Now, we'll look into what's going on inside a deep neural network. First, we'll verify the model accuracy. Then, we'll visualize and study the pixel weights. Finally, we'll look at the output weights as well.
After you've trained your deep neural network, let's take a look at the model accuracy. We'll do this the same way that we did for the single hidden layer model. The only difference this time, is that we have many more saved samples of the training and testing accuracy, having gone from many more epochs.
As always, don't worry if you don't have Matplotlib; printing parts of the arrays is fine.
Execute the following code to see the result:
# Plot the accuracy curves plt.figure(figsize=(6,6)) plt.plot(train_acc,'bo') plt.plot(test_acc,'rx')
From the preceding output graph, we reach about 68 percent training accuracy and maybe 63 percent validation accuracy. This isn't too bad, but it does leave room for some...
