def 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 ```python # 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`. """ return gen_sparse_ops._sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value=default_value, validate_indices=validate_indices, name=name)
def sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value=0, name=None): """Converts a sparse representation into a dense tensor. Builds an array `dense` with shape `output_shape` such that ```python # 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. 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. name: A name for the operation (optional). Returns: Dense `Tensor` of shape `output_shape`. Has the same type as `sparse_values`. """ return gen_sparse_ops._sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value, name=name)