basic.py 文件源码

def _forward(self):
        eps = self.eps

        param_size = (1, 1, self.n_output, 1, 1)
        self.gamma = self.declare(param_size)
        self.beta = self.declare(param_size)

        mean = self.inpt.mean(axis=[0, 1, 3, 4], keepdims=False)
        std = self.inpt.std(axis=[0, 1, 3, 4], keepdims=False)

        self._setup_running_metrics(self.n_output)
        self.running_mean.default_update = ifelse(
            self.training,
            (1.0 - self.alpha) * self.running_mean + self.alpha * mean,
            self.running_mean
        )
        self.running_std.default_update = ifelse(
            self.training,
            (1.0 - self.alpha) * self.running_std + self.alpha * std,
            self.running_std
        )

        # This will be optimized away, but ensures the running mean and the running std get updated.
        # Reference: https://gist.github.com/f0k/f1a6bd3c8585c400c190#file-batch_norm-py-L86
        mean += 0 * self.running_mean
        std += 0 * self.running_std

        use_mean = ifelse(self.training, mean, self.running_mean)
        use_std = ifelse(self.training, std, self.running_std)

        use_mean = use_mean.dimshuffle('x', 'x', 0, 'x', 'x')
        use_std = use_std.dimshuffle('x', 'x', 0, 'x', 'x')
        norm_inpt = (self.inpt - use_mean) / (use_std + eps)
        self.output = self.gamma * norm_inpt + self.beta