| /external/tensorflow/tensorflow/python/ops/ragged/ |
| D | ragged_reduce_op_test.py | 60 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
67 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
74 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
81 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
88 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
95 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
102 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
109 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
116 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
123 rt_input=[[3, 1, 4], [1, 5], [9], [2, 6]],
[all …]
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| D | ragged_row_lengths_op_test.py | 33 rt_input=[[[3, 1, 4], [1]], [], [[5, 9], [2]], [[6]], []],
36 rt_input=[[[3, 1, 4], [1]], [], [[5, 9], [2]], [[6]], []],
42 rt_input=[['a'], ['b', 'c', 'd'], ['e'], [], ['f']],
45 rt_input=[['a'], ['b', 'c', 'd'], ['e'], [], ['f']],
49 rt_input=[['a', 'b', 'c', 'd', 'e', 'f', 'g']],
52 rt_input=[[], ['a', 'b', 'c', 'd', 'e', 'f', 'g'], []],
55 rt_input=[],
59 rt_input=[],
66 rt_input=[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10]]],
71 rt_input=[[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10]]],
[all …]
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| D | ragged_tensor_shape.py | 177 def from_tensor(cls, rt_input, dim_size_dtype=None): argument
179 with ops.name_scope(None, 'RaggedTensorDynamicShapeFromTensor', [rt_input]):
180 rt_input = ragged_tensor.convert_to_tensor_or_ragged_tensor(rt_input)
181 if not ragged_tensor.is_ragged(rt_input):
182 return cls([], array_ops.shape(rt_input), dim_size_dtype=dim_size_dtype)
185 (rt_input.nrows(),) + rt_input.nested_row_lengths())
188 array_ops.shape(rt_input.flat_values)[1:],
475 def broadcast_to(rt_input, shape, broadcast_inner_dimensions=True): argument
494 rt_input = ragged_tensor.convert_to_tensor_or_ragged_tensor(rt_input)
498 return _broadcast_to_uniform_shape(rt_input, shape,
[all …]
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| D | ragged_tile_op_test.py | 38 rt_input=[[1, 2], [3]],
49 rt_input=[[[1, 2], [3]], [], [[4]]],
55 rt_input=[[[1, 2], [3]], [], [[4]]],
60 rt_input=[[[1, 2], [3]], [], [[4]]],
65 rt_input=[[[1, 2], [3]], [], [[4]]],
71 rt_input=[[[1, 2], [3]], [], [[4]]],
77 rt_input=[[[1, 2], [3]], [], [[4]]],
83 rt_input=[[['a', 'b'], ['c']], [], [['d']]],
92 rt_input=[[[1, 2], [3, 4]], [], [[5, 6]]],
99 rt_input=[[[1, 2], [3, 4]], [], [[5, 6]]],
[all …]
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| D | ragged_getitem.py | 35 def ragged_tensor_getitem(rt_input, key): argument
97 if not isinstance(rt_input, ragged_tensor.RaggedTensor):
99 scope_tensors = [rt_input] + list(_tensors_in_key_list(key))
105 return _ragged_getitem(rt_input, key)
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)
151 sliced_rt_input = _slice_ragged_row_dimension(rt_input, row_key)
[all …]
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| D | ragged_expand_dims_op_test.py | 46 dict(rt_input=[[1, 2], [3]],
50 dict(rt_input=[[1, 2], [3]],
54 dict(rt_input=[[1, 2], [3]],
61 dict(rt_input=[[1, 2], [3, 4], [5, 6]],
66 dict(rt_input=[[1, 2], [3, 4], [5, 6]],
71 dict(rt_input=[[1, 2], [3, 4], [5, 6]],
80 dict(rt_input=EXAMPLE4D,
85 dict(rt_input=EXAMPLE4D,
90 dict(rt_input=EXAMPLE4D,
95 dict(rt_input=EXAMPLE4D,
[all …]
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| D | ragged_concat_ops.py | 147 rt_input, name='rt_input') for rt_input in rt_inputs
202 splits = [[rt_input.row_splits] for rt_input in rt_inputs]
302 def _increase_ragged_rank_to(rt_input, ragged_rank, row_splits_dtype): argument
305 if not ragged_tensor.is_ragged(rt_input):
306 rt_input = ragged_tensor.RaggedTensor.from_tensor(
307 rt_input, row_splits_dtype=row_splits_dtype)
308 if rt_input.ragged_rank < ragged_rank:
309 rt_input = rt_input.with_values(
310 _increase_ragged_rank_to(rt_input.values, ragged_rank - 1,
312 return rt_input
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| D | ragged_conversion_ops.py | 44 def to_tensor(rt_input, default_value=None, name=None): argument
45 if ragged_tensor.is_ragged(rt_input):
46 return rt_input.to_tensor(default_value, name)
48 return rt_input
51 def ragged_to_dense(rt_input, default_value=None, shape=None): argument
53 return rt_input.to_tensor(default_value=default_value, shape=shape)
136 def to_sparse(rt_input, name=None): argument
137 return rt_input.to_sparse(name)
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| D | ragged_math_ops.py | 478 rt_input, argument
521 if not ragged_tensor.is_ragged(rt_input):
523 return reduce_op(rt_input, axis, keepdims=keepdims, name=name)
528 rt_input, axis, keepdims=keepdims, name=name, separator=separator)
539 result = reduce_op(rt_input.flat_values, None, keepdims=keepdims, name=name)
542 for _ in rt_input.shape[1:]:
546 with ops.name_scope(name, 'RaggedReduce', [rt_input, axis]):
549 return rt_input
558 array_ops.get_positive_axis(a, rt_input.shape.ndims, 'axis[%s]' % i,
568 rt_input, axis[-1], keepdims,
[all …]
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| D | ragged_where_op.py | 255 def _nrows(rt_input, out_type): argument
256 if isinstance(rt_input, ragged_tensor.RaggedTensor):
257 return rt_input.nrows(out_type=out_type)
259 return array_ops.shape(rt_input, out_type=out_type)[0]
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| D | ragged_to_tensor_op_test.py | 320 rt_input, argument
328 rt_input, ragged_rank=ragged_rank, inner_shape=inner_shape)
386 rt_input, argument
393 rt = ragged_factory_ops.constant(rt_input, ragged_rank=ragged_rank)
783 rt_input = self._generateRaggedTensor(shape, ragged_rank, dtype, fill)
796 rt = ragged_factory_ops.constant(rt_input, dtype, ragged_rank=ragged_rank)
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| D | ragged_concat_op_test.py | 38 ragged_factory_ops.constant(rt_input, ragged_rank=rrank)
39 if rrank != 0 else constant_op.constant(rt_input)
40 for (rt_input, rrank) in zip(rt_inputs, ragged_ranks)
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| D | ragged_array_ops.py | 258 def _tile_ragged_values(rt_input, multiples, const_multiples=None): argument
282 ragged_rank = rt_input.ragged_rank
283 nested_splits = rt_input.nested_row_splits
308 ragged_tiled_values = array_ops.gather(rt_input.flat_values, inner_value_ids)
317 def _tile_ragged_splits(rt_input, multiples, const_multiples=None): argument
341 ragged_rank = rt_input.ragged_rank
342 nested_splits = rt_input.nested_row_splits
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| D | ragged_stack_op_test.py | 350 ragged_factory_ops.constant(rt_input, ragged_rank=rrank) # pylint: disable=g-long-ternary
351 if rrank != 0 else constant_op.constant(rt_input)
352 for (rt_input, rrank) in zip(rt_inputs, ragged_ranks)
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| D | dynamic_ragged_shape.py | 1623 def broadcast_to(rt_input, shape: DynamicRaggedShape): argument
1640 rt_input = ragged_tensor.convert_to_tensor_or_ragged_tensor(rt_input)
1642 if ragged_tensor.is_ragged(rt_input):
1644 if rt_input.row_splits.dtype != shape.dtype:
1647 shape = shape.with_dtype(rt_input.row_splits.dtype)
1648 origin_shape = DynamicRaggedShape.from_tensor(rt_input)
1651 origin_shape = DynamicRaggedShape.from_tensor(rt_input, dtype=shape.dtype)
1653 origin_shape = DynamicRaggedShape.from_tensor(rt_input,
1658 return broadcaster.broadcast(rt_input)
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| D | ragged_tensor.py | 2956 def _get_row_partition_type_tensor_pairs(rt_input): argument
2969 partitions = rt_input._nested_row_partitions # pylint: disable=protected-access
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| /external/tensorflow/tensorflow/tools/api/golden/v2/ |
| D | tensorflow.__operators__.pbtxt | 21 argspec: "args=[\'rt_input\', \'key\'], varargs=None, keywords=None, defaults=None"
|