How to compress your Keras model x5 smaller with TensorFlow model optimization | DLology

eval_tflite_model.py

import numpy as np

interpreter = tf.lite.Interpreter(model_path=str(tflite_model_file))
interpreter.allocate_tensors()
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]

def eval_model(interpreter, x_test, y_test):
  total_seen = 0
  num_correct = 0

  for img, label in zip(x_test, y_test):
    inp = img.reshape((1, 28, 28, 1))
    total_seen += 1
    interpreter.set_tensor(input_index, inp)
    interpreter.invoke()
    predictions = interpreter.get_tensor(output_index)
    if np.argmax(predictions) == np.argmax(label):
      num_correct += 1

    if total_seen % 1000 == 0:
        print("Accuracy after %i images: %f" %
              (total_seen, float(num_correct) / float(total_seen)))

  return float(num_correct) / float(total_seen)

print(eval_model(interpreter, x_test, y_test))