tf.sparse_softmax( sp_input, name=None )
Defined in tensorflow/python/ops/sparse_ops.py
.
See the guide: Sparse Tensors > Math Operations
Applies softmax to a batched N-D SparseTensor
.
The inputs represent an N-D SparseTensor with logical shape [..., B, C]
(where N >= 2
), and with indices sorted in the canonical lexicographic order.
This op is equivalent to applying the normal tf.nn.softmax()
to each innermost logical submatrix with shape [B, C]
, but with the catch that the implicitly zero elements do not participate. Specifically, the algorithm is equivalent to:
(1) Applies tf.nn.softmax()
to a densified view of each innermost submatrix with shape [B, C]
, along the size-C dimension; (2) Masks out the original implicitly-zero locations; (3) Renormalizes the remaining elements.
Hence, the SparseTensor
result has exactly the same non-zero indices and shape.
Example:
# First batch: # [? e.] # [1. ? ] # Second batch: # [e ? ] # [e e ] shape = [2, 2, 2] # 3-D SparseTensor values = np.asarray([[[0., np.e], [1., 0.]], [[np.e, 0.], [np.e, np.e]]]) indices = np.vstack(np.where(values)).astype(np.int64).T result = tf.sparse_softmax(tf.SparseTensor(indices, values, shape)) # ...returning a 3-D SparseTensor, equivalent to: # [? 1.] [1 ?] # [1. ? ] and [.5 .5] # where ? means implicitly zero.
sp_input
: N-D SparseTensor
, where N >= 2
.name
: optional name of the operation.output
: N-D SparseTensor
representing the results.
© 2018 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_softmax