从 keras 模型转换为张量流精简版

Question

这里是一个初学者，你会看到:)我试图将我的keras (.h5)训练模型转换为TensorFlow Lite格式，但我有以下错误；

属性错误： 类型对象 “TFLite转换器” 没有属性 “from_keras_model”

    import os
    import sys
    import argparse
    import warnings
    
    def get_parent_dir(n=1):
        """returns the n-th parent dicrectory of the current
        working directory"""
        current_path = os.path.dirname(os.path.abspath(__file__))
        for _ in range(n):
            current_path = os.path.dirname(current_path)
        return current_path 

   src_path = os.path.join(get_parent_dir(0), "src")
   sys.path.append(src_path)

   utils_path = os.path.join(get_parent_dir(1), "Utils")
   sys.path.append(utils_path) 

   import numpy as np
   import keras.backend as K
   from keras.layers import Input, Lambda
   from keras.models import Model
   from keras.optimizers import Adam

   from keras.callbacks import (
     TensorBoard,
     ModelCheckpoint,
     ReduceLROnPlateau,
     EarlyStopping,
   )
   from keras_yolo3.yolo3.model import (
     preprocess_true_boxes,
     yolo_body,
     tiny_yolo_body,
     yolo_loss,
   )
   from keras_yolo3.yolo3.utils import get_random_data
   from PIL import Image
   from time import time
   import tensorflow.compat.v1 as tf
   import pickle

   from Train_Utils import (
     get_classes,
     get_anchors,
     create_model,
     create_tiny_model,
     data_generator,
     data_generator_wrapper,
     ChangeToOtherMachine,
   )
  

 gpu = tf.config.list_physical_devices("GPU")
       tf.config.experimental.set_memory_growth(gpu[0], True)

       keras_path = os.path.join(src_path, "keras_yolo3")
       Data_Folder = os.path.join(get_parent_dir(1), "Data")
       Image_Folder = os.path.join(Data_Folder, "Source_Images", "Training_Images")
       VoTT_Folder = os.path.join(Image_Folder, "vott-csv-export")
       YOLO_filename = os.path.join(VoTT_Folder, "data_train.txt")

       Model_Folder = os.path.join(Data_Folder, "Model_Weights")
       YOLO_classname = os.path.join(Model_Folder, "data_classes.txt")

       log_dir = Model_Folder
       anchors_path = os.path.join(keras_path, "model_data", "yolo_anchors.txt")
       weights_path = os.path.join(keras_path, "yolo.h5")

 # Convert the model.
    converter = tf.lite.TFLiteConverter.from_keras_model(model)
    tflite_model = converter.convert()

    # Save the model.
    with open('model.tflite', 'wb') as f:
        f.write(tflite_model)

model.save_weights(os.path.join(log_dir, "trained_weights_final.h5"))

Answer 1

既然你这样做：

import tensorflow.compat.v1 as tf

< code > TF . lite . tflite converter 解析为TF 1.x转换器API。

通常，我建议将所有内容迁移到TF2.x。

如果不可能，您可以尝试：

# Now `lite` is the V2 TFLite package, not `tf.compat.v1.lite`
from tensorflow import lite
converter = lite.TFLiteConverter.from_keras_model(model)