假设我将以下神经网络拟合为二元分类问题:
model = Sequential()
model.add(Dense(21, input_dim=19, init='uniform', activation='relu'))
model.add(Dense(80, init='uniform', activation='relu'))
model.add(Dense(80, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(x2, training_target, nb_epoch=10, batch_size=32, verbose=0,validation_split=0.1, shuffle=True,callbacks=[hist])
我如何使用 AdaBoost 增强神经网络？ keras 对此有任何命令吗？
最佳答案
这可以按如下方式完成: 首先创建一个模型(为了重现性，将其作为一个函数):
def simple_model():
# create model
model = Sequential()
model.add(Dense(25, input_dim=x_train.shape[1], kernel_initializer='normal', activation='relu'))
model.add(Dropout(0.2, input_shape=(x_train.shape[1],)))
model.add(Dense(10, kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam')
return model
然后将其放入 sklearn 包装器中:
ann_estimator = KerasRegressor(build_fn= simple_model, epochs=100, batch_size=10, verbose=0)
然后最后提升它:
boosted_ann = AdaBoostRegressor(base_estimator= ann_estimator)
boosted_ann.fit(rescaledX, y_train.values.ravel())# scale your training data
boosted_ann.predict(rescaledX_Test)
关于python - 如何使用 AdaBoost 提升基于 Keras 的神经网络？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/39063676/