Evaluation

results = neuralnetwork_cnn.evaluate(test_images, verbose=0)

print("    Test Loss: {:.5f}".format(results[0]))
print("Test Accuracy: {:.2f}%".format(results[1] * 100))


Test Loss: 0.06885
Test Accuracy: 99.53%


# Predict the label of the test_images
pred = neuralnetwork_cnn.predict(test_images)
pred = np.argmax(pred,axis=1)

# Map the label
labels = (train_images.class_indices)
labels = dict((v,k) for k,v in labels.items())
pred = [labels[k] for k in pred]

# Display the result
print(f'The first 5 predictions: {pred[:5]}')

The first 5 predictions: ['horse racing', 'figure skating men', 'billiards', 'rugby', 'bmx']