| /external/tensorflow/tensorflow/python/ops/ragged/ |
| D | ragged_reduce_op_test.py | 56 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
63 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
70 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
77 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
84 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
91 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
98 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
105 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
112 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
119 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
[all …]
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| D | ragged_row_lengths_op_test.py | 37 rt_input=[[[3, 1, 4], [1]], [], [[5, 9], [2]], [[6]], []],
40 rt_input=[[[3, 1, 4], [1]], [], [[5, 9], [2]], [[6]], []],
46 rt_input=[['a'], ['b', 'c', 'd'], ['e'], [], ['f']],
49 rt_input=[['a'], ['b', 'c', 'd'], ['e'], [], ['f']],
53 rt_input=[['a', 'b', 'c', 'd', 'e', 'f', 'g']],
56 rt_input=[[], ['a', 'b', 'c', 'd', 'e', 'f', 'g'], []],
59 rt_input=[],
63 rt_input=[],
70 rt_input=[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10]]],
75 rt_input=[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10]]],
[all …]
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| D | ragged_tile_op_test.py | 42 rt_input=[[1, 2], [3]],
53 rt_input=[[[1, 2], [3]], [], [[4]]],
59 rt_input=[[[1, 2], [3]], [], [[4]]],
64 rt_input=[[[1, 2], [3]], [], [[4]]],
69 rt_input=[[[1, 2], [3]], [], [[4]]],
75 rt_input=[[[1, 2], [3]], [], [[4]]],
81 rt_input=[[[1, 2], [3]], [], [[4]]],
87 rt_input=[[['a', 'b'], ['c']], [], [['d']]],
96 rt_input=[[[1, 2], [3, 4]], [], [[5, 6]]],
103 rt_input=[[[1, 2], [3, 4]], [], [[5, 6]]],
[all …]
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| D | ragged_tensor_shape.py | 181 def from_tensor(cls, rt_input, dim_size_dtype=None): argument
183 with ops.name_scope(None, 'RaggedTensorDynamicShapeFromTensor', [rt_input]):
184 rt_input = ragged_tensor.convert_to_tensor_or_ragged_tensor(rt_input)
185 if not ragged_tensor.is_ragged(rt_input):
186 return cls([], array_ops.shape(rt_input))
189 (rt_input.nrows(),) + rt_input.nested_row_lengths())
192 array_ops.shape(rt_input.flat_values)[1:],
476 def broadcast_to(rt_input, shape, broadcast_inner_dimensions=True): argument
495 rt_input = ragged_tensor.convert_to_tensor_or_ragged_tensor(rt_input)
499 return _broadcast_to_uniform_shape(rt_input, shape,
[all …]
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| D | ragged_getitem.py | 106 def _ragged_getitem(rt_input, key_list): argument
125 return rt_input
130 expanded_key_list = _expand_ellipsis(key_list, rt_input.shape.ndims)
131 return _ragged_getitem(rt_input, expanded_key_list)
136 inner_rt = _ragged_getitem(rt_input, inner_keys)
149 sliced_rt_input = _slice_ragged_row_dimension(rt_input, row_key)
150 if rt_input.uniform_row_length is not None:
156 sliced_rt_input.values, rt_input.uniform_row_length,
163 starts = rt_input.row_splits[:-1]
164 limits = rt_input.row_splits[1:]
[all …]
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| D | ragged_expand_dims_op_test.py | 50 dict(rt_input=[[1, 2], [3]],
54 dict(rt_input=[[1, 2], [3]],
58 dict(rt_input=[[1, 2], [3]],
65 dict(rt_input=[[1, 2], [3, 4], [5, 6]],
70 dict(rt_input=[[1, 2], [3, 4], [5, 6]],
75 dict(rt_input=[[1, 2], [3, 4], [5, 6]],
84 dict(rt_input=EXAMPLE4D,
89 dict(rt_input=EXAMPLE4D,
94 dict(rt_input=EXAMPLE4D,
99 dict(rt_input=EXAMPLE4D,
[all …]
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| D | ragged_math_ops.py | 436 rt_input, argument
479 if not ragged_tensor.is_ragged(rt_input):
481 return reduce_op(rt_input, axis, keepdims=keepdims, name=name)
486 rt_input, axis, keepdims=keepdims, name=name, separator=separator)
497 result = reduce_op(rt_input.flat_values, None, keepdims=keepdims, name=name)
500 for _ in rt_input.shape[1:]:
504 with ops.name_scope(name, 'RaggedReduce', [rt_input, axis]):
507 return rt_input
516 array_ops.get_positive_axis(a, rt_input.shape.ndims, 'axis[%s]' % i,
526 rt_input, axis[-1], keepdims,
[all …]
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| D | ragged_concat_ops.py | 145 rt_input, name='rt_input') for rt_input in rt_inputs
200 splits = [[rt_input.row_splits] for rt_input in rt_inputs]
300 def _increase_ragged_rank_to(rt_input, ragged_rank, row_splits_dtype): argument
303 if not ragged_tensor.is_ragged(rt_input):
304 rt_input = ragged_tensor.RaggedTensor.from_tensor(
305 rt_input, row_splits_dtype=row_splits_dtype)
306 if rt_input.ragged_rank < ragged_rank:
307 rt_input = rt_input.with_values(
308 _increase_ragged_rank_to(rt_input.values, ragged_rank - 1,
310 return rt_input
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| D | ragged_conversion_ops.py | 48 def to_tensor(rt_input, default_value=None, name=None): argument
49 if ragged_tensor.is_ragged(rt_input):
50 return rt_input.to_tensor(default_value, name)
52 return rt_input
55 def ragged_to_dense(rt_input, default_value=None, shape=None): argument
57 return rt_input.to_tensor(default_value=default_value, shape=shape)
140 def to_sparse(rt_input, name=None): argument
141 return rt_input.to_sparse(name)
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| D | ragged_where_op.py | 248 def _nrows(rt_input, out_type): argument
249 if isinstance(rt_input, ragged_tensor.RaggedTensor):
250 return rt_input.nrows(out_type=out_type)
252 return array_ops.shape(rt_input, out_type=out_type)[0]
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| D | ragged_array_ops.py | 253 def _tile_ragged_values(rt_input, multiples, const_multiples=None): argument
277 ragged_rank = rt_input.ragged_rank
278 nested_splits = rt_input.nested_row_splits
303 ragged_tiled_values = array_ops.gather(rt_input.flat_values, inner_value_ids)
312 def _tile_ragged_splits(rt_input, multiples, const_multiples=None): argument
336 ragged_rank = rt_input.ragged_rank
337 nested_splits = rt_input.nested_row_splits
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| D | ragged_to_tensor_op_test.py | 325 rt_input, argument
333 rt_input, ragged_rank=ragged_rank, inner_shape=inner_shape)
391 rt_input, argument
398 rt = ragged_factory_ops.constant(rt_input, ragged_rank=ragged_rank)
779 rt_input = self._generateRaggedTensor(shape, ragged_rank, dtype, fill)
792 rt = ragged_factory_ops.constant(rt_input, dtype, ragged_rank=ragged_rank)
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| D | ragged_concat_op_test.py | 42 ragged_factory_ops.constant(rt_input, ragged_rank=rrank)
43 if rrank != 0 else constant_op.constant(rt_input)
44 for (rt_input, rrank) in zip(rt_inputs, ragged_ranks)
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| D | ragged_stack_op_test.py | 354 ragged_factory_ops.constant(rt_input, ragged_rank=rrank) # pylint: disable=g-long-ternary
355 if rrank != 0 else constant_op.constant(rt_input)
356 for (rt_input, rrank) in zip(rt_inputs, ragged_ranks)
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| D | ragged_tensor.py | 2805 def _get_row_partition_type_tensor_pairs(rt_input): argument
2818 partitions = rt_input._nested_row_partitions # pylint: disable=protected-access
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