tf.sparse_split

tf.sparse_split(
    keyword_required=KeywordRequired(),
    sp_input=None,
    num_split=None,
    axis=None,
    name=None,
    split_dim=None
)

Defined in tensorflow/python/ops/sparse_ops.py.

See the guide: Upgrade to TensorFlow 1.0 > Upgrading your code manually

Split a SparseTensor into num_split tensors along axis. (deprecated arguments)

SOME ARGUMENTS ARE DEPRECATED. They will be removed in a future version. Instructions for updating: split_dim is deprecated, use axis instead

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, if axis = 1 and num_split = 2 and the input is:

input_tensor = shape = [2, 7]
[    a   d e  ]
[b c          ]

Graphically the output tensors are:

output_tensor[0] =
[    a ]
[b c   ]

output_tensor[1] =
[ d e  ]
[      ]

Args:

  • keyword_required: Python 2 standin for * (temporary for argument reorder)
  • 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.
  • name: A name for the operation (optional).
  • split_dim: Deprecated old name for axis.

Returns:

num_split SparseTensor objects resulting from splitting value.

Raises:

  • TypeError: If sp_input is not a SparseTensor.
  • ValueError: If the deprecated split_dim and axis are both non None.