tf.sparse.split | TensorFlow v2.16.1 (original) (raw)
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_spliteach 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 inputSparseTensor. |
| 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. |