Windows10下Spyder+TensorFlow交叉验证及网格搜索异常问题

我来帮你解决这两个Keras结合Scikit-learn时遇到的问题：

问题1：k折交叉验证设置n_jobs=-1导致Spyder控制台冻结 #

原因 #

Windows系统下，Spyder的IPython控制台对多进程的支持存在兼容性问题，加上TensorFlow/Keras的会话管理机制，当使用n_jobs=-1开启多进程交叉验证时，很容易出现死锁或者永久冻结的情况——这是Windows多进程（spawn启动方式）和TensorFlow初始化冲突导致的常见问题。

解决方案 #

• 优先推荐：用命令行运行脚本：把代码保存成.py文件，打开Anaconda Prompt，进入文件目录后用python your_script.py运行，多进程功能可以正常工作。
• 如果坚持在Spyder里运行：把n_jobs=-1改成n_jobs=1，用单进程执行，虽然速度慢，但不会冻结控制台。
• 可选尝试（不一定适配所有版本）：在代码最开头添加多进程启动方式设置：

import multiprocessing
multiprocessing.set_start_method('spawn', force=True)

问题2：GridSearchCV设置epochs=100却只运行1个epoch #

原因 #

你使用了旧版Keras的参数名nb_epoch，而在新版Keras（以及Keras被整合到TensorFlow后的版本）中，参数名已经改为epochs，所以nb_epoch=100不会被识别，模型默认只运行1个epoch。

解决方案 #

把所有nb_epoch替换成epochs，同时补全你未写完的GridSearchCV代码部分。

修改后的完整代码（重点标注修改处） #

# Part 1 - Data Preprocessing
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# 新增：解决Windows多进程问题的可选设置（如果用命令行运行可以不用）
import multiprocessing
multiprocessing.set_start_method('spawn', force=True)

# 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
# 修正：sklearn.cross_validation已废弃，改用sklearn.model_selection
from sklearn.model_selection 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

# 修改：把nb_epoch改成epochs，Spyder运行时设置n_jobs=1
classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, epochs = 100)
# 命令行运行可改为n_jobs=-1
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

# 修改：build_classifier接受optimizer参数，可扩展其他参数
def build_classifier(optimizer='adam'):
    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)
# 修改：参数网格用epochs替代nb_epoch
parameters = {
    'batch_size': [25, 32],
    'epochs': [100, 200],
    'optimizer': ['adam', 'rmsprop']
}

grid_search = GridSearchCV(estimator = classifier,
                           param_grid = parameters,
                           scoring = 'accuracy',
                           cv = 10)
# 命令行运行可加n_jobs=-1，Spyder用n_jobs=1
grid_search = grid_search.fit(X_train, y_train)
best_parameters = grid_search.best_params_
best_accuracy = grid_search.best_score_

内容的提问来源于stack exchange，提问作者BabisI