convolution_1d.py 文件源码

def backward(self, inputs, grad_outputs):
        x, V, g = inputs[:3]
        if hasattr(self, "W") == False:
            self.norm = _get_norm(V)
            self.V_normalized = V / self.norm
            self.W = g * self.V_normalized

        b = inputs[3] if len(inputs) == 4 else None
        if b is None:
            gx, gW = super(Convolution1DFunction, self).backward((x, self.W), grad_outputs)
        else:
            gx, gW, gb = super(Convolution1DFunction, self).backward((x, self.W, b), grad_outputs)

        xp = cuda.get_array_module(x)
        gg = xp.sum(gW * self.V_normalized, axis=(1, 2), keepdims=True)
        gV = g * (gW - gg * self.V_normalized) / self.norm

        if b is None:
            return gx, gV, gg
        else:
            return gx, gV, gg, gb