The next code block show how to use the VGG-16 model learned to predict the probability of whether an image is dog or cat from the test images dataset:
test_data = process_test_data()len(test_data)X_test = np.array([i for i in test_data]).reshape(-1,IMG_SIZE,IMG_SIZE,3)probs = model.predict(X_test)probs = np.round(probs,2)pylab.figure(figsize=(20,20))for i in range(100): pylab.subplot(10,10,i+1), pylab.imshow(X_test[i,:,:,::-1]), pylab.axis('off') pylab.title("{}, prob={:0.2f}".format('cat' if probs[i][1] < 0.5 else 'dog', max(probs[i][0],probs[i][1])))pylab.show()
The next screenshot shows the class predicted the first 100 test images along with the prediction probabilities. As can be seen in the following output, ...
