class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

prediction_values = model.predict_classes(x_test)

# set up the figure
fig = plt.figure(figsize=(15, 7))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)

# plot the images: each image is 28x28 pixels
for i in range(50):
    ax = fig.add_subplot(5, 10, i + 1, xticks=[], yticks=[])
    ax.imshow(x_test[i,:].reshape((28,28)),cmap=plt.cm.gray_r, interpolation='nearest')
  
    if prediction_values[i] == np.argmax(y_test[i]):
        # label the image with the blue text
        ax.text(0, 7, class_names[prediction_values[i]], color='blue')
    else:
        # label the image with the red text
        ax.text(0, 7, class_names[prediction_values[i]], color='red')