深度学习模型stacking模型融合python代码，看了你就会使

话不多说，直接上代码

 def stacking_first(train, train_y, test):
     savepath = './stack_op{}_dt{}_tfidf{}/'.format(args.option, args.data_type, args.tfidf)
     os.makedirs(savepath, exist_ok=True)

     count_kflod = 0
     num_folds = 6
     kf = KFold(n_splits=num_folds, shuffle=True, random_state=10)
     # 测试集上的预测结果
     predict = np.zeros((test.shape[0], config.n_class))
     # k折交叉验证集的预测结果
     oof_predict = np.zeros((train.shape[0], config.n_class))
     scores = []
     f1s = []

     for train_index, test_index in kf.split(train):
         # 训练集划分为6折，每一折都要走一遍。那么第一个是5份的训练集索引，第二个是1份的测试集，此处为验证集是索引

         kfold_X_train = {}
         kfold_X_valid = {}

         # 取数据的标签
         y_train, y_test = train_y[train_index], train_y[test_index]
         # 取数据
         kfold_X_train, kfold_X_valid = train[train_index], train[test_index]

         # 模型的前缀
         model_prefix = savepath + 'DNN' + str(count_kflod)
         if not os.path.exists(model_prefix):
             os.mkdir(model_prefix)

         M = 4  # number of snapshots
         alpha_zero = 1e-3  # initial learning rate
         snap_epoch = 16
         snapshot = SnapshotCallbackBuilder(snap_epoch, M, alpha_zero)

         # 使用训练集的size设定维度，fit一个模型出来
         res_model = get_model(train)
         res_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
         # res_model.fit(train_x, train_y, batch_size=BATCH_SIZE, epochs=EPOCH, verbose=1,  class_weight=class_weight)
         res_model.fit(kfold_X_train, y_train, batch_size=BATCH_SIZE, epochs=snap_epoch, verbose=1,
                       validation_data=(kfold_X_valid, y_test),
                       callbacks=snapshot.get_callbacks(model_save_place=model_prefix))

         # 找到这个目录下所有已经训练好的深度学习模型，通过".h5"
         evaluations = []
         for i in os.listdir(model_prefix):
             if '.h5' in i:
                 evaluations.append(i)

         # 给测试集和当前的验证集开辟空间，就是当前折的数据预测结果构建出这么多的数据集[数据个数，类别]
         preds1 = np.zeros((test.shape[0], config.n_class))
         preds2 = np.zeros((len(kfold_X_valid), config.n_class))
         # 遍历每一个模型，用他们分别预测当前折数的验证集和测试集，N个模型的结果求平均
         for run, i in enumerate(evaluations):
             res_model.load_weights(os.path.join(model_prefix, i))
             preds1 += res_model.predict(test, verbose=1) / len(evaluations)
             preds2 += res_model.predict(kfold_X_valid, batch_size=128) / len(evaluations)

         # 测试集上预测结果的加权平均
         predict += preds1 / num_folds
         # 每一折的预测结果放到对应折上的测试集中，用来最后构建训练集
         oof_predict[test_index] = preds2

         # 计算精度和F1
         accuracy = mb.cal_acc(oof_predict[test_index], np.argmax(y_test, axis=1))
         f1 = mb.cal_f_alpha(oof_predict[test_index], np.argmax(y_test, axis=1), n_out=config.n_class)
         print('the kflod cv is : ', str(accuracy))
         print('the kflod f1 is : ', str(f1))
         count_kflod += 1

         # 模型融合的预测结果，存起来，用以以后求平均值
         scores.append(accuracy)
         f1s.append(f1)
     # 指标均值，最为最后的预测结果
     print('total scores is ', np.mean(scores))
     print('total f1 is ', np.mean(f1s))
     return predict