我正在尝试使用.fit_generator训练自动编码器。我为此编写了自己的生成器，但是在调用VAE.fit_generator(generator=training_generator,validation_data=testing_generator)时发生以下错误：

  

ValueError：空的训练数据

这是我的生成器的代码：

class DataGenerator(tf.keras.utils.Sequence):
    'Generates data for Keras'
    def __init__(self,list_IDs,data_dir,batch_size=32,dim=(16,9216),shuffle=True,n_music=3,gen_dir="./generated/",epoch=0):
        'Initialization'
        self.dim = dim
        self.batch_size = batch_size
        self.list_IDs = list_IDs
        self.data_dir = data_dir
        self.shuffle = shuffle
        self.n_music = n_music
        self.gen_dir = gen_dir
        self.epoch = epoch
        self.on_epoch_end()

    def __len__(self):
        'Denotes the number of batches per epoch'
        return int(np.floor(len(self.list_IDs) / self.batch_size))

    def __getitem__(self,index):
        'Generate one batch of data'
        # Generate indexes of the batch
        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]

        # Find list of IDs
        list_IDs_temp = [self.list_IDs[k] for k in indexes]

        # Generate data
        X = self.__data_generation(list_IDs_temp)

        return X

    def on_epoch_end(self):
        'Updates indexes after each epoch'
        self.indexes = np.arange(len(self.list_IDs))
        if self.shuffle == True:
            np.random.shuffle(self.indexes)
            pass
        self.epoch += 1
        sample = decoder(np.random.normal(size=[self.n_music,120]))
        outputs_exp = np.expand_dims(sample,0)
        outputs_exp = np.expand_dims(outputs_exp,0)
        outputs_rsp = np.reshape(outputs_exp,(-1,96,16*96))
        for song in range(outputs_rsp.shape[0]):
            plt.imsave(self.gen_dir + str(self.epoch) + ".png",outputs_rsp[song])

    def __data_generation(self,list_IDs_temp):
        'Generates data containing batch_size samples' # X : (n_samples,*dim,n_channels)
        # Initialization
        X = np.empty((self.batch_size,*self.dim))

        # Generate data
        for i,ID in enumerate(list_IDs_temp):
            # Store sample
            X[i] = np.load(self.data_dir + ID + '.npy')


        return X

我按照this指南创建了生成器。