| /external/tensorflow/tensorflow/contrib/factorization/python/ops/ |
| gmm_ops.py | 32 from tensorflow.python.ops import math_ops
56 num_points = math_ops.to_float(array_ops.shape(x)[0])
57 x -= math_ops.reduce_mean(x, 0, keep_dims=True)
59 cov = math_ops.reduce_sum(
60 math_ops.square(x), 0, keep_dims=True) / (num_points - 1)
62 cov = math_ops.matmul(x, x, transpose_a=True) / (num_points - 1)
78 num_data = math_ops.add_n([array_ops.shape(inp)[0] for inp in data])
84 maxval=math_ops.cast(num_data, dtypes.int64),
87 indices %= math_ops.cast(num_data, dtypes.int64)
250 ret.append(math_ops.argmax(w, 1)
[all...] |
| /external/tensorflow/tensorflow/contrib/model_pruning/python/ |
| pruning.py | 73 from tensorflow.python.ops import math_ops
176 nbins_float = math_ops.cast(nbins, values.dtype)
179 scaled_values = math_ops.truediv(
186 indices = math_ops.floor(nbins_float * scaled_values, name='indices')
189 indices = math_ops.cast(
192 return math_ops.unsorted_segment_sum(
258 masked_weights = math_ops.multiply(mask, x, _MASKED_WEIGHT_NAME)
417 return math_ops.cast(graph_global_step, np.int32)
432 p = math_ops.minimum(1.0,
433 math_ops.maximum
[all...] |
| /external/tensorflow/tensorflow/contrib/metrics/python/ops/ |
| metric_ops.py | 34 from tensorflow.python.ops import math_ops
60 math_ops.greater(denominator, 0),
61 math_ops.truediv(numerator, denominator),
565 predictions=math_ops.cast(predictions, dtype=dtypes.bool),
566 labels=math_ops.cast(labels, dtype=dtypes.bool),
586 math_ops.greater(fp + tn, 0), math_ops.div(fp, fp + tn), 0, name)
653 predictions=math_ops.cast(predictions, dtype=dtypes.bool),
654 labels=math_ops.cast(labels, dtype=dtypes.bool),
674 math_ops.greater(fn + tp, 0), math_ops.div(fn, fn + tp), 0, name
[all...] |
| /external/tensorflow/tensorflow/contrib/opt/python/training/ |
| addsign.py | 24 from tensorflow.python.ops import math_ops
116 math_ops.cast(self._lr_t, var.dtype.base_dtype),
117 math_ops.cast(self._alpha_t, var.dtype.base_dtype),
118 math_ops.cast(self._sign_decay_t, var.dtype.base_dtype),
119 math_ops.cast(self._beta_t, var.dtype.base_dtype),
128 math_ops.cast(self._lr_t, var.dtype.base_dtype),
129 math_ops.cast(self._alpha_t, var.dtype.base_dtype),
130 math_ops.cast(self._sign_decay_t, var.dtype.base_dtype),
131 math_ops.cast(self._beta_t, var.dtype.base_dtype),
136 lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype
[all...] |
| external_optimizer_test.py | 27 from tensorflow.python.ops import math_ops
78 loss = math_ops.reduce_sum(
79 math_ops.square(vector - minimum_location[:2])) / 2.
80 loss += math_ops.reduce_sum(
81 math_ops.square(scalar - minimum_location[2])) / 2.
82 loss += math_ops.reduce_sum(
83 math_ops.square(
105 loss = math_ops.reduce_sum(math_ops.square(vector - minimum_location)) / 2.
148 s = math_ops.add
[all...] |
| /external/tensorflow/tensorflow/python/ops/distributions/ |
| dirichlet.py | 27 from tensorflow.python.ops import math_ops
162 self._total_concentration = math_ops.reduce_sum(self._concentration, -1)
201 return gamma_sample / math_ops.reduce_sum(gamma_sample, -1, keepdims=True)
209 return math_ops.exp(self._log_prob(x))
213 return math_ops.reduce_sum((self.concentration - 1.) * math_ops.log(x), -1)
219 k = math_ops.cast(self.event_shape_tensor()[0], self.dtype)
223 * math_ops.digamma(self.total_concentration))
224 - math_ops.reduce_sum(
225 (self.concentration - 1.) * math_ops.digamma(self.concentration)
[all...] |
| student_t.py | 30 from tensorflow.python.ops import math_ops
213 return constant_op.constant([], dtype=math_ops.int32)
234 samples = normal_sample * math_ops.rsqrt(gamma_sample / df)
242 return -0.5 * (self.df + 1.) * math_ops.log1p(y**2. / self.df)
245 return (math_ops.log(math_ops.abs(self.scale)) +
246 0.5 * math_ops.log(self.df) +
248 math_ops.lgamma(0.5 * self.df) -
249 math_ops.lgamma(0.5 * (self.df + 1.)))
252 return math_ops.exp(self._log_prob(x)
[all...] |
| gamma.py | 30 from tensorflow.python.ops import math_ops
197 return math_ops.exp(self._log_prob(x))
200 return math_ops.log(self._cdf(x))
206 return math_ops.igamma(self.concentration, self.rate * x)
210 return (self.concentration - 1.) * math_ops.log(x) - self.rate * x
213 return (math_ops.lgamma(self.concentration)
214 - self.concentration * math_ops.log(self.rate))
218 - math_ops.log(self.rate)
219 + math_ops.lgamma(self.concentration)
221 math_ops.digamma(self.concentration))
[all...] |
| /external/tensorflow/tensorflow/contrib/gan/python/eval/python/ |
| classifier_metrics_impl.py | 46 from tensorflow.python.ops import math_ops
104 si = array_ops.where(math_ops.less(s, eps), s, math_ops.sqrt(s))
108 return math_ops.matmul(
109 math_ops.matmul(u, array_ops.diag(si)), v, transpose_b=True)
135 images = math_ops.to_float(images)
178 return math_ops.reduce_sum(
179 p * (nn_ops.log_softmax(p_logits) - math_ops.log(q)), axis=1)
379 logits = math_ops.to_double(logits)
382 q = math_ops.reduce_mean(p, axis=0
[all...] |
| /external/tensorflow/tensorflow/contrib/data/python/ops/ |
| resampling.py | 30 from tensorflow.python.ops import math_ops
89 proportion_rejected = math_ops.reduce_sum(
92 math_ops.less(proportion_rejected, .5),
162 max_ratio = math_ops.reduce_max(ratio_l)
181 num_examples_per_class_seen = math_ops.add(
182 num_examples_per_class_seen, math_ops.reduce_sum(
184 init_prob_estimate = math_ops.truediv(
186 math_ops.reduce_sum(num_examples_per_class_seen))
187 dist = math_ops.cast(init_prob_estimate, dtypes.float32)
|
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/bijectors/ |
| softplus.py | 24 from tensorflow.python.ops import math_ops
111 hinge_softness = math_ops.cast(self.hinge_softness, x.dtype)
117 hinge_softness = math_ops.cast(self.hinge_softness, y.dtype)
123 # ildj = math_ops.reduce_sum(y - distribution_util.softplus_inverse(y),
132 y /= math_ops.cast(self.hinge_softness, y.dtype)
133 return -math_ops.reduce_sum(math_ops.log(-math_ops.expm1(-y)),
138 x /= math_ops.cast(self.hinge_softness, x.dtype)
139 return -math_ops.reduce_sum(nn_ops.softplus(-x)
[all...] |
| /external/tensorflow/tensorflow/contrib/layers/python/ops/ |
| sparse_ops.py | 25 from tensorflow.python.ops import math_ops
52 return math_ops.cast(ignore_value, dtype, name="ignore_value")
74 math_ops.not_equal(dense_tensor, ignore_value), name="indices")
149 missing_indicators = math_ops.equal(
166 math_ops.cumsum(binary_indicators, axis=-1), "row_index_indicators")
168 math_ops.reduce_max(row_index_indicators), shape=(1,),
182 values=math_ops.cast(
223 num_rows = math_ops.cast(sparse_input.dense_shape[row_axis], dtypes.int32)
224 row_envelope = math_ops.unsorted_segment_max(
|
| /external/tensorflow/tensorflow/python/training/ |
| momentum.py | 22 from tensorflow.python.ops import math_ops
94 math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
96 math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
104 math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype),
106 math_ops.cast(self._momentum_tensor, grad.dtype.base_dtype),
114 math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
116 math_ops.cast(self._momentum_tensor, var.dtype.base_dtype),
124 math_ops.cast(self._learning_rate_tensor, grad.dtype),
126 math_ops.cast(self._momentum_tensor, grad.dtype),
|
| adagrad_da.py | 23 from tensorflow.python.ops import math_ops
118 math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
119 math_ops.cast(self._l1_regularization_strength, var.dtype.base_dtype),
120 math_ops.cast(self._l2_regularization_strength, var.dtype.base_dtype),
134 math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype),
135 math_ops.cast(self._l1_regularization_strength, grad.dtype.base_dtype),
136 math_ops.cast(self._l2_regularization_strength, grad.dtype.base_dtype),
151 math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
152 math_ops.cast(self._l1_regularization_strength, var.dtype.base_dtype),
153 math_ops.cast(self._l2_regularization_strength, var.dtype.base_dtype)
[all...] |
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| kumaraswamy.py | 26 from tensorflow.python.ops import math_ops
59 return math_ops.digamma(x + one) - math_ops.digamma(one)
179 return math_ops.log1p(-(1 - x**a)**b)
195 return b * math_ops.log1p(-x**a)
201 return (a - 1) * math_ops.log(x) + (b - 1) * math_ops.log1p(-x**a)
206 return -(math_ops.log(a) + math_ops.log(b))
212 1 - 1. / b) * _harmonic_number(b) + math_ops.log(a) + math_ops.log(b
[all...] |
| negative_binomial.py | 26 from tensorflow.python.ops import math_ops
148 beta=math_ops.exp(-self.logits),
160 return math_ops.betainc(self.total_count, 1. + x,
161 math_ops.sigmoid(-self.logits))
170 return (self.total_count * math_ops.log_sigmoid(-self.logits)
171 + x * math_ops.log_sigmoid(self.logits))
176 return (-math_ops.lgamma(self.total_count + x)
177 + math_ops.lgamma(1. + x)
178 + math_ops.lgamma(self.total_count))
181 return self.total_count * math_ops.exp(self.logits
[all...] |
| /external/tensorflow/tensorflow/python/ops/ |
| clip_ops.py | 30 from tensorflow.python.ops import math_ops
64 t_min = math_ops.minimum(t, clip_value_max)
69 t_max = math_ops.maximum(t_min, clip_value_min, name=name)
113 l2norm = math_ops.sqrt(math_ops.reduce_sum(t * t, axes, keepdims=True))
119 intermediate / math_ops.maximum(l2norm, clip_norm), name=name)
163 half_squared_norm = math_ops.reduce_sum(array_ops.stack(half_squared_norms))
165 norm = math_ops.sqrt(
227 scale = clip_norm * math_ops.minimum(
287 n_element = math_ops.cast(array_ops.size(t), dtypes.float32
[all...] |
| math_ops_test.py | 15 """Tests for tensorflow.ops.math_ops."""
28 from tensorflow.python.ops import math_ops
45 y_tf = self.evaluate(math_ops.reduce_sum(x))
53 self.assertAllEqual(self.evaluate(math_ops.reduce_sum(x, axis=axis)),
56 self.assertAllEqual(self.evaluate(math_ops.reduce_sum(x, axis=axis)),
59 self.assertEqual(self.evaluate(math_ops.reduce_sum(x, axis=axis)), 21)
70 math_ops.reduce_sum(x, axis)
80 y_tf_np = math_ops.reduce_logsumexp(x_np).eval()
88 y_tf = math_ops.reduce_logsumexp(x_np, reduction_indices=[0])
98 y_tf = math_ops.reduce_logsumexp(x_np, reduction_indices=0
[all...] |
| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ |
| inline_test.py | 27 from tensorflow.python.ops import math_ops
38 forward_fn=math_ops.exp,
39 inverse_fn=math_ops.log,
41 lambda y: -math_ops.reduce_sum( # pylint: disable=g-long-lambda
42 math_ops.log(y), reduction_indices=-1)),
44 lambda x: math_ops.reduce_sum(x, reduction_indices=-1)),
|
| /external/tensorflow/tensorflow/contrib/graph_editor/tests/ |
| match_test.py | 23 from tensorflow.python.ops import math_ops
35 self.c = math_ops.add(self.a, self.b, name="c")
38 self.e = math_ops.add(self.c, self.d, name="e")
39 self.f = math_ops.add(self.c, self.d, name="f")
40 self.g = math_ops.add(self.c, self.a, name="g")
42 self.h = math_ops.add(self.f, self.g, name="h")
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| logging_ops_test.py | 27 from tensorflow.python.ops import math_ops
43 math_ops.less(epsilon, y), ["Divide-by-zero"])
45 out = math_ops.div(z, y)
53 math_ops.less(epsilon, x), ["Divide-by-zero", "less than x"])
55 out = math_ops.div(z, x)
72 wx = math_ops.matmul(w, inp, name="wx")
|
| /external/tensorflow/tensorflow/python/ops/linalg/ |
| linalg_impl.py | 25 from tensorflow.python.ops import math_ops
52 tensordot = math_ops.tensordot
53 trace = math_ops.trace
86 return 2.0 * math_ops.reduce_sum(
87 math_ops.log(math_ops.real(array_ops.matrix_diag_part(chol))),
|
| /external/tensorflow/tensorflow/contrib/py2tf/impl/ |
| api_test.py | 25 from tensorflow.python.ops import math_ops
32 config.DEFAULT_UNCOMPILED_MODULES.add((math_ops.__name__,))
50 while math_ops.reduce_sum(x) > s:
66 return math_ops.negative(a)
70 while math_ops.reduce_sum(x) > s:
87 return math_ops.negative(a)
91 while math_ops.reduce_sum(x) > s:
114 while math_ops.reduce_sum(x) > s:
136 while math_ops.reduce_sum(x) > s:
152 return math_ops.negative(a
[all...] |
| /external/tensorflow/tensorflow/contrib/quantize/python/ |
| quant_ops.py | 26 from tensorflow.python.ops import math_ops
127 batch_min = math_ops.reduce_min(
132 batch_min = math_ops.reduce_min(inputs, name='BatchMin')
134 batch_min = math_ops.minimum(batch_min, 0.0)
140 batch_max = math_ops.reduce_max(
145 batch_max = math_ops.reduce_max(inputs, name='BatchMax')
147 batch_max = math_ops.maximum(batch_max, 0.0)
239 batch_min = math_ops.reduce_min(
244 batch_min = math_ops.reduce_min(inputs, name='BatchMin')
246 batch_min = math_ops.minimum(batch_min, 0.0
[all...] |
| /external/tensorflow/tensorflow/contrib/crf/python/ops/ |
| crf.py | 60 from tensorflow.python.ops import math_ops
92 math_ops.range(batch_size, dtype=tag_indices.dtype), [-1, 1])
106 pred=math_ops.equal(inputs.shape[1].value or array_ops.shape(inputs)[1],
131 return math_ops.reduce_logsumexp(first_input, [1])
146 log_norm = math_ops.reduce_logsumexp(alphas, [1])
150 return control_flow_ops.cond(pred=math_ops.equal(max_seq_len, 1),
207 math_ops.range(batch_size) * max_seq_len * num_tags, 1)
208 offsets += array_ops.expand_dims(math_ops.range(max_seq_len) * num_tags, 0)
211 offsets = math_ops.to_int64(offsets)
222 unary_scores = math_ops.reduce_sum(unary_scores * masks, 1
[all...] |
