In the previous video, we looked at training
a small data set of cats versus dogs,
and saw how overfitting occurred
relatively early on in the training,
leading us to a false sense of security
about how well the neural network could perform.
Let's now take a look at the impact of
adding image augmentation to the training.
Here we have exactly the same code except
that we've added the image augmentation code to it.
I'll start the training, and we'll see that we have
2,000 training images in two classes.
As we start training,
we'll initially see that the accuracy is
lower than with the non-augmented version we did earlier.
This is because of the random effects of
the different image processing that's being done.
As it runs for a few more epochs,
you'll see the accuracy slowly climbing.
I'll skip forward to see the last few epochs,
and by the time we reach the last one,
our model's about 86 percent
accurate on the training data,
and about 81 percent on the test data.
So let's plot this.
We can see that the training and validation accuracy,
and loss are actually in step with each other.
This is a clear sign that we've
solved the overfitting that we had earlier.
While our accuracy is a little lower,
it's also trending upwards so
perhaps a more epochs will get us closer to 100 percent.
Why don't you go ahead and give it a try?