错误： TypeError：Fetch参数None具有无效类型
我认为当我在回调模型检查点中保存模型时会发生错误。在搜索错误时，this出现但我无法使用此答案，因为我使用keras因此我没有在tensorflow中显式调用sess.run（）。此时也完美地训练了这个时代，只有当它被保存时才会弹出错误。
代码：
完整的模型位于这里链接的kaggle笔记本中：https://www.kaggle.com/aevinq/cnn-batchnormalization-0-1646/
弹出错误的相关代码是：
early_stopping = EarlyStopping(monitor='val_loss', patience=5, mode='min')
mcp_save = ModelCheckpoint('md.hdf5', save_best_only=True, monitor='val_loss', mode='min')
reduce_lr_loss = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, verbose=1, epsilon=1e-4, mode='min')
history = model.fit(train_X, train_y, batch_size=32, epochs=20, verbose=1, validation_split=0.25, callbacks=[early_stopping, reduce_lr_loss, mcp_save])
错误：
Train on 4413 samples, validate on 1471 samples
Epoch 1/20
4384/4413 [============================>.] - ETA: 1s - loss: 0.5157 - acc: 0.7696
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-11-97f0757a1e9c> in <module>()
2 mcp_save = ModelCheckpoint('md.hdf5', save_best_only=True, monitor='val_loss', mode='min')
3 reduce_lr_loss = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, verbose=1, epsilon=1e-4, mode='min')
----> 4 history = model.fit(train_X, train_y, batch_size=32, epochs=20, verbose=1, validation_split=0.25, callbacks=[early_stopping, reduce_lr_loss, mcp_save])
/opt/conda/lib/python3.6/site-packages/Keras-2.1.2-py3.6.egg/keras/models.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, **kwargs)
970 initial_epoch=initial_epoch,
971 steps_per_epoch=steps_per_epoch,
--> 972 validation_steps=validation_steps)
973
974 def evaluate(self, x=None, y=None,
/opt/conda/lib/python3.6/site-packages/Keras-2.1.2-py3.6.egg/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, **kwargs)
1655 initial_epoch=initial_epoch,
1656 steps_per_epoch=steps_per_epoch,
-> 1657 validation_steps=validation_steps)
1658
1659 def evaluate(self, x=None, y=None,
/opt/conda/lib/python3.6/site-packages/Keras-2.1.2-py3.6.egg/keras/engine/training.py in _fit_loop(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
1231 for l, o in zip(out_labels, val_outs):
1232 epoch_logs['val_' + l] = o
-> 1233 callbacks.on_epoch_end(epoch, epoch_logs)
1234 if callback_model.stop_training:
1235 break
/opt/conda/lib/python3.6/site-packages/Keras-2.1.2-py3.6.egg/keras/callbacks.py in on_epoch_end(self, epoch, logs)
71 logs = logs or {}
72 for callback in self.callbacks:
---> 73 callback.on_epoch_end(epoch, logs)
74
75 def on_batch_begin(self, batch, logs=None):
/opt/conda/lib/python3.6/site-packages/Keras-2.1.2-py3.6.egg/keras/callbacks.py in on_epoch_end(self, epoch, logs)
413 self.model.save_weights(filepath, overwrite=True)
414 else:
--> 415 self.model.save(filepath, overwrite=True)
416 else:
417 if self.verbose > 0:
/opt/conda/lib/python3.6/site-packages/Keras-2.1.2-py3.6.egg/keras/engine/topology.py in save(self, filepath, overwrite, include_optimizer)
2563 """
2564 from ..models import save_model
-> 2565 save_model(self, filepath, overwrite, include_optimizer)
2566
2567 def save_weights(self, filepath, overwrite=True):
/opt/conda/lib/python3.6/site-packages/Keras-2.1.2-py3.6.egg/keras/models.py in save_model(model, filepath, overwrite, include_optimizer)
145 if symbolic_weights:
146 optimizer_weights_group = f.create_group('optimizer_weights')
--> 147 weight_values = K.batch_get_value(symbolic_weights)
148 weight_names = []
149 for i, (w, val) in enumerate(zip(symbolic_weights,
/opt/conda/lib/python3.6/site-packages/Keras-2.1.2-py3.6.egg/keras/backend/tensorflow_backend.py in batch_get_value(ops)
2208 """
2209 if ops:
-> 2210 return get_session().run(ops)
2211 else:
2212 return []
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
887 try:
888 result = self._run(None, fetches, feed_dict, options_ptr,
--> 889 run_metadata_ptr)
890 if run_metadata:
891 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1103 # Create a fetch handler to take care of the structure of fetches.
1104 fetch_handler = _FetchHandler(
-> 1105 self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)
1106
1107 # Run request and get response.
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in __init__(self, graph, fetches, feeds, feed_handles)
412 """
413 with graph.as_default():
--> 414 self._fetch_mapper = _FetchMapper.for_fetch(fetches)
415 self._fetches = []
416 self._targets = []
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in for_fetch(fetch)
232 elif isinstance(fetch, (list, tuple)):
233 # NOTE(touts): This is also the code path for namedtuples.
--> 234 return _ListFetchMapper(fetch)
235 elif isinstance(fetch, dict):
236 return _DictFetchMapper(fetch)
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in __init__(self, fetches)
339 """
340 self._fetch_type = type(fetches)
--> 341 self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
342 self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)
343
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in <listcomp>(.0)
339 """
340 self._fetch_type = type(fetches)
--> 341 self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
342 self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)
343
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in for_fetch(fetch)
229 if fetch is None:
230 raise TypeError('Fetch argument %r has invalid type %r' %
--> 231 (fetch, type(fetch)))
232 elif isinstance(fetch, (list, tuple)):
233 # NOTE(touts): This is also the code path for namedtuples.
TypeError: Fetch argument None has invalid type <class 'NoneType'>
答案 0 :(得分：1)
这是Keras的一个错误。最近更新后None中有model.optimizer.weights个值，这会在模型保存期间调用K.batch_get_value时导致错误。
我已经打开PR来修复它并将其合并。您可以在Github上安装最新的Keras来修复它。