Split a SparseTensor
into num_split
tensors along axis
.
tf.sparse.split(
sp_input=None, num_split=None, axis=None, name=None
)
If the sp_input.dense_shape[axis]
is not an integer multiple of num_split
each slice starting from 0:shape[axis] % num_split
gets extra one
dimension. For example:
indices = [[0, 2], [0, 4], [0, 5], [1, 0], [1, 1]]
values = [1, 2, 3, 4, 5]
t = tf.sparse.SparseTensor(indices=indices, values=values,
dense_shape=[2, 7])
tf.sparse.to_dense(t)
<tf.Tensor: shape=(2, 7), dtype=int32, numpy=
array([[0, 0, 1, 0, 2, 3, 0],
[4, 5, 0, 0, 0, 0, 0]], dtype=int32)>
output = tf.sparse.split(sp_input=t, num_split=2, axis=1)
tf.sparse.to_dense(output[0])
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
array([[0, 0, 1, 0],
[4, 5, 0, 0]], dtype=int32)>
tf.sparse.to_dense(output[1])
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[2, 3, 0],
[0, 0, 0]], dtype=int32)>
output = tf.sparse.split(sp_input=t, num_split=2, axis=0)
tf.sparse.to_dense(output[0])
<tf.Tensor: shape=(1, 7), dtype=int32, numpy=array([[0, 0, 1, 0, 2, 3, 0]],
dtype=int32)>
tf.sparse.to_dense(output[1])
<tf.Tensor: shape=(1, 7), dtype=int32, numpy=array([[4, 5, 0, 0, 0, 0, 0]],
dtype=int32)>
output = tf.sparse.split(sp_input=t, num_split=2, axis=-1)
tf.sparse.to_dense(output[0])
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
array([[0, 0, 1, 0],
[4, 5, 0, 0]], dtype=int32)>
tf.sparse.to_dense(output[1])
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[2, 3, 0],
[0, 0, 0]], dtype=int32)>
Args |
sp_input
|
The SparseTensor to split.
|
num_split
|
A Python integer. The number of ways to split.
|
axis
|
A 0-D int32 Tensor . The dimension along which to split. Must be in
range [-rank, rank), where rank is the number of dimensions in the input
SparseTensor .
|
name
|
A name for the operation (optional).
|
Returns |
num_split SparseTensor objects resulting from splitting value .
|
Raises |
TypeError
|
If sp_input is not a SparseTensor .
|