text: import tensorflow as tf
  from matplotlib import pyplot as plt
  from mnist_model import load_data, NETNAME, num2lab
  
  test_size = 1000
  # Load TFLite model and allocate tensors.
  interpreter = tf.lite.Interpreter(model_path="{}.tflite".format(NETNAME))
  interpreter.allocate_tensors()
  
  # Get input and output tensors.
  input_details = interpreter.get_input_details()
  output_details = interpreter.get_output_details()
  batch_size = input_details[0]["shape"][0]
  
  input_shape = input_details[0]['shape']
  # Load training and eval data
  
  _, _, eval_data, eval_labels = load_data(test_size)
  
  preds = []
  probs = []
  labs = []
  i = 0
  
  while i < test_size:
      batch_data = eval_data[i:i+batch_size]
      batch_labs = eval_labels[i:i+batch_size]
      interpreter.set_tensor(input_details[0]['index'], batch_data)
      interpreter.invoke()
      logits = interpreter.get_tensor(output_details[0]['index'])
      preds += list(logits.argmax(1))
      probs += list(logits.max(1))
      labs += list(batch_labs)
      i += batch_size
  
  print("accuracy: {}%".format(sum([i == j for i, j in zip(preds, labs)])/len(preds)*100))
  
  plt.subplots(figsize=(10, 5))
  for i in range(10):
      conf = round(float(probs[i]), 3)
      plt.subplot(2, 5, i+1)
      plt.imshow(eval_data[i])
      plt.title("--{}--\n{:2.4}".format(num2lab[preds[i]], conf))
      plt.axis("off")
      plt.suptitle("tflite test", fontsize=18)
  plt.savefig("./imgs/{}/res10-tflite.png".format(NETNAME))
  plt.close()