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/external/tensorflow/tensorflow/python/ops/ragged/
Dragged_operators.py33 ragged_tensor.RaggedTensor.__getitem__ = ragged_getitem.ragged_tensor_getitem
36 ragged_tensor.RaggedTensor.__ge__ = math_ops.greater_equal
37 ragged_tensor.RaggedTensor.__gt__ = math_ops.greater
38 ragged_tensor.RaggedTensor.__le__ = math_ops.less_equal
39 ragged_tensor.RaggedTensor.__lt__ = math_ops.less
42 ragged_tensor.RaggedTensor.__and__ = math_ops.logical_and
43 ragged_tensor.RaggedTensor.__rand__ = _right(math_ops.logical_and)
44 ragged_tensor.RaggedTensor.__invert__ = math_ops.logical_not
45 ragged_tensor.RaggedTensor.__ror__ = _right(math_ops.logical_or)
46 ragged_tensor.RaggedTensor.__or__ = math_ops.logical_or
[all …]
Dragged_from_tensor_op_test.py28 from tensorflow.python.ops.ragged.ragged_tensor import RaggedTensor
40 RaggedTensor.from_tensor(dt), [[5, 7, 0], [0, 3, 0], [6, 0, 0]])
43 RaggedTensor.from_tensor(dt, lengths=[1, 0, 3]), [[5], [], [6, 0, 0]])
46 RaggedTensor.from_tensor(dt, padding=0), [[5, 7], [0, 3], [6]])
52 RaggedTensor.from_tensor(dt_3d, lengths=([2, 0, 3], [1, 1, 2, 0, 1])),
315 rt = RaggedTensor.from_tensor(dt, lengths, padding, ragged_rank)
317 rt = RaggedTensor.from_tensor(dt, lengths, padding)
318 self.assertEqual(type(rt), RaggedTensor)
331 rt = RaggedTensor.from_tensor(dt, ragged_rank=ragged_rank)
332 self.assertEqual(type(rt), RaggedTensor)
[all …]
Dragged_from_sparse_op_test.py28 from tensorflow.python.ops.ragged.ragged_tensor import RaggedTensor
40 rt = RaggedTensor.from_sparse(st)
49 rt = RaggedTensor.from_sparse(st)
56 RaggedTensor.from_sparse, st1)
61 RaggedTensor.from_sparse, st2)
69 RaggedTensor.from_sparse, st3)
83 RaggedTensor.from_sparse(st1)
84 RaggedTensor.from_sparse(st2)
97 self.evaluate(RaggedTensor.from_sparse(st1))
103 self.evaluate(RaggedTensor.from_sparse(st2))
[all …]
Dragged_tensor_test.py37 from tensorflow.python.ops.ragged.ragged_tensor import RaggedTensor
126 rt = RaggedTensor.from_row_splits(
133 rt1 = RaggedTensor.from_row_splits(values, row_splits=[0, 4, 4, 7, 8, 8])
134 rt2 = RaggedTensor.from_row_lengths(values, row_lengths=[4, 0, 3, 1, 0])
135 rt3 = RaggedTensor.from_value_rowids(
137 rt4 = RaggedTensor.from_row_starts(values, row_starts=[0, 4, 4, 7, 8])
138 rt5 = RaggedTensor.from_row_limits(values, row_limits=[4, 4, 7, 8, 8])
144 inner_rt = RaggedTensor.from_row_splits(
146 outer_rt = RaggedTensor.from_row_splits(
155 rt = RaggedTensor.from_nested_row_splits(
[all …]
Dragged_tensor_bounding_shape_op_test.py39 rt1 = ragged_tensor.RaggedTensor.from_row_splits(values, [0, 2, 5, 6, 6, 7])
40 rt2 = ragged_tensor.RaggedTensor.from_row_splits(values, [0, 7])
41 rt3 = ragged_tensor.RaggedTensor.from_row_splits(values, [0, 0, 7, 7])
48 rt1 = ragged_tensor.RaggedTensor.from_row_splits(values, [0, 2, 5, 6, 6, 7])
49 rt2 = ragged_tensor.RaggedTensor.from_row_splits(values, [0, 7])
50 rt3 = ragged_tensor.RaggedTensor.from_row_splits(values, [0, 0, 7, 7])
56 rt = ragged_tensor.RaggedTensor.from_row_splits(b'a b c d e f g'.split(),
Dragged_to_sparse_op_test.py147 bad_rt1 = ragged_tensor.RaggedTensor.from_row_splits(
153 bad_rt2 = ragged_tensor.RaggedTensor.from_row_splits(
155 bad_rt3 = ragged_tensor.RaggedTensor.from_row_splits(
157 values=ragged_tensor.RaggedTensor.from_row_splits(
165 bad_rt4 = ragged_tensor.RaggedTensor.from_row_splits(
167 values=ragged_tensor.RaggedTensor.from_row_splits(
174 bad_rt5 = ragged_tensor.RaggedTensor.from_row_splits(
Dragged_where_op.py116 condition_is_ragged = isinstance(condition, ragged_tensor.RaggedTensor)
117 x_is_ragged = isinstance(x, ragged_tensor.RaggedTensor)
118 y_is_ragged = isinstance(y, ragged_tensor.RaggedTensor)
141 if not isinstance(condition, ragged_tensor.RaggedTensor):
162 if isinstance(rt_input, ragged_tensor.RaggedTensor):
Dragged_map_ops.py224 if isinstance(elems_flat[0], ragged_tensor.RaggedTensor):
370 if not isinstance(rt, ragged_tensor.RaggedTensor):
399 values = ragged_tensor.RaggedTensor.from_row_lengths(
401 return ragged_tensor.RaggedTensor.from_row_lengths(values, t.outer_row_length)
421 if isinstance(current, ragged_tensor.RaggedTensor):
437 if isinstance(current, ragged_tensor.RaggedTensor):
Dragged_array_ops.py166 masked_values = ragged_tensor.RaggedTensor.from_nested_row_splits(
189 return ragged_tensor.RaggedTensor.from_row_splits(masked_values,
211 masked_values = ragged_tensor.RaggedTensor.from_row_lengths(
221 masked_values = ragged_tensor.RaggedTensor.from_row_splits(
267 return ragged_tensor.RaggedTensor.from_nested_row_splits(
484 return ragged_tensor.RaggedTensor.from_row_splits(values, splits)
544 if isinstance(rt_input, ragged_tensor.RaggedTensor):
Dragged_dispatch_test.py145 if isinstance(x, ragged_tensor.RaggedTensor):
146 self.assertIsInstance(y, ragged_tensor.RaggedTensor)
238 dense_x = x.flat_values if isinstance(x, ragged_tensor.RaggedTensor) else x
245 if isinstance(result, ragged_tensor.RaggedTensor):
360 dense_x = x.flat_values if isinstance(x, ragged_tensor.RaggedTensor) else x
361 dense_y = y.flat_values if isinstance(y, ragged_tensor.RaggedTensor) else y
369 if isinstance(result, ragged_tensor.RaggedTensor):
425 x.flat_values if isinstance(x, ragged_tensor.RaggedTensor) else x
435 if isinstance(result, ragged_tensor.RaggedTensor):
445 y = ragged_tensor.RaggedTensor.from_row_splits(
Dragged_tensor.py49 class RaggedTensor(composite_tensor.CompositeTensor): class
243 if not isinstance(values, (RaggedTensor, ops.Tensor)):
663 values_is_ragged = isinstance(self._values, RaggedTensor)
739 while isinstance(rt_values, RaggedTensor):
771 while isinstance(rt_values, RaggedTensor):
917 elif isinstance(self.values, RaggedTensor):
940 while isinstance(rt, RaggedTensor):
1012 return RaggedTensor(
1435 while isinstance(rt, RaggedTensor):
1463 while isinstance(values, RaggedTensor):
[all …]
Dragged_concat_ops.py186 return ragged_tensor.RaggedTensor.from_row_splits(
222 return ragged_tensor.RaggedTensor.from_nested_row_splits(
266 return ragged_tensor.RaggedTensor.from_row_splits(permuted_rt,
273 return ragged_tensor.RaggedTensor.from_row_splits(permuted_rt.values,
Dragged_math_ops.py103 return ragged_tensor.RaggedTensor.from_row_splits(result.rt_dense_values,
241 return ragged_tensor.RaggedTensor.from_row_splits(output_values,
278 ones = ragged_tensor.RaggedTensor.from_nested_row_splits(
292 ones = ragged_tensor.RaggedTensor.from_nested_row_splits(
534 ones = ragged_tensor.RaggedTensor.from_nested_row_splits(
541 return ragged_tensor.RaggedTensor.from_nested_row_splits(
Dragged_conversion_ops.py28 return ragged_tensor.RaggedTensor.from_tensor(tensor, lengths, padding,
44 return ragged_tensor.RaggedTensor.from_sparse(st_input, name)
Dragged_getitem.py140 return ragged_tensor.RaggedTensor.from_row_splits(
208 return ragged_tensor.RaggedTensor.from_row_splits(
249 return ragged_tensor.RaggedTensor.from_row_splits(inner_rt,
368 if isinstance(values, ragged_tensor.RaggedTensor):
Dragged_string_ops.py117 ragged_input_tensor = ragged_tensor.RaggedTensor.from_row_splits(
388 codepoints = ragged_tensor.RaggedTensor.from_row_splits(
393 offsets = ragged_tensor.RaggedTensor.from_row_splits(
Dragged_batch_gather_ops.py90 return ragged_tensor.RaggedTensor.from_row_splits(
109 return ragged_tensor.RaggedTensor.from_row_splits(
Dragged_map_fn_op_test.py88 fn=lambda x: ragged_tensor.RaggedTensor.from_row_starts(x, [0]),
274 fn = lambda x: ragged_tensor.RaggedTensor.from_row_starts(x, [0])
289 t2 = ragged_tensor.RaggedTensor.from_sparse(s)
Dragged_tensor_shape.py472 if isinstance(rt_input, ragged_tensor.RaggedTensor):
487 if (isinstance(rt_input, ragged_tensor.RaggedTensor) and
504 rt_input = ragged_tensor.RaggedTensor.from_row_lengths(rt_input, [nrows])
571 return ragged_tensor.RaggedTensor.from_nested_row_lengths(
Dragged_map_flat_values_op_test.py199 x = ragged_tensor.RaggedTensor.from_row_splits([3, 1, 4, 1, 5], splits1)
200 y = ragged_tensor.RaggedTensor.from_row_splits([1, 2, 3, 4, 5], splits2)
/external/tensorflow/tensorflow/core/api_def/base_api/
Dapi_def_RaggedGather.pbtxt8 `params` RaggedTensor input.
14 The `flat_values` for the `params` RaggedTensor. There was a terminology change
30 returned RaggedTensor.
35 description: "The `flat_values` for the returned RaggedTensor."
40 The ragged rank of the `params` RaggedTensor. `params_nested_splits` should
48 The ragged rank of the output RaggedTensor. `output_nested_splits` will contain
57 Outputs a `RaggedTensor` output composed from `output_dense_values` and
Dapi_def_RaggedTensorToSparse.pbtxt6 description: "The `row_splits` for the `RaggedTensor`."
10 description: "The `flat_values` for the `RaggedTensor`."
23 `sparse_dense_shape` is a tight bounding box of the input `RaggedTensor`.
29 The ragged rank of the input RaggedTensor. `rt_nested_splits` should contain
35 Converts a `RaggedTensor` into a `SparseTensor` with the same values.
Dapi_def_RaggedRange.pbtxt18 description: "The `row_splits` for the returned `RaggedTensor`."
22 description: "The `flat_values` for the returned `RaggedTensor`."
25 Returns a `RaggedTensor` containing the specified sequences of numbers.
29 Returns a `RaggedTensor` `result` composed from `rt_dense_values` and
/external/tensorflow/tensorflow/python/keras/utils/
Dcomposite_tensor_support_test.py47 if isinstance(inputs, ragged_tensor.RaggedTensor):
67 return ragged_tensor.RaggedTensor.from_tensor(
/external/tensorflow/tensorflow/tools/api/golden/v2/
Dtensorflow.-ragged-tensor.pbtxt1 path: "tensorflow.RaggedTensor"
3 is_instance: "<class \'tensorflow.python.ops.ragged.ragged_tensor.RaggedTensor\'>"

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