nn.py 文件源码

def get_output_for(self, input, init=False, **kwargs):
        if init:
            m = T.mean(input, self.axes_to_sum)
            input -= m.dimshuffle(*self.dimshuffle_args)
            inv_stdv = self.init_stdv/T.sqrt(T.mean(T.square(input), self.axes_to_sum))
            input *= inv_stdv.dimshuffle(*self.dimshuffle_args)
            self.init_updates = [(self.b, -m*inv_stdv), (self.g, self.g*inv_stdv)]
        elif hasattr(self,'b'):
            input += self.b.dimshuffle(*self.dimshuffle_args)

        return self.nonlinearity(input)