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Testing
After you're satisfied with the training output and accuracy, you can run the network on the test dataset to measure its performance!
Keep in mind to perform this only after you've completed the training and are satisfied with the results.
A good result will obtain an accuracy higher than 95%. Some simple models have been known to achieve even up to 99.7% accuracy! We can test the model, as shown here:
# Comparing the labels predicted by our model with the actual labels score = model.evaluate(X_test, y_test, batch_size=32, verbose=1,sample_weight=None) # Printing the result print('Test score:', score[0]) print('Test accuracy:', score[1])