python调用模型代码_深度学习模型stacking模型融合python代码，看了你就会使

1 defstacking_first(train, train_y, test):2 savepath = './stack_op{}_dt{}_tfidf{}/'.format(args.option, args.data_type, args.tfidf)3 os.makedirs(savepath, exist_ok=True)4

5 count_kflod =06 num_folds = 6

7 kf = KFold(n_splits=num_folds, shuffle=True, random_state=10)8 #测试集上的预测结果

9 predict =np.zeros((test.shape[0], config.n_class))10 #k折交叉验证集的预测结果

11 oof_predict =np.zeros((train.shape[0], config.n_class))12 scores =[]13 f1s =[]14

15 for train_index, test_index inkf.split(train):16 #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

17

18 kfold_X_train ={}19 kfold_X_valid ={}20

21 #取数据的标签

22 y_train, y_test =train_y[train_index], train_y[test_index]23 #取数据

24 kfold_X_train, kfold_X_valid =train[train_index], train[test_index]25

26 #模型的前缀

27 model_prefix = savepath + 'DNN' +str(count_kflod)28 if notos.path.exists(model_prefix):29 os.mkdir(model_prefix)30

31 M = 4 #number of snapshots

32 alpha_zero = 1e-3 #initial learning rate

33 snap_epoch = 16

34 snapshot =SnapshotCallbackBuilder(snap_epoch, M, alpha_zero)35

36 #使用训练集的size设定维度，fit一个模型出来

37 res_model =get_model(train)38 res_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])39 #res_model.fit(train_x, train_y, batch_size=BATCH_SIZE, epochs=EPOCH, verbose=1, class_weight=class_weight)

40 res_model.fit(kfold_X_train, y_train, batch_size=BATCH_SIZE, epochs=snap_epoch, verbose=1,41 validation_data=(kfold_X_valid, y_test),42 callbacks=snapshot.get_callbacks(model_save_place=model_prefix))43

44 #找到这个目录下所有已经训练好的深度学习模型，通过".h5"

45 evaluations =[]46 for i inos.listdir(model_prefix):47 if '.h5' ini:48 evaluations.append(i)49

50 #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]

51 preds1 =np.zeros((test.shape[0], config.n_class))52 preds2 =np.zeros((len(kfold_X_valid), config.n_class))53 #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

54 for run, i inenumerate(evaluations):55 res_model.load_weights(os.path.join(model_prefix, i))56 preds1 += res_model.predict(test, verbose=1) /len(evaluations)57 preds2 += res_model.predict(kfold_X_valid, batch_size=128) /len(evaluations)58

59 #测试集上预测结果的加权平均

60 predict += preds1 /num_folds61 #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

62 oof_predict[test_index] =preds263

64 #计算精度和F1

65 accuracy = mb.cal_acc(oof_predict[test_index], np.argmax(y_test, axis=1))66 f1 = mb.cal_f_alpha(oof_predict[test_index], np.argmax(y_test, axis=1), n_out=config.n_class)67 print('the kflod cv is :', str(accuracy))68 print('the kflod f1 is :', str(f1))69 count_kflod += 1

70

71 #模型融合的预测结果，存起来，用以以后求平均值

72 scores.append(accuracy)73 f1s.append(f1)74 #指标均值，最为最后的预测结果

75 print('total scores is', np.mean(scores))76 print('total f1 is', np.mean(f1s))77 return predict