|TensorFlow 1 version||View source on GitHub|
Represents a ragged tensor.
Compat aliases for migration
See Migration guide for more details.
Used in the notebooks
|Used in the guide||Used in the tutorials|
RaggedTensor is a tensor with one or more ragged dimensions, which are
dimensions whose slices may have different lengths. For example, the inner
(column) dimension of
rt=[[3, 1, 4, 1], , [5, 9, 2], , ] is ragged,
since the column slices (
rt[0, :], ...,
rt[4, :]) have different lengths.
Dimensions whose slices all have the same length are called uniform
dimensions. The outermost dimension of a
RaggedTensor is always uniform,
since it consists of a single slice (and so there is no possibility for
differing slice lengths).
The total number of dimensions in a
RaggedTensor is called its rank,
and the number of ragged dimensions in a
RaggedTensor is called its
RaggedTensor's ragged-rank is fixed at graph creation
time: it can't depend on the runtime values of
Tensors, and can't vary
dynamically for different session runs.
Note that the
__init__ constructor is private. Please use one of the
following methods to construct a
Potentially Ragged Tensors
Many ops support both
(see tf.ragged for a
full listing). The term "potentially ragged tensor" may be used to refer to a
tensor that might be either a
Tensor or a
RaggedTensor. The ragged-rank
Tensor is zero.
Documenting RaggedTensor Shapes
When documenting the shape of a RaggedTensor, ragged dimensions can be
indicated by enclosing them in parentheses. For example, the shape of
RaggedTensor that stores the fixed-size word embedding for each
word in a sentence, for each sentence in a batch, could be written as
[num_sentences, (num_words), embedding_size]. The parentheses around
(num_words) indicate that dimension is ragged, and that the length
of each element list in that dimension may vary for each item.
RaggedTensor consists of a concatenated list