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基于KerasConv1D心电图检测开源教程 中国传统节日故事司南

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基于KerasConv1D心电图检测开源教程 中国传统节日故事司南

训练参数比较少,大家可以根据自己想法更改。 3.网络模型训练模型训练ifname__==“__main”:“”“dat1=get_feature(”“)print(”onedatashapeis“,)#onedatashapeis(12,5000)(dat1[0])()”“”if((MANIFEST_DIR)==False):create_csv()train_iter=xs_gen(train=True)test_iter=xs_gen(train=False)model=build_model()print(())ckpt=(filepath=‘best_model.{epoch:02d}-{val_acc:.2f}.h5’,monitor=‘val_acc’,save_best_only=True,verbose=1)(loss=‘categorical_crossentropy’,optimizer=‘adam’,metrics=[‘accuracy’])_generator(generator=train_iter,steps_per_epoch=500//Batch_size,epochs=20,initial_epoch=0,validation_data=test_iter,nb_val_samples=100//Batch_size,callbacks=[ckpt],)训练过程输出(最优结果:loss::_loss:_acc:)Epoch10/2025/25[==============================]-1s37ms/step-loss::_loss:_acc::val_,savingmodeltobest_/2025/25[==============================]-1s38ms/step-loss::_loss:_acc::val_/2025/25[==============================]-1s40ms/step-loss::_loss:_acc::val_/2025/25[==============================]-1s38ms/step-loss::_loss:_acc::val_/2025/25[==============================]-1s38ms/step-loss::_loss:_acc::val_/2025/25[==============================]-1s38ms/step-loss::_loss:_acc::val_,savingmodeltobest_/2025/25[==============================]-1s38ms/step-loss::_loss:_acc::val_/2025/25[==============================]-1s38ms/step-loss::_loss:_acc::val_/2025/25[==============================]-1s38ms/step-loss::_loss:_acc::val_/2025/25[==============================]-1s37ms/step-loss::_loss:_acc::val_/2025/25[==============================]-1s37ms/step-loss::_loss:_acc::val_模型应用预测结果预测数据ifname__==“__main”:“”“dat1=get_feature(”“)print(”onedatashapeis“,)#onedatashapeis(12,5000)(dat1[0])()”“”“”“if((MANIFEST_DIR)==False):create_csv()train_iter=xs_gen(train=True)test_iter=xs_gen(train=False)model=build_model()print(())ckpt=(filepath=‘best_model.{epoch:02d}-{val_acc:.2f}.h5’,monitor=‘val_acc’,save_best_only=True,verbose=1)(loss=‘categorical_crossentropy’,optimizer=‘adam’,metrics=[‘accuracy’])_generator(generator=train_iter,steps_per_epoch=500//Batch_size,epochs=20,initial_epoch=0,validation_data=test_iter,nb_val_samples=100//Batch_size,callbacks=[ckpt],)”“”PRE_DIR=“sample_codes/”model=load_model(“best_”)pre_lists=_csv(PRE_DIR,sep=r“”,header=None)print(pre_())pre_datas=([get_feature(item,BASE_DIR=“preliminary/TEST/”)foriteminpre_lists[0]])pre_result=_classes(pre_datas)#0-1概率预测print(pre_)pre_lists[1]=pre_resultpre__csv(“sample_codes/”,index=None,header=None)print(“predictfinish”)下面是前十条预测结果:TEST394,0TEST313,1TEST484,0TEST288,0TEST261,1TEST310,0TEST286,1TEST367,1TEST149,1TEST160,1展望此Baseline采用最简单的一维卷积达到了88%测试准确率(可能会因为随机初始化值上下波动),大家也可以多尝试GRU,Attention,和Resnet等结果,测试准确率会突破90+。

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