我正在尝试从http://www.superdatascience.com/wp-content/uploads/2017/03/Artificial_Neural_Networks.zip的文件夹中运行代码，该文件夹位于Artificial_Neural_Networks / evaluating_improving_tuning.py
该平台是使用python 3.6的最新视觉工作室，但是该脚本在
处停止（只是保持运行且不执行任何操作）accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
我卸载并重新安装了Theano，tensorflow，Keras软件包，但问题尚未解决。
# 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
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()
X = X[:, 1:]
# Splitting the dataset into the Training set and Test set
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
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
# classifier.add(Dropout(p = 0.1))
# Adding the second hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
# classifier.add(Dropout(p = 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)
# Predicting a single new observation
"""Predict if the customer with the following informations will leave the bank:
Geography: France
Credit Score: 600
Gender: Male
Age: 40
Tenure: 3
Balance: 60000
Number of Products: 2
Has Credit Card: Yes
Is Active Member: Yes
Estimated Salary: 50000"""
new_prediction = classifier.predict(sc.transform(np.array([[0.0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]])))
new_prediction = (new_prediction > 0.5)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
# 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, epochs = 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
# Dropout Regularization to reduce overfitting if needed
# 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],
'epochs': [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_
我尝试使用在线下载的Anaconda发行版，但仍然失败。以下是警告消息。
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
sklearn.externals.joblib.externals.loky.process_executor._RemoteTraceback:
"""
Traceback (most recent call last):
File "D:\Anaconda\lib\site-packages\sklearn\externals\joblib\externals\loky\process_executor.py", line 418, in _process_worker
r = call_item()
File "D:\Anaconda\lib\site-packages\sklearn\externals\joblib\externals\loky\process_executor.py", line 272, in __call__
return self.fn(*self.args, **self.kwargs)
File "D:\Anaconda\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 567, in __call__
return self.func(*args, **kwargs)
File "D:\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py", line 225, in __call__
for func, args, kwargs in self.items]
File "D:\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py", line 225, in <listcomp>
for func, args, kwargs in self.items]
File "D:\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py", line 528, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "D:\Anaconda\lib\site-packages\keras\wrappers\scikit_learn.py", line 210, in fit
return super(KerasClassifier, self).fit(x, y, **kwargs)
File "D:\Anaconda\lib\site-packages\keras\wrappers\scikit_learn.py", line 152, in fit
history = self.model.fit(x, y, **fit_args)
File "D:\Anaconda\lib\site-packages\keras\engine\training.py", line 1039, in fit
validation_steps=validation_steps)
File "D:\Anaconda\lib\site-packages\keras\engine\training_arrays.py", line 199, in fit_loop
outs = f(ins_batch)
File "D:\Anaconda\lib\site-packages\keras\backend\tensorflow_backend.py", line 2697, in __call__
if hasattr(get_session(), '_make_callable_from_options'):
File "D:\Anaconda\lib\site-packages\keras\backend\tensorflow_backend.py", line 186, in get_session
_SESSION = tf.Session(config=config)
File "D:\Anaconda\lib\site-packages\tensorflow\python\client\session.py", line 1551, in __init__
super(Session, self).__init__(target, graph, config=config)
File "D:\Anaconda\lib\site-packages\tensorflow\python\client\session.py", line 676, in __init__
self._session = tf_session.TF_NewSessionRef(self._graph._c_graph, opts)
tensorflow.python.framework.errors_impl.InternalError: CUDA runtime implicit initialization on GPU:0 failed. Status: out of memory
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py", line 402, in cross_val_score
error_score=error_score)
File "D:\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py", line 240, in cross_validate
for train, test in cv.split(X, y, groups))
File "D:\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py", line 930, in __call__
self.retrieve()
File "D:\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py", line 833, in retrieve
self._output.extend(job.get(timeout=self.timeout))
File "D:\Anaconda\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 521, in wrap_future_result
return future.result(timeout=timeout)
File "D:\Anaconda\lib\concurrent\futures\_base.py", line 432, in result
return self.__get_result()
File "D:\Anaconda\lib\concurrent\futures\_base.py", line 384, in __get_result
raise self._exception
tensorflow.python.framework.errors_impl.InternalError: CUDA runtime implicit initialization on GPU:0 failed. Status: out of memory
>>> mean = accuracies.mean()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'accuracies' is not defined
>>> variance = accuracies.std()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'accuracies' is not defined
spyder的错误消息
File "<ipython-input-3-e698aa09be33>", line 100, in <module>
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
File "D:\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py", line 402, in cross_val_score
error_score=error_score)
File "D:\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py", line 240, in cross_validate
for train, test in cv.split(X, y, groups))
File "D:\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py", line 930, in __call__
self.retrieve()
File "D:\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py", line 833, in retrieve
self._output.extend(job.get(timeout=self.timeout))
File "D:\Anaconda\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 521, in wrap_future_result
return future.result(timeout=timeout)
File "D:\Anaconda\lib\concurrent\futures\_base.py", line 432, in result
return self.__get_result()
File "D:\Anaconda\lib\concurrent\futures\_base.py", line 384, in __get_result
raise self._exception
BrokenProcessPool: A task has failed to un-serialize. Please ensure that the arguments of the function are all picklable.