A question I often get at
this point from programmers in particular when
experimenting with different numbers of epochs is, How can I stop training when I reach a point that
I want to be at? What do I always
have to hard code it to go for certain
number of epochs? Well, the good news is that, the training loop does
support callbacks. So in every epoch, you can callback to
a code function, having checked the metrics. If they're what you want to say, then you can cancel
the training at that point. Let's take a look. Okay, so here's
our code for training the neural network to
recognize the fashion images. In particular, keep an eye on the model.fit function that
executes the training loop. You can see that here. What we'll now do is write a callback in Python.
Here's the code. It's implemented as
a separate class, but that can be in-line
with your other code. It doesn't need to be
in a separate file. In it, we'll implement
the on_epoch_end function, which gets called by the callback whenever the epoch ends. It also sends a logs object
which contains lots of great information about the current state
of training. For example, the current loss
is available in the logs, so we can query it
for certain amount. For example, here I'm checking
if the loss is less than 0.4 and canceling
the training itself. Now that we have our callback, let's return to
the rest of the code, and there are two modifications
that we need to make. First, we instantiate the class
that we just created, we do that with this code. Then, in my model.fit, I used the callbacks parameter and pass it this instance
of the class. Let's see this in action.