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\', activat         


        

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)