我正在尝试用keras训练一个ResNet，tensorflow
我使用的模型是ResNet50
当我在训练过程中添加keras检查点时，我收到此错误消息 这是我的代码：
def train(batch_size, nb_classes = 5):
x_train, y_train, x_test, y_test = loadData(nb_classes)
x_train = (x_train.astype(np.float32) - 127.5)/127.5
x_test = (x_test.astype(np.float32) - 127.5)/127.5
r_model =model(nb_classes,dropout= True)
model = multi_gpu_model(r_model, gpus=4)
optim = SGD(lr=0.00001, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer = optim,metrics=['accuracy'])
model.load_weights('/../weights1',True)
checkpointer = ModelCheckpoint(filepath='/.../weights2', save_best_only=True)
model.fit(x_train, y_train, epochs=10, batch_size=128,validation_data=(x_test, y_test),shuffle=True,callbacks=[checkpointer])
添加此行时程序崩溃：
checkpointer = ModelCheckpoint(filepath='/.../weights2', save_best_only=True)
我在互联网上搜索，我认为在我使用的某些功能中必须使用deepcopy。
但我该如何解决呢？
错误讯息：
TypeError Traceback (most recent call last)
<ipython-input-11-d4fe46633cc3> in <module>()
----> 1 train(64)
<ipython-input-10-d02154ab30e5> in train(batch_size, nb_classes)
18 model.load_weights('/.../weights1',True)
19 checkpointer = ModelCheckpoint(filepath='/.../weights2', save_best_only=True)
---> 20 history = model.fit(x_train, y_train, epochs=10, batch_size=128,validation_data=(x_test, y_test),shuffle=True,callbacks=[checkpointer])
/usr/local/lib/python3.4/dist-packages/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)
1629 initial_epoch=initial_epoch,
1630 steps_per_epoch=steps_per_epoch,
-> 1631 validation_steps=validation_steps)
1632
1633 def evaluate(self, x=None, y=None,
/usr/local/lib/python3.4/dist-packages/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
/usr/local/lib/python3.4/dist-packages/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):
/usr/local/lib/python3.4/dist-packages/keras/callbacks.py in on_epoch_end(self, epoch, logs)
412 self.model.save_weights(filepath, overwrite=True)
413 else:
--> 414 self.model.save(filepath, overwrite=True)
415 else:
416 if self.verbose > 0:
/usr/local/lib/python3.4/dist-packages/keras/engine/topology.py in save(self, filepath, overwrite, include_optimizer)
2554 """
2555 from ..models import save_model
-> 2556 save_model(self, filepath, overwrite, include_optimizer)
2557
2558 def save_weights(self, filepath, overwrite=True):
/usr/local/lib/python3.4/dist-packages/keras/models.py in save_model(model, filepath, overwrite, include_optimizer)
105 f.attrs['model_config'] = json.dumps({
106 'class_name': model.__class__.__name__,
--> 107 'config': model.get_config()
108 }, default=get_json_type).encode('utf8')
109
/usr/local/lib/python3.4/dist-packages/keras/engine/topology.py in get_config(self)
2395 model_outputs.append([layer.name, new_node_index, tensor_index])
2396 config['output_layers'] = model_outputs
-> 2397 return copy.deepcopy(config)
2398
2399 @classmethod
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_dict(x, memo)
244 memo[id(x)] = y
245 for key, value in x.items():
--> 246 y[deepcopy(key, memo)] = deepcopy(value, memo)
247 return y
248 d[dict] = _deepcopy_dict
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_list(x, memo)
217 memo[id(x)] = y
218 for a in x:
--> 219 y.append(deepcopy(a, memo))
220 return y
221 d[list] = _deepcopy_list
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_dict(x, memo)
244 memo[id(x)] = y
245 for key, value in x.items():
--> 246 y[deepcopy(key, memo)] = deepcopy(value, memo)
247 return y
248 d[dict] = _deepcopy_dict
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_dict(x, memo)
244 memo[id(x)] = y
245 for key, value in x.items():
--> 246 y[deepcopy(key, memo)] = deepcopy(value, memo)
247 return y
248 d[dict] = _deepcopy_dict
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_tuple(x, memo)
224 y = []
225 for a in x:
--> 226 y.append(deepcopy(a, memo))
227 # We're not going to put the tuple in the memo, but it's still important we
228 # check for it, in case the tuple contains recursive mutable structures.
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_tuple(x, memo)
224 y = []
225 for a in x:
--> 226 y.append(deepcopy(a, memo))
227 # We're not going to put the tuple in the memo, but it's still important we
228 # check for it, in case the tuple contains recursive mutable structures.
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
180 raise Error(
181 "un(deep)copyable object of type %s" % cls)
--> 182 y = _reconstruct(x, rv, 1, memo)
183
184 # If is its own copy, don't memoize.
/usr/lib/python3.4/copy.py in _reconstruct(x, info, deep, memo)
298 if state:
299 if deep:
--> 300 state = deepcopy(state, memo)
301 if hasattr(y, '__setstate__'):
302 y.__setstate__(state)
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_dict(x, memo)
244 memo[id(x)] = y
245 for key, value in x.items():
--> 246 y[deepcopy(key, memo)] = deepcopy(value, memo)
247 return y
248 d[dict] = _deepcopy_dict
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
180 raise Error(
181 "un(deep)copyable object of type %s" % cls)
--> 182 y = _reconstruct(x, rv, 1, memo)
183
184 # If is its own copy, don't memoize.
/usr/lib/python3.4/copy.py in _reconstruct(x, info, deep, memo)
298 if state:
299 if deep:
--> 300 state = deepcopy(state, memo)
301 if hasattr(y, '__setstate__'):
302 y.__setstate__(state)
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_dict(x, memo)
244 memo[id(x)] = y
245 for key, value in x.items():
--> 246 y[deepcopy(key, memo)] = deepcopy(value, memo)
247 return y
248 d[dict] = _deepcopy_dict
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_dict(x, memo)
244 memo[id(x)] = y
245 for key, value in x.items():
--> 246 y[deepcopy(key, memo)] = deepcopy(value, memo)
247 return y
248 d[dict] = _deepcopy_dict
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_method(x, memo)
251
252 def _deepcopy_method(x, memo): # Copy instance methods
--> 253 return type(x)(x.__func__, deepcopy(x.__self__, memo))
254 _deepcopy_dispatch[types.MethodType] = _deepcopy_method
255
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
180 raise Error(
181 "un(deep)copyable object of type %s" % cls)
--> 182 y = _reconstruct(x, rv, 1, memo)
183
184 # If is its own copy, don't memoize.
/usr/lib/python3.4/copy.py in _reconstruct(x, info, deep, memo)
298 if state:
299 if deep:
--> 300 state = deepcopy(state, memo)
301 if hasattr(y, '__setstate__'):
302 y.__setstate__(state)
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_dict(x, memo)
244 memo[id(x)] = y
245 for key, value in x.items():
--> 246 y[deepcopy(key, memo)] = deepcopy(value, memo)
247 return y
248 d[dict] = _deepcopy_dict
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_dict(x, memo)
244 memo[id(x)] = y
245 for key, value in x.items():
--> 246 y[deepcopy(key, memo)] = deepcopy(value, memo)
247 return y
248 d[dict] = _deepcopy_dict
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
180 raise Error(
181 "un(deep)copyable object of type %s" % cls)
--> 182 y = _reconstruct(x, rv, 1, memo)
183
184 # If is its own copy, don't memoize.
/usr/lib/python3.4/copy.py in _reconstruct(x, info, deep, memo)
298 if state:
299 if deep:
--> 300 state = deepcopy(state, memo)
301 if hasattr(y, '__setstate__'):
302 y.__setstate__(state)
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_dict(x, memo)
244 memo[id(x)] = y
245 for key, value in x.items():
--> 246 y[deepcopy(key, memo)] = deepcopy(value, memo)
247 return y
248 d[dict] = _deepcopy_dict
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_dict(x, memo)
244 memo[id(x)] = y
245 for key, value in x.items():
--> 246 y[deepcopy(key, memo)] = deepcopy(value, memo)
247 return y
248 d[dict] = _deepcopy_dict
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
180 raise Error(
181 "un(deep)copyable object of type %s" % cls)
--> 182 y = _reconstruct(x, rv, 1, memo)
183
184 # If is its own copy, don't memoize.
/usr/lib/python3.4/copy.py in _reconstruct(x, info, deep, memo)
298 if state:
299 if deep:
--> 300 state = deepcopy(state, memo)
301 if hasattr(y, '__setstate__'):
302 y.__setstate__(state)
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
153 copier = _deepcopy_dispatch.get(cls)
154 if copier:
--> 155 y = copier(x, memo)
156 else:
157 try:
/usr/lib/python3.4/copy.py in _deepcopy_dict(x, memo)
244 memo[id(x)] = y
245 for key, value in x.items():
--> 246 y[deepcopy(key, memo)] = deepcopy(value, memo)
247 return y
248 d[dict] = _deepcopy_dict
/usr/lib/python3.4/copy.py in deepcopy(x, memo, _nil)
180 raise Error(
181 "un(deep)copyable object of type %s" % cls)
--> 182 y = _reconstruct(x, rv, 1, memo)
183
184 # If is its own copy, don't memoize.
/usr/lib/python3.4/copy.py in _reconstruct(x, info, deep, memo)
293 if deep:
294 args = deepcopy(args, memo)
--> 295 y = callable(*args)
296 memo[id(x)] = y
297
/usr/lib/python3.4/copyreg.py in __newobj__(cls, *args)
86
87 def __newobj__(cls, *args):
---> 88 return cls.__new__(cls, *args)
89
90 def __newobj_ex__(cls, args, kwargs):
TypeError: object.__new__(_hashlib.HASH) is not safe, use _hashlib.HASH.__new__()
答案 0 :(得分：0)
这不是答案，因为我无法从您的代码段中发现问题，但这里大概是发生了什么。 Keras正在尝试保存模型的检查点（正如您在checkpointer = ...行时所问的那样）。在执行此操作时，它会执行模型configuration的deepcopy。不知何故，在这个配置的深处，有一个无法复制的对象（_hashlib.HASH）。对象可以不可复制的原因有很多，例如，如果它们有锁，但它并不重要。重要的是弄清楚这样的对象是如何进入模型配置的。
您可以更改keras代码的副本（在/usr/local/lib/python3.4/dist-packages/keras/engine/topology.py中）以尝试复制{{1}的每个键/值}字典，看看哪一个是问题。例如，将其添加到this code
之上config如果需要深入挖掘并查看此对象的哪个属性是一个问题，等等。最后，尝试了解此对象在模型配置中的结果。