tf.sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value=0, validate_indices=True, name=None)
See the guide: Sparse Tensors > Conversion
Converts a sparse representation into a dense tensor.
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
.
Defined in tensorflow/python/ops/sparse_ops.py
.
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/sparse_to_dense