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Neural Network Foundations with TensorFlow 2.0
It is very simple; we just need to change a few lines:
# Compiling the model.
model.compile(optimizer='RMSProp',
loss='categorical_crossentropy', metrics=['accuracy'])
That's it. Let's test it:
Figure 19: Testing RMSProp
As you can see in the preceding screenshot, RMSProp is faster than SDG since we
are able to achieve in only 10 epochs an accuracy of 97.43% on training, 97.62%
on validation, and 97.64% on test. That's a significant improvement on SDG. Now
that we have a very fast optimizer, let us try to significantly increase the number of
epochs up to 250 and we get 98.99% accuracy on training, 97.66% on validation, and
97.77% on test:
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