Let's take a look at training cats versus dogs
with a smaller dataset over a longer cycle.
I'm going to start the training and we'll see it
picked up the 2,000 training images,
but note that we're training for 100 epochs.
After the first epoch,
We'll watch the accuracy and validation accuracy figures.
Let's watch them for a few more epochs.
Keep an eye on those figures.
After eight epochs, the accuracy is approaching 0.8,
but the validation accuracy has slowed its growth.
So now let's skip ahead to the end.
I'm going to plot the accuracy and
loss overall 100 epochs.
We can see from this figure that
the training reach close to
a 100 percent accuracy in a little over 20 epochs.
Meanwhile, the validation topped
out at around 70 percent,
and that's overfitting clearly been demonstrated.
In other words, the neural network was
terrific at finding a correlation between
the images and labels of cats versus
dogs for the 2,000 images that it was trained on,
but once it tried to predict the images
that it previously hadn't seen,
it was about 70 percent accurate.
It's a little bit like the example
of the shoes we spoke about earlier.
So in the next video,
we'll take a look at the impact of
adding augmentation to this.