for i in range(N_HIDDEN):               # build hidden layers and BN layers            input_size = 1 if i == 0 else 10            fc = nn.Linear(input_size, 10)            setattr(self, 'fc%i' % i, fc)       # IMPORTANT set layer to the Module            self._set_init(fc)                  # parameters initialization            self.fcs.append(fc)            if self.do_bn:                bn = nn.BatchNorm1d(10, momentum=0.5)                setattr(self, 'bn%i' % i, bn)   # IMPORTANT set layer to the Moduleself.bns.append(bn)

 上面的代码对每个隐层进行批标准化，setattr(self, 'fc%i' % i, fc)作用相当于self.fci=fc

每次生成的结果append到bns的最后面，结果的size 10×10，取出这些数据是非常方便

def forward(self, x):        pre_activation = [x]        if self.do_bn: x = self.bn_input(x)     # input batch normalization        layer_input = [x]        for i in range(N_HIDDEN):            x = self.fcs[i](x)            pre_activation.append(x)            if self.do_bn: x = self.bns[i](x)   # batch normalization            x = ACTIVATION(x)            layer_input.append(x)        out = self.predict(x)return out, layer_input, pre_activation

全部的