相关疑难解决方法(0)

我使用KerasClassifier来训练分类器.

代码如下:

import numpy
from pandas import read_csv
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import Pipeline
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load dataset
dataframe = read_csv("iris.csv", header=None)
dataset = dataframe.values
X = dataset[:,0:4].astype(float)
Y = dataset[:,4]
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
#print("encoded_Y") …

官方文件声明"不建议使用pickle或cPickle来保存Keras模型."

然而,我对酸洗Keras模型的需求源于使用sklearn的RandomizedSearchCV(或任何其他超参数优化器)的超参数优化.将结果保存到文件中至关重要,因为脚本可以在分离的会话中远程执行等.

基本上,我想:

trial_search = RandomizedSearchCV( estimator=keras_model, ... )
pickle.dump( trial_search, open( "trial_search.pickle", "wb" ) )