如何使用AdaBoost增强基于Keras的神经网络? [英] How to boost a Keras based neural network using AdaBoost?

问题描述

假设我将以下神经网络用于二进制分类问题:

Assuming I fit the following neural network for a binary classification problem:

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?

How would I boost the neural network using AdaBoost? Does keras have any commands for this?

推荐答案

这可以按以下步骤完成: 首先创建一个模型(为实现可复制性，将其作为一个函数):

This can be done as follows: First create a model (for reproducibility make it as a function):

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

Then put it inside the sklearn wrapper:

ann_estimator = KerasRegressor(build_fn= simple_model, epochs=100, batch_size=10, verbose=0)

然后最终将其增强:

boosted_ann = AdaBoostRegressor(base_estimator= ann_estimator)
boosted_ann.fit(rescaledX, y_train.values.ravel())# scale your training data 
boosted_ann.predict(rescaledX_Test)

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