基于sklearn和keras的数据切分与交叉验证

在训练深度学习模型的时候,通常将数据集切分为训练集和验证集.Keras提供了两种评估模型性能的方法:

  • 使用自动切分的验证集
  • 使用手动切分的验证集

 

一.自动切分

在Keras中,可以从数据集中切分出一部分作为验证集,并且在每次迭代(epoch)时在验证集中评估模型的性能.

具体地,调用model.fit()训练模型时,可通过validation_split参数来指定从数据集中切分出验证集的比例.

# MLP with automatic validation set
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
numpy.random.seed(7)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10)

validation_split:0~1之间的浮点数,用来指定训练集的一定比例数据作为验证集。验证集将不参与训练,并在每个epoch结束后测试的模型的指标,如损失函数、精确度等。

注意,validation_split的划分在shuffle之前,因此如果你的数据本身是有序的,需要先手工打乱再指定validation_split,否则可能会出现验证集样本不均匀。 

 

二.手动切分

Keras允许在训练模型的时候手动指定验证集.

例如,用sklearn库中的train_test_split()函数将数据集进行切分,然后在kerasmodel.fit()的时候通过validation_data参数指定前面切分出来的验证集.

# MLP with manual validation set
from keras.models import Sequential
from keras.layers import Dense
from sklearn.model_selection import train_test_split
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# split into 67% for train and 33% for test
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=seed)
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test,y_test), epochs=150, batch_size=10)

 

三.K折交叉验证(k-fold cross validation)

将数据集分成k份,每一轮用其中(k-1)份做训练而剩余1份做验证,以这种方式执行k轮,得到k个模型.将k次的性能取平均,作为该算法的整体性能.k一般取值为5或者10.

  • 优点:能比较鲁棒性地评估模型在未知数据上的性能.
  • 缺点:计算复杂度较大.因此,在数据集较大,模型复杂度较高,或者计算资源不是很充沛的情况下,可能不适用,尤其是在训练深度学习模型的时候.

sklearn.model_selection提供了KFold以及RepeatedKFold, LeaveOneOut, LeavePOut, ShuffleSplit, StratifiedKFold, GroupKFold, TimeSeriesSplit等变体.

下面的例子中用的StratifiedKFold采用的是分层抽样,它保证各类别的样本在切割后每一份小数据集中的比例都与原数据集中的比例相同.

# MLP for Pima Indians Dataset with 10-fold cross validation
from keras.models import Sequential
from keras.layers import Dense
from sklearn.model_selection import StratifiedKFold
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# define 10-fold cross validation test harness
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
cvscores = []
for train, test in kfold.split(X, Y):
  # create model
    model = Sequential()
    model.add(Dense(12, input_dim=8, activation='relu'))
    model.add(Dense(8, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    # Compile model
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    # Fit the model
    model.fit(X[train], Y[train], epochs=150, batch_size=10, verbose=0)
    # evaluate the model
    scores = model.evaluate(X[test], Y[test], verbose=0)
    print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
    cvscores.append(scores[1] * 100)
print("%.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores), numpy.std(cvscores)))

  

参考:

Evaluate the Performance Of Deep Learning Models in Keras

3.1. Cross-validation: evaluating estimator performance — scikit-learn 0.19.1 documentation

sklearn中的交叉验证与参数选择

posted @ 2018-05-14 15:45  焦距  阅读(22533)  评论(2编辑  收藏  举报