keras_v3.py 文件源码

def nn_model():
    model = Sequential()

    model.add(Dense(425, input_dim = xtrain.shape[1], init = 'he_normal')) #425
    model.add(PReLU())
    model.add(BatchNormalization())
    model.add(Dropout(0.4)) #0.4

    model.add(Dense(200, init = 'he_normal')) #225
    model.add(PReLU())
    model.add(BatchNormalization())    
    model.add(Dropout(0.3))                 #0.3

    model.add(Dense(40, init = 'he_normal')) #60
    model.add(PReLU())
    model.add(BatchNormalization())    
    model.add(Dropout(0.15))                 #0.1

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