tf.sparse.to_dense

Converts a SparseTensor into a dense tensor.

Compat aliases for migration

tf.sparse.to_dense(
    sp_input, default_value=None, validate_indices=True, name=None
)

Used in the guide	Used in the tutorials
Working with sparse tensors Migrate `tf.feature_column`s to Keras preprocessing layers Ragged tensors Introduction to Tensors Introduction to tensor slicing	Graph-based Neural Structured Learning in TFX Text generation with an RNN TFX Estimator Component Tutorial TFX Keras Component Tutorial

For this sparse tensor with three non-empty values:

sp_input = tf.sparse.SparseTensor(
  dense_shape=[3, 5],
  values=[7, 8, 9],
  indices =[[0, 1],
            [0, 3],
            [2, 0]])

The output will be a dense [3, 5] tensor with values:

tf.sparse.to_dense(sp_input).numpy()
array([[0, 7, 0, 8, 0],
       [0, 0, 0, 0, 0],
       [9, 0, 0, 0, 0]], dtype=int32)

Args
`sp_input`	The input `SparseTensor`.
`default_value`	Scalar value to set for indices not specified in `sp_input`. Defaults to zero.
`validate_indices`	A boolean value. If `True`, indices are checked to make sure they are sorted in lexicographic order and that there are no repeats.
`name`	A name prefix for the returned tensors (optional).

Returns
A dense tensor with shape `sp_input.dense_shape` and values specified by the non-empty values in `sp_input`. Indices not in `sp_input` are assigned `default_value`.

Raises
`TypeError`	If `sp_input` is not a `SparseTensor`.

Args