Before we go, let's have
a quick look at plotting
the learning history of this model.
The object has training accuracy and loss values as
well as validation accuracy and validation loss values.
So let's iterate over these and plot them.
Now, if you look closely,
we didn't just call model.fit,
we said history equals model.fit.
So we now have
a history object that we can query for data.
Here you can see I'm using the same history object,
and I'm calling its history property passing at
ACC which gets me the model accuracy.
When I run it and plot
the training and validation accuracy,
we can see that my training went towards one while
my validation leveled out into 0.7 to 0.75 range.
That shows that my model isn't bad,
but I didn't really earn anything after just two epochs.
It fits the training data very
well with the validation data needed work.
These results are borne out in
the loss where we can see that after two epochs,
my training loss went down nicely,
but my validation loss climbed.
So as it is, my model is
about 75 percent accurate-ish after two epochs,
and I don't really need to train any further.
Remember also that we just
used a subset of the full data.
Using the entire dataset would
likely yield better results.
But before we do that,
let's look at a few other options,
and we'll do that in the next lesson.