测试神经网络

使用Keras进行自动验证
使用Keras进行手工验证
使用Keras进行K折交叉验证

1 分割数据数组

数据量大和网络复杂会形成训练时间很长，因此须要将数据分红训练、测试或验证数据集。Keras提供两种办法：网络

自动验证
手工验证

Keras能够将数据自动分出一部分，每次训练后进行验证。在训练时用validation_split参数能够指定验证数据的比例，通常是总数据的20%或者33%。app

1 下面的代码加入了自动验证：dom

import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# MLP with automatic validation set
from keras.models import Sequential from keras.layers import Dense 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] # create model
model = Sequential() model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu')) model.add(Dense(8, kernel_initializer='uniform', activation='relu')) model.add(Dense(1, kernel_initializer='uniform', 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)

训练时，每轮会显示训练和测试数据的数据：机器学习

2 手工验证函数

Keras也能够手工进行验证。咱们定义一个train_test_split函数，将数据分红2：1的测试和验证数据集。在调用fit()方法时须要加入validation_data参数做为验证数据，数组的项目分别是输入和输出数据。性能

import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# MLP with manual validation set
from keras.models import Sequential from keras.layers import Dense # from sklearn.cross_validation import train_test_split # 因为cross_validation将要被移除This module will be removed in 0.20.因此使用下面的model_selection来导入train_test_split
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="\t") # 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, kernel_initializer='uniform', activation='relu')) model.add(Dense(8, kernel_initializer='uniform', activation='relu')) model.add(Dense(1, kernel_initializer='uniform', 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)

和自动化验证同样，每轮训练后，Keras会输出训练和验证结果：学习

3 手工K折交叉验证测试

机器学习的金科玉律是K折验证，以验证模型对将来数据的预测能力。K折验证的方法是：将数据分红K组，留下1组验证，其余数据用做训练，直到每种分发的性能一致。lua

深度学习通常不用交叉验证，由于对算力要求过高。例如，K折的次数通常是5或者10折：每组都须要训练并验证，训练时间成倍上升。然而，若是数据量小，交叉验证的效果更好，偏差更小。

scikit-learn有StratifiedKFold类，咱们用它把数据分红10组。抽样方法是分层抽样，尽量保证每组数据量一致。而后咱们在每组上训练模型，使用verbose=0参数关闭每轮的输出。

训练后，Keras会输出模型的性能，并存储模型。最终，Keras输出性能的平均值和标准差，为性能估算提供更准确的估计：

import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# MLP for Pima Indians Dataset with 10-fold cross validation
from keras.models import Sequential from keras.layers import Dense from sklearn.cross_validation import StratifiedKFold #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="\t") # split into input (X) and output (Y) variables
X = dataset[:,0:8] Y = dataset[:,8] # define 10-fold cross validation test harness
kfold = StratifiedKFold(y=Y,n_folds=10, shuffle=True, random_state=seed) cvscores = [] for i, (train, test) in enumerate(kfold): # create model
    model = Sequential() model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu')) model.add(Dense(8, kernel_initializer='uniform', activation='relu')) model.add(Dense(1, kernel_initializer='uniform', 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)))

输出是：