Keras多检查点

Question

我正在尝试使用keras在模型训练中实现不同的检查点。当我使用单个检查点时，它运行良好，但是当我将多个检查点作为列表提供时，它给了我一条错误消息。

model_1_checkpoint = ModelCheckpoint('model/model1/model.{epoch:02d}-{val_loss:.2f}.hdf5', monitor='val_loss', \
                                         save_best_only=True, verbose=1)
    model_1_tensorboard = TensorBoard(log_dir=str('logs/scalars/'+datetime.datetime.now().strftime("%Y%m%d-%H%M%S")),\
                                      histogram_freq=0, write_graph=True, write_images=True, embeddings_freq=0, \
                                      embeddings_layer_names=None, embeddings_metadata=None),

    keras_callbacks_1 = EarlyStopping(monitor='val_mean_absolute_error', patience=20, verbose=1, restore_best_weights=True)

    callback = [model_1_checkpoint, model_1_tensorboard, keras_callbacks_1]

    model_1.fit(x_train_array, y_train_array,
        batch_size=batch_size,
        epochs=epochs,
        shuffle=True,
        verbose=1, # Change it to 2, if wished to observe execution one line per epoch.  o if not. 1 for progress bar
        validation_data=(x_validation_array, y_validation_array),
        callbacks=callback,
        use_multiprocessing=True,
        workers=30)

错误消息：

    AttributeError                            Traceback (most recent call last)
    <ipython-input-203-3b2d923f4ffd> in <module>
          7     callbacks=callback,
          8     use_multiprocessing=True,
    ----> 9     workers=30)

    /app/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
        778           validation_steps=validation_steps,
        779           validation_freq=validation_freq,
    --> 780           steps_name='steps_per_epoch')
        781 
        782   def evaluate(self,

    /app/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_arrays.py in model_iteration(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)
        211       samples=num_samples_or_steps,
        212       verbose=0,  # Handle ProgBarLogger separately in this loop.
    --> 213       mode=mode)
        214   # TODO(omalleyt): Handle ProgBar as part of Callbacks once hooks are ready.
        215   progbar = training_utils.get_progbar(model, count_mode)

    /app/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/callbacks.py in configure_callbacks(callbacks, model, do_validation, batch_size, epochs, steps_per_epoch, samples, verbose, count_mode, mode)
        103   # Set callback model
        104   callback_model = model._get_callback_model()  # pylint: disable=protected-access
    --> 105   callback_list.set_model(callback_model)
        106 
        107   set_callback_parameters(

    /app/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/callbacks.py in set_model(self, model)
        229     self.model = model
        230     for callback in self.callbacks:
    --> 231       callback.set_model(model)
        232 
        233   def _call_batch_hook(self, mode, hook, batch, logs=None):

    AttributeError: 'tuple' object has no attribute 'set_model'