In Chapter 2, Using Deep Learning to Solve Regression Problems, we saw the .save() method, that allowed us to save our Keras model after we were done training. Wouldn't it be nice, though, if we could write our weights to disk every now and then so that we could go back in time in the preceding example and save a version of the model before it started to overfit? We could then stop right there and use the lowest variance version of the network.
That's exactly what the ModelCheckpoint callback does for us. Let's take a look:
checkpoint_callback = ModelCheckpoint(filepath="./model-weights.{epoch:02d}-{val_acc:.6f}.hdf5", monitor='val_acc', verbose=1, save_best_only=True)
What ModelCheckpoint will do for ...
