Testing

After you're satisfied with the training output and accuracy, you can run the network on the test dataset to measure its performance!

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])