企业官网建设流程全解析

网站开发软件开发,小说网站建设方案书ppt,昆明网站搜索优化,小程序制作价格目标#xff1a;利用切片实现AD诊断 数据划分#xff1a;143例用于训练#xff0c;62例用于验证#xff0c;51例用于测试 结果#xff1a; 训练30次后#xff0c;验证集准确度为80.65#xff05; 利用测试集数据进行测试#xff0c;51例数据可以正确识别出42例。 验证…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0.92ipynb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 moder encodingutf-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_historystore_history, silentsilent) 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 interactivityinteractivity, compilercompiler, resultresult) 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_fileslist(map(lambdax:ad_pathx,os.listdir(ad_path)))cn_fileslist(map(lambdax:cn_pathx,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_filesad_files cn_filescn_filesdata_1[]label_2[]forfileinad_files:datanib.load(file).get_data()data_1.append(cv2.resize(data,(121,121)))label_2.append([0,1])forfileincn_files:datanib.load(file).get_data()data_1.append(cv2.resize(data,(121,121)))label_2.append([1,0])data_1np.array(data_1)label_2np.array(label_2)data_1.shape(256, 121, 121, 121)data_2[]foriinrange(data_1.shape[0]):img1data_1[i,:,:,60]img2data_1[i,:,60,:]img3data_1[i,60,:,:]imgnp.stack((img1,img2,img3))data_2.append(img)data_2np.array(data_2)data_2.shape(256, 3, 121, 121)data_2data_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_2np.array(data_2)max_datanp.max(data_2)data_2data_2/max_datadata_2,label_2shuffle(data_2,label_2)data_2.shape(256, 121, 121, 3)size1121size2121channels3defbuild_model():inpInput(shape(size1,size2,channels))x1Conv2D(filters1,kernel_size(3,3),strides(1,1),activationrelu,input_shape(size1,size2,channels))(inp)x1Flatten()(x1)x1Dense(2,activationsoftmax)(x1)modelModel(inputinp,outputsx1)returnmodelinput_shapedata_2.shape[1:]output_shape2datadata_2 labellabel_2fromsklearn.metricsimportroc_curve,aucfromscipyimportinterp#定义n折交叉验证KFKFold(n_splits5)tprs[]aucs[]mean_fprnp.linspace(0,1,100)i0#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_testdata[train_index],data[test_index]Y_train,Y_testlabel[train_index],label[test_index]#建立模型(模型已经定义)modelbuild_model()#编译模型#定义优化器adamkeras.optimizers.Adam(lr0.00005,beta_10.9,beta_20.999,epsilon1e-08,decay0.0)model.compile(losscategorical_crossentropy,optimizeradam,metrics[acc])#训练模型model.fit(X_train,Y_train,batch_size32,validation_split0.3,epochs30)#利用model.predict获取测试集的预测值y_predmodel.predict(X_test,batch_size1)#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,thresholdsroc_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_aucauc(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,lw1,alpha0.3,labelROC fold %d(area%0.2f)%(i,roc_auc))i1#画对角线plt.plot([0,1],[0,1],linestyle--,lw2,colorr,labelLuck,alpha.8)mean_tprnp.mean(tprs,axis0)mean_tpr[-1]1.0mean_aucauc(mean_fpr,mean_tpr)#计算平均AUC值std_aucnp.std(tprs,axis0)plt.plot(mean_fpr,mean_tpr,colorb,labelrMean ROC (area%0.2f)%mean_auc,lw2,alpha.8)std_tprnp.std(tprs,axis0)tprs_uppernp.minimum(mean_tprstd_tpr,1)tprs_lowernp.maximum(mean_tpr-std_tpr,0)plt.fill_between(mean_tpr,tprs_lower,tprs_upper,colorgray,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(loclower 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(outputsTensor(de..., inputsTensor(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#[! pngy_predmodel.predict(X_test)y_pred[y_pred0.5]0y_pred[y_pred0.5]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?__bizMzU1MTkwNzIyOQmid2247486561idx1sn91df24bc64059f785856fc8f82945f58chksmfb8b7693ccfcff853936faccf0b42409c3dd7b2cef416fc623e051d4d81ee85d78702bd20325scene21#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 moder encodingutf-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_historystore_history, silentsilent) 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 interactivityinteractivity, compilercompiler, resultresult) 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datanp.load(AD_CN_102_154_LSTM_data_1.npy)labelnp.load(AD_CN_102_154_LSTM_label.npy)datanp.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_num205data_traindata[0:train_num,:,:]label_trainlabel[0:train_num,:]data_testdata[train_num:data.shape[0],:,:]label_testlabel[train_num:data.shape[0],:]#滤波变换示意图#非必需TR1.4plot_mun2TTimeSeries(data_train[plot_mun],sampling_intervalTR)FFilterAnalyzer(T,ub0.15,lb0.02)fig01plt.figure()ax01fig01.add_subplot(1,1,1)ax01.plot(F.data[plot_mun],labelunfiltered)ax01.plot(F.filtered_fourier.data[plot_mun],labelFourier)ax01.plot(F.iir.data[plot_mun],labelIIR)ax01.plot(F.filtered_boxcar.data[plot_mun],labelBoxcar 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 0x7fcf97d2f5f8data_train.shape(205, 140, 116)data_aug[]label_aug[]num_train205foriinrange(num_train):TTimeSeries(data_train[i],sampling_intervalTR)FFilterAnalyzer(T,ub0.15,lb0.02)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_augnp.array(data_aug)label_augnp.array(label_aug)data_aug.shape,label_aug.shape((615, 140, 116), (615, 2))data_allnp.concatenate((data_train,data_aug),axis0)label_allnp.concatenate((label_train,label_aug),axis0)data_allnp.expand_dims(data_all,axis3)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_test51foriinrange(num_test):TTimeSeries(data_test[i],sampling_intervalTR)FFilterAnalyzer(T,ub0.15,lb0.02)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_augnp.array(data_test_aug)label_test_augnp.array(label_test_aug)#拼接data_test_allnp.concatenate((data_test,data_test_aug),axis0)label_test_allnp.concatenate((label_test,label_test_aug),axis0)data_test_allnp.expand_dims(data_test_all,axis3)#打乱data_test_all,label_test_allshuffle(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):itersrange(len(self.losses[loss_type]))plt.figure()# accplt.plot(iters,self.accuracy[loss_type],r,labeltrain acc)# lossplt.plot(iters,self.losses[loss_type],g,labeltrain loss)ifloss_typeepoch:# val_accplt.plot(iters,self.val_acc[loss_type],b,labelval acc)# val_lossplt.plot(iters,self.val_loss[loss_type],k,labelval loss)plt.grid(True)plt.xlabel(loss_type)plt.ylabel(acc-loss)plt.legend(locupper right)plt.show()size1140size2116modelSequential()model.add(TimeDistributed(Conv1D(filters2,kernel_size2,activationrelu),input_shape(size1,size2,1)))model.add(TimeDistributed(Flatten()))model.add(LSTM(16,return_sequencesTrue))model.add(Flatten())model.add(Dropout(0.8))model.add(Dense(2,activationsoftmax))model.compile(losscategorical_crossentropy,optimizeradam,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historyhistoryLossHistory()X,ydata_all,label_all model.fit(X,y,batch_size16,epochs30,validation_split0.3,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_predmodel.predict(data_test_all)label_pred[label_pred0.5]1label_pred[label_pred0.5]0fromcollectionsimportCounter x((label_pred-label_test_all)[:,0])xx.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16156/204 76.47%