| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| shape_test.py | 52 def _get_expected(self, x, batch_ndims, event_ndims, expand_batch_dim):
56 n = x.ndim - batch_ndims - event_ndims
62 if batch_ndims == 0 and expand_batch_dim:
66 def _build_graph(self, x, batch_ndims, event_ndims, expand_batch_dim):
67 shaper = _DistributionShape(batch_ndims=batch_ndims,
75 def _test_dynamic(self, x, batch_ndims, event_ndims, expand_batch_dim=True):
85 batch_ndims_pl: batch_ndims,
88 x, batch_ndims, event_ndims, expand_batch_dim)
93 def _test_static(self, x, batch_ndims, event_ndims, expand_batch_dim)
[all...] |
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/bijectors/ |
| affine_linear_operator.py | 146 batch_ndims = scale.tensor_rank - 2
148 batch_ndims = scale.tensor_rank_tensor() - 2
149 graph_parents += [batch_ndims]
153 batch_ndims = 0 # We won't need shape inference when scale is None.
156 batch_ndims=batch_ndims,
|
| affine.py | 242 batch_ndims = scale.tensor_rank - 2
244 batch_ndims = scale.tensor_rank_tensor() - 2
248 batch_ndims = 0
251 batch_ndims=batch_ndims,
|
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| shape.py | 67 sample_ndims batch_ndims event_ndims
74 information: `batch_ndims` and `event_ndims`.
131 shaper = DistributionShape(batch_ndims=0, event_ndims=1)
142 shaper = DistributionShape(batch_ndims=0, event_ndims=2)
165 execution. (Same for `batch_ndims`). Constant `Tensor`s and non-`Tensor`
179 batch_ndims=None,
183 """Construct `DistributionShape` with fixed `batch_ndims`, `event_ndims`.
185 `batch_ndims` and `event_ndims` are fixed throughout the lifetime of a
188 If both `batch_ndims` and `event_ndims` are python scalars (rather than
193 batch_ndims: `Tensor`. Number of `dims` (`rank`) of the batch portion o
234 def batch_ndims(self): member in class:_DistributionShape
[all...] |
| independent.py | 127 `distribution.batch_ndims`
171 batch_ndims = (dim0
174 return batch_shape[:batch_ndims - self.reinterpreted_batch_ndims]
189 batch_ndims = (dim0
193 batch_shape[batch_ndims - self.reinterpreted_batch_ndims:],
238 batch_ndims = distribution.batch_shape.ndims
239 if batch_ndims is not None and static_reinterpreted_batch_ndims is not None:
240 if static_reinterpreted_batch_ndims > batch_ndims:
242 "distribution.batch_ndims({})".format(
243 static_reinterpreted_batch_ndims, batch_ndims))
[all...] |
| poisson_lognormal.py | 136 batch_ndims = dist.batch_shape.ndims
137 if batch_ndims is None:
138 batch_ndims = array_ops.shape(dist.batch_shape_tensor())[0]
147 [-1], array_ops.ones([batch_ndims], dtype=dtypes.int32)], axis=0))
151 math_ops.range(1, 1 + batch_ndims), [0]], axis=0)
|
| batch_reshape.py | 223 batch_ndims = (
226 sample_ndims = x_ndims - batch_ndims - event_ndims
291 batch_ndims = (
294 expected_batch_event_ndims = batch_ndims + event_ndims
|
| wishart.py | 225 batch_ndims = array_ops.shape(batch_shape)[0]
227 ndims = batch_ndims + 3 # sample_ndims=1, event_ndims=2
293 # sample_ndims = ndims - batch_ndims - event_ndims
|
| vector_diffeomixture.py | 181 batch_ndims = _get_batch_ndims()
199 [-1], array_ops.ones([batch_ndims], dtype=dtypes.int32)], axis=0))
204 math_ops.range(1, 1 + batch_ndims), [0]], axis=0)
[all...] |
| /external/tensorflow/tensorflow/python/ops/distributions/ |
| distribution.py | [all...] |
