attention_gru_cell.py 文件源码

def _linear(args, output_size, bias, bias_start=0.0):
    """Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.
    Args:
    args: a 2D Tensor or a list of 2D, batch x n, Tensors.
    output_size: int, second dimension of W[i].
    bias: boolean, whether to add a bias term or not.
    bias_start: starting value to initialize the bias; 0 by default.
    Returns:
    A 2D Tensor with shape [batch x output_size] equal to
    sum_i(args[i] * W[i]), where W[i]s are newly created matrices.
    Raises:
    ValueError: if some of the arguments has unspecified or wrong shape.
    """
    if args is None or (nest.is_sequence(args) and not args):
        raise ValueError("`args` must be specified")
    if not nest.is_sequence(args):
        args = [args]

    # Calculate the total size of arguments on dimension 1.
    total_arg_size = 0
    shapes = [a.get_shape() for a in args]
    for shape in shapes:
        if shape.ndims != 2:
            raise ValueError("linear is expecting 2D arguments: %s" % shapes)
        if shape[1].value is None:
            raise ValueError("linear expects shape[1] to be provided for shape %s, "
                "but saw %s" % (shape, shape[1]))
        else:
            total_arg_size += shape[1].value

    dtype = [a.dtype for a in args][0]

    # Now the computation.
    scope = vs.get_variable_scope()
    with vs.variable_scope(scope) as outer_scope:
        weights = vs.get_variable(
            "weights", [total_arg_size, output_size], dtype=dtype)
        if len(args) == 1:
            res = math_ops.matmul(args[0], weights)
        else:
            res = math_ops.matmul(array_ops.concat(args, 1), weights)
        if not bias:
            return res
        with vs.variable_scope(outer_scope) as inner_scope:
            inner_scope.set_partitioner(None)
            biases = vs.get_variable(
                        "biases", [output_size],
                      dtype=dtype,
                    initializer=init_ops.constant_initializer(bias_start, dtype=dtype))
        return nn_ops.bias_add(res, biases)