我在＆＃34; pickle＆＃34;。这是我的代码的结构：
RandomizedSearchCV调用GridSearchCV或n_jobs=-1的方法。
create_model，用于创建由KerasClassifier或KerasRegressor 我收到错误消息称本地对象无法被腌制。如果我改变n_jobs=1，那么没问题。所以我怀疑问题在于本地功能和并行处理。有没有解决这个问题？在谷歌搜索后，似乎序列化程序dill可以在这里工作（我甚至找到了一个名为multiprocessing_on_dill的包）。但我目前依赖的是sklearn套餐。
答案 0 :(得分：2)
我找到了一个＆＃34;解决方案＆＃34;我的问题。我真的很困惑为什么示例here与create_model一起使用，但我的代码并没有。似乎问题在于驻留在子类方法中的本地函数n_jobs > 1。如果我将局部函数作为子类的方法，我可以设置RandomizedSearchCV。
回顾一下，这里是我的代码结构：
GridSearchCV调用n_jobs=-1或create_model的方法。KerasClassifier，用于创建由KerasRegressor或from abc import ABCMeta
import numpy as np
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
class MLAlgorithms(metaclass=ABCMeta):
def __init__(self, X_train, y_train, X_test, y_test=None):
"""
Constructor with train and test data.
:param X_train: Train descriptor data
:param y_train: Train observed data
:param X_test: Test descriptor data
:param y_test: Test observed data
"""
...
@abstractmethod
def setmlalg(self, mlalg):
"""
Sets a machine learning algorithm.
:param mlalg: Dictionary of the machine learning algorithm.
"""
pass
@abstractmethod
def fitmlalg(self, mlalg, rid=None):
"""
Fits a machine learning algorithm.
:param mlalg: Machine learning algorithm
"""
pass
class MLClassification(MLAlgorithms):
"""
Main class for classification machine learning algorithms.
"""
def setmlalg(self, mlalg):
"""
Sets a classification machine learning algorithm.
:param mlalg: Dictionary of the classification machine learning algorithm.
"""
...
def fitmlalg(self, mlalg):
"""
Fits a classification machine learning algorithm.
:param mlalg: Classification machine learning algorithm
"""
...
# Function to create model, required for KerasClassifier
def create_model(self, n_layers=1, units=10, input_dim=10, output_dim=1,
optimizer="rmsprop", loss="binary_crossentropy",
kernel_initializer="glorot_uniform", activation="sigmoid",
kernel_regularizer="l2", kernel_regularizer_weight=0.01,
lr=0.01, momentum=0.0, decay=0.0, nesterov=False, rho=0.9, epsilon=1E-8,
beta_1=0.9, beta_2=0.999, schedule_decay=0.004):
from keras.models import Sequential
from keras.layers import Dense
from keras import regularizers, optimizers
# Create model
if kernel_regularizer.lower() == "l1":
kernel_regularizer = regularizers.l1(l=kernel_regularizer_weight)
elif kernel_regularizer.lower() == "l2":
kernel_regularizer = regularizers.l2(l=kernel_regularizer_weight)
elif kernel_regularizer.lower() == "l1_l2":
kernel_regularizer = regularizers.l1_l2(l1=kernel_regularizer_weight, l2=kernel_regularizer_weight)
else:
print("Warning: Kernel regularizer {0} not supported. Using default 'l2' regularizer.".format(
kernel_regularizer))
kernel_regularizer = regularizers.l2(l=kernel_regularizer_weight)
if optimizer.lower() == "sgd":
optimizer = optimizers.sgd(lr=lr, momentum=momentum, decay=decay, nesterov=nesterov)
elif optimizer.lower() == "rmsprop":
optimizer = optimizers.rmsprop(lr=lr, rho=rho, epsilon=epsilon, decay=decay)
elif optimizer.lower() == "adagrad":
optimizer = optimizers.adagrad(lr=lr, epsilon=epsilon, decay=decay)
elif optimizer.lower() == "adadelta":
optimizer = optimizers.adadelta(lr=lr, rho=rho, epsilon=epsilon, decay=decay)
elif optimizer.lower() == "adam":
optimizer = optimizers.adam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=epsilon, decay=decay)
elif optimizer.lower() == "adamax":
optimizer = optimizers.adamax(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=epsilon, decay=decay)
elif optimizer.lower() == "nadam":
optimizer = optimizers.nadam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=epsilon,
schedule_decay=schedule_decay)
else:
print("Warning: Optimizer {0} not supported. Using default 'sgd' optimizer.".format(optimizer))
optimizer = "sgd"
model = Sequential()
model.add(
Dense(units=units, input_dim=input_dim,
kernel_initializer=kernel_initializer, activation=activation,
kernel_regularizer=kernel_regularizer))
for layer_count in range(n_layers - 1):
model.add(
Dense(units=units, kernel_initializer=kernel_initializer, activation=activation,
kernel_regularizer=kernel_regularizer))
model.add(Dense(units=output_dim,
kernel_initializer=kernel_initializer, activation=activation,
kernel_regularizer=kernel_regularizer))
# Compile model
model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy'])
return model
class MLRegression(MLAlgorithms):
"""
Main class for regression machine learning algorithms.
"""
...
代码的一般概念：
games.equals(g) == false答案 1 :(得分：0)
当在jupyter笔记本/ ipython的Windows上以并行化（n_jobs> 1）在kerasClassifier模型上运行sklearn的网格搜索时，我可以确认相同的问题（在Unix上没有问题）。
我通过将导致酱菜问题的create_model函数放入模块并导入该模块，而不是在环境中定义该函数来解决了该问题。
要为Python创建一个简单的模块，
import my_module导入模块，然后使用my_module.create_model()从模块调用函数