如何为CNN架构填充序列？

如何解决如何为CNN架构填充序列？

我正在尝试使用CNN（卷积神经网络）使用Conv1D对时间序列进行回归。体系结构如下：

filters = 100

model_attn = Sequential()
model_attn.add(Conv1D(filters=filters,kernel_size=15,activation='relu',input_shape=(complete_inputs.shape[1],complete_inputs.shape[2])))
model_attn.add(MaxPooling1D(pool_size=2,strides=None))
model_attn.add(Conv1D(filters=filters,strides=None))
model_attn.add(Flatten())
model_attn.add(Dense(50,activation='relu'))
model_attn.add(Dense(1))
model_attn.compile(optimizer=optimizers.Adam(0.0001),loss ='mae')

我拥有的数据具有大量可变长度的时间序列的形式。我也在应用StandardScaler（）来标准化输入。

我面临的问题是，为了使输入的每个时间序列具有相同的长度以馈入CNN，我必须对输入进行填充。当我填充输入（其时间序列的长度各不相同）并且然后应用归一化时，零将变为浮点数，并且即使所有这些浮点都应表示为零（0），每个浮点值也会显着不同。我担心这可能是CNN巨大损失（损失，val_loss> 0.4）的原因，无论我对超参数进行什么更改。

（我无法在填充之前应用规范化，因为这会给我一个错误）

数据具有以下格式（显示了一个示例）：

array([0.,0.,1.0706892,1.0744922,1.0758405,1.0766355,1.0765879,1.0766382,1.0768414,1.0767081,1.0765858,1.0765196,1.0763972,1.0762045,1.0760512,1.0759902,1.0759126],dtype=float32)

缩放后，数据变为：

array([-0.0098773,-0.01396927,-0.01710961,-0.01975744,-0.02209057,-0.02420019,-0.02614048,-0.02794671,-0.02964341,-0.03124843,-0.03277523,-0.03563382,-0.0382806,-0.0407568,-0.04309184,-0.04530753,-0.04742062,-0.04944426,-0.05138901,-0.05326353,-0.05507497,-0.05682939,-0.05853189,-0.0601869,-0.06179822,-0.06336918,-0.06490272,-0.06640144,-0.06786764,-0.06930339,-0.07071052,-0.07209074,-0.07344555,-0.07477634,-0.07608435,-0.07737076,-0.07863662,-0.0798829,-0.08111052,-0.0823203,-0.08351303,-0.08468941,-0.08585013,-0.08699582,-0.08812705,-0.08924437,-0.0903483,-0.09143934,-0.09251794,-0.09358452,-0.09463949,-0.09568325,-0.09671614,-0.09773852,-0.0987507,-0.10025053,-0.10172912,-0.10366905,-0.10557488,-0.10744838,-0.1092912,-0.11110488,-0.11333311,-0.11552052,-0.11766941,-0.1197818,-0.12185962,-0.12390462,-0.12591837,-0.12790239,-0.129858,-0.13178651,-0.13368906,-0.13556679,-0.13742065,-0.13961521,-0.14177836,-0.14391151,-0.146016,-0.14809306,-0.1501438,-0.15216932,-0.15417059,-0.15614858,-0.15810414,-0.16035835,-0.16258444,-0.16478348,-0.1669566,-0.16910481,-0.17122902,-0.17333013,-0.17540897,-0.17746635,-0.179503,-0.18151961,-0.18351686,-0.18549539,-0.18745577,-0.18939863,-0.19132444,-0.19323374,-0.19512701,-0.19700477,-0.19886741,-0.20071536,-0.20254904,-0.20436884,-0.20617515,-0.20796828,-0.20974864,-0.2115165,-0.2132722,-0.21501584,-0.21699467,-0.2189588,-0.22090861,-0.22284448,-0.22476685,-0.226676,-0.2285723,-0.23045607,-0.23232761,-0.23418722,-0.23603524,-0.23787189,-0.23969747,-0.24151221,-0.24331637,-0.24511018,-0.24689364,-0.24866745,-0.25043166,-0.2521862,-0.2539317,-0.2556681,-0.25761104,-0.25954303,-0.2614643,-0.26337513,-0.2652757,-0.26716635,-0.26904663,-0.27091795,-0.2727799,-0.27463275,-0.27647632,-0.27831146,-0.28013793,-0.281956,-0.28376585,-0.28556734,-0.28736123,-0.2891473,-0.29132003,-0.29367775,-0.29602244,-0.29835477,-0.30067492,-0.3029831,-0.30527973,-0.30756497,-0.30983916,-0.31229043,-0.31472957,-0.317343,-0.32012805,-0.32308212,-0.32601935,-0.32948637,-0.33293125,-0.33653453,-0.34029344,-0.34402823,-0.347916,-0.35195422,-0.35614058,-0.36030012,-0.36460558,-0.36888412,-0.37313697,-0.37753382,-0.38190514,-0.38625202,-0.3905755,-0.3952066,-0.39981288,-0.4045587,-0.4094427,-0.41430196,-0.41913772,-0.42395112,-0.42874327,-0.43351525,-0.4387422,-0.44410446,-0.4497582,-0.45570102,-0.46177515,-0.4679805,-0.47416276,-0.48063117,-0.48738453,-0.49426895,-0.50113285,-0.50797826,-0.51480716,-0.5220752,-0.5293287,-0.5365699,-0.5438007,-0.5510232,-0.55838937,-0.565901,-0.5734096,-0.5812176,-0.5890266,-0.596989,-0.6051072,-0.613685,-0.6224249,-0.63117915,-0.6399503,-0.64874095,-0.6577059,-0.6666962,-0.67571455,-0.68491715,-0.6943084,-0.7037379,-0.71336436,-0.72319263,-0.73307097,-0.7430025,-0.75330853,-0.7636781,-0.77411455,-0.78462166,-0.79520303,-0.80635613,-0.81759876,-0.8294377,-0.84172213,-0.8546382,-0.86838096,-0.88280624,-0.897588,-0.9129222,-0.92883426,-0.94590706,-0.96363425,-0.9816685,-1.0002222,-1.0191268,-1.0384021,-1.0582769,-1.0785757,-1.0993239,-1.1205478,-1.1422764,-1.1645403,-1.1873723,-1.2108079,-1.2348853,-1.2596458,-1.2851342,-1.311399,-1.3387773,-1.3670616,-1.3963174,-1.4266162,-1.4580369,-1.491006,-1.5253086,-1.5610552,-1.5983696,-1.6373909,-1.6782749,-1.7211987,-1.7663625,-1.8139961,-1.8643622,-1.9177647,-1.9745562,-2.03515,-2.1000326,-2.1697834,-2.2450805,-2.3267937,-2.4159408,-2.5137994,-2.6219797,-2.7425473,-2.8781922,-3.032506,-3.212353,-3.4234576,-3.676436,-3.9877586,-4.3847356,-4.916755,-5.685551,-6.9478936,-9.667068,0.7664037,0.8944819,0.9429276,0.972402,0.97564405,0.9807707,0.98882246,0.9867822,0.9841371,0.9820615,0.9778804,0.9715033,0.96555257,0.9610835,0.95586026],dtype=float32)

有解决问题的方法吗？

编辑1：尽管我已将inverse_transform应用于缩放后的输入以获取原始输入，但CNN会识别出差异吗？

相同输入的

inverse_transform如下：

array([-3.82500585e-12,-6.33975728e-12,4.86057548e-12,5.84291122e-12,-3.65678798e-12,-7.03534193e-12,-2.00839432e-11,1.29974963e-11,-2.88795394e-11,3.08669236e-11,-1.98511346e-11,6.40846404e-11,-5.68959879e-11,1.49610324e-10,-8.36972922e-11,5.78726442e-11,2.94587202e-11,-9.54626100e-11,1.46498647e-11,1.31735204e-12,3.80215096e-11,-9.32821598e-11,-1.02480913e-10,9.24872123e-11,-1.94741001e-10,1.29327646e-10,2.05188963e-10,9.56670229e-11,1.42319462e-10,7.77692077e-11,-2.21269739e-10,-7.73603681e-11,-2.09277290e-10,-1.77569986e-10,1.89698715e-10,-1.72346012e-10,-5.98804728e-10,5.97441929e-10,-1.12701751e-10,1.23831112e-10,-3.82940658e-11,2.25948621e-10,-4.22097801e-10,-4.23188040e-10,-4.58075161e-10,-4.29729363e-10,1.51722628e-11,3.02445902e-10,1.44000228e-10,-2.06233763e-11,3.57047863e-10,3.83576615e-10,-3.20343724e-10,-2.94996028e-10,4.12376661e-10,1.87154858e-11,3.96114183e-11,1.57900554e-10,6.75029477e-11,-1.75980078e-10,-8.18575416e-11,-2.18953015e-11,-4.28820840e-10,3.64134306e-10,-3.55685092e-10,-3.42148171e-10,-6.85023149e-10,-5.14039922e-10,-2.07142276e-10,-1.72618558e-10,4.33545144e-10,3.73764630e-10,2.45481774e-10,-2.50623988e-09,4.42630321e-10,7.42441553e-10,-8.48374815e-10,4.90418428e-10,9.20692911e-10,5.92172533e-10,-6.45593412e-10,-7.53888840e-10,-7.12278708e-11,-8.05674416e-10,3.04717196e-10,5.20762933e-10,-2.71919665e-09,1.05351838e-09,5.64371827e-10,-2.34652231e-09,8.39289638e-10,-1.52522128e-09,-1.00300469e-10,-7.48619444e-10,4.13557744e-10,9.73932046e-10,-9.55034940e-10,-2.94360092e-10,1.52158719e-09,-1.37549738e-09,-1.66622338e-09,-3.79760834e-10,-4.34635383e-10,-4.57530042e-10,-1.04915743e-09,2.25312657e-10,1.21741506e-10,6.23607277e-10,9.34684108e-10,4.98595110e-10,-1.33879319e-09,-7.09734826e-10,7.52980345e-10,1.54484525e-09,1.57791535e-09,-9.04884709e-11,-1.83302740e-09,1.51904334e-09,-1.72618564e-09,-6.61401628e-11,1.76834092e-09,-1.14073617e-09,-1.19924473e-10,1.18834254e-09,-3.17254750e-10,1.04661363e-10,1.64914327e-09,-6.59584609e-10,3.73219511e-10,8.87441121e-10,1.23667576e-09,-8.79446183e-10,-1.89480676e-09,-2.89853830e-09,-1.63388014e-09,-1.72109793e-09,1.13165100e-09,-2.22405386e-10,-2.18989360e-09,-6.63582078e-10,-6.19973184e-10,-3.31645689e-09,1.02044828e-09,-2.49879006e-09,6.13431861e-10,2.10994400e-09,-1.56265223e-10,-9.91230298e-09,-1.19706434e-09,-1.42746470e-09,-1.82793969e-09,1.88971894e-11,-3.33535399e-09,-6.66634703e-09,-5.93081029e-10,6.06890538e-10,-2.73064393e-09,3.93206911e-10,-1.15563581e-09,3.15146975e-09,2.42538167e-09,-6.44757581e-09,-7.41714690e-09,3.42475226e-09,3.36951445e-09,-9.03285713e-09,-5.45111245e-09,-6.62128463e-10,-2.85056845e-09,2.67904010e-09,-1.51250201e-09,3.95605415e-09,5.75637493e-09,-1.88899230e-09,-9.44641432e-09,3.56212038e-09,-6.41777631e-10,-1.13455823e-09,8.15777135e-09,5.65316727e-09,7.29431537e-09,2.55838878e-10,6.90474233e-10,4.47863391e-09,3.82450072e-09,-1.76470683e-09,-7.01812564e-09,-3.60936325e-09,-6.45557074e-09,-6.56023236e-09,5.43657608e-09,5.52088686e-09,5.35371925e-09,-3.79106702e-09,-6.31747588e-09,2.36214870e-09,9.89922033e-10,-1.31989608e-09,1.30390609e-09,3.41748407e-09,-5.90755223e-09,-4.75482365e-09,-7.15767401e-09,-3.10350012e-09,6.51662324e-09,3.28375016e-09,2.29092079e-09,1.61847158e-08,1.31655273e-08,1.13906449e-08,1.81921802e-08,4.79261830e-09,-5.14585041e-10,1.09312976e-08,1.13935519e-08,1.11086402e-08,-8.55025206e-09,-8.57641691e-09,3.19217142e-09,-8.33220692e-09,1.19197665e-08,1.36234206e-08,-2.44209852e-09,-8.97761865e-09,-9.24508647e-09,1.43705865e-08,-1.16929995e-08,-2.40139686e-09,-6.85241197e-09,2.21823937e-09,2.56129606e-09,-1.03411235e-08,3.67186948e-09,-7.72749686e-09,-2.32290076e-09,1.03731033e-08,-9.31486088e-09,-1.06144062e-08,1.29663800e-08,-3.61663144e-09,-2.77061885e-09,-5.03246689e-09,5.50344303e-09,-3.77653064e-09,-2.72700995e-09,1.99438044e-09,6.62855271e-10,1.87460127e-08,8.40198133e-09,-2.32580799e-09,2.84504473e-08,-6.45411724e-09,1.45944448e-08,8.91365914e-09,-2.40430396e-08,-1.45130423e-08,-1.65190510e-08,4.16319645e-09,1.67923346e-08,-4.71557549e-09,-1.79552373e-08,-2.52931613e-08,-1.19197663e-09,-6.83787560e-09,1.06405720e-08,1.90309244e-08,-1.81180440e-08,-1.86646094e-08,7.29140837e-09,3.01192138e-09,-1.41350984e-08,1.36350495e-08,1.10475877e-08,-1.94728269e-08,2.68165667e-08,-2.88458342e-08,-6.86113388e-10,-2.10485620e-08,1.63969460e-08,2.59909037e-08,2.01124255e-08,-6.39015774e-09,-5.66741249e-08,7.72691564e-08,1.54956954e-08,2.57583235e-08,2.15137241e-08,-3.31427641e-09,-2.13974332e-08,-1.70656165e-08,-6.40178666e-09,7.01696266e-08,5.68078597e-08,-2.80841306e-09,-2.96714955e-08,5.96569727e-09,-2.45023877e-08,1.41118406e-08,-1.15069350e-08,2.51129126e-08,4.52718538e-08,3.16309889e-09,1.07068920e+00,1.07449222e+00,1.07584047e+00,1.07663548e+00,1.07658792e+00,1.07663822e+00,1.07684135e+00,1.07670808e+00,1.07658577e+00,1.07651961e+00,1.07639718e+00,1.07620454e+00,1.07605124e+00,1.07599020e+00,1.07591259e+00],dtype=float32)