TensorFlow2模型保存加载几个小坑

Dear 丶 2022-12-25 15:56 109阅读 0赞

最近学习TensorFlow2，利用tf2构建深度学习推荐系统算法，模型保存和加载遇到了几个问题，深感无力，也未找到直接的解决方法，特此记录一下，希望博友们给点提示。

### 1.通用模型参数保存加载（没毛病） ###

利用tf2的save\_weights和load\_weights，该方法加载参数，需要提前构建深度学习网络。伪代码代码案例如下：  
（1）自建神经网络

class Deep(Model):
        """ DNN网络 """
        def __init__(self, hiddens):
            """ :param hiddens: 每个隐藏层神经元数 """
            super(Deep, self).__init__()
            self.hiddens = hiddens
            self.deep_model = tf.keras.models.Sequential()
            for hidden in hiddens:
                self.deep_model.add(Dense(hidden, activation='relu', kernel_regularizer=tf.keras.regularizers.l2()))
    
        def call(self, inputs, training=None, mask=None):
            return self.deep_model(inputs)

（2）模型训练和保存模型

model = Deep([64, 64])
    model.compile(loss='binary_crossentropy', metrics=['accuracy', 'AUC'])
    
    checkpoint_save_path = 'model/deep_cross.ckpt'
    cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path, save_weights_only=True)
    model.fit(train_data, epochs=1, class_weight=class_weight, validation_data=dev_data, callbacks=cp_callback)

(3) 模型文件  
![在这里插入图片描述][20201204095326752.png]  
（4）加载模型进行预测

model = Deep([64, 64])
    model.compile(loss='binary_crossentropy', metrics=['accuracy', 'AUC'])
    
    checkpoint_save_path = 'model/deep_cross.ckpt'
    model.load_weights(checkpoint_save_path)
    
    result = model.predict(dev_data)

### 2.h5模型应用（坑坑不断，暂时放弃中） ###

采用h5的目的是不想使用网络搭建那部分代码，只想直接获取模型进行预测。

#### bug 1、Unable to create link (name already exist) ####

出现该问题是因为构建自己的网络，利用循环添加w，b参数，未添加变量名，导致的问题，解决方法和问题代码和注释如下：

class Cross(Model):
        """ cross网络 """
        def __init__(self, embed_dim, layer_num):
            """ :param embed_dim: 输入向量长度 :param layer_num: 网络层数 """
            super(Cross, self).__init__()
            self.embed_dim = embed_dim
            self.layer_num = layer_num
            self.ws = []
            self.bs = []
            # tf.Variable需要自己添加name参数，名称不能相同，不加即出现上述bug
            for i in range(layer_num):
                # 变量加了不同的name，否则保存h5模型会报错
                self.ws.append(tf.Variable(tf.random.truncated_normal(shape=(embed_dim, 1), stddev=0.01)), name='w' + str(i))
                self.bs.append(tf.Variable(tf.zeros(shape=(embed_dim, 1))), name='b' + str(i))
    
        def call(self, inputs, training=None, mask=None):
            x0 = inputs
            xl = x0
            for w, b in zip(self.ws, self.bs):
                xl_T = tf.reshape(xl, [-1, 1, self.embed_dim])
                xlTw = tf.tensordot(xl_T, w, axes=1)
                xl = x0 * xlTw + b + xl
            return xl
    
    # 模型工具间和训练
    model = Cross(256， 3)
    model.compile(loss='binary_crossentropy', metrics=['accuracy', 'AUC'])
    
    # 以h5为后缀，callbacks删除只保存weights
    checkpoint_save_path = 'model/deep_cross.h5'
    cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path)
    model.fit(train_data, epochs=1, class_weight=class_weight, validation_data=dev_data, callbacks=cp_callback)

#### bug 2、no model found in config file ####

接上面代码，利用tf.keras.models.load\_model(‘model/deep\_cross.h5’)加载h5模型直接报错。  
（1）尝试重写get\_config，无效

#### bug 3、NotImplementedError: Saving the model to HDF5 ####

完整bug  
NotImplementedError: Saving the model to HDF5 format requires the model to be a Functional model or a Sequential model. It does not work for subclassed models, because such models are defined via the body of a Python method, which isn’t safely serializable. Consider saving to the Tensorflow SavedModel format (by setting save\_format=“tf”) or using save\_weights

貌似和bug2是一个原因，不能保存自定义网络h5。不在训练过程中保存模型，训练完直接保存h5，直接报错。

model = Cross(256， 3)
    model.compile(loss='binary_crossentropy', metrics=['accuracy', 'AUC'])
    
    # 以h5为后缀，callbacks删除只保存weights
    checkpoint_save_path = 'model/deep_cross.h5'
    model.fit(train_data, epochs=1, class_weight=class_weight, validation_data=dev_data)
    
    model.save(checkpoint_save_path)

### 综上 ###

暂时还是好好用save\_weights和load\_weights吧，毕竟自定义网络，自己有代码的。

[20201204095326752.png]: /images/20221120/d59aefb437bc4be48dccb64ca93560e3.png