tf.raw_ops.SparseToDense

Converts a sparse representation into a dense tensor.

tf.raw_ops.SparseToDense(
    sparse_indices, output_shape, sparse_values, default_value,
    validate_indices=True, name=None
)

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.

Args:

  • sparse_indices: A Tensor. Must be one of the following types: int32, int64. 0-D, 1-D, or 2-D. sparse_indices[i] contains the complete index where sparse_values[i] will be placed.
  • output_shape: A Tensor. Must have the same type as sparse_indices. 1-D. Shape of the dense output tensor.
  • sparse_values: A Tensor. 1-D. Values corresponding to each row of sparse_indices, or a scalar value to be used for all sparse indices.
  • default_value: A Tensor. Must have the same type as sparse_values. Scalar value to set for indices not specified in sparse_indices.
  • validate_indices: An optional bool. Defaults to True. 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).

Returns:

A Tensor. Has the same type as sparse_values.