/external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
H A D | vector_exponential_diag.py | 21 from tensorflow.contrib.distributions.python.ops import distribution_util namespace 185 scale = distribution_util.make_diag_scale(
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H A D | vector_laplace_diag.py | 21 from tensorflow.contrib.distributions.python.ops import distribution_util namespace 220 scale = distribution_util.make_diag_scale(
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H A D | conditional_distribution.py | 22 from tensorflow.python.ops.distributions import util as distribution_util namespace 32 @distribution_util.AppendDocstring(kwargs_dict={ 39 @distribution_util.AppendDocstring(kwargs_dict={ 45 @distribution_util.AppendDocstring(kwargs_dict={ 51 @distribution_util.AppendDocstring(kwargs_dict={ 57 @distribution_util.AppendDocstring(kwargs_dict={ 63 @distribution_util.AppendDocstring(kwargs_dict={ 70 @distribution_util.AppendDocstring(kwargs_dict={
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H A D | mvn_diag.py | 21 from tensorflow.contrib.distributions.python.ops import distribution_util namespace 203 scale = distribution_util.make_diag_scale(
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H A D | mvn_diag_plus_low_rank.py | 22 from tensorflow.contrib.distributions.python.ops import distribution_util namespace 227 scale = distribution_util.make_diag_scale(
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H A D | mvn_tril.py | 24 from tensorflow.python.ops.distributions import util as distribution_util namespace 193 num_rows=distribution_util.dimension_size(loc, -1),
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H A D | vector_student_t.py | 22 from tensorflow.contrib.distributions.python.ops import distribution_util namespace 212 distribution_util.shapes_from_loc_and_scale( 214 override_batch_shape = distribution_util.pick_vector(
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H A D | autoregressive.py | 25 from tensorflow.python.ops.distributions import util as distribution_util namespace 196 seed = distribution_util.gen_new_seed(
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H A D | relaxed_bernoulli.py | 30 from tensorflow.python.ops.distributions import util as distribution_util namespace 173 self._logits, self._probs = distribution_util.get_logits_and_probs(
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H A D | sinh_arcsinh.py | 22 from tensorflow.contrib.distributions.python.ops import distribution_util namespace 148 batch_shape = distribution_util.get_broadcast_shape( 162 asserts = distribution_util.maybe_check_scalar_distribution(
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H A D | vector_sinh_arcsinh_diag.py | 22 from tensorflow.contrib.distributions.python.ops import distribution_util namespace 189 scale_linop = distribution_util.make_diag_scale( 195 batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale( 207 asserts = distribution_util.maybe_check_scalar_distribution(
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H A D | conditional_transformed_distribution.py | 26 from tensorflow.python.ops.distributions import util as distribution_util namespace 52 @distribution_util.AppendDocstring(kwargs_dict=_condition_kwargs_dict) 57 distribution_util.pick_vector(self._needs_rotation, self._empty, [n]), 60 distribution_util.pick_vector(self._needs_rotation, [n], self._empty)) 101 @distribution_util.AppendDocstring(kwargs_dict=_condition_kwargs_dict) 126 @distribution_util.AppendDocstring(kwargs_dict=_condition_kwargs_dict) 148 @distribution_util.AppendDocstring(kwargs_dict=_condition_kwargs_dict) 161 @distribution_util.AppendDocstring(kwargs_dict=_condition_kwargs_dict) 174 @distribution_util.AppendDocstring(kwargs_dict=_condition_kwargs_dict) 188 @distribution_util [all...] |
H A D | mixture_same_family.py | 23 from tensorflow.contrib.distributions.python.ops import distribution_util as distribution_utils 30 from tensorflow.python.ops.distributions import util as distribution_util namespace 172 distribution_util.pick_vector(
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H A D | mvn_linear_operator.py | 21 from tensorflow.contrib.distributions.python.ops import distribution_util namespace 183 batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale( 208 @distribution_util.AppendDocstring(_mvn_sample_note) 212 @distribution_util.AppendDocstring(_mvn_sample_note) 236 if distribution_util.is_diagonal_scale(self.scale): 242 if distribution_util.is_diagonal_scale(self.scale): 253 if distribution_util.is_diagonal_scale(self.scale):
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H A D | vector_exponential_linear_operator.py | 22 from tensorflow.contrib.distributions.python.ops import distribution_util namespace 188 batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale( 212 @distribution_util.AppendDocstring(_mvn_sample_note) 216 @distribution_util.AppendDocstring(_mvn_sample_note) 241 if distribution_util.is_diagonal_scale(self.scale): 247 if distribution_util.is_diagonal_scale(self.scale): 258 if distribution_util.is_diagonal_scale(self.scale):
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H A D | vector_laplace_linear_operator.py | 24 from tensorflow.contrib.distributions.python.ops import distribution_util namespace 204 batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale( 229 @distribution_util.AppendDocstring(_mvn_sample_note) 233 @distribution_util.AppendDocstring(_mvn_sample_note) 265 if distribution_util.is_diagonal_scale(self.scale): 271 if distribution_util.is_diagonal_scale(self.scale): 282 if distribution_util.is_diagonal_scale(self.scale):
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H A D | binomial.py | 29 from tensorflow.python.ops.distributions import util as distribution_util namespace 171 self._logits, self._probs = distribution_util.get_logits_and_probs( 218 @distribution_util.AppendDocstring(_binomial_sample_note) 222 @distribution_util.AppendDocstring(_binomial_sample_note) 255 @distribution_util.AppendDocstring( 270 distribution_util.assert_integer_form( 279 counts = distribution_util.embed_check_nonnegative_integer_form(counts)
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/external/tensorflow/tensorflow/contrib/distributions/python/ops/bijectors/ |
H A D | conditional_bijector.py | 22 from tensorflow.python.ops.distributions import util as distribution_util namespace 31 @distribution_util.AppendDocstring(kwargs_dict={ 37 @distribution_util.AppendDocstring(kwargs_dict={ 43 @distribution_util.AppendDocstring(kwargs_dict={ 50 @distribution_util.AppendDocstring(kwargs_dict={
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H A D | softplus.py | 27 from tensorflow.python.ops.distributions import util as distribution_util namespace 78 @distribution_util.AppendDocstring( 116 return distribution_util.softplus_inverse(y) 118 return hinge_softness * distribution_util.softplus_inverse( 123 # ildj = math_ops.reduce_sum(y - distribution_util.softplus_inverse(y),
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H A D | affine.py | 22 from tensorflow.contrib.distributions.python.ops import distribution_util namespace 307 shape_hint = distribution_util.dimension_size(perturb_factor, axis=-2) 319 scale = distribution_util.make_tril_scale( 391 event_size = distribution_util.pick_vector(
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H A D | cholesky_outer_product.py | 31 from tensorflow.python.ops.distributions import util as distribution_util namespace 231 distribution_util.pick_vector(
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H A D | softmax_centered.py | 23 from tensorflow.contrib.distributions.python.ops import distribution_util namespace 138 y = distribution_util.pad(y, axis=-1, back=True) 167 ndims = distribution_util.prefer_static_rank(y)
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/external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
H A D | autoregressive_test.py | 30 from tensorflow.python.ops.distributions import util as distribution_util namespace 43 return distribution_util.fill_triangular(0.25 * p)
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/external/tensorflow/tensorflow/contrib/bayesflow/python/ops/ |
H A D | csiszar_divergence_impl.py | 50 from tensorflow.python.ops.distributions import util as distribution_util namespace 1061 d_ok_result = logu + distribution_util.softplus_inverse(safe_d)
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H A D | layers_dense_variational.py | 33 from tensorflow.python.ops.distributions import util as distribution_util namespace 1024 seed=distribution_util.gen_new_seed(
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