text: # make a prediction for a new image.
  from keras.preprocessing.image import load_img
  from keras.preprocessing.image import img_to_array
  from keras.models import load_model
   
  # load and prepare the image
  def load_image(filename):
  	# load the image
  	img = load_img(filename, target_size=(32, 32))
  	# convert to array
  	img = img_to_array(img)
  	# reshape into a single sample with 3 channels
  	img = img.reshape(1, 32, 32, 3)
  	# center pixel data
  	img = img.astype('float32')
  	img = img - [123.68, 116.779, 103.939]
  	return img
   
  # load an image and predict the class
  def run_example(file):
  	# load the image
  	img = load_image(file)
  	# load model
  	model = load_model('small_model.h5')
  	# predict the class
  	result = model.predict(img)
  	print(result[0])
   
  # entry point, run the example
  if __name__ == "__main__":
  	for i in range(0, 10):
  		file = str(i) + '.png'
  		run_example(file)