How to boost a Keras based neural network using AdaBoost?

Question

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])

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

Answer 1

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

Then put it inside the sklearn wrapper:

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

Then and finally boost it:

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

Answer 2

Keras itself does not implement adaboost. However, Keras models are compatible with scikit-learn, so you probably can use AdaBoostClassifier from there: link. Use your model as the base_estimator after you compile it, and fit the AdaBoostClassifier instance instead of model.

This way, however, you will not be able to use the arguments you pass to fit, such as number of epochs or batch_size, so the defaults will be used. If the defaults are not good enough, you might need to build your own class that implements the scikit-learn interface on top of your model and passes proper arguments to fit.