python - “NoneType”对象没有属性“形状”Tensorflow

Question

我正在尝试训练一个模型，但每次它都会抛出这个错误。该错误通常发生在在 Google Colab 上进行训练时，但在我的本地 PC 上进行训练时，它运行正常。

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-25-3435b262f1ae> in <module>()
----> 1 trainer.train()

8 frames
/content/lstm_chem/trainer.py in train(self)
     43             # use_multiprocessing=True,
     44             shuffle=True,
---> 45             callbacks=self.callbacks)
     46 
     47         last_weight_file = glob(

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
     64   def _method_wrapper(self, *args, **kwargs):
     65     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
---> 66       return method(self, *args, **kwargs)
     67 
     68     # Running inside `run_distribute_coordinator` already.

/usr/local/lib/python3.6/dist-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_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
    813           workers=workers,
    814           use_multiprocessing=use_multiprocessing,
--> 815           model=self)
    816 
    817       # Container that configures and calls `tf.keras.Callback`s.

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model)
   1110         use_multiprocessing=use_multiprocessing,
   1111         distribution_strategy=ds_context.get_strategy(),
-> 1112         model=model)
   1113 
   1114     strategy = ds_context.get_strategy()

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, shuffle, workers, use_multiprocessing, max_queue_size, model, **kwargs)
    906         max_queue_size=max_queue_size,
    907         model=model,
--> 908         **kwargs)
    909 
    910   @staticmethod

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, workers, use_multiprocessing, max_queue_size, model, **kwargs)
    790       return tensor_shape.TensorShape([None for _ in shape.as_list()])
    791 
--> 792     output_shapes = nest.map_structure(_get_dynamic_shape, peek)
    793     output_types = nest.map_structure(lambda t: t.dtype, peek)
    794 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py in map_structure(func, *structure, **kwargs)
    615 
    616   return pack_sequence_as(
--> 617       structure[0], [func(*x) for x in entries],
    618       expand_composites=expand_composites)
    619 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py in <listcomp>(.0)
    615 
    616   return pack_sequence_as(
--> 617       structure[0], [func(*x) for x in entries],
    618       expand_composites=expand_composites)
    619 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/data_adapter.py in _get_dynamic_shape(t)
    784 
    785     def _get_dynamic_shape(t):
--> 786       shape = t.shape
    787       # Unknown number of dimensions, `as_list` cannot be called.
    788       if shape.rank is None:

AttributeError: 'NoneType' object has no attribute 'shape'

这是代码，任何帮助将不胜感激

from glob import glob
import os
from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard

class LSTMChemTrainer(object):
    def __init__(self, modeler, train_data_loader, valid_data_loader):
        self.model = modeler.model
        self.config = modeler.config
        self.train_data_loader = train_data_loader
        self.valid_data_loader = valid_data_loader
        self.callbacks = []
        self.init_callbacks()

    def init_callbacks(self):
        self.callbacks.append(
            ModelCheckpoint(
                filepath=os.path.join(
                    self.config.checkpoint_dir,
                    '%s-{epoch:02d}-{val_loss:.2f}.hdf5' %
                    self.config.exp_name),
                monitor=self.config.checkpoint_monitor,
                mode=self.config.checkpoint_mode,
                save_best_only=self.config.checkpoint_save_best_only,
                save_weights_only=self.config.checkpoint_save_weights_only,
                verbose=self.config.checkpoint_verbose,
            ))
        self.callbacks.append(
            TensorBoard(
                log_dir=self.config.tensorboard_log_dir,
                write_graph=self.config.tensorboard_write_graph,
            ))

    def train(self):
        history = self.model.fit(
            self.train_data_loader,
            steps_per_epoch=self.train_data_loader.__len__(),
            epochs=self.config.num_epochs,
            verbose=self.config.verbose_training,
            validation_data=self.valid_data_loader,
            validation_steps=self.valid_data_loader.__len__(),
            shuffle=True,
            callbacks=self.callbacks)

        last_weight_file = glob(
            os.path.join(
                f'{self.config.checkpoint_dir}',
                f'{self.config.exp_name}-{self.config.num_epochs:02}*.hdf5')
        )[0]

        assert os.path.exists(last_weight_file)
        self.config.model_weight_filename = last_weight_file

        with open(os.path.join(self.config.exp_dir, 'config.json'), 'w') as f:
            f.write(self.config.toJSON(indent=2))

标签： pythontensorflow2.0