Keras Early Stopping 教程

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from sklearn.datasets import make_moons
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
from matplotlib import pyplot
from keras.models import load_model
# 生成2D分类数据集
X, y = make_moons(n_samples=100, noise=0.2, random_state=1)
# 切割训练/测试集
n_train = 30
trainX, testX = X[:n_train, :], X[n_train:, :]
trainy, testy = y[:n_train], y[n_train:]
# 定义模型
model = Sequential()
model.add(Dense(500, input_dim=2, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# 设置early stop
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=200) # patience表示在n个epoch上没有提升时停止
mc = ModelCheckpoint('best_model.h5', monitor='val_acc', mode='max', verbose=1, save_best_only=True)
# 训练模型
history = model.fit(trainX, trainy, validation_data=(testX, testy), epochs=4000, verbose=0, callbacks=[es, mc])
# 加载模型
saved_model = load_model('best_model.h5')
# 评估模型性能
_, train_acc = saved_model.evaluate(trainX, trainy, verbose=0)
_, test_acc = saved_model.evaluate(testX, testy, verbose=0)
print('Train: %.3f, Test: %.3f' % (train_acc, test_acc))

[1].https://machinelearningmastery.com/how-to-stop-training-deep-neural-networks-at-the-right-time-using-early-stopping/

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