Keras 2，TypeError：无法腌制 _thread.lock 对象答案

【问题标题】：Keras 2, TypeError: can't pickle _thread.lock objectsKeras 2，TypeError：无法腌制 _thread.lock 对象
【发布时间】：2017-12-18 06:29:08
【问题描述】：

我正在使用 Keras 创建一个 ANN 并在网络上进行网格搜索。我在运行下面的代码时遇到了以下错误：

model = KerasClassifier(build_fn=create_model(input_dim), verbose=0)
# define the grid search parameters
batch_size = [10, 20]
epochs = [50, 100]
dropout = [0.3, 0.5, 0.7]
param_grid = dict(dropout_rate=dropout, batch_size=batch_size, nb_epoch=epochs)
pipe.append(('classify', model))
params.append(param_grid)
pipeline=Pipeline(pipe)
#the pipeline also contains feature selector, but for convenience I do not include code here
piped_classifier =  GridSearchCV(estimator=pipeline, param_grid=params, n_jobs=-1,
                        cv=nfold)
piped_classifier.fit(X_train, y_train) #this is line 246 of classifier_gridsearch.py causing error, see below,


def create_model(input_dim,dropout_rate=0.0):
    # create model
    model = Sequential()
    model.add(Dense(80,
                    input_dim=input_dim,
                    kernel_initializer='uniform', activation='relu'))
    model.add(Dropout(dropout_rate))
    model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
    # Compile model
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

第246行的错误抛出如下错误，堆栈跟踪很长：

Traceback (most recent call last):
  File "/home/zqz/Work/chase/python/src/exp/classifier_gridsearch_main.py", line 176, in <module>
    classifier.gridsearch()
  File "/home/zqz/Work/chase/python/src/exp/classifier_gridsearch_main.py", line 155, in gridsearch
    self.fs_option,self.fs_gridsearch)
  File "/home/zqz/Work/chase/python/src/ml/classifier_gridsearch.py", line 246, in learn_dnn
    piped_classifier.fit(X_train, y_train)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 945, in fit
    return self._fit(X, y, groups, ParameterGrid(self.param_grid))
  File "/home/zqz/Programs/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_search.py", line 550, in _fit
    base_estimator = clone(self.estimator)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/site-packages/sklearn/base.py", line 69, in clone
    new_object_params[name] = clone(param, safe=False)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/site-packages/sklearn/base.py", line 57, in clone
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "/home/zqz/Programs/anaconda3/lib/python3.6/site-packages/sklearn/base.py", line 57, in <listcomp>
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "/home/zqz/Programs/anaconda3/lib/python3.6/site-packages/sklearn/base.py", line 57, in clone
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "/home/zqz/Programs/anaconda3/lib/python3.6/site-packages/sklearn/base.py", line 57, in <listcomp>
    return estimator_type([clone(e, safe=safe) for e in estimator])
  File "/home/zqz/Programs/anaconda3/lib/python3.6/site-packages/sklearn/base.py", line 69, in clone
    new_object_params[name] = clone(param, safe=False)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/site-packages/sklearn/base.py", line 60, in clone
    return copy.deepcopy(estimator)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 215, in _deepcopy_list
    append(deepcopy(a, memo))
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 215, in _deepcopy_list
    append(deepcopy(a, memo))
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 180, in deepcopy
    y = _reconstruct(x, memo, *rv)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 280, in _reconstruct
    state = deepcopy(state, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 150, in deepcopy
    y = copier(x, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 240, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/home/zqz/Programs/anaconda3/lib/python3.6/copy.py", line 169, in deepcopy
    rv = reductor(4)
TypeError: can't pickle _thread.lock objects

请大家给点解决办法，谢谢

【问题讨论】：

Keras: TypeError: can't pickle _thread.lock objects with KerasClassifier的可能重复

标签： python keras

【解决方案1】：

好的，问题似乎是将方法作为参数传递，在这一行中：

model = KerasClassifier(build_fn=create_model(input_dim), verbose=0)

create_model 是要传递给 build_fn 的参数，但我想将另一个参数传递给 create_model。但这不是正确的做法，因此会导致错误。

不幸的是，在这种情况下，错误消息没有提供信息。

【讨论】：

你不应该传递函数调用（这是调用函数的结果），而是函数对象本身：KerasClassifier(build_fn=create_model, ...)。此外，要使其正常工作，您可能需要调整 create_model 以接受 kwargs。
我遇到了同样的问题。我需要动态调整输入的数量。所以我创建了一个类似你的函数：def create_model（input_dim），但是我们不允许在model = KerasClassifier（build_fn = create_model，verbose = 0）中包含参数，我们如何获得输入参数？你能告诉我你是怎么解决这个问题的吗？谢谢。