View source on GitHub |
Type specification for a tf.RaggedTensor
.
Inherits From: TypeSpec
tf.RaggedTensorSpec( shape=None, dtype=tf.dtypes.float32, ragged_rank=None, row_splits_dtype=tf.dtypes.int64, flat_values_spec=None )
Args | |
---|---|
shape | The shape of the RaggedTensor, or None to allow any shape. If a shape is specified, then all ragged dimensions must have size None . |
dtype | tf.DType of values in the RaggedTensor. |
ragged_rank | Python integer, the number of times the RaggedTensor's flat_values is partitioned. Defaults to shape.ndims - 1 . |
row_splits_dtype | dtype for the RaggedTensor's row_splits tensor. One of tf.int32 or tf.int64 . |
flat_values_spec | TypeSpec for flat_value of the RaggedTensor. It shall be provided when the flat_values is a CompositeTensor rather then Tensor. If both dtype and flat_values_spec and are provided, dtype must be the same as flat_values_spec.dtype . (experimental) |
Attributes | |
---|---|
dtype | The tf.dtypes.DType specified by this type for the RaggedTensor. rt = tf.ragged.constant([["a"], ["b", "c"]], dtype=tf.string) tf.type_spec_from_value(rt).dtype tf.string |
flat_values_spec | The TypeSpec of the flat_values of RaggedTensor. |
ragged_rank | The number of times the RaggedTensor's flat_values is partitioned. Defaults to values = tf.ragged.constant([[1, 2, 3], [4], [5, 6], [7, 8, 9, 10]]) tf.type_spec_from_value(values).ragged_rank 1 rt1 = tf.RaggedTensor.from_uniform_row_length(values, 2) tf.type_spec_from_value(rt1).ragged_rank 2 |
row_splits_dtype | The tf.dtypes.DType of the RaggedTensor's row_splits . rt = tf.ragged.constant([[1, 2, 3], [4]], row_splits_dtype=tf.int64) tf.type_spec_from_value(rt).row_splits_dtype tf.int64 |
shape | The statically known shape of the RaggedTensor. rt = tf.ragged.constant([[0], [1, 2]]) tf.type_spec_from_value(rt).shape TensorShape([2, None]) rt = tf.ragged.constant([[[0, 1]], [[1, 2], [3, 4]]], ragged_rank=1) tf.type_spec_from_value(rt).shape TensorShape([2, None, 2]) |
value_type | The Python type for values that are compatible with this TypeSpec. In particular, all values that are compatible with this TypeSpec must be an instance of this type. |
from_value
@classmethod from_value( value )
is_compatible_with
is_compatible_with( spec_or_value )
Returns true if spec_or_value
is compatible with this TypeSpec.
most_specific_compatible_type
most_specific_compatible_type( other )
Returns the most specific TypeSpec compatible with self
and other
.
Args | |
---|---|
other | A TypeSpec . |
Raises | |
---|---|
ValueError | If there is no TypeSpec that is compatible with both self and other . |
__eq__
__eq__( other )
Return self==value.
__ne__
__ne__( other )
Return self!=value.
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/RaggedTensorSpec