虽然有很多方法可以修改数据集，但我认为最好的选择是编写一个自定义模型类，这是从the official tutorial修改而来的：

class Autoencoder(tf.keras.Model):
    def train_step(self, data):
        # Unpack the data. Its structure depends on your model and
        # on what you pass to `fit()`.
        x = data  # CHANGE 1: changed from x, y = data

        with tf.GradientTape() as tape:
            y_pred = self(x, training=True)  # Forward pass
            # Compute the loss value
            # (the loss function is configured in `compile()`)
            loss = self.compiled_loss(x, y_pred, regularization_losses=self.losses)  # CHANGE 2: replaced y by x as label

        # Compute gradients
        trainable_vars = self.trainable_variables
        gradients = tape.gradient(loss, trainable_vars)
        # Update weights
        self.optimizer.apply_gradients(zip(gradients, trainable_vars))
        # Update metrics (includes the metric that tracks the loss)
        self.compiled_metrics.update_state(x, y_pred)  # CHANGE 3: like change 2
        # Return a dict mapping metric names to current value
        return {m.name: m.result() for m in self.metrics}

    def test_step(self, data):
        # CHANGED in the same way
        x = data
        # Compute predictions
        y_pred = self(x, training=False)
        # Updates the metrics tracking the loss
        self.compiled_loss(x, y_pred, regularization_losses=self.losses)
        # Update the metrics.
        self.compiled_metrics.update_state(x, y_pred)
        # Return a dict mapping metric names to current value.
        # Note that it will include the loss (tracked in self.metrics).
        return {m.name: m.result() for m in self.metrics}

这是函数API（tf.keras.Model），如果你使用的是Sequential模型，你应该从它继承，你可以直接用它来代替普通的模型构造函数。
另一种选择是使用train_zipped = tf.data.Dataset.zip((train_ds, train_ds))创建一个input, target数据集，您可以将其直接放入常用的模型和损失函数中。就我个人而言，我不喜欢复制。此外，我不确定这是否会正确地用于混洗数据（train_ds的两个副本是否会以相同的方式混洗？）
您可以通过在image_dataset_from_directory中设置shuffle=False，然后使用train_zipped = train_zipped.shuffle(buffer_size)来避免这种情况，但是，根据我的经验，这非常慢。