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Neural Network Foundations with TensorFlow 2.0
Okay, let's try the other optimizer, Adam(). Pretty simple:
# Compiling the model.
model.compile(optimizer='Adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
As we can see, Adam() is slightly better. With Adam we achieve 98.94% accuracy on
training, 97.89% on validation, and 97.82% on test with 20 iterations:
Figure 22: Testing with the Adam optimizer
One more time, let's plot how accuracy increases on training and test sets when the
number of epochs increases (see Figure 23). You'll notice that by choosing Adam as
an optimizer, we are able to stop after just about 12 epochs or steps:
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