Searched refs:math_ops (Results 1 - 25 of 657) sorted by relevance

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/external/tensorflow/tensorflow/contrib/opt/python/training/
H A Dsign_decay.py28 from tensorflow.python.ops import math_ops namespace
53 global_step = math_ops.minimum(global_step, decay_steps)
54 remaining_steps = math_ops.to_int32(decay_steps) - math_ops.to_int32(
56 decayed = math_ops.to_float(remaining_steps) / math_ops.to_float(
58 return math_ops.maximum(0.0, decayed)
94 global_step = math_ops.minimum(global_step, decay_steps)
95 completed_fraction = math_ops.to_float(global_step) / math_ops
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H A Dnadam_optimizer.py22 from tensorflow.python.ops import math_ops namespace
42 math_ops.cast(beta1_power, var.dtype.base_dtype),
43 math_ops.cast(beta2_power, var.dtype.base_dtype),
44 math_ops.cast(self._lr_t, var.dtype.base_dtype),
45 math_ops.cast(self._beta1_t, var.dtype.base_dtype),
46 math_ops.cast(self._beta2_t, var.dtype.base_dtype),
47 math_ops.cast(self._epsilon_t, var.dtype.base_dtype),
60 math_ops.cast(beta1_power, grad.dtype.base_dtype),
61 math_ops.cast(beta2_power, grad.dtype.base_dtype),
62 math_ops
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/external/tensorflow/tensorflow/contrib/sparsemax/python/ops/
H A Dsparsemax.py24 from tensorflow.python.ops import math_ops namespace
52 z = logits - math_ops.reduce_mean(logits, axis=1)[:, array_ops.newaxis]
58 z_cumsum = math_ops.cumsum(z_sorted, axis=1)
59 k = math_ops.range(
60 1, math_ops.cast(dims, logits.dtype) + 1, dtype=logits.dtype)
64 k_z = math_ops.reduce_sum(math_ops.cast(z_check, dtypes.int32), axis=1)
67 indices = array_ops.stack([math_ops.range(0, obs), k_z - 1], axis=1)
69 tau_z = (tau_sum - 1) / math_ops.cast(k_z, logits.dtype)
72 return math_ops
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H A Dsparsemax_loss.py23 from tensorflow.python.ops import math_ops namespace
51 math_ops.reduce_mean(logits, axis=1)[:, array_ops.newaxis]
54 support = math_ops.cast(sparsemax > 0, sparsemax.dtype)
60 return math_ops.reduce_sum(sum_s + q_part, axis=1)
/external/tensorflow/tensorflow/python/training/
H A Dlearning_rate_decay.py26 from tensorflow.python.ops import math_ops namespace
98 global_step = math_ops.cast(global_step, dtype)
99 decay_steps = math_ops.cast(decay_steps, dtype)
100 decay_rate = math_ops.cast(decay_rate, dtype)
103 p = math_ops.floor(p)
104 return math_ops.multiply(
105 learning_rate, math_ops.pow(decay_rate, p), name=name)
161 b = math_ops.cast(b, x.dtype.base_dtype)
275 global_step = math_ops.cast(global_step, dtype)
276 decay_steps = math_ops
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H A Dftrl.py22 from tensorflow.python.ops import math_ops namespace
152 math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
153 math_ops.cast(self._l1_regularization_strength_tensor,
155 math_ops.cast(self._l2_regularization_strength_tensor,
157 math_ops.cast(self._learning_rate_power_tensor, var.dtype.base_dtype),
165 math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
166 math_ops.cast(self._l1_regularization_strength_tensor,
168 math_ops.cast(self._l2_regularization_strength_tensor,
170 math_ops.cast(self._l2_shrinkage_regularization_strength_tensor,
172 math_ops
[all...]
H A Drmsprop.py47 from tensorflow.python.ops import math_ops namespace
140 math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
141 math_ops.cast(self._decay_tensor, var.dtype.base_dtype),
142 math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
143 math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype),
151 math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
152 math_ops.cast(self._decay_tensor, var.dtype.base_dtype),
153 math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
154 math_ops.cast(self._epsilon_tensor, var.dtype.base_dtype),
168 math_ops
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/external/tensorflow/tensorflow/contrib/distributions/python/ops/bijectors/
H A Dsinh_arcsinh.py27 from tensorflow.python.ops import math_ops namespace
38 math_ops.abs(x) * np.sqrt(np.finfo(x.dtype.as_numpy_dtype).eps) <= 1.,
39 math_ops.sqrt(x**2. + 1.),
55 math_ops.abs(x))
141 return math_ops.sinh((math_ops.asinh(x) + self.skewness) * self.tailweight)
144 return math_ops.sinh(math_ops.asinh(y) / self.tailweight - self.skewness)
153 return math_ops.reduce_sum(
156 math_ops
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H A Dsigmoid.py21 from tensorflow.python.ops import math_ops namespace
39 return math_ops.sigmoid(x)
42 return math_ops.log(y) - math_ops.log1p(-y)
45 return -math_ops.log(y) - math_ops.log1p(-y)
H A Dpower_transform.py25 from tensorflow.python.ops import math_ops namespace
85 return math_ops.exp(x)
88 return math_ops.exp(math_ops.log1p(x * self.power) / self.power)
93 return math_ops.log(y)
96 return math_ops.expm1(math_ops.log(y) * self.power) / self.power
101 return (self.power - 1.) * math_ops.reduce_sum(
102 math_ops.log(y), axis=event_dims)
108 return math_ops
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H A Dgumbel.py25 from tensorflow.python.ops import math_ops namespace
97 return math_ops.exp(-math_ops.exp(-z))
101 return self.loc - self.scale * math_ops.log(-math_ops.log(y))
106 return math_ops.reduce_sum(
107 math_ops.log(self.scale / (-math_ops.log(y) * y)), axis=event_dims)
112 return math_ops.reduce_sum(
113 -z - math_ops
[all...]
/external/tensorflow/tensorflow/contrib/distributions/python/ops/
H A Dnormal_conjugate_posteriors.py21 from tensorflow.python.ops import math_ops namespace
75 n = math_ops.cast(n, prior.dtype)
76 scale0_2 = math_ops.square(prior.scale)
77 scale_2 = math_ops.square(scale)
81 scale=math_ops.sqrt(scalep_2))
141 n = math_ops.cast(n, prior.dtype)
142 scale0_2 = math_ops.square(prior.scale)
143 scale_2 = math_ops.square(scale)
147 scale=math_ops.sqrt(scalep_2 + scale_2))
/external/tensorflow/tensorflow/contrib/metrics/python/metrics/
H A Dclassification.py24 from tensorflow.python.ops import math_ops namespace
57 is_correct = math_ops.cast(
58 math_ops.equal(predictions, labels), dtypes.float32)
60 is_correct = math_ops.multiply(is_correct, weights)
61 num_values = math_ops.multiply(weights, array_ops.ones_like(is_correct))
62 return math_ops.div(math_ops.reduce_sum(is_correct),
63 math_ops.reduce_sum(num_values))
64 return math_ops.reduce_mean(is_correct)
/external/tensorflow/tensorflow/contrib/losses/python/losses/
H A Dloss_ops.py27 from tensorflow.python.ops import math_ops namespace
62 reduced_losses = math_ops.reduce_sum(
64 reduced_losses = math_ops.multiply(reduced_losses, weights)
65 return math_ops.reduce_sum(reduced_losses)
85 math_ops.greater(denominator, 0),
86 math_ops.div(numerator,
88 math_ops.equal(denominator, 0),
105 total_loss = math_ops.reduce_sum(losses)
129 losses = math_ops.to_float(losses)
130 weights = math_ops
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/external/tensorflow/tensorflow/contrib/layers/python/ops/
H A Dbucketization_op.py20 from tensorflow.python.ops import math_ops namespace
40 return math_ops._bucketize( # pylint: disable=protected-access
/external/tensorflow/tensorflow/contrib/losses/python/metric_learning/
H A Dmetric_loss_ops.py27 from tensorflow.python.ops import math_ops namespace
52 pairwise_distances_squared = math_ops.add(
53 math_ops.reduce_sum(
54 math_ops.square(feature),
57 math_ops.reduce_sum(
58 math_ops.square(
61 keepdims=True)) - 2.0 * math_ops.matmul(
65 pairwise_distances_squared = math_ops.maximum(pairwise_distances_squared, 0.0)
67 error_mask = math_ops.less_equal(pairwise_distances_squared, 0.0)
73 pairwise_distances = math_ops
[all...]
/external/tensorflow/tensorflow/python/kernel_tests/
H A Dbincount_op_test.py15 """Tests for math_ops.bincount."""
25 from tensorflow.python.ops import math_ops namespace
34 math_ops.bincount([], minlength=5).eval(), [0, 0, 0, 0, 0])
35 self.assertAllEqual(math_ops.bincount([], minlength=1).eval(), [0])
36 self.assertAllEqual(math_ops.bincount([], minlength=0).eval(), [])
38 math_ops.bincount([], minlength=0, dtype=np.float32).eval().dtype,
41 math_ops.bincount([], minlength=3, dtype=np.float64).eval().dtype,
47 math_ops.bincount([1, 1, 1, 2, 2, 3]).eval(), [0, 3, 2, 1])
49 self.assertAllEqual(math_ops.bincount(arr).eval(), [0, 5, 4, 3, 2, 1])
51 self.assertAllEqual(math_ops
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/external/tensorflow/tensorflow/python/ops/
H A Dspectral_grad.py25 from tensorflow.python.ops import math_ops namespace
30 return math_ops.reduce_prod(array_ops.shape(grad)[-rank:])
35 size = math_ops.cast(_FFTSizeForGrad(grad, 1), dtypes.float32)
36 return spectral_ops.ifft(grad) * math_ops.complex(size, 0.)
41 rsize = 1. / math_ops.cast(_FFTSizeForGrad(grad, 1), dtypes.float32)
42 return spectral_ops.fft(grad) * math_ops.complex(rsize, 0.)
47 size = math_ops.cast(_FFTSizeForGrad(grad, 2), dtypes.float32)
48 return spectral_ops.ifft2d(grad) * math_ops.complex(size, 0.)
53 rsize = 1. / math_ops.cast(_FFTSizeForGrad(grad, 2), dtypes.float32)
54 return spectral_ops.fft2d(grad) * math_ops
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H A Dmath_grad.py15 """Gradients for operators defined in math_ops.py."""
30 from tensorflow.python.ops import math_ops namespace
35 return x // math_ops.maximum(y, 1)
61 output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
70 output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
78 indicators = math_ops.cast(math_ops.equal(y, op.inputs[0]), grad.dtype)
80 math_ops.reduce_sum(indicators, op.inputs[1]), output_shape_kept_dims)
82 return [math_ops.div(indicators, num_selected) * grad, None]
112 math_ops
[all...]
H A Dlinalg_grad.py34 from tensorflow.python.ops import math_ops namespace
42 return -math_ops.matmul(
43 ainv, math_ops.matmul(grad, ainv, adjoint_b=True), adjoint_a=True)
72 middle = math_ops.matmul(l, grad, adjoint_a=True)
77 grad_a = math_ops.matmul(
78 math_ops.matmul(l_inverse, middle, adjoint_a=True), l_inverse)
97 qdq = math_ops.matmul(q, dq, adjoint_a=True)
99 rdr = math_ops.matmul(r, dr, adjoint_b=True)
109 grad_a = math_ops.matmul(q, dr + _TriangularSolve(tril, r))
110 grad_b = _TriangularSolve(dq - math_ops
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H A Dmetrics_impl.py29 from tensorflow.python.ops import math_ops namespace
106 math_ops.equal(rank_diff, -1),
118 math_ops.equal(rank_diff, 1), maybe_squeeze_weights,
124 math_ops.equal(weights_rank_tensor, 0), lambda: weights,
151 math_ops.equal(
175 math_ops.equal(array_ops.rank(predictions),
191 t = math_ops.truediv(numerator, denominator)
193 condition = math_ops.greater(denominator, zero)
194 zero = math_ops.cast(zero, t.dtype)
212 math_ops
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/external/tensorflow/tensorflow/contrib/timeseries/python/timeseries/state_space_models/
H A Dkalman_filter.py30 from tensorflow.python.ops import math_ops namespace
131 min_diag = math_ops.reduce_min(diag)
170 math_ops.matmul(
207 prior_variance_transitioned = math_ops.matmul(
208 math_ops.matmul(transition_matrices, prior_state_var),
243 kalman_solve_rhs = math_ops.matmul(
253 math_ops.matmul(
258 gain_obs = math_ops.matmul(
266 posterior_state_var = math_ops.matmul(identity_minus_factor,
280 left_multiplied_state_var = math_ops
[all...]
/external/tensorflow/tensorflow/contrib/bayesflow/python/ops/
H A Dhalton_sequence_impl.py29 from tensorflow.python.ops import math_ops namespace
157 max_sizes_by_axes = _base_expansion_size(math_ops.reduce_max(indices),
160 max_size = math_ops.reduce_max(max_sizes_by_axes)
172 exponents_by_axes = array_ops.tile([math_ops.range(max_size)], [dim, 1])
177 coeffs = math_ops.floor_div(indices, weights)
178 coeffs *= 1 - math_ops.cast(weight_mask, dtype)
180 return math_ops.reduce_sum(coeffs / weights, axis=-1)
209 sample_indices = math_ops.range(n, dtype=dtype)
211 sample_indices = math_ops.cast(sample_indices, dtype)
241 return math_ops
[all...]
/external/tensorflow/tensorflow/contrib/boosted_trees/python/utils/
H A Dlosses.py24 from tensorflow.python.ops import math_ops namespace
40 labels = math_ops.to_float(labels)
68 labels = math_ops.to_int64(labels)
75 labels = math_ops.reduce_sum(
77 labels = math_ops.to_float(labels)
80 unnormalized_probs = math_ops.exp(logits)
81 normalizers = math_ops.reduce_sum(unnormalized_probs, 1, keepdims=True)
82 softmax_predictions = math_ops.divide(unnormalized_probs,
83 math_ops.add(normalizers, eps))
86 probs_for_real_class = math_ops
[all...]
/external/tensorflow/tensorflow/contrib/kfac/python/kernel_tests/
H A Dop_queue_test.py23 from tensorflow.python.ops import math_ops namespace
33 math_ops.add(1, 2),
34 math_ops.subtract(1, 2),
35 math_ops.reduce_mean([1, 2]),

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