Build Deep Learning Framework:Step11 Support Variable Length Parameter

Before step 11, the function we realize is only one parameter, input or output. While in practice, multiparameter is very common, so we need support variable length parameter.

The Code Use Add function as an example:

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import numpy as np


class Variable:
    def __init__(self, data):
        if data is not None:
            if not isinstance(data, np.ndarray):
                raise TypeError('{} is not supported'.format(type(data)))

        self.data = data
        self.grad = None
        self.creator = None

    def set_creator(self, func):
        self.creator = func

    def backward(self):
        if self.grad is None:
            self.grad = np.ones_like(self.data)

        funcs = [self.creator]
        while funcs:
            f = funcs.pop()
            x, y = f.input, f.output
            x.grad = f.backward(y.grad)

            if x.creator is not None:
                funcs.append(x.creator)


def as_array(x):
    if np.isscalar(x):
        return np.array(x)
    return x


class Function:
    def __call__(self, inputs):
        xs = [x.data for x in inputs]  # Get data from Variable
        ys = self.forward(xs)
        outputs = [Variable(as_array(y)) for y in ys]  # Wrap data

        for output in outputs:
            output.set_creator(self)
        self.inputs = inputs
        self.outputs = outputs
        return outputs

    def forward(self, xs):
        raise NotImplementedError()

    def backward(self, gys):
        raise NotImplementedError()


class Add(Function):
    def forward(self, xs):
        x0, x1 = xs
        y = x0 + x1
        return (y,)


xs = [Variable(np.array(2)), Variable(np.array(3))]
f = Add()
ys = f(xs)
y = ys[0]
print(y.data)