Code snippet for medium article

celeba_train.py

#Mixed precision model to train
learn = cnn_learner(dls, resnet18, pretrained=True, metrics=[accuracy_multi]).to_fp16()

#Train only the classifier using one cycle policy, with default optimizer(ADAM) and learning rate.
learn.fit_one_cycle(4)
#Save the trained model
learn.save('stage_1')

#Unfreeze the model, now entire model is available for training
learn.unfreeze()
#Find optimal leaning rate
learn.lr_find()

#Train the entire model with variable learning rate across the layers
learn.fit_one_cycle(4, slice(1e-6, 1e-4))