predict_2017_07_05_3.py 文件源码

def keras_mlp1(train2, y, test2, v, z):
    from keras import layers
    from keras import models
    from keras import optimizers
    cname = sys._getframe().f_code.co_name
    num_splits = 9
    scaler = preprocessing.RobustScaler()
    train3 = scaler.fit_transform(train2)
    test3 = scaler.transform(test2)
    input_dims = train3.shape[1]
    def build_model():
        input_ = layers.Input(shape=(input_dims,))
        model = layers.Dense(256, kernel_initializer='Orthogonal')(input_)
        #model = layers.BatchNormalization()(model)
        #model = layers.advanced_activations.PReLU()(model)
        model = layers.Activation('selu')(model)
        #model = layers.Dropout(0.7)(model)

        model = layers.Dense(64, kernel_initializer='Orthogonal')(model)
        #model = layers.BatchNormalization()(model)
        model = layers.Activation('selu')(model)
        #model = layers.advanced_activations.PReLU()(model)
        #model = layers.Dropout(0.9)(model)

        model = layers.Dense(16, kernel_initializer='Orthogonal')(model)
        #model = layers.BatchNormalization()(model)
        model = layers.Activation('selu')(model)
        #model = layers.advanced_activations.PReLU()(model)

        model = layers.Dense(1, activation='sigmoid')(model)

        model = models.Model(input_, model)
        model.compile(loss = 'binary_crossentropy', optimizer = optimizers.Nadam())
        #print(model.summary(line_length=120))
        return model
    keras_common(train3, y, test3, v, z, num_splits, cname, build_model)