan,
52 math_ops.atanh,
53 math_ops.ceil,
[all …]
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| D | ragged_operators.py | 21 from tensorflow.python.ops import math_ops
36 ragged_tensor.RaggedTensor.__ge__ = math_ops.greater_equal
37 ragged_tensor.RaggedTensor.__gt__ = math_ops.greater
38 ragged_tensor.RaggedTensor.__le__ = math_ops.less_equal
39 ragged_tensor.RaggedTensor.__lt__ = math_ops.less
42 ragged_tensor.RaggedTensor.__and__ = math_ops.logical_and
43 ragged_tensor.RaggedTensor.__rand__ = _right(math_ops.logical_and)
44 ragged_tensor.RaggedTensor.__invert__ = math_ops.logical_not
45 ragged_tensor.RaggedTensor.__ror__ = _right(math_ops.logical_or)
46 ragged_tensor.RaggedTensor.__or__ = math_ops.logical_or
[all …]
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| /external/tensorflow/tensorflow/python/ops/ |
| D | math_grad.py | 30 from tensorflow.python.ops import math_ops
35 return x // math_ops.maximum(y, 1)
85 output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
94 output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
102 indicators = math_ops.cast(math_ops.equal(y, op.inputs[0]), grad.dtype)
104 math_ops.reduce_sum(indicators, op.inputs[1]), output_shape_kept_dims)
106 return [math_ops.divide(indicators, num_selected) * grad, None]
136 math_ops.reduce_prod(input_shape), math_ops.reduce_prod(output_shape))
137 return math_ops.truediv(sum_grad, math_ops.cast(factor, sum_grad.dtype)), None
153 output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
[all …]
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| D | metrics_impl.py | 30 from tensorflow.python.ops import math_ops
141 math_ops.equal(rank_diff, -1),
153 math_ops.equal(rank_diff, 1), maybe_squeeze_weights,
159 math_ops.equal(weights_rank_tensor, 0), lambda: weights,
186 math_ops.equal(
210 math_ops.equal(array_ops.rank(predictions),
228 return math_ops.div_no_nan(numerator, denominator, name=name)
259 predictions = math_ops.cast(predictions, dtypes.int64)
260 labels = math_ops.cast(labels, dtypes.int64)
261 num_classes = math_ops.cast(num_classes, dtypes.int64)
[all …]
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| D | linalg_grad.py | 34 from tensorflow.python.ops import math_ops
42 return -math_ops.matmul(
43 ainv, math_ops.matmul(grad, ainv, adjoint_b=True), adjoint_a=True)
76 shape_slice_size = [math_ops.subtract(array_ops.size(b1_shape), 2)]
94 matrix_count = math_ops.reduce_prod(shape[0:-2])
107 ksum = math_ops.add(k1, k2)
110 shape_slice_size = [math_ops.subtract(array_ops.size(shape), 2)]
148 middle = math_ops.matmul(l, grad, adjoint_a=True)
153 grad_a = math_ops.matmul(
154 math_ops.matmul(l_inverse, middle, adjoint_a=True), l_inverse)
[all …]
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| /external/tensorflow/tensorflow/python/data/experimental/kernel_tests/optimization/ |
| D | map_vectorization_test.py | 41 from tensorflow.python.ops import math_ops
50 logical_cases = [("LogicalNot", math_ops.logical_not)]
52 ("Angle", math_ops.angle),
53 ("ComplexAbs", math_ops.abs),
54 ("Conj", math_ops.conj),
55 ("Imag", math_ops.imag),
56 ("Real", math_ops.real),
59 ("Abs", math_ops.abs),
60 ("Acos", math_ops.acos),
61 ("Acosh", lambda x: math_ops.acosh(1 + math_ops.square(x))),
[all …]
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| /external/tensorflow/tensorflow/contrib/losses/python/losses/ |
| D | loss_ops.py | 28 from tensorflow.python.ops import math_ops
64 reduced_losses = math_ops.reduce_sum(losses, axis=axis)
65 reduced_losses = math_ops.multiply(reduced_losses, weights)
66 return math_ops.reduce_sum(reduced_losses)
80 total_loss = math_ops.reduce_sum(losses)
81 return math_ops.div_no_nan(total_loss, num_present, name="value")
104 losses = math_ops.cast(losses, dtypes.float32)
105 weights = math_ops.cast(ops.convert_to_tensor(weights), dtypes.float32)
120 mean_loss = math_ops.cast(mean_loss, input_dtype)
150 num_per_batch = math_ops.div(
[all …]
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| /external/tensorflow/tensorflow/python/ops/losses/ |
| D | losses_impl.py | 28 from tensorflow.python.ops import math_ops
92 total_loss = math_ops.reduce_sum(losses)
93 return math_ops.div_no_nan(total_loss, num_present, name="value")
120 and not math_ops.equal(weights, 0.0))):
123 weights = math_ops.cast(weights, dtype=dtypes.float32)
125 math_ops.equal(weights, 0.0),
130 return math_ops.reduce_sum(
132 axis=math_ops.range(1, array_ops.rank(present)),
135 return math_ops.reduce_sum(present, name=scope)
141 return math_ops.cast(array_ops.size(losses, name=scope), dtype=losses.dtype)
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| /external/tensorflow/tensorflow/contrib/gan/python/eval/python/ |
| D | classifier_metrics_impl.py | 47 from tensorflow.python.ops import math_ops
111 si = array_ops.where(math_ops.less(s, eps), s, math_ops.sqrt(s))
115 return math_ops.matmul(
116 math_ops.matmul(u, array_ops.diag(si)), v, transpose_b=True)
143 images = math_ops.cast(images, dtypes.float32)
186 return math_ops.reduce_sum(
187 p * (nn_ops.log_softmax(p_logits) - math_ops.log(q)), axis=1)
390 logits = math_ops.cast(logits, dtypes.float64)
393 q = math_ops.reduce_mean(p, axis=0)
396 log_score = math_ops.reduce_mean(kl)
[all …]
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| /external/tensorflow/tensorflow/python/keras/optimizer_v2/ |
| D | learning_rate_schedule.py | 28 from tensorflow.python.ops import math_ops
151 decay_steps = math_ops.cast(self.decay_steps, dtype)
152 decay_rate = math_ops.cast(self.decay_rate, dtype)
154 global_step_recomp = math_ops.cast(step, dtype)
157 p = math_ops.floor(p)
158 return math_ops.multiply(
159 initial_learning_rate, math_ops.pow(decay_rate, p), name=name)
255 b = math_ops.cast(b, x_recomp.dtype.base_dtype)
400 end_learning_rate = math_ops.cast(self.end_learning_rate, dtype)
401 power = math_ops.cast(self.power, dtype)
[all …]
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| /external/tensorflow/tensorflow/compiler/tf2xla/python/ |
| D | xla.py | 37 from tensorflow.python.ops import math_ops
76 abs = _unary_op(math_ops.abs)
78 conj = _unary_op(math_ops.conj)
79 cos = _unary_op(math_ops.cos)
80 ceil = _unary_op(math_ops.ceil)
81 digamma = _unary_op(math_ops.digamma)
82 erf = _unary_op(math_ops.erf)
83 erfc = _unary_op(math_ops.erfc)
85 exp = _unary_op(math_ops.exp)
86 expm1 = _unary_op(math_ops.expm1)
[all …]
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| /external/tensorflow/tensorflow/contrib/metrics/python/ops/ |
| D | metric_ops.py | 34 from tensorflow.python.ops import math_ops
560 predictions=math_ops.cast(predictions, dtype=dtypes.bool),
561 labels=math_ops.cast(labels, dtype=dtypes.bool),
581 math_ops.greater(fp + tn, 0), math_ops.div(fp, fp + tn), 0, name)
648 predictions=math_ops.cast(predictions, dtype=dtypes.bool),
649 labels=math_ops.cast(labels, dtype=dtypes.bool),
669 math_ops.greater(fn + tp, 0), math_ops.div(fn, fn + tp), 0, name)
749 math_ops.cast(labels, dtype=dtypes.bool), [1, -1])
762 pred_is_pos = math_ops.greater(
766 pred_is_neg = math_ops.logical_not(pred_is_pos)
[all …]
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| /external/tensorflow/tensorflow/contrib/opt/python/training/ |
| D | sign_decay.py | 30 from tensorflow.python.ops import math_ops
55 global_step = math_ops.minimum(global_step, decay_steps)
56 remaining_steps = math_ops.cast(
57 decay_steps, dtypes.int32) - math_ops.cast(global_step, dtypes.int32)
58 decayed = (math_ops.cast(remaining_steps, dtypes.float32) /
59 math_ops.cast(decay_steps, dtypes.float32))
60 return math_ops.maximum(0.0, decayed)
96 global_step = math_ops.minimum(global_step, decay_steps)
97 completed_fraction = (math_ops.cast(global_step, dtypes.float32) /
98 math_ops.cast(decay_steps, dtypes.float32))
[all …]
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| D | matrix_functions.py | 23 from tensorflow.python.ops import math_ops
47 return math_ops.logical_and(i < iter_count, err < old_err)
51 current_iterate = 0.5 * (3.0 * identity - math_ops.matmul(mat_z, mat_y))
52 current_mat_y = math_ops.matmul(mat_y, current_iterate)
53 current_mat_z = math_ops.matmul(current_iterate, mat_z)
55 mat_sqrt_a = current_mat_y * math_ops.sqrt(norm)
56 mat_a_approx = math_ops.matmul(mat_sqrt_a, mat_sqrt_a)
58 current_err = math_ops.sqrt(math_ops.reduce_sum(residual * residual)) / norm
61 identity = linalg_ops.eye(math_ops.cast(mat_a_size, dtypes.int32))
63 norm = math_ops.sqrt(math_ops.reduce_sum(mat_a * mat_a))
[all …]
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| D | ggt.py | 29 from tensorflow.python.ops import math_ops
137 initial_value=math_ops.cast(0., dtype=var_list[0].dtype.base_dtype),
193 flat_grad, math_ops.range(start_index, end_index), new_flat_grad)
207 flat_grad, math_ops.range(start_index, end_index), new_flat_grad)
227 next_grad_index = math_ops.floormod(
228 math_ops.cast(update_global_step - 1., dtypes.int32), window)
238 denom = math_ops.sqrt(
239 math_ops.minimum(
241 ops.convert_to_tensor(math_ops.cast(window, dtype=var_dtype))))
247 m = array_ops.transpose(math_ops.divide(update_grad_buffer, denom))
[all …]
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| /external/tensorflow/tensorflow/python/training/ |
| D | ftrl.py | 22 from tensorflow.python.ops import math_ops
156 math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
157 math_ops.cast(self._l1_regularization_strength_tensor,
159 math_ops.cast(self._l2_regularization_strength_tensor,
161 math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
169 math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
170 math_ops.cast(self._l1_regularization_strength_tensor,
172 math_ops.cast(self._l2_regularization_strength_tensor,
174 math_ops.cast(self._l2_shrinkage_regularization_strength_tensor,
176 math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
[all …]
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| D | rmsprop.py | 47 from tensorflow.python.ops import math_ops
150 math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
151 math_ops.cast(self._decay_tensor, var.dtype.base_dtype),
152 math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
153 math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype),
161 math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
162 math_ops.cast(self._decay_tensor, var.dtype.base_dtype),
163 math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
164 math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype),
178 math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype),
[all …]
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| /external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/ |
| D | periodic.py | 30 from tensorflow.python.ops import math_ops
76 math_ops.range(self._periodicity - 1, dtype=powers.dtype),
83 is_row_negative = math_ops.equal(range_shape_padded + 1, powers[..., None])
92 is_one = math_ops.equal(coord_diff % self._periodicity,
101 return math_ops.cast(positive_ones + negative_row_indicator[..., None],
141 math_ops.range(self._periodicity, dtype=num_steps.dtype),
153 self.dtype)[..., None] * noise_addition_scalar * math_ops.cast(
265 value = math_ops.cast(value, self.dtype)
266 return math_ops.less(
267 math_ops.abs(value - gen_math_ops.round(value)),
[all …]
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| /external/tensorflow/tensorflow/python/ops/linalg/ |
| D | linalg_impl.py | 29 from tensorflow.python.ops import math_ops
57 tensordot = math_ops.tensordot
58 trace = math_ops.trace
91 return 2.0 * math_ops.reduce_sum(
92 math_ops.log(math_ops.real(array_ops.matrix_diag_part(chol))),
133 matrix_2 = math_ops.matmul(matrix, matrix)
135 matrix_u = math_ops.matmul(matrix, tmp)
147 matrix_2 = math_ops.matmul(matrix, matrix)
148 matrix_4 = math_ops.matmul(matrix_2, matrix_2)
150 matrix_u = math_ops.matmul(matrix, tmp)
[all …]
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| /external/tensorflow/tensorflow/contrib/training/python/training/ |
| D | sgdr_learning_rate_decay.py | 25 from tensorflow.python.ops import math_ops, control_flow_ops
136 global_step = math_ops.cast(global_step, dtype)
137 t_0 = math_ops.cast(initial_period_steps, dtype)
138 t_mul = math_ops.cast(t_mul, dtype)
139 m_mul = math_ops.cast(m_mul, dtype)
141 c_one = math_ops.cast(constant_op.constant(1.0), dtype)
142 c_half = math_ops.cast(constant_op.constant(0.5), dtype)
143 c_pi = math_ops.cast(constant_op.constant(math.pi), dtype)
146 x_val = math_ops.div(global_step, t_0)
163 i_restart = math_ops.floor(
[all …]
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