目标：利用切片实现AD诊断
数据划分：143例用于训练，62例用于验证，51例用于测试
结果：
训练30次后，验证集准确度为80.65％
利用测试集数据进行测试，51例数据可以正确识别出42例。
验证方法：使用五折交叉验证
ROC曲线下面积为0.92
ipynb文件：
```python#使用自己搭建的网络来分析切片#目的：与融合之后的label实现例数上的对应#载入模块#matplotlib:用于画图importmatplotlib#从matplotlib中载入子模块frommatplotlibimportpyplotasplt#nibabel:读取和编写NIFTI文件importnibabelasnib#os:处理文件和目录importos#numpy:矩阵操作importnumpyasnp#cv2:用于图像处理importcv2#keras:把很多深度学习要用到的函数封装起来,使用keras可以快速调用函数搭建网络,精简代码importkeras#从keras中载入3D卷积层(Conv),全连接层(Dense),池化层(Pool)#Flatten:Flatten是指将多维的矩阵拉开,变成一维向量来表示fromkeras.layersimportConv3D,Conv2D,Dense,Input,MaxPool2D,MaxPool3D,Flatten#载入模型fromkeras.modelsimportModel#载入模块#KFold:K折交叉模块fromsklearn.model_selectionimportKFold#shuffle:将数据打乱fromsklearn.utilsimportshuffle#metrics:用于定义评估模型的指标fromsklearnimportmetricsimportmatplotlibfrommatplotlibimportpylabaspltimportnibabelasnibimportosimportnumpyasnpimportcv2importkerasfromkeras.layersimportConv3D,Conv2D,Dense,Input,MaxPool2D,MaxPool3D,Flattenfromkeras.modelsimportModelimportosimportnumpyasnpfromnilearnimportdatasetsfromnilearnimportinput_dataimportcv2fromsklearn.utilsimportshufflefromkerasimportmodelsfromkeras.modelsimportload_modelimportnilearnfromnilearn.imageimportnew_img_likefromnilearnimportdatasetsfromnilearnimportinput_datafromnilearn.connectomeimportConnectivityMeasureimportmatplotlib.pyplotaspltfromnilearnimportimageimportnibabelasnibimportskimage.ioasioimportnumpyasnpfromnilearnimportdatasets,plotting,imageimportrandom#nilearn:一个用于神经影像的机器学习工具包#nibabel:用于读取nii文件,提取图像数据的工具包#numpy:用于进行矩阵操作#skimage:用于进行图像处理的模块importkerasfromkeras.layersimportConv3D,Conv2D,Dense,Input,MaxPool2D,MaxPool3D,Flattenfromkeras.modelsimportModelfromkeras.layersimportDense,Input,Add,LSTM,Flatten,GRUfromkeras.optimizersimportAdam,SGDfromkeras.modelsimportSequentialfromkeras.layersimportDense,Dropoutfromkeras.layersimportconcatenatefromkeras.layersimportConv1D,GlobalAveragePooling1D,MaxPooling1Dfromkeras.layersimportConv2D,GlobalAveragePooling2D,MaxPooling2Dfromkeras.layersimportTimeDistributedfromkerasimportregularizersfromkeras.layers.coreimportDense,Dropout,Activation,Flattenfromkeras.optimizersimportAdam,RMSpropfromkeras.layersimportDense,Input,Add,LSTM,Flatten,GRU/usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88 return f(*args, **kwds) Using TensorFlow backend. /usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88 return f(*args, **kwds) /usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88 return f(*args, **kwds) /usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88 return f(*args, **kwds) /usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88 return f(*args, **kwds) /usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88 return f(*args, **kwds) /usr/local/lib/python3.5/dist-packages/skimage/__init__.py:71: ResourceWarning: unclosed file <_io.TextIOWrapper name='/usr/local/lib/python3.5/dist-packages/pytest.py' mode='r' encoding='utf-8'> imp.find_module('pytest') /usr/local/lib/python3.5/dist-packages/nilearn/plotting/__init__.py:20: UserWarning: This call to matplotlib.use() has no effect because the backend has already been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot, or matplotlib.backends is imported for the first time. The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code: File "/usr/lib/python3.5/runpy.py", line 184, in _run_module_as_main "__main__", mod_spec) File "/usr/lib/python3.5/runpy.py", line 85, in _run_code exec(code, run_globals) File "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py", line 16, in <module> app.launch_new_instance() File "/usr/local/lib/python3.5/dist-packages/traitlets/config/application.py", line 658, in launch_instance app.start() File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelapp.py", line 486, in start self.io_loop.start() File "/usr/local/lib/python3.5/dist-packages/tornado/platform/asyncio.py", line 132, in start self.asyncio_loop.run_forever() File "/usr/lib/python3.5/asyncio/base_events.py", line 345, in run_forever self._run_once() File "/usr/lib/python3.5/asyncio/base_events.py", line 1312, in _run_once handle._run() File "/usr/lib/python3.5/asyncio/events.py", line 125, in _run self._callback(*self._args) File "/usr/local/lib/python3.5/dist-packages/tornado/ioloop.py", line 758, in _run_callback ret = callback() File "/usr/local/lib/python3.5/dist-packages/tornado/stack_context.py", line 300, in null_wrapper return fn(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 536, in <lambda> self.io_loop.add_callback(lambda : self._handle_events(self.socket, 0)) File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 450, in _handle_events self._handle_recv() File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 480, in _handle_recv self._run_callback(callback, msg) File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 432, in _run_callback callback(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tornado/stack_context.py", line 300, in null_wrapper return fn(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher return self.dispatch_shell(stream, msg) File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell handler(stream, idents, msg) File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request user_expressions, allow_stdin) File "/usr/local/lib/python3.5/dist-packages/ipykernel/ipkernel.py", line 208, in do_execute res = shell.run_cell(code, store_history=store_history, silent=silent) File "/usr/local/lib/python3.5/dist-packages/ipykernel/zmqshell.py", line 537, in run_cell return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2662, in run_cell raw_cell, store_history, silent, shell_futures) File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2785, in _run_cell interactivity=interactivity, compiler=compiler, result=result) File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2901, in run_ast_nodes if self.run_code(code, result): File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2961, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-2-9541018185df>", line 5, in <module> from matplotlib import pyplot as plt File "/usr/local/lib/python3.5/dist-packages/matplotlib/pyplot.py", line 71, in <module> from matplotlib.backends import pylab_setup File "/usr/local/lib/python3.5/dist-packages/matplotlib/backends/__init__.py", line 16, in <module> line for line in traceback.format_stack() matplotlib.use('Agg') /usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88 return f(*args, **kwds)#定义数据路径ad_path='/home/ADNI/outcome/AD/T1ImgNewSegment/'cn_path='/home/ADNI/outcome/CN/T1ImgNewSegment/'#读取数据路径下的所有文件#os.listdir表示遍历文件夹下的文件(只有文件名,不包含路径)#lambda x:定义一个变量x,并遍历目录#将文件路径和文件名拼接起来作为结果返回#结果保存于一个list(列表文件)中ad_files=list(map(lambdax:ad_path+x,os.listdir(ad_path)))cn_files=list(map(lambdax:cn_path+x,os.listdir(cn_path)))#移除文件夹中的无关文件ad_files.remove(ad_path+'.ipynb_checkpoints')#cn_files.remove(cn_path + '.ipynb_checkpoints')ad_files=ad_files cn_files=cn_filesdata_1=[]label_2=[]forfileinad_files:data=nib.load(file).get_data()data_1.append(cv2.resize(data,(121,121)))label_2.append([0,1])forfileincn_files:data=nib.load(file).get_data()data_1.append(cv2.resize(data,(121,121)))label_2.append([1,0])data_1=np.array(data_1)label_2=np.array(label_2)data_1.shape(256, 121, 121, 121)data_2=[]foriinrange(data_1.shape[0]):img1=data_1[i,:,:,60]img2=data_1[i,:,60,:]img3=data_1[i,60,:,:]img=np.stack((img1,img2,img3))data_2.append(img)data_2=np.array(data_2)data_2.shape(256, 3, 121, 121)data_2=data_2.transpose(0,3,2,1)data_2.shape(256, 121, 121, 3)#数据归一化(除以最大值,将最大值转化为1)data_2=np.array(data_2)max_data=np.max(data_2)data_2=data_2/max_datadata_2,label_2=shuffle(data_2,label_2)data_2.shape(256, 121, 121, 3)size1=121size2=121channels=3defbuild_model():inp=Input(shape=(size1,size2,channels))x1=Conv2D(filters=1,kernel_size=(3,3),strides=(1,1),activation='relu',input_shape=(size1,size2,channels))(inp)x1=Flatten()(x1)x1=Dense(2,activation='softmax')(x1)model=Model(input=inp,outputs=x1)returnmodelinput_shape=data_2.shape[1:]output_shape=2data=data_2 label=label_2fromsklearn.metricsimportroc_curve,aucfromscipyimportinterp#定义n折交叉验证KF=KFold(n_splits=5)tprs=[]aucs=[]mean_fpr=np.linspace(0,1,100)i=0#data为数据集,利用KF.split划分训练集和测试集fortrain_index,test_indexinKF.split(data):#建立模型，并对训练集进行测试，求出预测得分#划分训练集和测试集X_train,X_test=data[train_index],data[test_index]Y_train,Y_test=label[train_index],label[test_index]#建立模型(模型已经定义)model=build_model()#编译模型#定义优化器adam=keras.optimizers.Adam(lr=0.00005,beta_1=0.9,beta_2=0.999,epsilon=1e-08,decay=0.0)model.compile(loss='categorical_crossentropy',optimizer=adam,metrics=['acc'])#训练模型model.fit(X_train,Y_train,batch_size=32,validation_split=0.3,epochs=30)#利用model.predict获取测试集的预测值y_pred=model.predict(X_test,batch_size=1)#计算fpr(假阳性率),tpr(真阳性率),thresholds(阈值)[绘制ROC曲线要用到这几个值]fpr,tpr,thresholds=roc_curve(Y_test[:,1],y_pred[:,1])#interp:插值 把结果添加到tprs列表中tprs.append(interp(mean_fpr,fpr,tpr))tprs[-1][0]=0.0#计算aucroc_auc=auc(fpr,tpr)aucs.append(roc_auc)#画图，只需要plt.plot(fpr,tpr),变量roc_auc只是记录auc的值，通过auc()函数计算出来plt.plot(fpr,tpr,lw=1,alpha=0.3,label='ROC fold %d(area=%0.2f)'%(i,roc_auc))i+=1#画对角线plt.plot([0,1],[0,1],linestyle='--',lw=2,color='r',label='Luck',alpha=.8)mean_tpr=np.mean(tprs,axis=0)mean_tpr[-1]=1.0mean_auc=auc(mean_fpr,mean_tpr)#计算平均AUC值std_auc=np.std(tprs,axis=0)plt.plot(mean_fpr,mean_tpr,color='b',label=r'Mean ROC (area=%0.2f)'%mean_auc,lw=2,alpha=.8)std_tpr=np.std(tprs,axis=0)tprs_upper=np.minimum(mean_tpr+std_tpr,1)tprs_lower=np.maximum(mean_tpr-std_tpr,0)plt.fill_between(mean_tpr,tprs_lower,tprs_upper,color='gray',alpha=.2)plt.xlim([-0.05,1.05])plt.ylim([-0.05,1.05])plt.xlabel('False Positive Rate')plt.ylabel('True Positive Rate')plt.title('ROC')plt.legend(loc='lower right')plt.show()/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:9: UserWarning: Update your `Model` call to the Keras 2 API: `Model(outputs=Tensor("de..., inputs=Tensor("in...)` if __name__ == '__main__': Train on 142 samples, validate on 62 samples Epoch 1/30 142/142 [==============================] - 3s 22ms/step - loss: 0.7119 - acc: 0.4225 - val_loss: 0.7087 - val_acc: 0.5161 Epoch 2/30 142/142 [==============================] - 0s 949us/step - loss: 0.6651 - acc: 0.6127 - val_loss: 0.7267 - val_acc: 0.5161 Epoch 3/30 142/142 [==============================] - 0s 842us/step - loss: 0.6601 - acc: 0.6127 - val_loss: 0.7481 - val_acc: 0.5161 Epoch 4/30 142/142 [==============================] - 0s 824us/step - loss: 0.6549 - acc: 0.6127 - val_loss: 0.7354 - val_acc: 0.5161 Epoch 5/30 142/142 [==============================] - 0s 817us/step - loss: 0.6435 - acc: 0.6127 - val_loss: 0.7097 - val_acc: 0.5161 Epoch 6/30 142/142 [==============================] - 0s 792us/step - loss: 0.6344 - acc: 0.6127 - val_loss: 0.6866 - val_acc: 0.5161 Epoch 7/30 142/142 [==============================] - 0s 936us/step - loss: 0.6242 - acc: 0.6127 - val_loss: 0.6746 - val_acc: 0.5161 Epoch 8/30 142/142 [==============================] - 0s 946us/step - loss: 0.6148 - acc: 0.6127 - val_loss: 0.6711 - val_acc: 0.5161 Epoch 9/30 142/142 [==============================] - 0s 837us/step - loss: 0.6069 - acc: 0.6197 - val_loss: 0.6615 - val_acc: 0.5161 Epoch 10/30 142/142 [==============================] - 0s 845us/step - loss: 0.5974 - acc: 0.6127 - val_loss: 0.6600 - val_acc: 0.5161 Epoch 11/30 142/142 [==============================] - 0s 886us/step - loss: 0.5899 - acc: 0.6127 - val_loss: 0.6582 - val_acc: 0.5161 Epoch 12/30 142/142 [==============================] - 0s 812us/step - loss: 0.5822 - acc: 0.6127 - val_loss: 0.6386 - val_acc: 0.5323 Epoch 13/30 142/142 [==============================] - 0s 784us/step - loss: 0.5721 - acc: 0.6338 - val_loss: 0.6297 - val_acc: 0.5323 Epoch 14/30 142/142 [==============================] - 0s 838us/step - loss: 0.5633 - acc: 0.6479 - val_loss: 0.6222 - val_acc: 0.5323 Epoch 15/30 142/142 [==============================] - 0s 925us/step - loss: 0.5559 - acc: 0.6761 - val_loss: 0.6097 - val_acc: 0.5484 Epoch 16/30 142/142 [==============================] - 0s 923us/step - loss: 0.5475 - acc: 0.6831 - val_loss: 0.6046 - val_acc: 0.5484 Epoch 17/30 142/142 [==============================] - 0s 889us/step - loss: 0.5393 - acc: 0.6901 - val_loss: 0.5993 - val_acc: 0.5645 Epoch 18/30 142/142 [==============================] - 0s 882us/step - loss: 0.5313 - acc: 0.7042 - val_loss: 0.5878 - val_acc: 0.6290 Epoch 19/30 142/142 [==============================] - 0s 880us/step - loss: 0.5227 - acc: 0.7465 - val_loss: 0.5785 - val_acc: 0.6613 Epoch 20/30 142/142 [==============================] - 0s 883us/step - loss: 0.5154 - acc: 0.8028 - val_loss: 0.5693 - val_acc: 0.7097 Epoch 21/30 142/142 [==============================] - 0s 828us/step - loss: 0.5068 - acc: 0.8310 - val_loss: 0.5670 - val_acc: 0.6613 Epoch 22/30 142/142 [==============================] - 0s 855us/step - loss: 0.5019 - acc: 0.7465 - val_loss: 0.5679 - val_acc: 0.6129 Epoch 23/30 142/142 [==============================] - 0s 827us/step - loss: 0.4923 - acc: 0.7535 - val_loss: 0.5543 - val_acc: 0.6935 Epoch 24/30 142/142 [==============================] - 0s 844us/step - loss: 0.4839 - acc: 0.8310 - val_loss: 0.5448 - val_acc: 0.7581 Epoch 25/30 142/142 [==============================] - 0s 841us/step - loss: 0.4762 - acc: 0.8732 - val_loss: 0.5366 - val_acc: 0.7581 Epoch 26/30 142/142 [==============================] - 0s 816us/step - loss: 0.4688 - acc: 0.8803 - val_loss: 0.5284 - val_acc: 0.7581 Epoch 27/30 142/142 [==============================] - 0s 847us/step - loss: 0.4624 - acc: 0.9155 - val_loss: 0.5157 - val_acc: 0.8065 Epoch 28/30 142/142 [==============================] - 0s 1ms/step - loss: 0.4538 - acc: 0.9366 - val_loss: 0.5127 - val_acc: 0.7903 Epoch 29/30 142/142 [==============================] - 0s 815us/step - loss: 0.4465 - acc: 0.9155 - val_loss: 0.5098 - val_acc: 0.7581 Epoch 30/30 142/142 [==============================] - 0s 777us/step - loss: 0.4399 - acc: 0.9155 - val_loss: 0.5018 - val_acc: 0.7903 Train on 143 samples, validate on 62 samples Epoch 1/30 143/143 [==============================] - 1s 4ms/step - loss: 0.7019 - acc: 0.4476 - val_loss: 0.6939 - val_acc: 0.4355 Epoch 2/30 143/143 [==============================] - 0s 863us/step - loss: 0.6875 - acc: 0.5315 - val_loss: 0.6905 - val_acc: 0.5484 Epoch 3/30 143/143 [==============================] - 0s 872us/step - loss: 0.6782 - acc: 0.6294 - val_loss: 0.6886 - val_acc: 0.5161 Epoch 4/30 143/143 [==============================] - 0s 835us/step - loss: 0.6674 - acc: 0.6154 - val_loss: 0.6870 - val_acc: 0.5161 Epoch 5/30 143/143 [==============================] - 0s 770us/step - loss: 0.6592 - acc: 0.6154 - val_loss: 0.6863 - val_acc: 0.5161 Epoch 6/30 143/143 [==============================] - 0s 839us/step - loss: 0.6504 - acc: 0.6154 - val_loss: 0.6852 - val_acc: 0.5161 Epoch 7/30 143/143 [==============================] - 0s 810us/step - loss: 0.6438 - acc: 0.6154 - val_loss: 0.6847 - val_acc: 0.5161 Epoch 8/30 143/143 [==============================] - 0s 840us/step - loss: 0.6373 - acc: 0.6154 - val_loss: 0.6827 - val_acc: 0.5161 Epoch 9/30 143/143 [==============================] - 0s 847us/step - loss: 0.6320 - acc: 0.6154 - val_loss: 0.6813 - val_acc: 0.5161 Epoch 10/30 143/143 [==============================] - 0s 810us/step - loss: 0.6260 - acc: 0.6154 - val_loss: 0.6773 - val_acc: 0.5161 Epoch 11/30 143/143 [==============================] - 0s 858us/step - loss: 0.6202 - acc: 0.6154 - val_loss: 0.6734 - val_acc: 0.5161 Epoch 12/30 143/143 [==============================] - 0s 830us/step - loss: 0.6142 - acc: 0.6154 - val_loss: 0.6686 - val_acc: 0.5161 Epoch 13/30 143/143 [==============================] - 0s 901us/step - loss: 0.6080 - acc: 0.6154 - val_loss: 0.6622 - val_acc: 0.5161 Epoch 14/30 143/143 [==============================] - 0s 907us/step - loss: 0.6022 - acc: 0.6154 - val_loss: 0.6550 - val_acc: 0.5161 Epoch 15/30 143/143 [==============================] - 0s 1ms/step - loss: 0.5956 - acc: 0.6154 - val_loss: 0.6507 - val_acc: 0.5161 Epoch 16/30 143/143 [==============================] - 0s 781us/step - loss: 0.5886 - acc: 0.6224 - val_loss: 0.6430 - val_acc: 0.5161 Epoch 17/30 143/143 [==============================] - 0s 874us/step - loss: 0.5820 - acc: 0.6224 - val_loss: 0.6359 - val_acc: 0.5323 Epoch 18/30 143/143 [==============================] - 0s 974us/step - loss: 0.5756 - acc: 0.6364 - val_loss: 0.6267 - val_acc: 0.5323 Epoch 19/30 143/143 [==============================] - 0s 949us/step - loss: 0.5682 - acc: 0.6503 - val_loss: 0.6200 - val_acc: 0.5323 Epoch 20/30 143/143 [==============================] - 0s 856us/step - loss: 0.5612 - acc: 0.6503 - val_loss: 0.6119 - val_acc: 0.5323 Epoch 21/30 143/143 [==============================] - 0s 826us/step - loss: 0.5539 - acc: 0.6573 - val_loss: 0.6047 - val_acc: 0.5323 Epoch 22/30 143/143 [==============================] - 0s 814us/step - loss: 0.5467 - acc: 0.6783 - val_loss: 0.5961 - val_acc: 0.5645 Epoch 23/30 143/143 [==============================] - 0s 849us/step - loss: 0.5391 - acc: 0.6923 - val_loss: 0.5890 - val_acc: 0.5645 Epoch 24/30 143/143 [==============================] - 0s 821us/step - loss: 0.5313 - acc: 0.7063 - val_loss: 0.5827 - val_acc: 0.5645 Epoch 25/30 143/143 [==============================] - 0s 848us/step - loss: 0.5238 - acc: 0.7133 - val_loss: 0.5754 - val_acc: 0.5806 Epoch 26/30 143/143 [==============================] - 0s 864us/step - loss: 0.5160 - acc: 0.7552 - val_loss: 0.5677 - val_acc: 0.6129 Epoch 27/30 143/143 [==============================] - 0s 832us/step - loss: 0.5085 - acc: 0.7832 - val_loss: 0.5591 - val_acc: 0.6774 Epoch 28/30 143/143 [==============================] - 0s 811us/step - loss: 0.5004 - acc: 0.8252 - val_loss: 0.5514 - val_acc: 0.6774 Epoch 29/30 143/143 [==============================] - 0s 855us/step - loss: 0.4924 - acc: 0.8252 - val_loss: 0.5440 - val_acc: 0.6935 Epoch 30/30 143/143 [==============================] - 0s 750us/step - loss: 0.4846 - acc: 0.8601 - val_loss: 0.5334 - val_acc: 0.7581 Train on 143 samples, validate on 62 samples Epoch 1/30 143/143 [==============================] - 1s 4ms/step - loss: 0.6923 - acc: 0.5245 - val_loss: 0.6942 - val_acc: 0.4032 Epoch 2/30 143/143 [==============================] - 0s 678us/step - loss: 0.6903 - acc: 0.5524 - val_loss: 0.6944 - val_acc: 0.4355 Epoch 3/30 143/143 [==============================] - 0s 601us/step - loss: 0.6882 - acc: 0.6154 - val_loss: 0.6947 - val_acc: 0.5161 Epoch 4/30 143/143 [==============================] - 0s 640us/step - loss: 0.6861 - acc: 0.6224 - val_loss: 0.6949 - val_acc: 0.5000 Epoch 5/30 143/143 [==============================] - 0s 715us/step - loss: 0.6838 - acc: 0.6154 - val_loss: 0.6952 - val_acc: 0.5161 Epoch 6/30 143/143 [==============================] - 0s 684us/step - loss: 0.6817 - acc: 0.6154 - val_loss: 0.6954 - val_acc: 0.5161 Epoch 7/30 143/143 [==============================] - 0s 660us/step - loss: 0.6794 - acc: 0.6154 - val_loss: 0.6956 - val_acc: 0.5161 Epoch 8/30 143/143 [==============================] - 0s 706us/step - loss: 0.6770 - acc: 0.6154 - val_loss: 0.6958 - val_acc: 0.5161 Epoch 9/30 143/143 [==============================] - 0s 740us/step - loss: 0.6745 - acc: 0.6154 - val_loss: 0.6959 - val_acc: 0.5161 Epoch 10/30 143/143 [==============================] - 0s 621us/step - loss: 0.6723 - acc: 0.6154 - val_loss: 0.6962 - val_acc: 0.5161 Epoch 11/30 143/143 [==============================] - 0s 582us/step - loss: 0.6695 - acc: 0.6154 - val_loss: 0.6961 - val_acc: 0.5161 Epoch 12/30 143/143 [==============================] - 0s 581us/step - loss: 0.6672 - acc: 0.6154 - val_loss: 0.6962 - val_acc: 0.5161 Epoch 13/30 143/143 [==============================] - 0s 666us/step - loss: 0.6644 - acc: 0.6154 - val_loss: 0.6964 - val_acc: 0.5161 Epoch 14/30 143/143 [==============================] - 0s 665us/step - loss: 0.6616 - acc: 0.6154 - val_loss: 0.6966 - val_acc: 0.5161 Epoch 15/30 143/143 [==============================] - 0s 709us/step - loss: 0.6587 - acc: 0.6154 - val_loss: 0.6968 - val_acc: 0.5161 Epoch 16/30 143/143 [==============================] - 0s 692us/step - loss: 0.6560 - acc: 0.6154 - val_loss: 0.6972 - val_acc: 0.5161 Epoch 17/30 143/143 [==============================] - 0s 625us/step - loss: 0.6530 - acc: 0.6154 - val_loss: 0.6974 - val_acc: 0.5161 Epoch 18/30 143/143 [==============================] - 0s 650us/step - loss: 0.6502 - acc: 0.6154 - val_loss: 0.6979 - val_acc: 0.5161 Epoch 19/30 143/143 [==============================] - 0s 807us/step - loss: 0.6472 - acc: 0.6154 - val_loss: 0.6982 - val_acc: 0.5161 Epoch 20/30 143/143 [==============================] - 0s 639us/step - loss: 0.6441 - acc: 0.6154 - val_loss: 0.6984 - val_acc: 0.5161 Epoch 21/30 143/143 [==============================] - 0s 652us/step - loss: 0.6411 - acc: 0.6154 - val_loss: 0.6985 - val_acc: 0.5161 Epoch 22/30 143/143 [==============================] - ETA: 0s - loss: 0.6344 - acc: 0.632 - 0s 734us/step - loss: 0.6382 - acc: 0.6154 - val_loss: 0.6986 - val_acc: 0.5161 Epoch 23/30 143/143 [==============================] - 0s 687us/step - loss: 0.6349 - acc: 0.6154 - val_loss: 0.6982 - val_acc: 0.5161 Epoch 24/30 143/143 [==============================] - 0s 743us/step - loss: 0.6323 - acc: 0.6154 - val_loss: 0.6982 - val_acc: 0.5161 Epoch 25/30 143/143 [==============================] - 0s 747us/step - loss: 0.6290 - acc: 0.6154 - val_loss: 0.6976 - val_acc: 0.5161 Epoch 26/30 143/143 [==============================] - 0s 632us/step - loss: 0.6261 - acc: 0.6154 - val_loss: 0.6968 - val_acc: 0.5161 Epoch 27/30 143/143 [==============================] - 0s 864us/step - loss: 0.6229 - acc: 0.6154 - val_loss: 0.6956 - val_acc: 0.5161 Epoch 28/30 143/143 [==============================] - 0s 865us/step - loss: 0.6199 - acc: 0.6154 - val_loss: 0.6949 - val_acc: 0.5161 Epoch 29/30 143/143 [==============================] - 0s 661us/step - loss: 0.6167 - acc: 0.6154 - val_loss: 0.6943 - val_acc: 0.5161 Epoch 30/30 143/143 [==============================] - 0s 731us/step - loss: 0.6134 - acc: 0.6154 - val_loss: 0.6931 - val_acc: 0.5161 Train on 143 samples, validate on 62 samples Epoch 1/30 143/143 [==============================] - 1s 4ms/step - loss: 0.6688 - acc: 0.6713 - val_loss: 0.7584 - val_acc: 0.5645 Epoch 2/30 143/143 [==============================] - 0s 898us/step - loss: 0.6615 - acc: 0.6713 - val_loss: 0.7669 - val_acc: 0.5645 Epoch 3/30 143/143 [==============================] - 0s 870us/step - loss: 0.6502 - acc: 0.6713 - val_loss: 0.7273 - val_acc: 0.5645 Epoch 4/30 143/143 [==============================] - 0s 908us/step - loss: 0.6400 - acc: 0.6713 - val_loss: 0.7230 - val_acc: 0.5645 Epoch 5/30 143/143 [==============================] - 0s 838us/step - loss: 0.6340 - acc: 0.6713 - val_loss: 0.7121 - val_acc: 0.5645 Epoch 6/30 143/143 [==============================] - 0s 828us/step - loss: 0.6266 - acc: 0.6713 - val_loss: 0.7065 - val_acc: 0.5645 Epoch 7/30 143/143 [==============================] - 0s 816us/step - loss: 0.6202 - acc: 0.6713 - val_loss: 0.7013 - val_acc: 0.5645 Epoch 8/30 143/143 [==============================] - 0s 807us/step - loss: 0.6169 - acc: 0.6713 - val_loss: 0.6839 - val_acc: 0.5645 Epoch 9/30 143/143 [==============================] - 0s 777us/step - loss: 0.6090 - acc: 0.6713 - val_loss: 0.6784 - val_acc: 0.5645 Epoch 10/30 143/143 [==============================] - 0s 841us/step - loss: 0.5996 - acc: 0.6713 - val_loss: 0.6886 - val_acc: 0.5645 Epoch 11/30 143/143 [==============================] - 0s 1ms/step - loss: 0.5948 - acc: 0.6713 - val_loss: 0.6986 - val_acc: 0.5645 Epoch 12/30 143/143 [==============================] - 0s 814us/step - loss: 0.5902 - acc: 0.6713 - val_loss: 0.6866 - val_acc: 0.5645 Epoch 13/30 143/143 [==============================] - 0s 885us/step - loss: 0.5836 - acc: 0.6713 - val_loss: 0.6792 - val_acc: 0.5645 Epoch 14/30 143/143 [==============================] - 0s 815us/step - loss: 0.5744 - acc: 0.6713 - val_loss: 0.6522 - val_acc: 0.5645 Epoch 15/30 143/143 [==============================] - 0s 833us/step - loss: 0.5708 - acc: 0.6713 - val_loss: 0.6340 - val_acc: 0.5645 Epoch 16/30 143/143 [==============================] - 0s 875us/step - loss: 0.5650 - acc: 0.6713 - val_loss: 0.6322 - val_acc: 0.5645 Epoch 17/30 143/143 [==============================] - 0s 801us/step - loss: 0.5576 - acc: 0.6713 - val_loss: 0.6350 - val_acc: 0.5645 Epoch 18/30 143/143 [==============================] - 0s 877us/step - loss: 0.5514 - acc: 0.6713 - val_loss: 0.6381 - val_acc: 0.5645 Epoch 19/30 143/143 [==============================] - 0s 822us/step - loss: 0.5472 - acc: 0.6713 - val_loss: 0.6347 - val_acc: 0.5645 Epoch 20/30 143/143 [==============================] - 0s 886us/step - loss: 0.5398 - acc: 0.6713 - val_loss: 0.6198 - val_acc: 0.5645 Epoch 21/30 143/143 [==============================] - 0s 1ms/step - loss: 0.5364 - acc: 0.6713 - val_loss: 0.5980 - val_acc: 0.5645 Epoch 22/30 143/143 [==============================] - 0s 832us/step - loss: 0.5302 - acc: 0.6853 - val_loss: 0.5905 - val_acc: 0.5806 Epoch 23/30 143/143 [==============================] - 0s 852us/step - loss: 0.5255 - acc: 0.7063 - val_loss: 0.5849 - val_acc: 0.6129 Epoch 24/30 143/143 [==============================] - 0s 828us/step - loss: 0.5172 - acc: 0.6783 - val_loss: 0.5946 - val_acc: 0.5645 Epoch 25/30 143/143 [==============================] - 0s 822us/step - loss: 0.5129 - acc: 0.6713 - val_loss: 0.6004 - val_acc: 0.5645 Epoch 26/30 143/143 [==============================] - 0s 892us/step - loss: 0.5067 - acc: 0.6713 - val_loss: 0.5862 - val_acc: 0.5645 Epoch 27/30 143/143 [==============================] - 0s 854us/step - loss: 0.5004 - acc: 0.6923 - val_loss: 0.5652 - val_acc: 0.6613 Epoch 28/30 143/143 [==============================] - 0s 859us/step - loss: 0.4977 - acc: 0.7902 - val_loss: 0.5542 - val_acc: 0.6935 Epoch 29/30 143/143 [==============================] - 0s 874us/step - loss: 0.4906 - acc: 0.8182 - val_loss: 0.5558 - val_acc: 0.6935 Epoch 30/30 143/143 [==============================] - 0s 1ms/step - loss: 0.4842 - acc: 0.7552 - val_loss: 0.5563 - val_acc: 0.6774 Train on 143 samples, validate on 62 samples Epoch 1/30 143/143 [==============================] - 1s 4ms/step - loss: 0.6682 - acc: 0.6643 - val_loss: 0.7258 - val_acc: 0.4839 Epoch 2/30 143/143 [==============================] - 0s 774us/step - loss: 0.6358 - acc: 0.6713 - val_loss: 0.7755 - val_acc: 0.4839 Epoch 3/30 143/143 [==============================] - 0s 786us/step - loss: 0.6300 - acc: 0.6713 - val_loss: 0.7771 - val_acc: 0.4839 Epoch 4/30 143/143 [==============================] - 0s 713us/step - loss: 0.6253 - acc: 0.6713 - val_loss: 0.7634 - val_acc: 0.4839 Epoch 5/30 143/143 [==============================] - 0s 811us/step - loss: 0.6153 - acc: 0.6713 - val_loss: 0.7258 - val_acc: 0.4839 Epoch 6/30 143/143 [==============================] - 0s 817us/step - loss: 0.6084 - acc: 0.6713 - val_loss: 0.7010 - val_acc: 0.4839 Epoch 7/30 143/143 [==============================] - 0s 957us/step - loss: 0.6037 - acc: 0.6713 - val_loss: 0.6951 - val_acc: 0.4839 Epoch 8/30 143/143 [==============================] - 0s 748us/step - loss: 0.5958 - acc: 0.6713 - val_loss: 0.6969 - val_acc: 0.4839 Epoch 9/30 143/143 [==============================] - 0s 842us/step - loss: 0.5884 - acc: 0.6713 - val_loss: 0.7086 - val_acc: 0.4839 Epoch 10/30 143/143 [==============================] - 0s 826us/step - loss: 0.5883 - acc: 0.6713 - val_loss: 0.7194 - val_acc: 0.4839 Epoch 11/30 143/143 [==============================] - 0s 850us/step - loss: 0.5780 - acc: 0.6713 - val_loss: 0.6947 - val_acc: 0.4839 Epoch 12/30 143/143 [==============================] - 0s 784us/step - loss: 0.5709 - acc: 0.6713 - val_loss: 0.6670 - val_acc: 0.4839 Epoch 13/30 143/143 [==============================] - 0s 853us/step - loss: 0.5633 - acc: 0.6713 - val_loss: 0.6605 - val_acc: 0.4839 Epoch 14/30 143/143 [==============================] - 0s 1ms/step - loss: 0.5571 - acc: 0.6713 - val_loss: 0.6549 - val_acc: 0.4839 Epoch 15/30 143/143 [==============================] - 0s 811us/step - loss: 0.5523 - acc: 0.6713 - val_loss: 0.6401 - val_acc: 0.4839 Epoch 16/30 143/143 [==============================] - 0s 865us/step - loss: 0.5447 - acc: 0.6713 - val_loss: 0.6428 - val_acc: 0.4839 Epoch 17/30 143/143 [==============================] - 0s 827us/step - loss: 0.5383 - acc: 0.6713 - val_loss: 0.6447 - val_acc: 0.4839 Epoch 18/30 143/143 [==============================] - 0s 817us/step - loss: 0.5322 - acc: 0.6713 - val_loss: 0.6335 - val_acc: 0.4839 Epoch 19/30 143/143 [==============================] - 0s 834us/step - loss: 0.5256 - acc: 0.6713 - val_loss: 0.6301 - val_acc: 0.4839 Epoch 20/30 143/143 [==============================] - 0s 822us/step - loss: 0.5198 - acc: 0.6783 - val_loss: 0.6132 - val_acc: 0.5000 Epoch 21/30 143/143 [==============================] - 0s 847us/step - loss: 0.5140 - acc: 0.6783 - val_loss: 0.6141 - val_acc: 0.4839 Epoch 22/30 143/143 [==============================] - 0s 835us/step - loss: 0.5096 - acc: 0.6783 - val_loss: 0.5978 - val_acc: 0.5161 Epoch 23/30 143/143 [==============================] - 0s 706us/step - loss: 0.5015 - acc: 0.6923 - val_loss: 0.5930 - val_acc: 0.5323 Epoch 24/30 143/143 [==============================] - 0s 686us/step - loss: 0.4947 - acc: 0.6853 - val_loss: 0.6010 - val_acc: 0.5000 Epoch 25/30 143/143 [==============================] - 0s 882us/step - loss: 0.4907 - acc: 0.6783 - val_loss: 0.6018 - val_acc: 0.4839 Epoch 26/30 143/143 [==============================] - 0s 797us/step - loss: 0.4854 - acc: 0.6783 - val_loss: 0.5906 - val_acc: 0.5000 Epoch 27/30 143/143 [==============================] - 0s 857us/step - loss: 0.4787 - acc: 0.6853 - val_loss: 0.5746 - val_acc: 0.6129 Epoch 28/30 143/143 [==============================] - 0s 821us/step - loss: 0.4726 - acc: 0.7483 - val_loss: 0.5589 - val_acc: 0.7097 Epoch 29/30 143/143 [==============================] - 0s 808us/step - loss: 0.4672 - acc: 0.8112 - val_loss: 0.5495 - val_acc: 0.7742 Epoch 30/30 143/143 [==============================] - 0s 825us/step - loss: 0.4624 - acc: 0.8531 - val_loss: 0.5401 - val_acc: 0.8065#[! png
y_pred=model.predict(X_test)y_pred[y_pred<0.5]=0y_pred[y_pred>0.5]=1y_pred-Y_testarray([[ 1., -1.], [ 0., 0.], [ 1., -1.], [ 1., -1.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 1., -1.], [ 1., -1.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 1., -1.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 1., -1.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 1., -1.], [ 1., -1.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.], [ 0., 0.]])Y_testarray([[0, 1], [1, 0], [0, 1], [0, 1], [0, 1], [1, 0], [1, 0], [1, 0], [1, 0], [0, 1], [1, 0], [0, 1], [1, 0], [0, 1], [0, 1], [1, 0], [0, 1], [1, 0], [0, 1], [1, 0], [0, 1], [0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1], [0, 1], [0, 1], [1, 0], [1, 0], [1, 0], [1, 0], [1, 0], [0, 1], [0, 1], [1, 0], [1, 0]])目标：利用长短期记忆网络实现AD诊断
使用了数据增强
数据集划分：共得到1024个样本数据，820个用于训练，204个用于测试
结果：训练30次后，测试集准确度为84.96%测试结果：
利用测试集数据进行测试，204例数据可以正确识别出156例。
测试集准确率为76.47%
#对时间序列数据进行样本扩充#方法:先划分数据集,再利用nitime进行滤波#https://mp.weixin.qq.com/s?__biz=MzU1MTkwNzIyOQ==&mid=2247486561&idx=1&sn=91df24bc64059f785856fc8f82945f58&chksm=fb8b7693ccfcff853936faccf0b42409c3dd7b2cef416fc623e051d4d81ee85d78702bd20325&scene=21#wechat_redirect#对测试集进行样本扩充#利用3种滤波方式增强数据#训练集和验证集分开增强#五折交叉验证怎样划分数据?-不使用交叉验证网络搭建：
importwarnings warnings.filterwarnings("ignore")importmatplotlibfrommatplotlibimportpylabaspltimportnibabelasnibimportosimportnumpyasnpimportcv2importkerasfromkeras.layersimportConv3D,Conv2D,Dense,Input,MaxPool2D,MaxPool3D,Flattenfromkeras.modelsimportModelimportosimportnumpyasnpfromnilearnimportdatasetsfromnilearnimportinput_dataimportcv2fromsklearn.utilsimportshufflefromkerasimportmodelsfromkeras.modelsimportload_modelimportnilearnfromnilearn.imageimportnew_img_likefromnilearnimportdatasetsfromnilearnimportinput_datafromnilearn.connectomeimportConnectivityMeasureimportmatplotlib.pyplotaspltfromnilearnimportimageimportnibabelasnibimportskimage.ioasioimportnumpyasnpfromnilearnimportdatasets,plotting,imageimportrandom#nilearn:一个用于神经影像的机器学习工具包#nibabel:用于读取nii文件,提取图像数据的工具包#numpy:用于进行矩阵操作#skimage:用于进行图像处理的模块importkerasfromkeras.layersimportConv3D,Conv2D,Dense,Input,MaxPool2D,MaxPool3D,Flattenfromkeras.modelsimportModelfromkeras.layersimportDense,Input,Add,LSTM,Flatten,GRUfromkeras.optimizersimportAdam,SGDfromkeras.modelsimportSequentialfromkeras.layersimportDense,Dropoutfromkeras.layersimportconcatenatefromkeras.layersimportConv1D,GlobalAveragePooling1D,MaxPooling1Dfromkeras.layersimportConv2D,GlobalAveragePooling2D,MaxPooling2Dfromkeras.layersimportTimeDistributedfromkeras.layersimportBatchNormalizationfromkerasimportregularizersfromkeras.layers.coreimportDense,Dropout,Activation,Flattenfromkeras.optimizersimportAdam,RMSpropfromkeras.layersimportDense,Input,Add,LSTM,Flatten,GRUimportnitime# Import the time-series objects:fromnitime.timeseriesimportTimeSeries# Import the analysis objects:fromnitime.analysisimportSpectralAnalyzer,FilterAnalyzer,NormalizationAnalyzerUsing TensorFlow backend. /usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88 return f(*args, **kwds) /usr/local/lib/python3.5/dist-packages/skimage/__init__.py:71: ResourceWarning: unclosed file <_io.TextIOWrapper name='/usr/local/lib/python3.5/dist-packages/pytest.py' mode='r' encoding='utf-8'> imp.find_module('pytest') /usr/local/lib/python3.5/dist-packages/nilearn/plotting/__init__.py:20: UserWarning: This call to matplotlib.use() has no effect because the backend has already been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot, or matplotlib.backends is imported for the first time. The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code: File "/usr/lib/python3.5/runpy.py", line 184, in _run_module_as_main "__main__", mod_spec) File "/usr/lib/python3.5/runpy.py", line 85, in _run_code exec(code, run_globals) File "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py", line 16, in <module> app.launch_new_instance() File "/usr/local/lib/python3.5/dist-packages/traitlets/config/application.py", line 658, in launch_instance app.start() File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelapp.py", line 486, in start self.io_loop.start() File "/usr/local/lib/python3.5/dist-packages/tornado/platform/asyncio.py", line 132, in start self.asyncio_loop.run_forever() File "/usr/lib/python3.5/asyncio/base_events.py", line 345, in run_forever self._run_once() File "/usr/lib/python3.5/asyncio/base_events.py", line 1312, in _run_once handle._run() File "/usr/lib/python3.5/asyncio/events.py", line 125, in _run self._callback(*self._args) File "/usr/local/lib/python3.5/dist-packages/tornado/ioloop.py", line 758, in _run_callback ret = callback() File "/usr/local/lib/python3.5/dist-packages/tornado/stack_context.py", line 300, in null_wrapper return fn(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 536, in <lambda> self.io_loop.add_callback(lambda : self._handle_events(self.socket, 0)) File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 450, in _handle_events self._handle_recv() File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 480, in _handle_recv self._run_callback(callback, msg) File "/usr/local/lib/python3.5/dist-packages/zmq/eventloop/zmqstream.py", line 432, in _run_callback callback(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/tornado/stack_context.py", line 300, in null_wrapper return fn(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 283, in dispatcher return self.dispatch_shell(stream, msg) File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell handler(stream, idents, msg) File "/usr/local/lib/python3.5/dist-packages/ipykernel/kernelbase.py", line 399, in execute_request user_expressions, allow_stdin) File "/usr/local/lib/python3.5/dist-packages/ipykernel/ipkernel.py", line 208, in do_execute res = shell.run_cell(code, store_history=store_history, silent=silent) File "/usr/local/lib/python3.5/dist-packages/ipykernel/zmqshell.py", line 537, in run_cell return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2662, in run_cell raw_cell, store_history, silent, shell_futures) File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2785, in _run_cell interactivity=interactivity, compiler=compiler, result=result) File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2901, in run_ast_nodes if self.run_code(code, result): File "/usr/local/lib/python3.5/dist-packages/IPython/core/interactiveshell.py", line 2961, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-5-a2293080d056>", line 4, in <module> from matplotlib import pylab as plt File "/usr/local/lib/python3.5/dist-packages/matplotlib/pylab.py", line 252, in <module> from matplotlib import cbook, mlab, pyplot as plt File "/usr/local/lib/python3.5/dist-packages/matplotlib/pyplot.py", line 71, in <module> from matplotlib.backends import pylab_setup File "/usr/local/lib/python3.5/dist-packages/matplotlib/backends/__init__.py", line 16, in <module> line for line in traceback.format_stack() matplotlib.use('Agg') /usr/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88 return f(*args, **kwds)#从本地载入文件#打乱过label的数据data=np.load('AD_CN_102_154_LSTM_data_1.npy')label=np.load('AD_CN_102_154_LSTM_label.npy')data=np.squeeze(data)#先做标准化再划分数据集 from sklearn import preprocessing from sklearn.preprocessing import MinMaxScaler num_data = 256 minmax = preprocessing.MinMaxScaler() for i in range(num_data): data[i] = minmax.fit_transform(data[i]) #标准化处理2 from sklearn import preprocessing from sklearn.preprocessing import MaxAbsScaler num_data = 256 maxabs = preprocessing.MaxAbsScaler() for i in range(num_data): data[i] = maxabs.fit_transform(data[i]) #标准化处理3 from sklearn import preprocessing from sklearn.preprocessing import StandardScaler num_data = 256 standard = preprocessing.StandardScaler() for i in range(num_data): data[i] = standard.fit_transform(data[i])#划分数据集train_num=205data_train=data[0:train_num,:,:]label_train=label[0:train_num,:]data_test=data[train_num:data.shape[0],:,:]label_test=label[train_num:data.shape[0],:]#滤波变换示意图#非必需TR=1.4plot_mun=2T=TimeSeries(data_train[plot_mun],sampling_interval=TR)F=FilterAnalyzer(T,ub=0.15,lb=0.02)fig01=plt.figure()ax01=fig01.add_subplot(1,1,1)ax01.plot(F.data[plot_mun],label='unfiltered')ax01.plot(F.filtered_fourier.data[plot_mun],label='Fourier')ax01.plot(F.iir.data[plot_mun],label='IIR')ax01.plot(F.filtered_boxcar.data[plot_mun],label='Boxcar filter')ax01.set_xlabel('Time (TR)')ax01.set_ylabel('Signal amplitude (a.u.)')ax01.legend()/usr/local/lib/python3.5/dist-packages/nitime/utils.py:1028: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer. return np.linspace(0, float(Fs) / 2, float(n) / 2 + 1) /usr/local/lib/python3.5/dist-packages/scipy/signal/filter_design.py:3462: RuntimeWarning: divide by zero encountered in true_divide (stopb * (passb[0] - passb[1]))) /usr/local/lib/python3.5/dist-packages/scipy/signal/_arraytools.py:45: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result. b = a[a_slice] <matplotlib.legend.Legend at 0x7fcf97d2f5f8>data_train.shape(205, 140, 116)data_aug=[]label_aug=[]num_train=205foriinrange(num_train):T=TimeSeries(data_train[i],sampling_interval=TR)F=FilterAnalyzer(T,ub=0.15,lb=0.02)data_aug.append(F.filtered_fourier.data)label_aug.append(label_train[i])data_aug.append(F.iir.data)label_aug.append(label_train[i])data_aug.append(F.filtered_boxcar.data)label_aug.append(label_train[i])/usr/local/lib/python3.5/dist-packages/nitime/utils.py:1028: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer. return np.linspace(0, float(Fs) / 2, float(n) / 2 + 1) /usr/local/lib/python3.5/dist-packages/scipy/signal/filter_design.py:3462: RuntimeWarning: divide by zero encountered in true_divide (stopb * (passb[0] - passb[1]))) /usr/local/lib/python3.5/dist-packages/scipy/signal/_arraytools.py:45: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result. b = a[a_slice]data_aug=np.array(data_aug)label_aug=np.array(label_aug)data_aug.shape,label_aug.shape((615, 140, 116), (615, 2))data_all=np.concatenate((data_train,data_aug),axis=0)label_all=np.concatenate((label_train,label_aug),axis=0)data_all=np.expand_dims(data_all,axis=3)data_all.shape,label_all.shape((820, 140, 116, 1), (820, 2))测试集数据增强
data_test_aug=[]label_test_aug=[]num_test=51foriinrange(num_test):T=TimeSeries(data_test[i],sampling_interval=TR)F=FilterAnalyzer(T,ub=0.15,lb=0.02)data_test_aug.append(F.filtered_fourier.data)label_test_aug.append(label_test[i])data_test_aug.append(F.iir.data)label_test_aug.append(label_test[i])data_test_aug.append(F.filtered_boxcar.data)label_test_aug.append(label_test[i])#转换格式data_test_aug=np.array(data_test_aug)label_test_aug=np.array(label_test_aug)#拼接data_test_all=np.concatenate((data_test,data_test_aug),axis=0)label_test_all=np.concatenate((label_test,label_test_aug),axis=0)data_test_all=np.expand_dims(data_test_all,axis=3)#打乱data_test_all,label_test_all=shuffle(data_test_all,label_test_all)data_test_all.shape,label_test_all.shape/usr/local/lib/python3.5/dist-packages/nitime/utils.py:1028: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer. return np.linspace(0, float(Fs) / 2, float(n) / 2 + 1) /usr/local/lib/python3.5/dist-packages/scipy/signal/filter_design.py:3462: RuntimeWarning: divide by zero encountered in true_divide (stopb * (passb[0] - passb[1]))) /usr/local/lib/python3.5/dist-packages/scipy/signal/_arraytools.py:45: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result. b = a[a_slice] ((204, 140, 116, 1), (204, 2))#画结果图用#写一个LossHistory类，保存loss和accclassLossHistory(keras.callbacks.Callback):defon_train_begin(self,logs={}):self.losses={'batch':[],'epoch':[]}self.accuracy={'batch':[],'epoch':[]}self.val_loss={'batch':[],'epoch':[]}self.val_acc={'batch':[],'epoch':[]}defon_batch_end(self,batch,logs={}):self.losses['batch'].append(logs.get('loss'))self.accuracy['batch'].append(logs.get('acc'))self.val_loss['batch'].append(logs.get('val_loss'))self.val_acc['batch'].append(logs.get('val_acc'))defon_epoch_end(self,batch,logs={}):self.losses['epoch'].append(logs.get('loss'))self.accuracy['epoch'].append(logs.get('acc'))self.val_loss['epoch'].append(logs.get('val_loss'))self.val_acc['epoch'].append(logs.get('val_acc'))defloss_plot(self,loss_type):iters=range(len(self.losses[loss_type]))plt.figure()# accplt.plot(iters,self.accuracy[loss_type],'r',label='train acc')# lossplt.plot(iters,self.losses[loss_type],'g',label='train loss')ifloss_type=='epoch':# val_accplt.plot(iters,self.val_acc[loss_type],'b',label='val acc')# val_lossplt.plot(iters,self.val_loss[loss_type],'k',label='val loss')plt.grid(True)plt.xlabel(loss_type)plt.ylabel('acc-loss')plt.legend(loc="upper right")plt.show()size1=140size2=116model=Sequential()model.add(TimeDistributed(Conv1D(filters=2,kernel_size=2,activation='relu'),input_shape=(size1,size2,1)))model.add(TimeDistributed(Flatten()))model.add(LSTM(16,return_sequences=True))model.add(Flatten())model.add(Dropout(0.8))model.add(Dense(2,activation='softmax'))model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['acc'])print(model.summary())_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= time_distributed_1 (TimeDist (None, 140, 115, 2) 6 _________________________________________________________________ time_distributed_2 (TimeDist (None, 140, 230) 0 _________________________________________________________________ lstm_1 (LSTM) (None, 140, 16) 15808 _________________________________________________________________ flatten_2 (Flatten) (None, 2240) 0 _________________________________________________________________ dropout_1 (Dropout) (None, 2240) 0 _________________________________________________________________ dense_1 (Dense) (None, 2) 4482 ================================================================= Total params: 20,296 Trainable params: 20,296 Non-trainable params: 0 _________________________________________________________________ None#创建一个实例historyhistory=LossHistory()X,y=data_all,label_all model.fit(X,y,batch_size=16,epochs=30,validation_split=0.3,callbacks=[history])#绘制acc-loss曲线history.loss_plot('epoch')Train on 574 samples, validate on 246 samples Epoch 1/30 574/574 [==============================] - 30s 53ms/step - loss: 0.7603 - acc: 0.5906 - val_loss: 0.6899 - val_acc: 0.6098 Epoch 2/30 574/574 [==============================] - 25s 44ms/step - loss: 0.6172 - acc: 0.6672 - val_loss: 0.6334 - val_acc: 0.6260 Epoch 3/30 574/574 [==============================] - 24s 42ms/step - loss: 0.5209 - acc: 0.7404 - val_loss: 0.5912 - val_acc: 0.6545 Epoch 4/30 574/574 [==============================] - 27s 47ms/step - loss: 0.4298 - acc: 0.8188 - val_loss: 0.5260 - val_acc: 0.7398 Epoch 5/30 574/574 [==============================] - 26s 46ms/step - loss: 0.3918 - acc: 0.8223 - val_loss: 0.4847 - val_acc: 0.7805 Epoch 6/30 574/574 [==============================] - 27s 47ms/step - loss: 0.3411 - acc: 0.8554 - val_loss: 0.4703 - val_acc: 0.8089 Epoch 7/30 574/574 [==============================] - 28s 48ms/step - loss: 0.3219 - acc: 0.8746 - val_loss: 0.4335 - val_acc: 0.8008 Epoch 8/30 574/574 [==============================] - 26s 46ms/step - loss: 0.2235 - acc: 0.9077 - val_loss: 0.4634 - val_acc: 0.7683 Epoch 9/30 574/574 [==============================] - 27s 47ms/step - loss: 0.2467 - acc: 0.8972 - val_loss: 0.4185 - val_acc: 0.8171 Epoch 10/30 574/574 [==============================] - 26s 46ms/step - loss: 0.1952 - acc: 0.9268 - val_loss: 0.3867 - val_acc: 0.8374 Epoch 11/30 574/574 [==============================] - 26s 46ms/step - loss: 0.2236 - acc: 0.9199 - val_loss: 0.3699 - val_acc: 0.8130 Epoch 12/30 574/574 [==============================] - 27s 46ms/step - loss: 0.1392 - acc: 0.9460 - val_loss: 0.3485 - val_acc: 0.8374 Epoch 13/30 574/574 [==============================] - 26s 46ms/step - loss: 0.1197 - acc: 0.9582 - val_loss: 0.3428 - val_acc: 0.8333 Epoch 14/30 574/574 [==============================] - 27s 47ms/step - loss: 0.0972 - acc: 0.9652 - val_loss: 0.3566 - val_acc: 0.8333 Epoch 15/30 574/574 [==============================] - 26s 46ms/step - loss: 0.0935 - acc: 0.9617 - val_loss: 0.3420 - val_acc: 0.8374 Epoch 16/30 574/574 [==============================] - 27s 47ms/step - loss: 0.0624 - acc: 0.9739 - val_loss: 0.3002 - val_acc: 0.8618 Epoch 17/30 574/574 [==============================] - 27s 46ms/step - loss: 0.0699 - acc: 0.9791 - val_loss: 0.3374 - val_acc: 0.8455 Epoch 18/30 574/574 [==============================] - 27s 47ms/step - loss: 0.0633 - acc: 0.9756 - val_loss: 0.2348 - val_acc: 0.8740 Epoch 19/30 574/574 [==============================] - 26s 45ms/step - loss: 0.0353 - acc: 0.9878 - val_loss: 0.2971 - val_acc: 0.8740 Epoch 20/30 574/574 [==============================] - 28s 48ms/step - loss: 0.0646 - acc: 0.9721 - val_loss: 0.2745 - val_acc: 0.8699 Epoch 21/30 574/574 [==============================] - 27s 48ms/step - loss: 0.0802 - acc: 0.9669 - val_loss: 0.3214 - val_acc: 0.8577 Epoch 22/30 574/574 [==============================] - 26s 46ms/step - loss: 0.0647 - acc: 0.9774 - val_loss: 0.3369 - val_acc: 0.8577 Epoch 23/30 574/574 [==============================] - 26s 46ms/step - loss: 0.0622 - acc: 0.9791 - val_loss: 0.3870 - val_acc: 0.8415 Epoch 24/30 574/574 [==============================] - 26s 45ms/step - loss: 0.0506 - acc: 0.9791 - val_loss: 0.2629 - val_acc: 0.8780 Epoch 25/30 574/574 [==============================] - 24s 42ms/step - loss: 0.0390 - acc: 0.9861 - val_loss: 0.3160 - val_acc: 0.8618 Epoch 26/30 574/574 [==============================] - 23s 41ms/step - loss: 0.0254 - acc: 0.9930 - val_loss: 0.4207 - val_acc: 0.8415 Epoch 27/30 574/574 [==============================] - 23s 40ms/step - loss: 0.0351 - acc: 0.9895 - val_loss: 0.3660 - val_acc: 0.8699 Epoch 28/30 574/574 [==============================] - 22s 39ms/step - loss: 0.0477 - acc: 0.9791 - val_loss: 0.4207 - val_acc: 0.8659 Epoch 29/30 574/574 [==============================] - 23s 40ms/step - loss: 0.0419 - acc: 0.9843 - val_loss: 0.4053 - val_acc: 0.8618 Epoch 30/30 574/574 [==============================] - 23s 39ms/step - loss: 0.0371 - acc: 0.9861 - val_loss: 0.4967 - val_acc: 0.8496#model.save('model_time_series_dataaugmentation.h5')label_pred=model.predict(data_test_all)label_pred[label_pred>0.5]=1label_pred[label_pred<0.5]=0fromcollectionsimportCounter x=((label_pred-label_test_all)[:,0])x=x.tolist()x.count(0)print(x.count(0))acc=(x.count(0))/204print(acc)156 0.7647058823529411#150/204 73.53%#LSTM层输出参数设置为16：156/204 76.47%
