Dear All,
While watching stanford course on convolutional neural networks, i noticed that they recommend to use batch normalization after the conv or fc layers but before the nonlinearity
When i get back to the code used for vgg16bn.py
i found that the fcblock defines the batch normalization after the nonlinearity
def FCBlock(self):
model = self.model
model.add(Dense(4096, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
i googled a little bit, and found that there’s a debate around this point, so that’s why i’m asking why had you preferred puting it after the non linearity unlike what the paper says
Regards,
Omar