Wolfram Language

Neuronale Netze

Klassifizierung von Objekten

Trainieren Sie unter der Verwendung CIFAR-10-Datenbank beschrifteter Bilder ein Convolutional Neural Network zur Vorhersage der Klasse jedes Objekts.

Erstellen Sie Trainingsdaten.

In[1]:=
Click for copyable input
obj = ResourceObject["CIFAR-10"]; trainingData = ResourceData[obj, "TrainingData"]; RandomSample[trainingData, 5]
Out[1]=

Extrahieren Sie die einzelnen Klassen.

In[2]:=
Click for copyable input
classes = Union@Values[trainingData]
Out[2]=

Erzeugen Sie ein Convolutional Neural Network, das die Klasse eines gegebenen Bildes vorhersagt.

In[3]:=
Click for copyable input
lenet = NetChain[ {ConvolutionLayer[20, 5], Ramp, PoolingLayer[2, 2], ConvolutionLayer[50, 5], Ramp, PoolingLayer[2, 2], FlattenLayer[], 500, Ramp, 10, SoftmaxLayer[]}, "Output" -> NetDecoder[{"Class", classes}], "Input" -> NetEncoder[{"Image", {32, 32}}] ]
Out[3]=

Trainieren Sie das Netz mit den Trainingsdaten.

In[4]:=
Click for copyable input
trained = NetTrain[lenet, trainingData, MaxTrainingRounds -> 4]
Out[5]=

Erstellen Sie Vorhersagen für die wahrscheinlichsten Klassen einer Gruppe von Bildern.

In[6]:=
Click for copyable input
trained[{\!\(\* GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJylkglQU1kWhruma1qrHRQhiLIIKCKyiQKKSlS2gIiEAMoi+2ID4sYiChbt LqiIYqugKKDYYiubrIEIhMgWQkKWlxVM2EkAQUhIINtcwJpyqnqmempO/e/k 5VXd7/z3v9cg/IzXib/98MMPCStB8wpLto+PD7vorQr+HD2dEPPL6ajIQ6cT o36JircN/xF81P/2LL53f3hDrCym1JVAjRWc1rr+rsaRHtwYtX2cQfjaRxH1 0+eH2bLRPtlY3wK/b2GcKxXwZAKelM9dGO0T8znicY6EzxYO0ma5lJle8iSz exzCj0NdfCB61xitE/BJVW9p6FJ64wfAHyA0jZJxfFoH4H9hk2a5NPEAQzrE ko/1ycY/Syd5oMsES++CXsnEosRjLNEgJOqnCT9Tp9mkKRYRaHJJANJd8Tvg Q/Vly3zgf5jUAvzzgQ0GYYrTA0bM9zMkg8xF/xPchYk+6eSiFgB5nCPks4TD kHCAKuLRRFzabB8FjJjm9Ez3koGAQ+C/p/od1FC+zOd2Yga7m0fIrWBrAnrX BLMbpDT3mSbk0oSDDNEoa07AlEwwJZMsIKGA+XUYmhmkAb6QRwX+gWZ6e0BQ M5+pYCEYAcxTat8zMBX0pj/hgy1Mc8iAL+IBk/TZYYZwDBKNQ3PjdNBn+PTp IdrXAeosjzLL/cYHW5gFHeyFSwNTSDXvyIvhV9KbKpf5Q0Qsn9I6AY4Awgug rik2eaGfPsvpGV0c2v6FQxJyqfMjDPEXtmiSLZngzI2xxSPs2SHmFI8GyIA5 xaNM91NnBugiLkRCl5IbyqGmKga2GvB5+I+AL6C2TUEdE3T8OJP49TPEp3RQ MRVtZUUNr3IbCp5/Kv6dWFXKJTRPcyl8JoHV3jhEIUzzGHOjbNkwSzrKEk+w RXzWzDBTNMAg15dRP36gN1ezcLW9bejl+ymgtU/QO/mMrjFWD6sL115b0kto EbB76K2YyhfPinN/+/3B7aLzSUWXUmM9PfzhB1KDo16l3/lYlI8vK+5Bl9Db agQsomSYNTfCAuYBn4Gt4bSil8MZJX8SQB0COn6I0knBYTBlf0B43GgvJOAx x3rB0qqMtJT7F5MKw2Pi9znbqOvu0dh0UMcMaWl/3AkV4+X/a2xs3s2rdY+y hpurpSN0CqaC1ljJwALz9cA84AP/XyD8NIOEeV1Q+vQJq6ONB1HARYDwHa3o 2jdPck4HBEYedHyIOn4Ipr/tJ5j1av3tK3VM/2FoDDOzM95z+cS5gouXX4QG UbOuiTvrgHnAZ7bU9raiQfjg8vNp7V+ZRHZD3YPExMKb6YQaAP2jMPu3m8kX z4ZHBrt7um+xOLPL4TrymPkKFXMVTbNVWmardIxX6liobjpmBX8UEvlHWFR3 ctLU/Wvsq2ep6AoIU0kH/tvr+zsx/WBKYzWvurz2xo17ISF3gkKS3D3CXQ9H unuGuyFDXI5429ghtY3yAmOSD3sizCx3amy0VtNx0TONsoE/DQ1rTjnPSk0a TDo9khTXG+TViXKk1lQw0JVsbB21srg1L7s561Zr+jVS+k367XTanVuEq5fq zsYWRv3yOCg0yz840R4RtMXk6n6XNxEng4xNYw44JTi5ZaGOVkXHdp9P+Jya yI2JgFBuJMfdXfZWBDcEK9iXWFfOwHxoz3tUEhP9ISIMHRv1KTG2J+UsMy2p 99algd9uTTzLVrzIF+bmDN+73ZWSgA4P46SkfQqLyndwIiaeYyaeYp8Mo/gf xiHs6nftarSwwJqZEuG2nSjPN84unJMnSA1lFQ+uP3B1emXv2IDyavX2xPsh SSFe9BMBQ+fj+DcvDd+9MpR+cSQjWXA7afxW/OyV5LnzSQMBASRHR+IBW6yZ AXrLOvQWVbSJbpOVDWm/fR/Ksz887O4eZxcV2BtXx5YXjzI8EJnbthbZ2ryz tUFbWmJ3WLY72EFHkYMBfpyj3t2+SFqc/8DZkMGwoxwPRCfcGmu2tdlAD6On g9HXxZkY4Xdtpzja0lEurOO+vSGBnOgIbMjxZCdUNAJ1125fQWjwXVOTx/q6 eZu0ivS0S3UN3hsZF+zY/srGutrEAm+zi+PpxnRzwlntfL9x88t1uu82alXp bcBuMyTstSa7ODJRSLbfUdZxP3qoPyXcnxIWSDl54iXSrSDtavOr9zk+vg/h 9llr1uWugxXBVpeuVs1X08y0MH/o45HtYF9pu4/pimixNH6vqZmhDvP/WcV9 jdpjk609Lg60I65UXyQ5AEUP9mWGB9LDghgRgcyIAFZEMOtkTMFhl6qc+8yG pqrL1zL32j1S0y6EaZWsVa9UUStar5tjb/fp6gV8dGSbvUOVnt5bDdV8A/1j sA27VXVMVTRDNm5u8zxC90YR/bwpQT6MED9m0DFGgA/T14OFcmJ4IaiRQcXB gXwykfEJh6+rTt60OX21+nMNzZcwWKn6+hqdzTU7rZuOuDbs31NruLnBcFOb 7c5cC/MkV2TuxdvhDkiPdbpoF3uurzd0zLvHw6HTeW+rgy3uoG2rE7zjkAPR 5wguIuxJUCi+urq5soJMaE8xNkldsfK55trMDWo3VNfkq2uU6mpXm29tg+/G W23H7zZvhG/PtLV5cC6BjSMU3roXarSt3gVOR7pi4fvaD1h3udh1HkFg3d0+ uLrl2dk/sXO4BkcE7rTLvnzldW5Ofy8z29/niqZmgbr6fc0N8StW3VNb+1Jf 87n2WrSpIc7UkGxv1XxoT7aX56OrvxIxjQ9S0xL3H6xwgXciD2EcnRvd3d4h XDN27I7VNfGF6Xlr6h833Bm8y9kH7nrh1BlMdbVUIn57Ky3Pw+OBruET2JZc daOX2ltKjU0K9bTLTQxxO8w69+8sO2jVcDdzhAPNDHDLnz5LRHg8Qzg+PWif YGAcDNM+tEbLdYORl8X+OA/fh8mpdYXve5rxDBL1M5sjEc0pFcrCyyk50dGp cMc4XaMYNa2EdTrXdA0eGhm9tTRH77Bs2mOdY7G1JCOzC4vhEdp60OgAK8cA LX3U+o0eeubRcMT1qJOv7z1sqa7lMWjCLwKpRCqXyRWKBaVSqQC/csWjxIQL vn6hdgcRBoa716jvXaXq8LOqh4pG8FqdOM3NiXrbgtfrn/P0y7yQWvu8oOD6 bXcT21PO7o8vpKFfl7DxxOnRMalIpJRKlIp5hUIqlwMkaAsKxSIc+H+blXn3 VNxZ9yMBVntd9U3sYBttVDS3/7zeeIXW1p82GP19veGPGuZrNpzy9E8Lj4tD +tU9fyVg9cqFQqVSrlyqRSZ4FArlnxUDW9dRXlyfn/P2TkZ2fOKlwODTSM8o V/dIN6/TPkGXwk9ejo1PiTlzIyn14bWM1tqPilmxUiZXyqRS2YJMLlP+B+y/ appHHud0j7MJYxBhmITv62hhtGCgT82srq4hBn1qaFA0OSmc+iqcmpkXSZQy BRBIGASxAAL+7+ilkk4PiKd4kq8D89PD8plx5dyUUjylnJ9VysRK+VKq8nnF Uqzf+lIuMrAQnOBf4CslX2TzkzL5tEIGjkmsWBArpBK5fF4mX5ArpIpF0r9h 5EtS/EX44gKxXCaSyefAoS8f1XKk8u9Q39f/QF4ucGXFQtm8SCZbkH9Xsu/0 /9Q/Aart9CQ= "], {{0, 32}, {32, 0}}, {0, 255}, ColorFunction->RGBColor], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{32, 32}, PlotRange->{{0, 32}, {0, 32}}]\), \!\(\* GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJyNVnlUU2cW78z8MdPO/GGt59RacStgoGxhk1VJ2JKwE0A22QwJARJCWEKA rGYhCUnIRthRFnGhjtqKom2ncqptrZ6q7VRrxVotQhGRVZK3ZV6IaJ1Oz5l7 vrfkvZvfu/d+9/t+v+1FFanUP7/22mtVf0NPqYUcHJtdyCWvQ3+kM6voNGYx hcisLqYVs4OK/oI+3Pb8sN/bbDYIgmAYhhwGQhAAgSA6VgDrMmC1WK0WAARA ED3Qm2cAuLL62u4JOhwhAB32v/3GYBj1gGx/ZIgNdYCBhYXZCcCyACMQgNhA xIbA9oG+XfOCYBts9109v3zxX2AIsnpCIAQBIRgNCwatS/OTX4+dONwh+f76 pyD49BmwaLEuw5YVBABsqz5rSTiGvQAwYkEQ6yoMGooD9hWzlwcAEMiyvPDw s9EOcRWJmubRWJF09dLhq199cPXz4999efr+rcuTP98CLYs2NCcYRFFskA22 2mAAhOFFGFlCEOCPEoFBEAGeLc6Mnz+pbBbg64pd9ie8VZzipOXHaPgEvYCk 5kQqOPENjJTTRzsWZn6yLD94dP/bmUf3wKUF2LIIQk9A6Cmaxe/wV9OBITSk pemfD7fxKgvcTQd8TSJvWYVrS52XscHb1Oijrd4pY2xtYnszcjzSojFKXv5w X61amNPUkHvp3MEfbly4du3swuIvCLKC2CfpZVUQtHgraI5zloXvx07ragoD qambTIKAdrGvhLmZV7JOVPqmjLkJBZcw3xOx3IVsLLccm5fwbmm6WyMtvITs KWeTDKJMLjP+X6ODMLRssT5Dp3Ct/mgZAWQF+unOxyMnGTrZbikHqxeF6XjB zTVeItomKWOjoWGngLpZUurcVPl+XTGmvtTDLCcqqvHFJGcKcUdG2DucfT4q Nr48y5tTmvzjd1eslkW0k9eyQLt0eXb6+2NDTJMxSC5x6evCD/cltTaFi8td FAxnXS3mkCzYxPXTc3x0HF8dJ0TLwen5CRpOQj5ua0WKe22WT32eV0OBFyvL Yy/eWcAqvH3jCgJb1/DR6+PPLxq72pOHBsndnaThIXKPOUKvwBrFnoZ6TBPL Scd17RR7q2tcG/dv4WY75+Pezo7aws71K47dUUl252R58wp8ypK2lia7FpPc 4wN28Cqpj399+GIWlubvosEP9GQZVOnDA8xeM1kt9WnTerapMAaxS3P9di3f ubvZu1sdoG0IYKVsS/V/s6owWMTeIykLrM312Bf5rrQsvDbXL9H/bZLHhngf p93Y9z4Y7kcXtQP/0f3rxwcYH/RXsPaHqUVxndo4vdRPJ3HVN2H62vH9bbEK vmdfO+70MNmsiqUQnWjxLkd66a36RDU/SMkNL0raruelNdJIezCbfdb/NcFn S2KUT3V96dT0pAN/9td77Xoahx5bX+6vlwa0iLDdquAeZWBbc8iFEcbF841m XWqHKX5oKL3NENdI9W+k7jKb0kfOl547md9rTKHsdaFl+qdFentu3OC1/vXc SO/8LFw+Pf3e/XH7mrKBKwj48cdn88ghwkoPDd+7Uxk6ZIoZNEY2Cz1M+tjP PpOdPy88c6p0oDfZ2BRVmevWWBJaV4FrkiV8eLRUWR+djt+UQ8JEYN+J9N2Y FonJIPrGRvpVcRhTU5Or3WNZsQFPZh53qmnSajeDJOBoO+l4Z1ybYpde7mc2 xl0cU164IPvkXH2vOUUl3FOQtEXAxKvEOZXlOC4rhEJ2oad5leUEE8O2dTbT GIW4yDBMUjy+p6dzeXl5delaQOTJ5L3L3bJkg9C3z4g/0hF7tJPQ0RzSZcCN nKYdOVzG4xIPtlNEDbvrawLpeW48Nk6nKdJoCorz3POTnalpPvsz/BmUaI2Y lh4flEgIk4oFd+7cgeHn6+vRxBdnj1Xran149K0asb+Ui1HyPVoVYX0dxMFD iWpFRDnNs1lC4FQFKWTEenZgBd1PKCK3tpdTCnzZtAh+RVJxzu46dvb+3MS9 qQSl/MCN6zcAAFrbg4BLY+ZufZSR75kX//cGlqdK5CviuAmrsW06gka5S68K 0CtCukzxrboMuZgk5+NkgiiVMqutk13NTpY2FDAK4tMTQjMzCPSS/d1d7XfH 71qtALpZP4/eMnfmFM+kw+rlAdI6PKccq5aE8qq9ucxAQS2urSVBI/RuUwVr m3B1leFcRphJRpTUhUj48Xl5YcFBLrsD3cJ83NISiEIh/9OxsenZJxaUzmAA drAAujUsTp88wdG2eCiaAge7SlukiXJ+CJeFldfHkQnvc8qi9EKcUbKHuu+9 nFRXIQuv5UWwCrZX03fFRu184/U/OW/ZmBQb0240P5yYWkY5FYKs9l3eCqNE s7pLr1imRs4JDa3hLbpdxhZCV2titznWqCLIuYkR2B1Rgc5SLqG+0o9Jc2dQ /RWiBK0ssqHSq5LimxTp6rxlHQ4XUM1hfHP9i4cPbn9z7fLSwjwEokyG8ibk wLeCj09/JJbIggcHSO2mmA4zqctM6GlNryuL9nd2cn57HaskUiogtKjI7TqK WZuvkUc3VnlIOKGUNExM0Oa8TOI/T/SfPXPw+ICiv0f1zdefAxarnaYdHIn2 Pzx/brRFqYxtNUS1GxMHenMPdWb1d1Ep2eHbNqx3Wr8uNgIjEWQe6W9UiSlN Ddnm5jR5o3+nNvpAFTYdtyF5j5eQVSbn5rc2FyrltIPdmke/PIDslPKCfq1X vjpmNuV3mdNUB2K6jfkHO2hyUQ4R7xni67cnKCQmwp9ZmtZYV0gvTOHQMxtZ MQp++GBHglbgnxe7juTzbug2p6IErE5KFgsyteq6r69cAtH2gZEXHxj/8Uut iqKWJbNLAqk5vhW0qESib1jQ+9TCIrPB3GE2tpnUrUZlX5f5/KljpuYqQS3B 0Jyok+Kr8nfuDdse7PRWHgnLr46r52QoZNU3r19D64MqoBccvDw/2dPGE9bF MQtD/V3/kRSNZdDz6CWU4eHh2dmn8/Pzc3NP5+ZnFxfmAav13t1vDVpuRRmh lhVVVxqeH+ceuO2NTGJQUW5sXlYUh00f+fDMs2UL9Jv4Ueodv3W5SZgvqklN iNiJD8KcONZ3+/a/n87NQgjkKCQ6WWhDW9HegJBbt27qWqTM8sxyKpFFiYrb 7UzEB6WRk1Uy/sipkyeGT01MTK3JqucEBgLLIx8dPdgpVEoKCnKCDRrWzasj K89+RdUgaBdPCLpeUMoGV1UUAACTkxNjY5+0mtRcDiM3K7mwYJ/ZbP7p3rjV YnkyM7u0tPxcljjCX52MmZnHH314qN1cIxbsNaipcl7e0KDm4cQPsF1E2b3Q MOw5w3aphl5RRTozM3Pp0uW+vr7R0dHp6WmHjkXW7KWsQiFAu8p7cP/W2TO9 TTKqmJ/dUE0uKozsH9IuLj1+jvwbc8hh9MZqtc7NzaFbMWhPDnbo5BdfeUVi oR+CgOmp8YFDyuyMYAaNJBYXagw1N7+97FCn/1PyOer8O7RX7IWototw69LN 6xfLS9LTE8OrqrJULbVnR08sLs6jcaJlfynj/z9Dwf8DjJrXkg== "], {{0, 32}, { 32, 0}}, {0, 255}, ColorFunction->RGBColor], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{32, 32}, PlotRange->{{0, 32}, {0, 32}}]\), \!\(\* GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJw1k3tUE2fexyEXZNuqba3Yi6h46WohISQhIZCQAHI1gIjc1GrxWrcWXVvX 2rracpN7uORCyGQyk8xkJpkk5B5ynYQShCCKitbubnfb49lz9j2n57znvH+8 f+9gd8/5zPc8M398fs/veX6Tc/bqsYu0tLS0LzKpONZ5s+z69c5bzW9SLy1d X1y+1HXhfG3XlxcuXbhedJZOfdzzn2djDTljKjwwYnLfg5x94GyP3k7R/Yoe 0NEN2rtBogckegGiDyD6QWLAYB+E7MNG5yjiUZh9E3hgyjqntoe0s1HATRq8 SWNgGYs8tJKPZxeee5f/Mo54h2Bnv8HRpyd6dJb/AFg30L8CtPTpLX06vB/A 7+ktgwbrIEQMw45Rk1Nh9kziPqXFr7bNTdtDOmdU70nA/kVzOIXHVh3zjz33 fxhDfIMbfnu/3tqrw3t1WM9/2dDqLf16rB/ABgBsUI8NgdiwAR+BLGNG27hp dgJ1KTGPyuKdts1p7UHdbBh0k0b/91h4yRpbsS+suZefK1DvEGQfoDYJmHt1 SK8O3UitqW8GuQeYN9CjAwA6qEOHAHQENI8azGMGTAFZxo22KcShwlxq3DNN +LW2wIw9CLoisI80h5LW6LIj8dCbfDyJecaMxIgBG9Cj/QDlfIXONEABIK/S NAggQzpkFDQrIGwcxikUMOUnJhG7CnNqLJ5pq3/GFgAcAcgVMvtjttC8h1yM fr/64MGzV37biAEfotrXmynP0CvngM44qDNSeW/DbxwBTWMQMm5Ef2PCiE0a rVMmQoXaNZhrxuLR29xGp4fw+qPxxOpS8seHSy/XH/3604sxzDdsdAwaiHug 9Z4eGwTMg3qUaqFfaxzQmgZ1yCBgGgHgCRDSwLAWhmeMRh1iglDEjKOYFbMQ OG61Wgm702kPh3xLifD6UuKXtfu//vDg//72+P9/ea6zODWIRQkhEyA8ZYA1 sFEDQWq9QQNCWhCeASEAMphMIIGBYTcWc5nnfZbknG0pZE+FbCth+0rYlgrN Pgi710jPi4XAz6nwP1fJ/1lb+PXp8v++2CiRCHoibsJvM7ktoJ+AQg44YAF8 uC5kM8TscJQwJJzwfY8x5YXWAsbnQdP6nPHZnHE9bH4atTwnifWodT1mexF3 /m3e/Y8F78tF38uluZfL0Zcr8/98lPzXeupH0vM8ZF+fszwJoGs++IkXeuo1 PPLoH7mBx26QWj/1Q0/80EMPeN8+nXLqVlzA8uzMkgtc9iErAXMqYF72Iyt+ 9FHQsh6xP487n5KuJ3Hf+nzoxSL504Pks5DjsQ9f8yCrLmhlVv/AoXvo1K84 9SmHbsWhW3XqH7gMy04w6TAk7CDpAOOzUMwOxmxg3GFMuJCEC427TAk3suDB 7vvwVNCeirhWSd+jRPBpMvbD8vcpL744Cy/YQNKijZjVYVQVQVVRVBNB1VFU TWLauBWIW3UUJDETcwBRmz5sBSJWIEboSTtEOhA/NoPrFKQTm3eji35LKji7 GnWvUS0shH9YTiTdWMIGkRYgjGnnEI0XVnoME3PQRNA4GTIpqRJxbDqOaeKY MoYrIxZ1GNeEsOkIro1ZdFSJhNOE6xX3/vzHeY9l0Yc9CFqeRGefJzx/WQz8 tBz5eXV+3omShCGK64LodMCo9sEqn2EqCE1EjFNRkzKGKElkkkQnSEQRRRVh 80QEU1JtxvDpuFUbt+uTHkQ72XPrxqX7c5aU3/woiD+LEj/GZ/++6Pt5ee7l amxh1kSdAInPxMzaKKKJmNRhozIMT4Th8TA0HoEnYuhUDB0nzeMkPh7DxuP4 VBxXxXF1gpiedwBJN9x99+rVa5987zEuOsEVN7zmR5+FrX9NOP++4PklFUq5 jElCN4+p49RWjZMxSmicmNOPBPUjUXjcqenX99/wGUaCiMKuuRtFR0mzgmon jk2FkDE/PEoS2kuX20+fawriqkd+ZC1gfhqyPIsQL0jHXxc8/1gKLjvApEW7 YNYk4KmYYTxiUIShMYeqGxn+Guj709eXOpori+9cOXf7yplbV9pNyjsxm5q0 KuOEGhy51X2t0zY9fKypUloumOr90msYdRvGXYbxIKqK4ZpFJ0RdxIJdH8c1 JKqJwpNhgyIIjM4Bo37d0Kx2EFP199z6TMA5WHAgRyrKLyrOlR+RDPXfQIEB VHevtly4f9e7Ym7uvpzst99+M2/fTkHuHkFeTrU4f3rgKy88Rg0kddcxOzU5 yqBpwgsPeQ1DfnDMox0lNCOzeoULUiDqvu4vLyq+6/r0Ukf2gT2b39l6kL2/ srb4eGvVB9nb6Zm0Awf37N67s1hU2Hmy5frn57+6dmHoztWYA1yNWNei9vV5 bwyfCZqmqIH0QiOEfnBG2XP7dtelz88O9N4kZoac2iHXzLAPGvj62tnNb25N o9HSMumMLYy3sjbveHfbpi2Zuw9kt7fJYe2o32qIus2k15ycwxcDWCpoWY3Y 1+KeCKrxm5QOcFI19O2NG5fbLrSVn6wXtcobP26+e+tzcLzHMNlHaPox1UCt TMRhH8zNO/C7Nzbt3btr+ztvb9m2tUCYrxi9G7DOBBB1AFUF8OmwDUi44KQP XQoSK1GXA1ZoprpvfnO1qfPk4fZjsvajkvajxW3Npa2N9aeaTl4+efoPp25/ dQWe6lX2XQfH/jz87RdtR6vv3L5RWiHhy4rO//H8V3euTYzcdUBTIXwmYgOo w4k7oXmPKRmwLoWdXTcvN59vLT/RUHKiqbiDMjeUtNRLjtdLWuSyjoaKU42l rTWVrXWfdp1RK74xq78zTH430HvjYtc5+cct9Z3tTedPNp/rOPPZmZvfXNOp hnB40oEq4y54yYss+4mVsKe0tU7S0SBqr+c313CPVglb6opajhS3yEta5eLW ellHo7TtSHHbEVlb7fHTjWcvnTh94YT842ZZO1W6qfqTtppzp+QXTzf+4czx K5+cv3bx6q3Pb969NqnsMxunbKjabddvP7Rzt+Cj3UV5O7kH32Pt/72Yl1dW xK2TFdTJ2NUSXq00V1qYWynmHpGKGktLj5eJj5eL26qLqb+isaLsRENlZ2vV +ZO1n55p+OyT5iudrVc6O66ea/n01OWvu/7UffPb4e483q6DnOwDrA/2/v69 nTnbdua8s+/Q+4cK9n7E278vL/tAXvZ7u97atmtb1r6sd/dnZR/c9f6HO/fn f7jnYHaVXCys4BbVCOs+bqg5UX+4tbrmZF1ZU5mwSpgv41SfqC0/XnHsYktD 44d1dfuqqnIqKnJk0l0SyQdSaXapeJdUspvKEtH7JaIPCgU7Obz3uNwd+ezt bG5WAW8HtyDrWBNfVn6gqGSPvFlQJWdX1eYeOcqRN7KlZXvEZTkVdbkVcnZF Q4GoNF1QnM4XpvFEDG4RnSei80VMoXCTUPAKYaZQ9FpR0dZCwZZCwRsC4eu8 ks188RvCki1FxVtLxNtKS3dIyrNkh7dXV2ZVVW+vq8uqP7JDfuTdhobsjhMf njp9qKycKZYwRCK6oCiDL2Bw+TReIZ3Hp3O56QUUBek8Do3LphWw0tl5aSxW ei6blstOz89nFOQzuQUZAn4mX5ghKM4QipgCEV1UQqdspVJmWUVmdV1GdUNm aTGzuJAp5DL5BUweh8HNp1NwKAMrjYKdl87Kpf9Gbh7tECv9Nz5ipeexaWwO LZ9L5/LofB5dKKSLRIySErpEwpDKGLIyenklraJuk1TIFPMzijhMIZspYDMK WYyNZKfzWWlU8lg0DouRn8fIZzHycmkf5dHz2IxcFp3FpnEKaDw+g1/IKCpk iAsZ0iJmWQmzXMw4LGVWlTEry+jVlZuqa35XJ8usLNlULswo52WWczOl+Uwp hynhMCT5dGlBhpTDkObTS9k0CYsmZtFFucwSVoaYvamUk1HGZRwWZFSJNtUU Z8olrzXIXm8se/3o4deaa95oqdvcKt/S3vBWx9G3/g145Pw2 "], {{0, 32}, {32, 0}}, {0, 255}, ColorFunction->RGBColor], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{32, 32}, PlotRange->{{0, 32}, {0, 32}}]\), \!\(\* GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJyllnlMVEcYwE37T/8xaZr0SIxWghgjlBKbttQWKdUieBAB8eCSJd4cBtAi SMDaUkWstVHEArZQaMGjtlJARVkFFBSoRcFlUTTh3MXl2N333pxvod9DsEtq ccHJ7OTtezO/75vvmrEL3+G3+aVp06bFvQKDnyreIzZWleD/KvwJiI7btiV6 00bv6J2btmyKdQ1/GV7OHv0pz5RSSZIQNBgkIkl0rBMBC2ZkNiMkICogpkyS lLnSZJrFYqH/NjbWOSEwSEyWGIcnRgijU2qyLJPxDWMMcinFg/1dWk1Dq6bR PNjHCYYPZPJtPB/YCEZZ5j3dHYfTkgN8PHyXeeRlHePITMmL8jEWKZO6u9tr aqtTUhLed5493+41u7emuy2YX1VewhnFk9+CFZ8igjmT2tq0ufl5fr7Lnee8 4ThruqPd6/Pt3owOX6vvaaeKCIoJtV3MUz6sEsAb2EywqB8wpR9I9fzYyd1l hovj7K1bVEGr3EuKC2QLR4gjIiObJVjx2QhfIFhCfKjqSmnIyoV+n84LCfYv U1eEBq5MSYpgFL4yRDiiU9FffMIHP8hDXS11x2J8ftuvenD78vXaytU+7qtX LGysU1u44gRQZgp8iXKORXhAlOsby8u/cL211+1WXsKeiAB/97lL3pu5OzJI 367hDEQA36Y9WPMRYRyMA7pRqq8tup/kqImbcWy9fehnc4I97Ncscli92Ck/ 64Bo7seYT4EPnREoEpxSYqwrHDy4oG+fQ3aoQ7i3U7zKa/sq10BPp4gw77u3 a2U+bGOoWvEJYkgZicyY2Nd8rj9niXTcpTLJOXalfVzo8sStvoeSVPER/rkn 0kfcJEoEIaV0wIbRc/mgvsQFCeZjC2fm/u7Kx2WhUpFrX96HZ3c7b1w2d/O6 Twqzkr//entEiFdLw2VGBwUiilxZDl77v+y2yi+MmIjA9MSixKGoETTp+HqI +YL3/dNex3e7BHrNjAh0jQ52C/Z2St+j6u3SQJwKlI7wJWIDHyJoxGtQMUXG dLj/mtSaIdXvNNZuay5Vpe5wW7f47aDP54YunRe24p0rxbkYDYhKyYIFNthH QT/JS4opwkyQUK+x86bYeprczxEeZDZdPXI0eUOw57v+i+zXLrbPTNvZp3uI sIQnTAZrPoeZSlhgRCHXIAuY0aB72Hit416J7lGxsaOirf7C4T2x6zw/WPbR rJgwr+YGNVbyZaLSPY4PeTXKZ5DLsAuCBG3TX2Wlv1y49KO6LEf9R2FBRmZC 5OaApe/5uDkc3BujbbkjiFCy0Ij5n5ER4+yDR2couQAHDEGiqR8NGjraWmuq r54/nZdx6JuC7BPFpwqKcjOO7I+PjQhO/XLXrRvl5kG9IgFDEI0BnsW3bk/U Meg6ux5omMnMBPxIey8747v6G1eR0WDu79F1ttRUluxNjIqLCisq+KGvT0eh 8v3H0RPwKWOm/t5z+Se1f9cjk7FKfTn/p+zmxoYhKFSSkeEB0aRvbqw9cuir 0GDfo8dT2zs1YNeR1cwWPgiQiZiXefjXkxnV6ksXS4vLSop7ujpkBpaDQm2C zoh5oE939szPQaHeKfti9L3tjDElwp/PB/uz4SGmLjm1K3JDxcXf6+tqa2pq KH0qH6IMrixQKOCOIdXcvJSWnqzV3uWcUxv0H/E2H7bQ8vMFIQFLzhRmVV5T P35sUN6Peo8+7UqOUWQ0DQiCGRSglNrAB1fh4SFa/mfReh/31KSoe013KNTv 0SijY+FGR19CwNHRom2Fn4APWSAyC22+27BDFfBt4naDrouyCTKVPjPJJtQf DltuMOhSEyPjVD51NdWcy5O9okxkfywjYhGROTcrLTFqzY2qign1nywfGpeI TDhraqy+eC5H1w33H2btuxfjKxEIxw0EnygOiANdGIk2nrm28qkJTkyBcgTB IQlKESOTviL+A/g9nZk= "], {{0, 32}, {32, 0}}, {0, 255}, ColorFunction->RGBColor], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{32, 32}, PlotRange->{{0, 32}, {0, 32}}]\), \!\(\* GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJxFVQk0Ve0aNs8yz/N4ijJn/GWIkCIziRCODOFQIeGXWWSex4OQIQ2/oZAk lELKnByd+Zx95mNIde/d/v/eddd69rvevdfaz/O+z/fstVVCYt3D2VhYWBJ4 wOIefNsGBgtO9hAGb7xuJFyH3ggLdbqRGAYNg5mGsIMPlf97HfX03/+mHP6i /vxN+fmb/OM36eBfeOZvDP0Xiv4LzfgNAkn7iaAdHoF6+JW8v0phVrS2qmiq Oft5z3xDfcIx5tGUBQx1Hkubx9PnsNR3aNIsEphGECe/A59wFJCZuHcA7P8A 9g+Iez/wzEMM9QeKfICkHKDAhnrU7FD2EWQQe1ukvWVg/9WLoZqMeBcH6+HZ j5/xtHkUsIgmLWLJC1jyRwzwAQ3MIQnvdgjTSGAJR6L9+hdITjo4BCtx7xDH +AHyo8n7KNIuiryLJu9hqPsYGqi1h6LsIUi7Wzjm6sz0zqtnNdnps0sry3jq Egb4giUt4ymf8eQlLAnkXEQT51HEOSxplfj/+fG7+3jmPp5xgKUdYqmHWMou hsxAEWgogI6mgFoMFImOAGhrGMLK2kpHY1Xy7dgNDHYZR1nGklZw5FUCZZlA +YInH1UcCRRawFM2SVTa4S/S7gGwu49lHqDIlK0dxJdNxNLazqeV1Y/LK9+2 ASSZ+R1gooh0JI4ManUPjfgFBZ3QOq6gIvf45dg6gb6CA9aJpA2AsgHQ14n/ gLZ6pEX5RqZRD34CzH0iYw9g7iLwxKzCYmhwoP952wt25sZmxqFQ2PQacoe4 h8AztrH070RmSlYumAo2NlawpuTkbxDpy1jiGoG4QSRvAoz/4UhiBU/dJtMo +4cgOYG+S2fQ3nxY0DWzttZWynA9ddVExu6UvKIwr9NFt4XlrW3C7jqWuYoE LgeHsLCycHNxsbCzZhSWbOJpq2jCOo6wSSB9JdC3iIxtEvMbifn170W+kxkg P0iOpzFpDPrg6GtTc+vAs0Y1QaY3LRUCDRQDTORlOFnjY2I3keDrexMzC7p6 uuDkHGzsnDx8de09WzjaOoqwgcF/xQJbOOo2gbED7O4AzC0CbZNARx/x/yTQ 97AUJpG2O/PxkzYEYqYscdVIzllZzJCfI0BP8pKupqa0WH1j29IG2u9KkIyU uI7WcU52Dml55dG3H7cwlDUkfgON30QTNzEUUA5BYCDARXCUTRwNDdApP36B mcHRDkjUvQ9rmzr6BieEeBzVxXWFuV2PS951VC8OsXM7rfKHvkFUNAxyXPW8 vaW9tQU7O7url+/Gd+wGkrCOJm2AwJDWsSAn9StoO3iyGOAbDswegwp+VjQa gUbBk5g7RFKQzyVTeSELVXGICJ/bSYmGEJP+FM+aaB+ItKiRgU5cuH9qYpSP p6uHp7v9+QtLWzvrGGADA6yhQYsABJH2DU8F6xYW2D7ip4L8DOYvgMQgEMko wh4CjU665uGko2BrrO3hcNZWQzzdGVIWbH/DyVpFRtTewdbXxbm0IIfJpExO TsgrKmbeL61ohpdUVgyOjb559z75bprn5YCW7r5tHGkbQ9zCHvEjsDgUgYIF mDjq/gYScfmivZWWEiwqpDAv10xT0c9A3k5NSp2PR46b9ayJQQw0bGX5M41G efLksaKivICQqJSYKERG+IKViaeLk7ycFBsHm5aewYs3M9/xlC0sBUVi3CtO LKkvaO1pG387+m51wcPHz+GMRUtT9fDL8ZPqqurHuCS5OPRE+VzkBIKM1YM8 naprK6Nirj/saMvNypQUEVYV5HGESJrL8esqSFqZ6YsIC7Kwcabey8GQ6P/M /1d/3kN4WlV5/P28yOrq9MzshISkiKcjvfDOdllpKX5WFjleTm+ITKa1Yr63 kY6i9DEhAfuLNk/6egnI77k3w82FuUL0FNy0BCD8Ag5mOsZGuiysvMHQiO8A GcwSksBAfh7YnOtYnWlYniifHsga789+2pk50JmdngLlFzzGxcKmI8ILs9C4 +Yf8TSftP9RkBDi5jmuppyVCn3eWdeRAI3QkA1UFIi3ETnFzWEOUHc6YsbDy eAZe/oLaXkKitnDE1bdtS2OVK2MlG69LN6cq1qeqlierNqYqi9ICuXj4uFi4 DcT57JVEtIS4TkoJpUYGmZsbCguwR9iq3PfWTnPUTDur6iHFEWkiZiMucOoY n7K4MOiPur5mXNatuJyUgrry7Q8dX15XL7x88HH0wdxo8dzL4tnhwsXRktKM a8KCfJysnGKcbNIC3OI83CqS0pWlReZWphL8XCmO6gUOytbHOH10ZR1khfx1 xB2VxFV4OGRFBdi4OEXkBfTP6pg4G3uHeiEX29dnGz+9qV56U7n0qnj+Rf7c 4L13Q/eLU8PUxfi4WY9+iwJ83BBxIVMNVdiNaGUFteOyEEslQRM5XhEWbg0J QU0h4T8URME1ZQV5Thue4ODm0DWFRN0ODIx0jU8K35pp2JyqXRkvXRwrWBzN WxrJW3yR834wpyrBJcBAVl1UwMRIT1NdSYybXVVK5NQJDRVZeQt9QzkRfk0l CTCcnOxsgjw8qmKiElxsUiKCjucsuXjZrc+ZDwyUvRyqnproWwSdGcxbHM6b G8p4//zO+4HUqf5UeGFAQYB+rodWbpTLcGduY0mSioK0hopcfNila142qfFX 7sIul+dHXvayYmPhkZeV8nc/Azovws9vYWbMw8vueN786cCDV8NVC+/+Whgr mhvK+TB4b+ZZymRv3Otu2LNmWJCrbuAZuY4Mj4m+zJfdNyd7M4yNtQ30NZ80 Jg40xA603h7sTBnsvFVWeFVcSFpGWiI1yc3BUpeXg42Pl1dG6ljm3ZDHffnP +wumxrvG+lNe9adM9N560hLxpDV86GE8LMJBT1si6bpdbw30ETxhAH59uPuW leVJfV2lrtqI5+0xvU2R3XXh/c3Xe9punLM15OZk9/EyuRnrDFETA4/Lyd64 p/Vub/e954/zp151dtZF9DRe76m9VpByNjvFOi/tgrGehJfb6aayq+0Vfu21 QZ3V/vC6MFsr7dMGCi210J5m6KOmsK76sO76iEctkVl/+kiKC53Skk+74xQe bi0tJ3ze3vjpw4ze7oxnj3PfvIJHhJkGBxkmRlneiTHLTnX0dFX3u6TVWBrS XnsVXn25rfZyV/2V7oZrFx2M7G3VG8v9Goq8awovNpZ4tJZd6aoJaa4LNzSC aKpIpic7Fd33d3M3NzPSaqiIb22E9XWlT040hEKNTW3Ej+vxmpyWsLNS0zkl 4u9rcjPGPiXeNu2W3Z8p9rmpDnWFPl7OJld9dRtLL9QUuJfnOpXm2JVk29cV uVYU+Zubavi6m1QWeRUVXEpMcLUy1y4rDGusieqG3x58XjQ3fr+hOsrZVUdR QUBBhl9DVUBNhUNBkU1GnkNFg0f9BL+aJp+ZqfQJiLzbpZO59+xKc91rH7g/ yLHNTbfISTsTEWxsZiiTknC2LM8p70+b1GRnKwtIdoZfXXlYW90N0CLEbMXc SG4yzPWKt0VWkndVXkDGLUdoqKWDo5aJqdQFF21ff3MfvzNqCtKujicjg3Vj Qg1vxxmlJ5nnpNlXlfheOnci2Ot0Y7l3XZFLRZ5DfpabpZlqxDXr0jy/ysKA no478yP3pp+ldjbEddbHjnbffNt/a6g9+ll7wkBLYvOD4I6qsIc1kbDrDpes 9WqyQzrKg+uLAwsznRMjjaFXdCKCjM11ZHOTvDvrr8IrfBuK3WpKQ87Z6AT4 WLXWRLZVQh93Zox3xY13xw+3RU8PJL17mjzZCxtsgT5vgo7AY8cewgabox7X QIuT3WqyfIfbY8d64l8+ShjtuQXGuC7fMy7YPDnKqb44uLbYu6HEvSLfuao4 wMfd7LSBUlNV6EBr1Iv+gqG26yMd0S8exow9ip3oi3/VG/eyK7qvLuhpE3QQ HvWkMfxFR+x0350X8Oin9UEjbWFPW6F9jWF9TWGtpX7wyqsddaHNFVfg1QEd dYHN5X7NVdfSk92goTYNtUEd9SGD/fmIz8/W5nq+zHYtz3auzXWv/o1PU22r 7x+tf+j9MtO5Od//benJ5mLfp+mW5fdti2/b5iZapkZq3gxXvx4ufz1S9nqk cmKoanq06f1E2+xE+/xM3/vpnrdv4NOT8C8L4/8BwYKA+g== "], {{0, 32}, {32, 0}}, {0, 255}, ColorFunction->RGBColor], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{32, 32}, PlotRange->{{0, 32}, {0, 32}}]\)}]
Out[6]=

Geben Sie die Wahrscheinlichkeiten für die wahrscheinlichsten Zuordnung eines bestimmten Bildes an.

In[7]:=
Click for copyable input
trained[\!\(\* GraphicsBox[ TagBox[RasterBox[CompressedData[" 1:eJyVlllzE1cWgGHmJTVPgdQUAa8YhrHBBowkLNmyrNWyZe371i2p1d1qdatb +756lRdZMrbBgA1eMMasBoYtLAFMErJMVVIzU5WqecwvmUvIzNO8pOv0rXu7 ur+z9LnnnsNIUI//Yc+ePdFPwKD3JEWRiCdt+BQsTEzU72MwdJCJYT4swkX+ CB42/3Z/mO/du3fP778+fPRR/rf8/6/t3b9/f1dXF5fL7ericDjsjzeLw2ax WGw2m8vj9vTyBcI+IHx+D4/HZbNZ7R0n2o4fO37iryfaW4GA+bHWo61tfwGT 1uMfxvaOtlOdJ9kc1v7PPmtubqYoKhaLxBOhaCyUSCRSmUwylw1FwpFoNJ3L jpTHZ2rV2bna5NTE6GgpFKYh2KbTK/TGISA649CAUiIZECi1/RqjAoyDKonG OIT6PbFU9PCRlvr6erPZYnfYUAyiAr5YNJYvlgpjI/F0KpaIx1LJVCGXHxku jo2UhvPpTJzwo07IrNHJ1FqpQimUDfL5Ek6vhDOoEWnNg2pjv1wlVOqlHp8z nAg2tjTX1dcZjHqL1eRBHD7CEwzSmVw+nc9FE7FQmAnHIrFUIpXL5kvFfCEb iQYJArPa9Eq1cFDZK5FzBVJ2t+h0j/j0gJqvs8j0ln61QaIzy924lY74mw43 1TXUma1GB2RDvLAXhTDcGwyFw7EoRZMY7iH8Pj9FBRga+BJPRGiG9BGo0aSW K/gi2Vm+uLNb3MkTneIJO2QKjsYo0Bj71Hqh1iS1wGoEdzY21dc31NmdVtjt RDE34nV6PDAVoCOJmJ8igEYUQ3DChxM4UMcEyQBNAKUGo0om7+4VsXh9nT1i TreY3SM6I5GfHVR3K7W9Kp1IY5BqTAMmq7a+4RDgA+PdCEQQKO5zYxjCBEPx VBIA3R6nG4FRHPFiHsznJSncT6IeBNLqBgFfoRIbrJq+/l6BtFvY3yORd8sG uQOqHsDXGuVGu8bqNAL4Rz7idQVDVCzOxOLhVDoNoh2OhoBHrg8qIMhlBxMU cwEHQfBVatngUF8giK9uruZHCyiJBUKk0aIeUPYOKHuUWpHBMgQhDjcKNzSC +NRDLgfuR7P59Hh5ZKI8PjlTmaxWM/kMReEejwPwnbDNCVlcbividUCQ2WbX BRj0/sM7P//753/+/K9vvvv6ze6rq2uXCco1oASJKrI6NF4C8fmxxqYGoMKN uuhIYGxqvDo/V5mbm7+0vLC6OloeCzEE7rURBEIHyWiYwDEnw/jyueSFC3PP nz/+6acff/j7t9++f/v9+7fv3r549/bl+sZVyG3WGCQ2p9ZHonSQAvkD+C4v zESZ8emJ2sK52vzC8sa1levXx8uj+XSkPJIaLiaXzle3N5erM+PnF2v37tz8 5uvd7394//r16wcPd7ZvXNm5s35z68rW5tVXX76ozU87XQaHy+insFAkCPgg Pi4EJoP+0kh+ujK9eOH8pasrEzPlYjG9UC3f3lzeuHzuyc71Jztbt29sPP3b /W+/2n3+7PH2za21tfWlixfOzU2sXK4sX5xdvjT/6NHOvQe3A4zPi8MEiTGh wK/8OhgkD+2LxUPFUm6mMl2eHE+lo8Ds87Xy+sXa2uLUvfXzW1cW7t3aePPy 8ZOHd6uVyUI+l0pmgsEg5ffGo+TYcGphrgz8fvb86Xh5DCe8gP8xPvX1dQ6X 3evzgNxLJMOJRDQUplDMiaOOQio4Vy7MjcSXJjOXa2NPHm7du7VaSIdIwoP7 8H75ICiAvT08p9WcSQYvL1Vu3d788vXrtfVroQgDVFC0/zf73TYPYsdwiImS JI2jGCzt5wt72R6nKRX0xSjXaIZcquSWz4+WIniCQIrpmMmgbWisO/Dn/Z2n OpxWYz5Bba0vvnr16M2bNzv3H+WLBbANvagL5E9jYwPh9/pJDw74EcJPIZBN b1T2d3ee6GOddNm1NGYbSxAzBTqG26KQZbFUWpiYxGyOzva2tqPNAzIRApsm 8uFrV+ZePn+wu/vm+YtXtbkaTiAwbK+vP9Tc3BQO0+FwgKLQeJyOhH12vQLS K2X8rrPHj5i00mwAWR1OTYawmNuWI32TuWy1PEkTpEmvlYv4uiEZ6bVVRuLX VxdefnH/h+++evfV2/WNNbBbEcQFinNLS0s6nU6lErEoQxMe2gd5HBoENtot OlHXyZDLeG0sv5GIXqHIVZy8HAhOYr4FMrSEhycoxmHUWI1DNGqdKoZBfO7d Xn/7+vHu7ssvnj0tlfIEgTc2NgF+qTQ8OjY2NlpMhUjKY8VhHWRVWXWqgEX7 eGbiCk1lpNIndOyVP7zigNJy6YzesGR3lTEUtRk1g0LEopgZDq8sTV9bPf/o weaL5w/e7b6Zna0EAlRT0+HDLS2xRCzAkKjL5jarHVqpXSM0DQkjsHP3woV/ LF7cppgdin4fjD+FkRm1MqiSZ7zOCOoMeCHEadENiglIN5EPVCbi1cropYuz 2zdXXr54Mn+uSgeDTc0f+JgfFUr4XHa7gNuhELOsWmGadr1cW/pl5+4vG1s/ zi8+SMQWTbphqZjo63bKgME6HHUGaZwJ4JjLGiKgVNgzkiPHR1K12tily5Vn T+6urV5KpNPNLS1NzY0kQ5jsepVK7rDrCcxB+aCVauHdRuXLpenVaDCpUxn4 bAn7uIDdKuC19/eyNLJuvVbosCkxxAZOS8JjjQTcmRg2WghWJlMLteGnD7dv 39osjo4cPnIE1B8v4abCBEkTuUJ6eno8lQwFEf0UY4y7hrQSjqznpITfKZd0 qxTiIZVIr+/XqYX9kjNDgzyTXua0qlCXiSaggM8RD8HpCDI1HN/ZvrJz98Zo eeLD+d5wyGTXORELqHjZbGJiYjgeDRiGBIhRqlfwNJpeg6ZPJeMpxFxNv1Ax ILTYtG6XyWoY0Kv6dKpeh3WAwMw+1IwhxiDloHB7OkasLlfu3N4sjoyA33vw 0AGFWmq0qjxeeyRKFfKJXCYU8LtBYfF4LGatGNaKIHUfZJLBdo3TocdRiCG8 MZrA3WazTmwzS2BIATtViMfgw2we2EQHXLVqcWNjJZXNgN37+ecHxLKzaq3E bFURJJxMB7JZUIICyVS0WMoSXithlTOO/hCmiVDOKOVO0GiS8RVS0VwyhCEW h01hNfVbjErIaXW7rYQPjoSJanX87r2bxVIe7K/PDx4QSTvlCq5WL0VxRyIV yuaSoNHKZJOz1elkgvbD6qBLHsVVCcqWZuB82BslnKUkPTWeCQQQFLVCdo1e N2SxmFwuB00T6XRs/tzsg4d3C8XcoUMHD/7Kl8rZKo0Yw2GgdLZaGR4tpTLJ mdnpsYlhErOS8GDIq4oSpjQDZYPuJAWVEv6ZcjYcIXEcxrwOECmn0wG7YIoi s9lMuTxxY/taLpcG9Lq6QzI5T9bfo9UpaMY/NVVeXVudv7BYGCmBnrA6V4uE SNJr8bsNNGqJBdwxCslHyeEUMzmaTSYjfj9GBwiv1w25YA8CTlKmWCpNTk1e 31qfnplsaGj49NN9x/56+Nixo8ePt7LYnT09PJFEJBCLurp5/D6BoFfIYbHO nDrR2d7KOtnGOd3OOdXBZZ3md7HUg/JeHvcsh8Xjdp3tOtt+sgN0xZwuDl/A 7xMK+uUSgYC/b9++/zbZv69L/9Mnn/T1Ajzgcxrq61tbW48cPQKk80wnkI6O 9lOnTra1tQH+fwAN6ImN "], {{0, 32}, {32, 0}}, {0, 255}, ColorFunction->RGBColor], BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True], Selectable->False], DefaultBaseStyle->"ImageGraphics", ImageSizeRaw->{32, 32}, PlotRange->{{0, 32}, {0, 32}}]\), {"TopProbabilities", 3}]
Out[7]=

Wählen Sie aus zufälligen Bildern jene aus, für die das Netz die höchste und niedrigste Entropie vorhersagt. Inputs mit hoher Entropie können wie solche interpretiert werden, bei denen das Netz sich am unsichersten bei der Zuordnung in die korrekte Klasse ist.

In[8]:=
Click for copyable input
images = RandomSample[Keys[trainingData], 5000];
In[9]:=
Click for copyable input
entropies = trained[images, "Entropy"];
In[10]:=
Click for copyable input
Labeled[images[[Ordering[entropies, -10]]], "high entropy"] Labeled[images[[Ordering[entropies, 10]]], "low entropy"]
Out[10]=
Out[10]=

Verwandte Beispiele

en es fr ja ko pt-br ru zh