Python spyder +张量流交叉验证在Windows 10上冻结

Question

在Windows 10上，我已经安装Anaconda并启动了Spyder。自从我执行

以来，我还成功安装了Theano，Tensorflow和Keras

进口喀拉拉邦

控制台输出

使用Tensorflow后端

当我编译并拟合神经网络时，它运行良好。但是，当我尝试运行k-fold交叉验证，通过keras包装器组合scikit-learn并使用参数n_jobs = -1（通常为n_jobs具有任何值，从而具有多处理功能）时，控制台将永久冻结，直到重新启动内核手动或终止Spyder。

另一个问题，当我尝试使用GridSearchCV运行某些参数调整时，例如100个时期，它不会冻结，但会输出时期1/1而不是1/100，通常会给出不好的结果，而不合逻辑（即只运行几分钟，而通常需要几个小时！）。>

我的代码是：

# Part 1 - Data Preprocessing

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values

# Encoding categorical data
# Encoding the Independent Variable
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
onehotencoder = OneHotEncoder(categorical_features = [1])
X = onehotencoder.fit_transform(X).toarray()

# Avoiding the dummy variable trap
X = X[:, 1:]

# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Part 2 - Now let's make the ANN!

# Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
# Initialising the ANN
classifier = Sequential()

# Adding the input layer and the first hidden layer with dropout
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dropout(rate = 0.1))    # p should vary from 0.1 to 0.4, NOT HIGHER, because then we will have under-fitting.

# Adding the second hidden layer with dropout
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dropout(rate = 0.1))

# Adding the output layer
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))

# Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)

# Part 3 - Making predictions and evaluating the model

# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

new_prediction = classifier.predict(sc.transform(np.array([[0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]])))
new_prediction = (new_prediction > 0.5)

#Part 4 = Evaluating, Improving and Tuning the ANN

# Evaluating the ANN
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from keras.models import Sequential
from keras.layers import Dense
def build_classifier():
    classifier = Sequential()
    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
    classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
    classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
    return classifier
classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, nb_epoch = 100)
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
mean = accuracies.mean()
variance = accuracies.std()

# Improving the ANN

# Tuning the ANN
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
from keras.models import Sequential
from keras.layers import Dense
def build_classifier(optimizer):
    classifier = Sequential()
    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
    classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
    classifier.compile(optimizer = optimizer, loss = 'binary_crossentropy', metrics = ['accuracy'])
    return classifier
classifier = KerasClassifier(build_fn = build_classifier)
parameters = {"batch_size": [25, 32],
              "nb_epoch": [100, 500],
              "optimizer": ["adam", "rmsprop"]}
grid_search = GridSearchCV(estimator = classifier,
                           param_grid = parameters,
                           scoring = "accuracy",
                           cv = 10)
grid_search = grid_search.fit(X_train, y_train)
best_parameters = grid_search.best_params_
best_accuracy = grid_search.best_score_
[另外，对于n_jobs = 1，它运行，但表示时代1/1并运行10次，这是k倍值。这意味着由于某种原因它可以识别nb_epoch = 1而不是100。最后，我尝试将cross_val_score（）封装到一个类中：

class run():
    def __init__(self):
            cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)

if __name__ == '__main__':
    run()
或仅具有if条件：

if __name__ == '__main__':
   cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
但是它也不起作用，它再次冻结。

有人可以帮我解决这些问题吗？发生了什么，我该怎么办才能解决这些问题，以便一切正常运行？预先谢谢你。

在Windows 10上，我已经安装了Anaconda并启动了Spyder。我还成功安装了Theano，Tensorflow和Keras，因为当我执行import keras时，控制台输出使用...

Answer 1

似乎Windows的“ n_jobs”存在问题，请在您的“ accuracies =”代码中将其删除，它将起作用，缺点是可能需要一段时间，但至少会起作用。

Answer 2

我也有同样的问题，但是删除n_jobs没有帮助。