我想自定义模型的fit函数，以便只有当模型在验证数据上的预测有所改善时，才对权重应用梯度下降。我这样做的原因是为了防止过拟合。

根据这个指南，应该可以自定义模型的fit函数。然而，以下代码运行时会出现错误：

class CustomModel(tf.keras.Model):    def train_step(self, data):        x, y = data        with tf.GradientTape() as tape:            y_pred = self(x, training=True)            loss = self.compiled_loss(y, y_pred, regularization_losses=self.losses)        trainable_vars = self.trainable_variables        gradients = tape.gradient(loss, trainable_vars)        ### 检查并应用梯度        Y_pred_val = self.predict(X_val)                 # 这行代码不起作用        acc_val = calculate_accuracy(Y_val, Y_pred_val)        if acc_val > last_acc_val:            self.optimizer.apply_gradients(zip(gradients, trainable_vars))        ###        self.compiled_metrics.update_state(y, y_pred)        return_obj = {m.name: m.result() for m in self.metrics}        return_obj["acc_val"] = acc_val        return return_obj

如何才能在fit函数内评估模型呢？

回答：

你不需要子类化fit()来实现这个功能。你可以创建一个自定义的训练循环。看看我是怎么做的：

import osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'from tensorflow.keras import Modelimport tensorflow as tffrom tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten, Concatenateimport tensorflow_datasets as tfdsfrom tensorflow.keras.regularizers import l1, l2, l1_l2from collections import dequedataset, info = tfds.load('mnist',                          with_info=True,                          split='train',                          as_supervised=False)TAKE = 1_000data = dataset.map(lambda x: (tf.cast(x['image'],                       tf.float32), x['label'])).shuffle(TAKE).take(TAKE)len_train = int(8e-1*TAKE)train = data.take(len_train).batch(8)test = data.skip(len_train).take(info.splits['train'].num_examples - len_train).batch(8)class CNN(Model):    def __init__(self):        super(CNN, self).__init__()        self.layer1 = Dense(32, activation=tf.nn.relu,                            kernel_regularizer=l1(1e-2),                            input_shape=info.features['image'].shape)        self.layer2 = Conv2D(filters=16,                             kernel_size=(3, 3),                             strides=(1, 1),                             activation='relu',                             input_shape=info.features['image'].shape)        self.layer3 = MaxPooling2D(pool_size=(2, 2))        self.layer4 = Conv2D(filters=32,                             kernel_size=(3, 3),                             strides=(1, 1),                             activation=tf.nn.elu,                             kernel_initializer=tf.keras.initializers.glorot_normal)        self.layer5 = MaxPooling2D(pool_size=(2, 2))        self.layer6 = Flatten()        self.layer7 = Dense(units=64,                            activation=tf.nn.relu,                            kernel_regularizer=l2(1e-2))        self.layer8 = Dense(units=64,                            activation=tf.nn.relu,                            kernel_regularizer=l1_l2(l1=1e-2, l2=1e-2))        self.layer9 = Concatenate()        self.layer10 = Dense(units=info.features['label'].num_classes)    def call(self, inputs, training=None, **kwargs):        b = self.layer1(inputs)        a = self.layer2(inputs)        a = self.layer3(a)        a = self.layer4(a)        a = self.layer5(a)        a = self.layer6(a)        a = self.layer8(a)        b = self.layer7(b)        b = self.layer6(b)        x = self.layer9([a, b])        x = self.layer10(x)        return xcnn = CNN()loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)train_loss = tf.keras.metrics.Mean()test_loss = tf.keras.metrics.Mean()train_acc = tf.keras.metrics.SparseCategoricalAccuracy()test_acc = tf.keras.metrics.SparseCategoricalAccuracy()optimizer = tf.keras.optimizers.Nadam()template = 'Epoch {:3} Train Loss {:7.4f} Test Loss {:7.4f} ' \           'Train Acc {:6.2%} Test Acc {:6.2%} 'epochs = 5early_stop = epochs//50loss_hist = deque()acc_hist = deque(maxlen=1)acc_hist.append(0)for epoch in range(1, epochs + 1):    train_loss.reset_states()    test_loss.reset_states()    train_acc.reset_states()    test_acc.reset_states()    for images, labels in train:        with tf.GradientTape() as tape:            logits = cnn(images, training=True)            loss = loss_object(labels, logits)            train_loss(loss)            train_acc(labels, logits)            current_acc = tf.metrics.SparseCategoricalAccuracy()(labels, logits)            if tf.greater(current_acc, acc_hist[-1]):                print('IMPROVEMENT.')                gradients = tape.gradient(loss, cnn.trainable_variables)                optimizer.apply_gradients(zip(gradients, cnn.trainable_variables))                acc_hist.append(current_acc)    for images, labels in test:        logits = cnn(images, training=False)        loss = loss_object(labels, logits)        test_loss(loss)        test_acc(labels, logits)    print(template.format(epoch,                          train_loss.result(),                          test_loss.result(),                          train_acc.result(),                          test_acc.result()))    if len(loss_hist) > early_stop and loss_hist.popleft() < min(loss_hist):        print('Early stopping. No validation loss decrease in %i epochs.' % early_stop)        break