keras_v1.py 文件源码

def nn_model():
    model = Sequential()

    model.add(Dense(400, input_dim = xtrain.shape[1], init = 'he_normal')) #400
    model.add(PReLU())
    model.add(BatchNormalization())
    model.add(Dropout(0.4))

    model.add(Dense(120, init = 'he_normal')) #200
    model.add(PReLU())
    model.add(BatchNormalization())    
    model.add(Dropout(0.2))

    model.add(Dense(30, init = 'he_normal')) #50
    model.add(PReLU())
    model.add(BatchNormalization())    
    model.add(Dropout(0.1))                 #0.2

    model.add(Dense(1, init = 'he_normal'))
    model.compile(loss = 'mae', optimizer = 'adadelta')
    return(model)