Tensorflow Keras 中的 model.fit() 方法中的 validation_data 是否必须是一个元组？

Question

I'm implementing a complicated loss function so I use a custom layer to pass the loss. Something like：

class SIAMESE_LOSS(Layer):
    def __init__(self, **kwargs):
        super(SIAMESE_LOSS, self).__init__(**kwargs)

    @staticmethod
    def mmd_loss(source_samples, target_samples):
        return mmd(source_samples, target_samples)

    @staticmethod
    def regression_loss(pred, labels):
        return K.mean(mae(pred, labels))

    def call(self, inputs, **kwargs):
        source_labels = inputs[0]
        target_labels = inputs[1]
        source_pred = inputs[2]
        target_pred = inputs[3]
        source_samples = inputs[4]
        target_samples = inputs[5]
        source_loss = self.regression_loss(source_pred, source_labels)
        target_loss = self.regression_loss(target_pred, target_labels)
        mmd_loss = self.mmd_loss(source_samples, target_samples)
        self.add_loss(source_loss)
        self.add_loss(target_loss)
        self.add_loss(mmd)
        self.add_metric(source_loss, aggregation='mean', name='source_mae')
        self.add_metric(target_loss, aggregation='mean', name='target_mae')
        self.add_metric(mmd_loss, aggregation='mean', name='MMD')
        return mmd_loss+target_loss+source_loss

So the labels are sent to the model as inputs.
Therefore fitting the model will be like:

        history = model.fit(
            x=[train_data_s, train_data_t, self.train_labels, self.train_data_t],
            y=None,
            batch_size=self.batch_size,
            epochs=base_epochs,
            verbose=2,
            callbacks=cp_callback,
            validation_data=[val_data_s, val_data_t, self.val_labels, self.val_labels_t],
            shuffle=True
        )

However, according to the official document in Tensorflow, validation_data should be:

Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. validation_data will override validation_split. validation_data could be: tuple (x_val, y_val) of Numpy arrays or tensors tuple (x_val, y_val, val_sample_weights) of Numpy arrays dataset For the first two cases, batch_size must be provided. For the last case, validation_steps could be provided. Note that validation_data does not support all the data types that are supported in x, eg, dict, generator or keras.utils.Sequence.

There's no 'label' that should be passed since they're already handled by the model as inputs. How can I solve the problem if I still wanna use validation data?

Answer 1

to write your own loss you need to inherit from class Loss and then implement your loss calculation in the init and call methods. https://www.tensorflow.org/api_docs/python/tf/keras/losses/Loss

so you dont need to train without passing y in model.fit()