Now, this is a new data set that I created for learning
opportunities. It's freely available, and it consists of about 3,000 images. They've all been generated using CGI with a diverse
array of models, male and female, and lots
of different skin tones, and here's some examples. If you want to download
the data sets, you can find them at this URL. It will contain a training
set, a validation set, and some extra images
that you can download to test
the network for yourself. Once your directory is set up, you need to set up
your image generator. Here's the code that
you used earlier but, note the class mode
was set to binary. For multiple classes,
you'll have to change this to
categorical like this. The next change comes in your model definition where you'll need to change
the output layer. For a binary classifier, it was more efficient
for you to just have one neuron and use a sigmoid
function to activate it. This meant that it
would output close to zero for one class and
close to one for the other. Now, that doesn't
fit for multi-class, so we need to change it,
but it's pretty simple. Now, we have an output layer
that has three neurons, one for each of
the classes rock, paper, and scissors, and
it's activated by softmax which turns
all the values into probabilities that
will sum up to one. So what does that really mean? Consider a hand like this one. It's most likely a paper, but because she has
her first two fingers open, and the rest joined, it could also be
mistaken as scissors. The output of
a neural network with three neurons and a softmax
would reflect that, and maybe look like this with a very low
probability of rock, a really high one for paper, and a decent one for scissors. All three probabilities
would still add up to one. The final change then comes when you compile your network. If you recall with
the earlier examples, your loss function was
binary cross entropy. Now, you'll change it's a categorical cross
entropy like this. There are other
categorical loss functions including sparse categorical, cross entropy that you used
in the fashion example, and you can of course
also use those. Around this for 100 epochs, and I got this chart, it shows the training hits
a max at about 25 epochs. So I'd recommend
just using not many, and that's all really
that you have to do. So let's take a look
at it in the workbook.