from keras.backend import clear_session
import numpy as np
import tensorflow as tf
clear_session()
np.set_printoptions(suppress=True)
input_graph_name = "../models/weights.best_mobilenet224.h5"
output_graph_name = input_graph_name[:-3] + '.tflite'
converter = tf.lite.TFLiteConverter.from_keras_model_file(model_file=input_graph_name)
converter.post_training_quantize = True
#在windows平台这个函数有问题，无法正常使用
tflite_model = converter.convert()
open(output_graph_name, "wb").write(tflite_model)
print ("generate:",output_graph_name)

converter = tf.lite.TFLiteConverter.from_keras_model_file('newModel.h5')
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)

import tensorflow as tf
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.utils import CustomObjectScope

def relu6(x):
 return K.relu(x, max_value=6)

with CustomObjectScope({'relu6': relu6}):
  converter = tf.lite.TFLiteConverter.from_keras_model_file('newModel.h5')
  tflite_model = converter.convert()
  open("newModel.tflite", "wb").write(tflite_model)

import numpy as np
import tensorflow as tf

# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="newModel.tflite")
interpreter.allocate_tensors()

# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

print(input_details)
print(output_details)