qrnn.py 文件源码

def __call__(self, x):
        if not hasattr(self, 'encoding') or self.encoding is None:
            self.batch_size = x.shape[0]
            self.init()
        dims = len(x.shape) - 1
        f, z, o = F.split_axis(self.pre(x), 3, axis=dims)
        f = F.sigmoid(f)
        z = (1 - f) * F.tanh(z)
        o = F.sigmoid(o)

        if dims == 2:
            self.c = strnn(f, z, self.c[:self.batch_size])
        else:
            self.c = f * self.c + z

        if self.attention:
            context = attention_sum(self.encoding, self.c)
            self.h = o * self.o(F.concat((self.c, context), axis=dims))
        else:
            self.h = self.c * o

        self.x = x
        return self.h