Question

我刚刚用Keras创建了一个人工神经网络，我想将Scikit-learn函数cross_val_score传递给它，以对数据集的某些X_train和y_train进行训练。

import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score

def build_classifier():
    classifier = Sequential()
    classifier.add(Dense(units = 16, kernel_initializer = 'uniform', activation = 'relu', input_dim = 30))
    classifier.add(Dense(units = 16, kernel_initializer = 'uniform', activation = 'relu'))
    classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
    classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])
    return classifier

classifier = KerasClassifier(build_fn = build_classifier, batch_size=25, epochs = 10)

results = cross_val_score(classifier, X_train, y_train, cv=10, n_jobs=-1)

我得到的输出是时代1/1重复了4次（我有4个内核），没有别的，因为在那之后它卡住了，计算永远也不会结束。我使用任何其他Scikit学习算法测试了n_jobs = -1，并且工作正常。我不使用GPU，仅使用CPU。

要测试代码，只需添加以下标准化数据集：

from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()
df = pd.DataFrame(data['data'])
target = pd.DataFrame(data['target'])

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(df, target, 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)

玩完n_jobs（设置为1,2,3或-1）后，我得到了一些奇怪的结果，例如时代1/1仅重复了3次而不是4次（即使使用n_jobs = -1）或重复了3次这就是我得到的内核：

Process ForkPoolWorker-33:
Traceback (most recent call last):
  File "/home/myname/anaconda3/lib/python3.6/multiprocessing/process.py", line 258, in _bootstrap
self.run()
  File "/home/myname/anaconda3/lib/python3.6/multiprocessing/process.py", line 93, in run
self._target(*self._args, **self._kwargs)
  File "/home/myname/anaconda3/lib/python3.6/multiprocessing/pool.py", line 108, in worker
task = get()
  File "/home/myname/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/pool.py", line 362, in get
return recv()
  File "/home/myname/anaconda3/lib/python3.6/multiprocessing/connection.py", line 250, in recv
buf = self._recv_bytes()
  File "/home/myname/anaconda3/lib/python3.6/multiprocessing/connection.py", line 407, in _recv_bytes
buf = self._recv(4)
  File "/home/myname/anaconda3/lib/python3.6/multiprocessing/connection.py", line 379, in _recv
chunk = read(handle, remaining)
KeyboardInterrupt

这可能是多处理中的某件事，但我不知道如何解决。

Answer 1

上面的代码对我来说很好。请升级您的模块。

步骤1） pip install --upgrade tensorflow

第2步） pip install keras

我尝试过，并且可以在TensorFlow后端中使用。

我有：

输入[7]：sklearn。版本输出[7]：“ 0.19.1”

在[8]中：keras。版本在[8]中：“ 2.2.4”

并且：

import keras

/anaconda2/lib/python2.7/site-packages/h5py/ init .py：36：   FutureWarning：将issubdtype的第二个参数转换为   已弃用float至np.floating。将来会被治疗   为np.float64 == np.dtype(float).type。从._conv导入   register_converters作为_register_converters

使用TensorFlow后端。

Answer 2

我切换到sklearn版本= 0.20.1

由于命令运行和完成的时间比n_jobs = 1更短，因此n_jobs现在可以正常运行。

尽管如此：

1）n_jobs = 2或更高时，计算时间没有明显改善

2）在某些情况下，我会收到此警告：

[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.
/home/my_name/anaconda3/lib/python3.6/site-packages/sklearn/externals/joblib/externals/loky/process_executor.py:706: 
UserWarning: A worker stopped while some jobs were given to the executor. 
This can be caused by a too short worker timeout or by a memory leak.
  "timeout or by a memory leak.", UserWarning

最后一句话：在Jupyter笔记本中，对于n_jobs！= 1而言，神经网络与历元的交互计算不再显示，而是在终端（！？）中显示

scikit学习和keras多核命令n_jobs = -1

2 个答案: