View source on GitHub |
Converts a sparse representation into a dense tensor. (deprecated)
tf.compat.v1.sparse_to_dense(
sparse_indices, output_shape, sparse_values, default_value=0,
validate_indices=True, name=None
)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Create a tf.sparse.SparseTensor
and use tf.sparse.to_dense
instead.
Builds an array dense
with shape output_shape
such that
# If sparse_indices is scalar
dense[i] = (i == sparse_indices ? sparse_values : default_value)
# If sparse_indices is a vector, then for each i
dense[sparse_indices[i]] = sparse_values[i]
# If sparse_indices is an n by d matrix, then for each i in [0, n)
dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
All other values in dense
are set to default_value
. If sparse_values
is a scalar, all sparse indices are set to this single value.
Indices should be sorted in lexicographic order, and indices must not
contain any repeats. If validate_indices
is True, these properties
are checked during execution.
sparse_indices
: A 0-D, 1-D, or 2-D Tensor
of type int32
or int64
.
sparse_indices[i]
contains the complete index where sparse_values[i]
will be placed.output_shape
: A 1-D Tensor
of the same type as sparse_indices
. Shape
of the dense output tensor.sparse_values
: A 0-D or 1-D Tensor
. Values corresponding to each row of
sparse_indices
, or a scalar value to be used for all sparse indices.default_value
: A 0-D Tensor
of the same type as sparse_values
. Value
to set for indices not specified in sparse_indices
. 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 for the operation (optional).Dense Tensor
of shape output_shape
. Has the same type as
sparse_values
.