tf.sparse_to_dense()

tf.sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value=0, validate_indices=True, name=None)

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.

Args:

• 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).

Returns:

Dense Tensor of shape output_shape. Has the same type as sparse_values.