tf.sparse_to_indicator(sp_input, vocab_size, name=None)
See the guide: Sparse Tensors > Conversion
Converts a SparseTensor
of ids into a dense bool indicator tensor.
The last dimension of sp_input.indices
is discarded and replaced with the values of sp_input
. If sp_input.dense_shape = [D0, D1, ..., Dn, K]
, then output.shape = [D0, D1, ..., Dn, vocab_size]
, where
output[d_0, d_1, ..., d_n, sp_input[d_0, d_1, ..., d_n, k]] = True
and False elsewhere in output
.
For example, if sp_input.dense_shape = [2, 3, 4]
with non-empty values:
[0, 0, 0]: 0 [0, 1, 0]: 10 [1, 0, 3]: 103 [1, 1, 2]: 150 [1, 1, 3]: 149 [1, 1, 4]: 150 [1, 2, 1]: 121
and vocab_size = 200
, then the output will be a [2, 3, 200]
dense bool tensor with False everywhere except at positions
(0, 0, 0), (0, 1, 10), (1, 0, 103), (1, 1, 149), (1, 1, 150), (1, 2, 121).
Note that repeats are allowed in the input SparseTensor. This op is useful for converting SparseTensor
s into dense formats for compatibility with ops that expect dense tensors.
The input SparseTensor
must be in row-major order.
sp_input
: A SparseTensor
with values
property of type int32
or int64
.vocab_size
: A scalar int64 Tensor (or Python int) containing the new size of the last dimension, all(0 <= sp_input.values < vocab_size)
.name
: A name prefix for the returned tensors (optional)A dense bool indicator tensor representing the indices with specified value.
TypeError
: If sp_input
is not a SparseTensor
.Defined in tensorflow/python/ops/sparse_ops.py
.
© 2017 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/api_docs/python/tf/sparse_to_indicator