标签：amp repr 迭代 nbsp shu std optimize core nump
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：
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)
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keras的model.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．
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
标签：amp repr 迭代 nbsp shu std optimize core nump
原文地址：https://www.cnblogs.com/bymo/p/9026198.html
