#convert keras model to tflite
def convert_keras_tfliet(model):
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)
print("converted_model.tflite")
return tflite_model
#inferece tflite model
def inference_tflite(tflite_model, input_data):
interpreter = tf.lite.Interpreter(model_path=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
input_data = np.array(input_data, dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])
return output_data