| /external/tensorflow/tensorflow/python/ops/ragged/ |
| D | ragged_reduce_op_test.py | 57 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
63 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
69 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
75 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
81 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
87 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
93 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
99 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
105 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
111 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
[all …]
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| D | ragged_row_lengths_op_test.py | 38 rt_input=[[[3, 1, 4], [1]], [], [[5, 9], [2]], [[6]], []],
41 rt_input=[[[3, 1, 4], [1]], [], [[5, 9], [2]], [[6]], []],
47 rt_input=[['a'], ['b', 'c', 'd'], ['e'], [], ['f']],
50 rt_input=[['a'], ['b', 'c', 'd'], ['e'], [], ['f']],
54 rt_input=[['a', 'b', 'c', 'd', 'e', 'f', 'g']],
57 rt_input=[[], ['a', 'b', 'c', 'd', 'e', 'f', 'g'], []],
60 rt_input=[],
64 rt_input=[],
71 rt_input=[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10]]],
76 rt_input=[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10]]],
[all …]
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| D | ragged_tensor_shape.py | 161 def from_tensor(cls, rt_input): argument
163 with ops.name_scope(None, 'RaggedTensorDynamicShapeFromTensor', [rt_input]):
164 rt_input = ragged_tensor.convert_to_tensor_or_ragged_tensor(rt_input)
165 if not ragged_tensor.is_ragged(rt_input):
166 return cls([], array_ops.shape(rt_input))
169 (rt_input.nrows(),) + rt_input.nested_row_lengths())
172 array_ops.shape(rt_input.flat_values)[1:])
440 def broadcast_to(rt_input, shape, broadcast_inner_dimensions=True): argument
459 rt_input = ragged_tensor.convert_to_tensor_or_ragged_tensor(rt_input)
463 return _broadcast_to_uniform_shape(rt_input, shape,
[all …]
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| D | ragged_tile_op_test.py | 43 rt_input=[[1, 2], [3]],
54 rt_input=[[[1, 2], [3]], [], [[4]]],
60 rt_input=[[[1, 2], [3]], [], [[4]]],
65 rt_input=[[[1, 2], [3]], [], [[4]]],
70 rt_input=[[[1, 2], [3]], [], [[4]]],
76 rt_input=[[[1, 2], [3]], [], [[4]]],
82 rt_input=[[[1, 2], [3]], [], [[4]]],
88 rt_input=[[['a', 'b'], ['c']], [], [['d']]],
97 rt_input=[[[1, 2], [3, 4]], [], [[5, 6]]],
104 rt_input=[[[1, 2], [3, 4]], [], [[5, 6]]],
[all …]
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| D | ragged_getitem.py | 108 def _ragged_getitem(rt_input, key_list): argument
127 return rt_input
132 expanded_key_list = _expand_ellipsis(key_list, rt_input.shape.ndims)
133 return _ragged_getitem(rt_input, expanded_key_list)
138 inner_rt = _ragged_getitem(rt_input, inner_keys)
146 sliced_rt_input = _slice_ragged_row_dimension(rt_input, row_key)
152 starts = rt_input.row_splits[:-1]
153 limits = rt_input.row_splits[1:]
175 row = rt_input.values[starts[row_key]:limits[row_key]]
179 def _slice_ragged_row_dimension(rt_input, row_key): argument
[all …]
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| D | ragged_expand_dims_op_test.py | 51 dict(rt_input=[[1, 2], [3]],
55 dict(rt_input=[[1, 2], [3]],
59 dict(rt_input=[[1, 2], [3]],
66 dict(rt_input=[[1, 2], [3, 4], [5, 6]],
71 dict(rt_input=[[1, 2], [3, 4], [5, 6]],
76 dict(rt_input=[[1, 2], [3, 4], [5, 6]],
85 dict(rt_input=EXAMPLE4D,
90 dict(rt_input=EXAMPLE4D,
95 dict(rt_input=EXAMPLE4D,
100 dict(rt_input=EXAMPLE4D,
[all …]
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| D | ragged_array_ops.py | 272 def _tile_ragged_values(rt_input, multiples, const_multiples=None): argument
297 ragged_rank = rt_input.ragged_rank
298 nested_splits = rt_input.nested_row_splits
323 ragged_tiled_values = array_ops.gather(rt_input.flat_values, inner_value_ids)
332 def _tile_ragged_splits(rt_input, multiples, const_multiples=None): argument
356 ragged_rank = rt_input.ragged_rank
357 nested_splits = rt_input.nested_row_splits
522 def _increase_ragged_rank_to(rt_input, ragged_rank): argument
525 if not ragged_tensor.is_ragged(rt_input):
526 rt_input = ragged_conversion_ops.from_tensor(rt_input)
[all …]
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| D | ragged_concat_ops.py | 136 rt_input, name='rt_input') for rt_input in rt_inputs
184 splits = [[rt_input.row_splits] for rt_input in rt_inputs]
284 def _increase_ragged_rank_to(rt_input, ragged_rank): argument
287 if not ragged_tensor.is_ragged(rt_input):
288 rt_input = ragged_conversion_ops.from_tensor(rt_input)
289 if rt_input.ragged_rank < ragged_rank:
290 rt_input = rt_input.with_values(
291 _increase_ragged_rank_to(rt_input.values, ragged_rank - 1))
292 return rt_input
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| D | ragged_math_ops.py | 401 rt_input, argument
440 if not ragged_tensor.is_ragged(rt_input):
441 return reduce_op(rt_input, axis, name=name)
455 return reduce_op(rt_input.flat_values, None, name=name)
457 with ops.name_scope(name, 'RaggedReduce', [rt_input, axis]):
460 return rt_input
470 rt_input, axis[-1], keepdims)
474 rt_input = ragged_tensor.convert_to_tensor_or_ragged_tensor(
475 rt_input, name='rt_input')
477 axis = ragged_util.get_positive_axis(axis, rt_input.shape.ndims)
[all …]
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| D | ragged_conversion_ops.py | 32 def to_tensor(rt_input, default_value=None, name=None): argument
33 if ragged_tensor.is_ragged(rt_input):
34 return rt_input.to_tensor(default_value, name)
36 return rt_input
39 def to_sparse(rt_input, name=None): argument
40 return rt_input.to_sparse(name)
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| D | ragged_where_op.py | 161 def _nrows(rt_input, out_type=dtypes.int64, name=None): argument
162 if isinstance(rt_input, ragged_tensor.RaggedTensor):
163 return rt_input.nrows(out_type=out_type, name=name)
165 with ops.name_scope(name, 'RaggedNRows', [rt_input]):
166 return array_ops.shape(rt_input, out_type=out_type)[0]
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| D | ragged_to_tensor_op_test.py | 98 rt_input, argument
103 rt = ragged_factory_ops.constant(rt_input, ragged_rank=ragged_rank)
131 def testError(self, rt_input, default, error, ragged_rank=None): argument
132 rt = ragged_factory_ops.constant(rt_input, ragged_rank=ragged_rank)
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| D | ragged_concat_op_test.py | 43 ragged_factory_ops.constant(rt_input, ragged_rank=rrank)
44 if rrank != 0 else constant_op.constant(rt_input)
45 for (rt_input, rrank) in zip(rt_inputs, ragged_ranks)
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| D | ragged_stack_op_test.py | 329 ragged_factory_ops.constant(rt_input, ragged_rank=rrank) # pylint: disable=g-long-ternary
330 if rrank != 0 else constant_op.constant(rt_input)
331 for (rt_input, rrank) in zip(rt_inputs, ragged_ranks)
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