ikki_NN_1.py 文件源码

def build_model(self):
            model = Sequential()
            model.add(Dropout(0.2, input_shape=(nn_input_dim_NN,)))
            model.add(Dense(input_dim=nn_input_dim_NN, output_dim=100, init='he_normal'))
            model.add(PReLU(init='zero'))
            model.add(BatchNormalization())
            model.add(Dropout(0.2))
            model.add(Dense(input_dim=100,output_dim=180, init='he_normal'))
            model.add(PReLU(init='zero'))
            model.add(BatchNormalization())
            model.add(Dropout(0.6))
            model.add(Dense(input_dim=180,output_dim=50, init='he_normal'))
            model.add(PReLU(init='zero'))
            model.add(BatchNormalization())
            model.add(Dropout(0.5))
            model.add(Dense(input_dim=50,output_dim=30, init='he_normal'))
            model.add(PReLU(init='zero'))
            model.add(BatchNormalization())
            model.add(Dropout(0.2))
            model.add(Dense(input_dim=30,output_dim=2, init='he_normal', activation='softmax'))    
            sgd = SGD(lr=0.01, decay=1e-10, momentum=0.9, nesterov=True)

            model.compile(optimizer=sgd, loss='binary_crossentropy',class_mode='binary')

            return KerasClassifier(nn=model,**self.params)