| /external/tensorflow/tensorflow/python/keras/utils/ |
| D | conv_utils_test.py | 169 ndims = len(input_shape)
170 strides = (1,) * ndims
184 ndims = len(input_shape)
185 kernel_shape = (1,) * ndims
186 strides = (1,) * ndims
203 ndims = len(input_shape)
204 kernel_shape = (1,) * ndims
224 ndims = len(input_shape)
225 kernel_shape = (1,) * ndims
247 ndims = len(input_shape)
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| /external/tensorflow/tensorflow/python/ops/ragged/ |
| D | ragged_util.py | 43 def get_positive_axis(axis, ndims): argument
65 if ndims is not None:
66 if 0 <= axis < ndims:
68 elif -ndims <= axis < 0:
69 return axis + ndims
72 "axis=%s out of bounds: expected %s<=axis<%s" % (axis, -ndims, ndims))
152 axis = get_positive_axis(axis, data.shape.ndims)
155 if repeats.shape.ndims == 1:
159 if repeats.shape.ndims == 0:
167 if repeats.shape.ndims != axis + 1:
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| D | ragged_batch_gather_with_default_op.py | 86 if (default_value.shape.ndims is not 0
87 and default_value.shape.ndims is not 1):
90 if indices.shape.ndims is None:
92 if params.shape.ndims is None:
95 num_batch_dimensions = indices.shape.ndims - 1
125 params_shape[num_batch_dimensions + 1:params.shape.ndims]
149 num_batch_dimensions = indices.shape.ndims - 1
154 if params.shape.ndims == indices.shape.ndims:
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| D | ragged_concat_ops.py | 147 ndims = None
149 if ndims is None:
150 ndims = rt.shape.ndims
152 rt.shape.assert_has_rank(ndims)
154 out_ndims = ndims if (ndims is None or not stack_values) else ndims + 1
161 if ndims is not None and (axis == out_ndims - 1 or axis == ndims - 1):
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| D | ragged_tensor_shape.py | 113 if dimension_size.shape.ndims is None:
117 if partitioned_dim_sizes[0].shape.ndims == 1:
119 if partitioned_dim_sizes[-1].shape.ndims == 0:
153 if dim_size.shape.ndims == 1:
155 elif dim_size.shape.ndims != 0:
195 self._partitioned_dim_sizes[axis].shape.ndims == 1)
296 if lengths.shape.ndims is None:
298 elif lengths.shape.ndims > 1:
301 lengths_is_scalar = (lengths.shape.ndims == 0)
361 if lengths.shape.ndims == 0:
[all …]
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| D | ragged_array_ops.py | 127 if mask.shape.ndims is None:
129 elif mask.shape.ndims == 0:
149 if mask.shape.ndims > 2:
173 elif ragged_tensor.is_ragged(data) and mask.shape.ndims == 1:
196 mask, ragged_rank=min(data.ragged_rank, mask.shape.ndims - 1))
204 if mask.shape.ndims >= 2 and keepdims:
215 if mask.shape.ndims > 2 and keepdims:
218 for dim in range(mask.shape.ndims - 3, -1, -1):
472 ndims = None if input.shape.ndims is None else input.shape.ndims + 1
473 axis = ragged_util.get_positive_axis(axis, ndims)
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| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| D | shape.py | 260 ndims = x.get_shape().ndims
261 if ndims is None:
263 return ops.convert_to_tensor(ndims, dtype=dtypes.int32, name="ndims")
279 ndims = self.get_ndims(x, name=name)
280 if self._is_all_constant_helper(ndims, self.batch_ndims,
282 ndims = tensor_util.constant_value(ndims)
283 sample_ndims = (ndims - self._batch_ndims_static -
288 (self._batch_ndims_static, self._event_ndims_static, ndims))
292 sample_ndims = ndims - self.batch_ndims - self.event_ndims
359 if (x.get_shape().ndims is not None and
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| D | batch_reshape.py | 219 x_ndims = (array_ops.rank(x) if x.shape.ndims is None else x.shape.ndims)
221 if self.event_shape.ndims is None
222 else self.event_shape.ndims)
225 if self.batch_shape.ndims is None else self.batch_shape.ndims)
254 if (static_sample_shape.ndims is not None and
255 self.batch_shape.ndims is not None):
274 if (self.batch_shape.ndims is not None and
275 self.event_shape.ndims is not None):
287 x_ndims = (array_ops.rank(x) if x.shape.ndims is None else x.shape.ndims)
289 if self.event_shape.ndims is None
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| D | sample_stats.py | 402 ndims = x.get_shape().ndims
403 if ndims is None:
406 ndims = shape_const.ndim
408 if ndims is None:
418 if ndims != expect_ndims:
425 if ndims < expect_ndims_at_least:
432 if ndims > expect_ndims_no_more_than:
435 return ndims
468 def _make_static_axis_non_negative(axis, ndims): argument
484 if d >= ndims:
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| D | mixture_same_family.py | 161 if (mixture_distribution.event_shape.ndims is not None
162 and mixture_distribution.event_shape.ndims != 0):
174 if mdbs.ndims != 0 and mdbs != cdbs:
258 self._event_shape().ndims) # [n, B, k, [1]*e]
275 self._event_shape().ndims) # [B, k, [1]*e]
293 self._event_shape().ndims) # [B, k, [1]*e]
305 static_event_ndims = self.event_shape.ndims
315 self._event_shape().ndims),
317 self._event_shape().ndims) # [B, k, 1, 1]
330 ndims = x.shape.ndims if x.shape.ndims is not None else array_ops.rank(x)
[all …]
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| D | independent.py | 179 or batch_shape.ndims is None):
181 d = batch_shape.ndims - self._static_reinterpreted_batch_ndims
200 or batch_shape.ndims is None):
202 d = batch_shape.ndims - self._static_reinterpreted_batch_ndims
238 batch_ndims = distribution.batch_shape.ndims
267 ndims = distribution.batch_shape.ndims
268 if ndims is None:
270 ndims = array_ops.shape(distribution.batch_shape_tensor())[0]
273 return which_maximum(0, ndims - 1)
316 num_reduce_dims = a.event_shape.ndims - p.event_shape.ndims
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| D | wishart.py | 138 if (self._scale_operator.shape.ndims is None or
227 ndims = batch_ndims + 3 # sample_ndims=1, event_ndims=2
259 perm = array_ops.concat([math_ops.range(1, ndims), [0]], 0)
274 perm = array_ops.concat([[ndims - 1], math_ops.range(0, ndims - 1)], 0)
292 ndims = array_ops.rank(x_sqrt)
294 sample_ndims = ndims - array_ops.shape(batch_shape)[0] - 2
311 perm = array_ops.concat([math_ops.range(sample_ndims, ndims),
331 perm = array_ops.concat([math_ops.range(ndims - sample_ndims, ndims),
332 math_ops.range(0, ndims - sample_ndims)], 0)
359 if x.get_shape().ndims is not None:
[all …]
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | tile_functor_gpu.h | 35 const int32 ndims, T* dst) { in TileKernel() argument
37 const int32* out_strides = buf + ndims; in TileKernel()
38 const int32* in_dim_sizes = buf + ndims * 2; in TileKernel()
42 for (int i = 0; i < ndims; ++i) { in TileKernel()
58 const int32 ndims = in.dims(); in TileSimple() local
59 gtl::InlinedVector<int32, 24> host_buf(ndims * 3); in TileSimple()
62 for (int i = 0; i < ndims; ++i) { in TileSimple()
64 host_buf[ndims + i] = out_strides[i]; in TileSimple()
65 host_buf[ndims * 2 + i] = in.dim_size(i); in TileSimple()
81 reinterpret_cast<const int32*>(dev_buf), ndims, q)); in TileSimple()
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| D | qr_op_impl.h | 141 const int ndims = input.dims(); in ComputeAsync() local
142 const int64 m = input.dim_size(ndims - 2); in ComputeAsync()
143 const int64 n = input.dim_size(ndims - 1); in ComputeAsync()
150 context, ndims >= 2, in ComputeAsync()
151 errors::InvalidArgument("Input must have rank >= 2, got ", ndims), in ComputeAsync()
159 q_shape.set_dim(ndims - 1, full_matrices_ ? m : min_size); in ComputeAsync()
164 r_shape.set_dim(ndims - 2, full_matrices_ ? m : min_size); in ComputeAsync()
179 transposed_shape.set_dim(ndims - 2, input.dim_size(ndims - 1)); in ComputeAsync()
180 transposed_shape.set_dim(ndims - 1, input.dim_size(ndims - 2)); in ComputeAsync()
|
| D | transpose_functor_gpu.cu.cc | 36 const int32 ndims, T* dst) { in TransposeKernel() argument
38 const int32* out_strides = buf + ndims; in TransposeKernel()
39 const int32* perm = buf + ndims * 2; in TransposeKernel()
43 for (int32 i = 0; i < ndims; ++i) { in TransposeKernel()
63 const int32 ndims = in.dims(); in TransposeSimple() local
64 gtl::InlinedVector<int32, 24> host_buf(ndims * 3); in TransposeSimple()
68 for (int i = 0; i < ndims; ++i) { in TransposeSimple()
70 host_buf[ndims + i] = out_strides[i]; in TransposeSimple()
71 host_buf[ndims * 2 + i] = perm[i]; in TransposeSimple()
86 reinterpret_cast<const int32*>(dev_buf), ndims, q)); in TransposeSimple()
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| D | determinant_op.cc | 135 const int ndims = input.dims(); in ComputeAsync() local
136 const int64 n = input.dim_size(ndims - 1); in ComputeAsync()
139 context, ndims >= 2, in ComputeAsync()
140 errors::InvalidArgument("Input must have rank >= 2, got ", ndims), in ComputeAsync()
143 context, input.dim_size(ndims - 2) == n, in ComputeAsync()
145 input.dim_size(ndims - 2), " != ", n), in ComputeAsync()
150 for (int dim = 0; dim < ndims - 2; ++dim) { in ComputeAsync()
275 const int ndims = input.dims(); in ComputeAsync() local
276 const int64 n = input.dim_size(ndims - 1); in ComputeAsync()
279 context, ndims >= 2, in ComputeAsync()
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| D | matrix_solve_op.cc | 131 const int ndims = input.dims(); in ComputeAsync() local
132 const int64 n = input.dim_size(ndims - 1); in ComputeAsync()
133 const int64 nrhs = rhs.dim_size(ndims - 1); in ComputeAsync()
136 context, ndims >= 2, in ComputeAsync()
137 errors::InvalidArgument("Input must have rank >= 2, got ", ndims), in ComputeAsync()
139 OP_REQUIRES_ASYNC(context, rhs.dims() == ndims, in ComputeAsync()
142 ndims, " != ", rhs.dims()), in ComputeAsync()
145 context, input.dim_size(ndims - 2) == n, in ComputeAsync()
147 input.dim_size(ndims - 2), " != ", n), in ComputeAsync()
149 OP_REQUIRES_ASYNC(context, rhs.dim_size(ndims - 2) == n, in ComputeAsync()
[all …]
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| /external/tensorflow/tensorflow/stream_executor/ |
| D | dnn.cc | 238 BatchDescriptor::BatchDescriptor(int ndims) in BatchDescriptor() argument
242 tensor_.mutable_dimensions()->Resize(ndims + 2, 0); in BatchDescriptor()
249 std::vector<int64> bdyx_dims(ndims() + 2); in full_dims()
269 phys_strides[ndims() + 1] = 1; in full_strides()
270 for (int i = ndims(); i >= 0; i--) { in full_strides()
285 for (int i = 0; i < ndims(); i++) { in ToString()
303 for (int i = 0; i < ndims(); i++) { in ToShortString()
334 for (int i = 0; i < ndims(); i++) { in NodesPerFeatureMap()
383 FilterDescriptor::FilterDescriptor(int ndims) { in FilterDescriptor() argument
384 tensor_.mutable_dimensions()->Resize(ndims + 2, 0); in FilterDescriptor()
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| /external/tensorflow/tensorflow/compiler/xla/service/ |
| D | triangular_solve_expander.cc | 43 int ndims = shape.rank(); in DiagonalBlocks() local
52 std::vector<int64> permutation(ndims); in DiagonalBlocks()
68 MakeEdgePaddingConfig({{0, 0}, {ndims - 2, 0}}); in DiagonalBlocks()
73 std::vector<int64> slice_sizes(ndims); in DiagonalBlocks()
75 for (int i = 0; i < ndims - 2; ++i) { in DiagonalBlocks()
80 slice_sizes[ndims - 2] = slice_sizes[ndims - 1] = block_size; in DiagonalBlocks()
81 dim_numbers.add_offset_dims(ndims - 1); in DiagonalBlocks()
82 dim_numbers.add_offset_dims(ndims); in DiagonalBlocks()
83 dim_numbers.add_start_index_map(ndims - 2); in DiagonalBlocks()
84 dim_numbers.add_start_index_map(ndims - 1); in DiagonalBlocks()
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| /external/tensorflow/tensorflow/python/ops/distributions/ |
| D | distribution.py | 1212 if self.batch_shape.ndims is not None
1215 if self.event_shape.ndims is not None
1247 ndims = x.get_shape().ndims # != sample_ndims
1248 if ndims is None:
1250 ndims = array_ops.rank(x)
1252 math_ops.equal(ndims, 0),
1255 elif ndims == 0:
1263 elif ndims != 1:
1274 ndims = x.get_shape().ndims
1275 sample_ndims = sample_shape.ndims
[all …]
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| /external/tensorflow/tensorflow/python/tpu/ |
| D | tpu_sharding.py | 173 ndims = shape.ndims
174 if ndims is None:
176 if ndims <= self._shard_dimension:
209 ndims = shape.ndims
210 if ndims is None:
212 if ndims <= self._shard_dimension:
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| /external/tensorflow/tensorflow/contrib/gan/python/features/python/ |
| D | virtual_batchnorm_impl.py | 84 if reference_batch.shape.ndims is None:
87 ndims = reference_batch.shape.ndims
89 used_axis = ndims + axis
92 if used_axis < 0 or used_axis >= ndims:
94 ' is out of range for input with rank ' + str(ndims))
198 ndims = input_shape.ndims
199 reduction_axes = list(range(ndims))
205 self._example_reduction_axes = list(range(ndims))
214 sorted(self._example_reduction_axes) != list(range(ndims))[:-2])
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| /external/tensorflow/tensorflow/core/framework/ |
| D | ops_util.h | 91 const int ndims = shape.dims(); in ComputeStride() local
92 gtl::InlinedVector<T, 8> strides(ndims); in ComputeStride()
94 for (int i = ndims - 1; i >= 0; --i) { in ComputeStride()
104 const int ndims = shape.rank(); in ComputeEigenStrides() local
105 gtl::InlinedVector<T, 8> strides(ndims); in ComputeEigenStrides()
107 for (int i = ndims - 1; i >= 0; --i) { in ComputeEigenStrides()
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| /external/tensorflow/tensorflow/go/ |
| D | operation.go | 100 ndims := C.TF_GraphGetTensorNumDims(p.Op.g.c, port, status.c)
107 if ndims < 0 {
110 if ndims == 0 {
113 dims := make([]C.int64_t, ndims)
114 C.TF_GraphGetTensorShape(p.Op.g.c, port, &dims[0], ndims, status.c)
119 ret := Shape{dims: make([]int64, ndims)}
120 for i := 0; i < int(ndims); i++ {
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| /external/tensorflow/tensorflow/python/ops/ |
| D | image_ops_impl.py | 217 if image.get_shape().ndims is None:
325 if shape.ndims == 3 or shape.ndims is None:
335 elif shape.ndims == 4:
414 if shape.ndims == 3 or shape.ndims is None:
416 elif shape.ndims == 4:
446 if shape.ndims == 3 or shape.ndims is None:
448 elif shape.ndims == 4:
544 if shape.ndims == 3 or shape.ndims is None:
546 elif shape.ndims == 4:
589 rank = image.get_shape().ndims
[all …]
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