tf.sparse.split

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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.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.