Keras KerasClassifier gridsearch TypeError：无法腌制_thread.lock对象

Question

以下代码引发错误：TypeError：无法腌制_thread.lock对象

我可以看到它可能与将先前的方法作为函数传递给def fit（self，c_m）有关。但是我认为通过文档是正确的：https : //keras.io/scikit-learn-api/

如果有人在我的代码中看到错误，我可能会犯一个菜鸟错误，我将不胜感激。

np.random.seed(7)
y_dic = []

class NN:
    def __init__(self):
        self.X = None
        self.y = None
        self.model = None

    def clean_data(self):
        seed = 7
        np.random.seed(seed)
        dataset = pd.read_csv('/Users/isaac/pca_rfe_tsne_comparisons/Vital_intrusions.csv', delimiter=',', skiprows=0)
        dataset = dataset.iloc[:,1:6]
        self.X = dataset.iloc[:, 1:5]
        Y = dataset.iloc[:, 0]

        for y in Y:
            if y >= 8:
                y_dic.append(1)
            else:
                y_dic.append(0)
        self.y = y_dic

        self.X = np.asmatrix(stats.zscore(self.X, axis=0, ddof=1))
        self.y = to_categorical(self.y)


    def create_model(self):
        self.model = Sequential()
        self.model.add(Dense(4, input_dim=4, activation='relu'))
        self.model.add(Dense(4, activation='relu'))
        self.model.add(Dense(2, activation='sigmoid'))
        self.model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
        pass

    def fit(self, c_m):
        model = KerasClassifier(build_fn=c_m, verbose=0)
        batch_size = [10, 20, 40, 60, 80, 100]
        epochs = [10, 50, 100]
        param_grid = dict(batch_size=batch_size, epochs=epochs)
        grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1)
        pdb.set_trace()
        grid_result = grid.fit(self.X, self.y)
        return (grid_result)

    def results(self, grid_results):
        print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
        means = grid_result.cv_results_['mean_test_score']
        stds = grid_result.cv_results_['std_test_score']
        params = grid_result.cv_results_['params']
        for mean, stdev, param in zip(means, stds, params):
            print("%f (%f) with: %r" % (mean, stdev, param))


def main():
    nn = NN()
    nn.clean_data()
    nn.create_model()
    grid_results = nn.fit(nn.create_model)
    nn.results(grid_results)

if __name__ == "__main__":
    main()

好的，对此进行跟进。感谢您的评论@MarcinMo？ejko。您对此是正确的。我应该提到更多错误。在def fit（）中，我编写了model = KerasClassifier，而不是self.model = Keras Classifier。我想提一下，以防万一有人在看代码。我现在在同一行上收到一个新错误：

AttributeError：'NoneType'对象没有属性'loss'。

我可以将其追溯到scikit_learn.py：

loss_name = self.model.loss
        if hasattr(loss_name, '__name__'):
            loss_name = loss_name.__name__
        if loss_name == 'categorical_crossentropy' and len(y.shape) != 2:
            y = to_categorical(y) 

我不确定如何解决此问题，因为我在self.model.compile中设置了损失项。我尝试将其更改为binary_crossentropy，但这没有任何效果。还有什么想法吗？

Answer 1

问题出在这行代码中：

grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1)

不幸的是，目前keras尚不支持将pickle其sklearn应用于应用多处理所需的模型（您可以在此处阅读有关此内容的讨论）。为了使此代码起作用，您应该设置：

grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=1)