predict_2017_07_03_5.py 文件源码

def keras_mlp1(train2, y, test2, v, z):
    cname = sys._getframe().f_code.co_name
    def build_model(input_dims):
        from keras import layers
        from keras import models
        from keras import optimizers
        input_ = layers.Input(shape=(input_dims,))
        model = layers.Dense(1024, kernel_initializer='Orthogonal')(input_)
        model = layers.BatchNormalization()(model)
        model = layers.advanced_activations.PReLU()(model)
        #model = layers.Dropout(0.7)(model)
        model = layers.Dense(256, kernel_initializer='Orthogonal')(model)
        model = layers.BatchNormalization()(model)
        model = layers.advanced_activations.PReLU()(model)
        #model = layers.Dropout(0.9)(model)
        model = layers.Dense(64, kernel_initializer='Orthogonal')(model)
        model = layers.BatchNormalization()(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(),
                      #optimizer = optimizers.SGD(),
                      metrics = ['binary_accuracy'])
        #print(model.summary(line_length=120))
        return model
    keras_base(train2, y, test2, v, z, build_model, 9, cname, base_seed=42)

#@tf_force_cpu