class_names = ['bike', 'ship']

x_valid, label_batch  = next(iter(valid_generator))

prediction_values = model.predict_classes(x_valid)

# set up the figure
fig = plt.figure(figsize=(10, 6))
fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)

# plot the images: each image is 227x227 pixels
for i in range(8):
    ax = fig.add_subplot(2, 4, i + 1, xticks=[], yticks=[])
    ax.imshow(x_valid[i,:],cmap=plt.cm.gray_r, interpolation='nearest')
  
    if prediction_values[i] == np.argmax(label_batch[i]):
        # label the image with the blue text
        ax.text(3, 17, class_names[prediction_values[i]], color='blue', fontsize=14)
    else:
        # label the image with the red text
        ax.text(3, 17, class_names[prediction_values[i]], color='red', fontsize=14)