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TensorFlow 1 version
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View source on GitHub
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Applies softmax to a batched N-D SparseTensor.
tf.sparse.softmax(
sp_input, name=None
)
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.sparse.SparseTensor(indices, values, shape))
# ...returning a 3-D SparseTensor, equivalent to:
# [? 1.] [1 ?]
# [1. ? ] and [.5 .5]
# where ? means implicitly zero.
Args | |
|---|---|
sp_input
|
N-D SparseTensor, where N >= 2.
|
name
|
optional name of the operation. |
Returns | |
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output
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N-D SparseTensor representing the results.
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