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本文记录了在TensorFlow框架中自定义训练函数的模板并简述了使用自定义训练函数的优势与劣势。

首先需要说明的是，本文中所记录的训练函数模板参考自https://stackoverflow.com/questions/59438904/applying-callbacks-in-a-custom-training-loop-in-tensorflow-2-0中的回答以及Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow一书中第12.3.9节的内容，如有错漏，欢迎指正。

为什么和什么时候需要自定义训练函数

除非你真的需要额外的灵活性，否则应该更倾向使用fit()方法，为不是实现你自己的循环，尤其是在团队合作中。

如果你还在困惑为什么需要自定义训练函数的时候，那说明你还不需要自定义训练函数。通常只有在搭建一些结构奇特的模型时，我们才会发现model.fit()无法完全满足需求，接下来首先该尝试的方法是去看TensorFlow相关部分的源码，看看有没有认识之外的参数或方法，其次才是考虑使用自定义训练函数。毫无疑问，自定义训练函数会让代码更长、更难维护、更难懂。

但是，自定义训练函数的灵活性是fit()方法无法比拟的。比如，在自定义函数中你可以实现使用多个不同优化器的训练循环或是在多个数据集上计算验证循环。

自定义训练函数模板

模板设计的目的在于让我们通过对代码块的复用以及对关键部位的填空快速完成自定义训练函数，以使我们更专注于训练函数结构本身而非一些细枝末节的部分（如未知长度训练集的处理）并实现一些fit()方法支持的功能（如Callback类的使用）。

 def train(model:keras.Model,train\_batchs,epochs=1,initial\_epoch=0,callbacks=None,steps\_per\_epoch=None,val\_batchs=None):
    callbacks = tf.keras.callbacks.CallbackList(
        callbacks, add_history=True, model=model)

    logs_dict = {}
    
    # init optimizer, loss function and metrics
    optimizer = keras.optimizers.Nadam(learning_rate=0.0005)
    loss_fn = keras.losses.MeanSquaredError
    
    train_loss_tracker = keras.metrics.Mean(name="train\_loss")
    val_loss_tracker = keras.metrics.Mean(name="val\_loss")
    # train\_acc\_metric = tf.keras.metrics.BinaryAccuracy(name="train\_acc")
    # val\_acc\_metric = tf.keras.metrics.BinaryAccuracy(name="val\_acc")
    
    def count(): # infinite iter
        x = 0
        while True:yield x;x+=1
    
    def print\_status\_bar(iteration, total, metrics=None):
        metrics = " - ".join(["{}:{:.4f}".format(m.name,m.result()) for m in (metrics or [])])
        end = "" if iteration < total or float('inf') else "\n"
        print("\r{}/{} - ".format(iteration,total) + metrics, end=end)
   	
    def train\_step(x,y,loss\_tracker:keras.metrics.Metric):
        with tf.GradientTape() as tape:
            outputs = model(x)
            main_loss = tf.reduce_mean(loss_fn(y,outputs))
            
            loss = tf.add_n([main_loss] + model.losses)
        gradients = tape.gradient(loss, model.trainable_variables)
        optimizer.apply_gradients(zip(gradients,model.trainable_variables))
        loss_tracker.update_state(loss)
        return {loss_tracker.name:loss_tracker.result()}
    
    def val\_step(x,y,loss\_tracker:keras.metrics.Metric):
        outputs = model.predict(x,verbose=0)
        main_loss = tf.reduce_mean(loss_fn(y,outputs))
        
        loss = tf.add_n([main_loss] + model.losses)
        loss_tracker.update_state(loss)
        return {loss_tracker.name:loss_tracker.result()}
    
    # init train\_batchs
    train_iter = iter(train_batchs)
    
    callbacks.on_train_begin(logs=logs_dict)
    for i_epoch in range(initial_epoch, epochs):
    
        # init steps
        infinite_flag = False
        if steps_per_epoch is None:
            infinite_flag = True
            step_iter = count()
        else:
            step_iter = range(steps_per_epoch)

		# train\_loop
        for i_step in step_iter:
            callbacks.on_batch_begin(i_step, logs=logs_dict)
            callbacks.on_train_batch_begin(i_step, logs=logs_dict)

            try:
                X_batch, y_batch = train_iter.next()
            except StopIteration:
                train_iter = iter(train_batchs)
                if infinite_flag is True:
                    break
                else:
                    X_batch, y_batch = train_iter.next()
            
            train_logs_dict = train_step(x=X_batch,y=y_batch,loss_tracker=train_loss_tracker)
            logs_dict.update(train_logs_dict)

            print_status_bar(i_step, steps_per_epoch or i_step, [train_loss_tracker])
            
            callbacks.on_train_batch_end(i_step, logs=logs_dict)
            callbacks.on_batch_end(i_step, logs=logs_dict)

        if steps_per_epoch is None:
            print()
            steps_per_epoch = i_step
            
        if val_batchs is not None:
        	# val\_loop
            for i_step,(X_batch,y_batch) in enumerate(iter(val_batchs)):
                callbacks.on_batch_begin(i_step, logs=logs_dict)
                callbacks.on_test_batch_begin(i_step, logs=logs_dict)

                val_logs_dict = val_step(x=X_batch,y=y_batch,loss_tracker=val_loss_tracker)
                logs_dict.update(val_logs_dict)

                callbacks.on_test_batch_end(i_step, logs=logs_dict)
                callbacks.on_batch_end(i_step, logs=logs_dict)
            
            logs_dict.update(val_logs_dict)

        print_status_bar(steps_per_epoch, steps_per_epoch, [train_loss_tracker, val_loss_tracker])
        callbacks.on_epoch_end(i_epoch, logs=logs_dict)

        for metric in [train_loss_tracker, val_loss_tracker]:
            metric.reset_states()

    callbacks.on_train_end(logs=logs_dict)
    
    # Fetch the history object we normally get from keras.fit
    history_object = None
    for cb in callbacks:
        if isinstance(cb, tf.keras.callbacks.History):
            history_object = cb
    return history_object
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