layers.py 文件源码

def forward(self, input):
        return F.batch_norm(input, 
                        running_mean=self.running_mean[:input.size(1)], 
                        running_var=self.running_var[:input.size(1)],
                        weight=self.weight[:input.size(1)],
                        bias=self.bias[:input.size(1)],
                        training=self.training)

# A convenience wrapper to prevent the forward() method of SMASH from
# being annoyingly verbose. This version of a fully-connected layer simply 
# slices its weights according to the size of the incoming tensor.
# Note that the bias does not need slicing, as it's defined wrt the output dim.