After we define the VGG model with keras, we are then loading the pre-trained weights into the model we just defined. VGG was trained on the entire image net database, right? But we are dealing with dogs / cats here so we want to improve the performance of the model specifically for dogs / cats
And so whats the difference between fine-tuning and fitting? The fitting part looks roughly what I'd expect, but the fine tuning part I havent seen before.
This is what I am guessing (Jeremy / Rachel please tell me if I am right). We are taking everything we can from the VGG model, up until the last layer, because we want the nice abstract/ high-level / object-like image features of the last layer of the network, but we just want to output different categories (now, dogs / cats , before 1000 different object categories).
So we are in a sense, modifying the VGG model such that we take the high level VGG network, doing no further training on it, but instead training a one layer neural network which maps the pretrained VGG features to a dogs / cats output.
If this is correct, then, the very fast performance per epoch actually makes more sense now.
def finetune(self, batches):
model = self.model
for layer in model.layers:
model.add(Dense(batches.nb_class, activation='softmax', input_shape=(1000,)))
def fit(self, batches, val_batches, nb_epoch=1):
self.model.fit_generator(batches, samples_per_epoch=batches.nb_sample, nb_epoch=nb_epoch,