Details about the TrainingResNet Notebook

for param in model.parameters():
    param.requires_grad = False
model.fc = nn.Linear(512, len(classes))

In these three lines of code, we basically replace the last layer of our network with one that fits our problem. In this len(classes) gives us the number of classes we loaded in with our data set. In this example, it would be two (classes =['Cats', 'Dogs']). The other two lines beforehand basically tell the network, not to change any values in the network when it tries to tweak the parameters. Since we replace the last layer after this loop .requires_grad is automatically set to true again.

for i in range(5):  #loop over the dataset multiple times