| /external/tensorflow/tensorflow/python/ops/ |
| D | linalg_ops_impl.py | 35 batch_shape=None, argument
43 name, default_name='eye', values=[num_rows, num_columns, batch_shape]):
45 batch_shape = [] if batch_shape is None else batch_shape
62 if isinstance(batch_shape, ops.Tensor) or isinstance(diag_size, ops.Tensor):
63 batch_shape = ops.convert_to_tensor(
64 batch_shape, name='shape', dtype=dtypes.int32)
65 diag_shape = array_ops.concat((batch_shape, [diag_size]), axis=0)
67 shape = array_ops.concat((batch_shape, [num_rows, num_columns]), axis=0)
70 batch_shape = list(batch_shape)
71 diag_shape = batch_shape + [diag_size]
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| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| D | independent.py | 168 batch_shape = self.distribution.batch_shape_tensor()
170 batch_shape.shape.with_rank_at_least(1)[0])
173 else array_ops.shape(batch_shape)[0])
174 return batch_shape[:batch_ndims - self.reinterpreted_batch_ndims]
177 batch_shape = self.distribution.batch_shape
179 or batch_shape.ndims is None):
181 d = batch_shape.ndims - self._static_reinterpreted_batch_ndims
182 return batch_shape[:d]
186 batch_shape = self.distribution.batch_shape_tensor()
188 batch_shape.shape.with_rank_at_least(1)[0])
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| D | batch_reshape.py | 86 batch_shape, argument
117 with ops.name_scope(name, values=[batch_shape]) as name:
120 batch_shape, dtype=dtypes.int32, name="batch_shape")
122 batch_shape, batch_shape_static, runtime_assertions = calculate_reshape(
126 self._batch_shape_ = batch_shape
225 if self.batch_shape.ndims is None else self.batch_shape.ndims)
255 self.batch_shape.ndims is not None):
256 new_shape = static_sample_shape.concatenate(self.batch_shape)
274 if (self.batch_shape.ndims is not None and
280 self.batch_shape.concatenate(event_shape))
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| /external/tensorflow/tensorflow/python/kernel_tests/linalg/ |
| D | linear_operator_zeros_test.py | 59 batch_shape = shape[:-2]
63 num_rows, batch_shape=batch_shape, dtype=dtype)
104 linalg_lib.LinearOperatorZeros(num_rows=2, batch_shape=2)
108 linalg_lib.LinearOperatorZeros(num_rows=2, batch_shape=[2.])
112 linalg_lib.LinearOperatorZeros(num_rows=2, batch_shape=[-2])
140 batch_shape = array_ops.placeholder(dtypes.int32)
142 num_rows=2, batch_shape=batch_shape, assert_proper_shapes=True)
144 operator.to_dense().eval(feed_dict={batch_shape: 2})
149 batch_shape = array_ops.placeholder(dtypes.int32)
151 num_rows=2, batch_shape=batch_shape, assert_proper_shapes=True)
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| D | linear_operator_identity_test.py | 55 batch_shape = shape[:-2]
59 num_rows, batch_shape=batch_shape, dtype=dtype)
60 mat = linalg_ops.eye(num_rows, batch_shape=batch_shape, dtype=dtype)
106 linalg_lib.LinearOperatorIdentity(num_rows=2, batch_shape=2)
110 linalg_lib.LinearOperatorIdentity(num_rows=2, batch_shape=[2.])
114 linalg_lib.LinearOperatorIdentity(num_rows=2, batch_shape=[-2])
137 batch_shape = array_ops.placeholder(dtypes.int32)
139 num_rows=2, batch_shape=batch_shape, assert_proper_shapes=True)
141 operator.to_dense().eval(feed_dict={batch_shape: 2})
146 batch_shape = array_ops.placeholder(dtypes.int32)
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| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| D | quantized_distribution_test.py | 133 batch_shape = (5, 5)
141 low=array_ops.zeros(batch_shape, dtype=dtypes.float32),
142 high=10 * array_ops.ones(batch_shape, dtype=dtypes.float32))
147 x = rng.randint(-3, 13, size=batch_shape).astype(np.float32)
151 expected_pmf = (1 / 10) * np.ones(batch_shape)
167 batch_shape = (2,)
171 batch_shape, dtype=dtypes.float32),
173 batch_shape, dtype=dtypes.float32))
243 batch_shape = (3, 3)
244 mu = rng.randn(*batch_shape)
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| D | mixture_test.py | 110 def make_univariate_mixture(batch_shape, num_components, use_static_graph): argument
111 batch_shape = ops.convert_to_tensor(batch_shape, dtypes.int32)
113 array_ops.concat((batch_shape, [num_components]), axis=0),
117 loc=random_ops.random_normal(batch_shape),
118 scale=10 * random_ops.random_uniform(batch_shape))
125 def make_multivariate_mixture(batch_shape, num_components, event_shape, argument
128 batch_shape_tensor = batch_shape
134 tensor_shape.TensorShape(batch_shape).concatenate(num_components))
136 tensor_shape.TensorShape(batch_shape).concatenate(event_shape))
156 for batch_shape in ([], [1], [2, 3, 4]):
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| D | relaxed_onehot_categorical_test.py | 32 def make_relaxed_categorical(batch_shape, num_classes, dtype=dtypes.float32): argument
34 list(batch_shape) + [num_classes], -10, 10, dtype=dtype) - 50.
36 list(batch_shape), 0.1, 10, dtype=dtypes.float32)
140 for batch_shape in ([], [1], [2, 3, 4]):
141 dist = make_relaxed_categorical(batch_shape, 10)
142 self.assertAllEqual(batch_shape, dist.batch_shape.as_list())
143 self.assertAllEqual(batch_shape, dist.batch_shape_tensor().eval())
147 for batch_shape in ([], [1], [2, 3, 4]):
149 batch_shape, constant_op.constant(10, dtype=dtypes.int32))
150 self.assertAllEqual(len(batch_shape), dist.batch_shape.ndims)
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| D | cauchy_test.py | 99 self.assertAllEqual(cauchy.batch_shape, log_pdf.shape)
100 self.assertAllEqual(cauchy.batch_shape, log_pdf.eval().shape)
105 self.assertAllEqual(cauchy.batch_shape, pdf.shape)
106 self.assertAllEqual(cauchy.batch_shape, pdf.eval().shape)
129 self.assertAllEqual(cauchy.batch_shape, log_pdf.shape)
130 self.assertAllEqual(cauchy.batch_shape, log_pdf.eval().shape)
137 self.assertAllEqual(cauchy.batch_shape, pdf.shape)
138 self.assertAllEqual(cauchy.batch_shape, pdf_values.shape)
157 self.assertAllEqual(cauchy.batch_shape, cdf.shape)
158 self.assertAllEqual(cauchy.batch_shape, cdf.eval().shape)
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| D | mvn_full_covariance_test.py | 114 self.assertEqual((3, 5), tuple(mvn.batch_shape.as_list()))
120 def _random_mu_and_sigma(self, batch_shape, event_shape): argument
122 mat_shape = batch_shape + event_shape + event_shape
128 mu_shape = batch_shape + event_shape
134 batch_shape = [2]
137 mu_a, sigma_a = self._random_mu_and_sigma(batch_shape, event_shape)
138 mu_b, sigma_b = self._random_mu_and_sigma(batch_shape, event_shape)
149 self.assertEqual(batch_shape, kl.get_shape())
160 batch_shape = [2]
163 mu_a, sigma_a = self._random_mu_and_sigma(batch_shape, event_shape)
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| D | transformed_distribution_test.py | 310 batch_shape=(), argument
322 feed_dict = {batch_shape_pl: np.array(batch_shape, dtype=np.int32),
328 batch_shape=batch_shape_pl,
336 batch_shape=batch_shape,
355 self.assertAllEqual([2], fake_mvn_static.batch_shape)
360 fake_mvn_dynamic.batch_shape)
419 batch_shape=[2],
428 batch_shape=[2],
437 batch_shape=[2],
454 batch_shape=[2],
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| D | mvn_tril_test.py | 218 self.assertEqual((3, 5), tuple(mvn.batch_shape.as_list()))
224 def _random_mu_and_sigma(self, batch_shape, event_shape): argument
226 mat_shape = batch_shape + event_shape + event_shape
232 mu_shape = batch_shape + event_shape
238 batch_shape = []
241 mu_a, sigma_a = self._random_mu_and_sigma(batch_shape, event_shape)
242 mu_b, sigma_b = self._random_mu_and_sigma(batch_shape, event_shape)
253 self.assertEqual(batch_shape, kl.get_shape())
260 batch_shape = [2]
263 mu_a, sigma_a = self._random_mu_and_sigma(batch_shape, event_shape)
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| D | autoregressive_test.py | 54 batch_shape = []
57 batch_event_shape = np.concatenate([batch_shape, [event_size]], axis=0)
68 batch_shape = np.int32([])
71 batch_event_shape = np.concatenate([batch_shape, [event_size]], axis=0)
84 batch_shape=batch_shape,
87 [sample_shape, batch_shape, [event_size]], axis=0)
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| D | batch_reshape_test.py | 54 batch_shape=new_batch_shape_ph,
66 batch_shape = reshape_wishart.batch_shape_tensor()
86 batch_shape,
100 self.assertAllEqual(new_batch_shape, reshape_wishart.batch_shape)
181 batch_shape=new_batch_shape_ph,
192 batch_shape = reshape_normal.batch_shape_tensor()
212 batch_shape,
225 self.assertAllEqual(new_batch_shape, reshape_normal.batch_shape)
302 batch_shape=new_batch_shape_ph,
313 batch_shape = reshape_mvn.batch_shape_tensor()
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| /external/tensorflow/tensorflow/core/ops/ |
| D | linalg_ops.cc | 37 ShapeHandle batch_shape; in MakeBatchSquareMatrix() local
38 TF_RETURN_IF_ERROR(c->Subshape(s, 0, -2, &batch_shape)); in MakeBatchSquareMatrix()
39 TF_RETURN_IF_ERROR(c->Concatenate(batch_shape, c->Matrix(d, d), out)); in MakeBatchSquareMatrix()
95 ShapeHandle batch_shape; in SelfAdjointEigV2ShapeFn() local
96 TF_RETURN_IF_ERROR(c->Subshape(input, 0, -2, &batch_shape)); in SelfAdjointEigV2ShapeFn()
98 TF_RETURN_IF_ERROR(c->Concatenate(batch_shape, c->Vector(n), &e_shape)); in SelfAdjointEigV2ShapeFn()
104 TF_RETURN_IF_ERROR(c->Concatenate(batch_shape, c->Matrix(n, n), &v_shape)); in SelfAdjointEigV2ShapeFn()
122 ShapeHandle batch_shape; in LuShapeFn() local
123 TF_RETURN_IF_ERROR(c->Subshape(input, 0, -2, &batch_shape)); in LuShapeFn()
128 TF_RETURN_IF_ERROR(c->Concatenate(batch_shape, c->Matrix(n, n), &lu_shape)); in LuShapeFn()
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| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | linalg_ops_test.py | 154 batch_shape = (2, 3)
157 linalg_ops.eye(num_rows=2, batch_shape=batch_shape).shape)
161 num_rows=2, num_columns=3, batch_shape=batch_shape).shape)
179 batch_shape = (2, 3)
183 batch_shape=batch_shape)
210 def test_eye_no_placeholder(self, num_rows, num_columns, batch_shape, dtype): argument
212 if batch_shape is not None:
213 eye_np = np.tile(eye_np, batch_shape + [1, 1])
217 batch_shape=batch_shape,
241 self, num_rows, num_columns, batch_shape, dtype): argument
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| D | matrix_solve_ls_op_test.py | 46 batch_shape = matrix_shape[:-2]
55 np.tile(matrix, batch_shape + (1, 1)), trainable=False)
56 rhs = variables.Variable(np.tile(rhs, batch_shape + (1, 1)), trainable=False)
88 batch_shape=()): argument
107 if batch_shape is not ():
108 a = np.tile(a, batch_shape + (1, 1))
109 b = np.tile(b, batch_shape + (1, 1))
110 np_ans = np.tile(np_ans, batch_shape + (1, 1))
111 np_r_norm = np.tile(np_r_norm, batch_shape)
183 for batch_shape in (), (2, 3):
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| /external/tensorflow/tensorflow/python/ops/linalg/ |
| D | linear_operator_kronecker.py | 250 batch_shape = self.operators[0].batch_shape
252 batch_shape = common_shapes.broadcast_shape(
253 batch_shape, operator.batch_shape)
255 return batch_shape.concatenate(matrix_shape)
270 batch_shape = self.operators[0].batch_shape_tensor()
272 batch_shape = array_ops.broadcast_dynamic_shape(
273 batch_shape, operator.batch_shape_tensor())
275 return array_ops.concat((batch_shape, matrix_shape), 0)
319 batch_shape = array_ops.concat(
321 x += array_ops.zeros(batch_shape, dtype=x.dtype.base_dtype)
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| D | linear_operator_composition.py | 204 batch_shape = self.operators[0].batch_shape
206 batch_shape = common_shapes.broadcast_shape(
207 batch_shape, operator.batch_shape)
209 return batch_shape.concatenate(matrix_shape)
229 batch_shape = array_ops.shape(zeros)
231 return array_ops.concat((batch_shape, matrix_shape), 0)
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| D | linear_operator_zeros.py | 129 batch_shape=None, argument
221 if batch_shape is None:
225 batch_shape, name="batch_shape_arg")
236 batch_shape = tensor_shape.TensorShape(self._batch_shape_static)
237 return batch_shape.concatenate(matrix_shape)
275 special_shape = self.batch_shape.concatenate([1, 1])
319 if self.batch_shape.is_fully_defined():
320 return array_ops.zeros(shape=self.batch_shape, dtype=self.dtype)
326 if self.batch_shape.is_fully_defined():
327 return array_ops.zeros(shape=self.batch_shape, dtype=self.dtype)
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| D | linear_operator_identity.py | 92 d_shape = self.batch_shape.concatenate([self._min_matrix_dim()])
209 batch_shape=None, argument
281 if batch_shape is None:
285 batch_shape, name="batch_shape_arg")
296 batch_shape = tensor_shape.TensorShape(self._batch_shape_static)
297 return batch_shape.concatenate(matrix_shape)
331 special_shape = self.batch_shape.concatenate([1, 1])
367 if self.batch_shape.is_fully_defined():
368 batch_of_ones = array_ops.ones(shape=self.batch_shape, dtype=self.dtype)
630 batch_shape = self.multiplier.get_shape()
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| /external/tensorflow/tensorflow/compiler/tests/ |
| D | matrix_band_part_test.py | 169 def testMatrixBandPart(self, batch_shape, rows, cols): argument
171 if self.device == 'XLA_CPU' and cols == 7 and rows == 1 and batch_shape == [
177 mat = np.ones(batch_shape + [rows, cols]).astype(dtype)
178 batch_mat = np.tile(mat, batch_shape + [1, 1])
186 if batch_shape:
187 band_np = np.tile(band_np, batch_shape + [1, 1])
|
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/bijectors/ |
| D | fill_triangular.py | 109 batch_shape, d = (input_shape[:-1],
115 return batch_shape.concatenate([n, n])
118 batch_shape, n1, n2 = (output_shape[:-2],
127 return batch_shape.concatenate([m])
130 batch_shape, d = input_shape_tensor[:-1], input_shape_tensor[-1]
132 return array_ops.concat([batch_shape, [n, n]], axis=0)
135 batch_shape, n = output_shape_tensor[:-2], output_shape_tensor[-1]
142 return array_ops.concat([batch_shape, [d]], axis=0)
|
| /external/tensorflow/tensorflow/python/kernel_tests/distributions/ |
| D | normal_test.py | 124 self.assertAllEqual(normal.batch_shape, log_pdf.get_shape())
125 self.assertAllEqual(normal.batch_shape, self.evaluate(log_pdf).shape)
133 self.assertAllEqual(normal.batch_shape, pdf.get_shape())
134 self.assertAllEqual(normal.batch_shape, self.evaluate(pdf).shape)
160 self.assertAllEqual(normal.batch_shape, log_pdf.get_shape())
161 self.assertAllEqual(normal.batch_shape, self.evaluate(log_pdf).shape)
170 self.assertAllEqual(normal.batch_shape, pdf.get_shape())
171 self.assertAllEqual(normal.batch_shape, pdf_values.shape)
194 self.assertAllEqual(normal.batch_shape, cdf.get_shape())
195 self.assertAllEqual(normal.batch_shape, self.evaluate(cdf).shape)
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | linalg_ops_common.cc | 92 TensorShape batch_shape; in Compute() local
93 AnalyzeInputs(context, &inputs, &input_matrix_shapes, &batch_shape); in Compute()
97 PrepareOutputs(context, input_matrix_shapes, batch_shape, &outputs, in Compute()
110 batch_shape.num_elements(), GetCostPerUnit(input_matrix_shapes), shard); in Compute()
117 TensorShape* batch_shape) { in AnalyzeInputs() argument
131 batch_shape->AddDim(in.dim_size(dim)); in AnalyzeInputs()
140 context, in.dim_size(dim) == batch_shape->dim_size(dim), in AnalyzeInputs()
161 const TensorShape& batch_shape, TensorOutputs* outputs, in PrepareOutputs() argument
193 output_tensor_shape = batch_shape; in PrepareOutputs()
|