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week4.html
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<div id='write' class = 'is-node'><div class='md-toc' mdtype='toc'><p class="md-toc-content"><span class="md-toc-item md-toc-h1" data-ref="n2"><a class="md-toc-inner" href="#header-n2">8 神经网络：表达(Neural Networks: Representation)</a></span><span class="md-toc-item md-toc-h2" data-ref="n3"><a class="md-toc-inner" href="#header-n3">8.1 非线性假设(Non-linear Hypotheses)</a></span><span class="md-toc-item md-toc-h2" data-ref="n9"><a class="md-toc-inner" href="#header-n9">8.2 神经网络和大脑(Neurons and the Brain)</a></span><span class="md-toc-item md-toc-h2" data-ref="n17"><a class="md-toc-inner" href="#header-n17">8.3 模型表示1(Model Representation I)</a></span><span class="md-toc-item md-toc-h2" data-ref="n57"><a class="md-toc-inner" href="#header-n57">8.4 模型表示2(Model Representation II)</a></span><span class="md-toc-item md-toc-h2" data-ref="n87"><a class="md-toc-inner" href="#header-n87">8.5 例子和直观理解1(Examples and Intuitions I)</a></span><span class="md-toc-item md-toc-h2" data-ref="n99"><a class="md-toc-inner" href="#header-n99">8.6 例子和直观理解2(Examples and Intuitions II)</a></span><span class="md-toc-item md-toc-h2" data-ref="n107"><a class="md-toc-inner" href="#header-n107">8.7 多类别分类(Multiclass Classification)</a></span></p></div><h1><a name='header-n2' class='md-header-anchor '></a>8 神经网络：表达(Neural Networks: Representation)</h1><h2><a name='header-n3' class='md-header-anchor '></a>8.1 非线性假设(Non-linear Hypotheses)</h2><p>理论上我们可以用多项式函数去近似任意函数（泰勒极数(Taylor series)），从而可得到任意问题的拟合曲线。</p><p>在实际处理时，特征量通常会很多，如果再构造高阶多项式等，特征数量将会急剧增加，这使得回归模型的复杂度太高，可见并不合适。神经网络无需构造高阶多项式，在特征量很大时也可以处理的很好。</p><p>那特征能有多大呢？下面是一个计算机视觉中的例子：</p><p><img src='images/20180115_084326.png' alt='' referrerPolicy='no-referrer' /></p><p>如上图，如果选取一小块 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="6.839ex" height="1.994ex" viewBox="0 -755.9 2944.4 858.4" role="img" focusable="false" style="vertical-align: -0.238ex;"><defs><path stroke-width="0" id="E275-MJMAIN-35" d="M164 157Q164 133 148 117T109 101H102Q148 22 224 22Q294 22 326 82Q345 115 345 210Q345 313 318 349Q292 382 260 382H254Q176 382 136 314Q132 307 129 306T114 304Q97 304 95 310Q93 314 93 485V614Q93 664 98 664Q100 666 102 666Q103 666 123 658T178 642T253 634Q324 634 389 662Q397 666 402 666Q410 666 410 648V635Q328 538 205 538Q174 538 149 544L139 546V374Q158 388 169 396T205 412T256 420Q337 420 393 355T449 201Q449 109 385 44T229 -22Q148 -22 99 32T50 154Q50 178 61 192T84 210T107 214Q132 214 148 197T164 157Z"></path><path stroke-width="0" id="E275-MJMAIN-30" d="M96 585Q152 666 249 666Q297 666 345 640T423 548Q460 465 460 320Q460 165 417 83Q397 41 362 16T301 -15T250 -22Q224 -22 198 -16T137 16T82 83Q39 165 39 320Q39 494 96 585ZM321 597Q291 629 250 629Q208 629 178 597Q153 571 145 525T137 333Q137 175 145 125T181 46Q209 16 250 16Q290 16 318 46Q347 76 354 130T362 333Q362 478 354 524T321 597Z"></path><path stroke-width="0" id="E275-MJMAIN-2217" d="M229 286Q216 420 216 436Q216 454 240 464Q241 464 245 464T251 465Q263 464 273 456T283 436Q283 419 277 356T270 286L328 328Q384 369 389 372T399 375Q412 375 423 365T435 338Q435 325 425 315Q420 312 357 282T289 250L355 219L425 184Q434 175 434 161Q434 146 425 136T401 125Q393 125 383 131T328 171L270 213Q283 79 283 63Q283 53 276 44T250 35Q231 35 224 44T216 63Q216 80 222 143T229 213L171 171Q115 130 110 127Q106 124 100 124Q87 124 76 134T64 161Q64 166 64 169T67 175T72 181T81 188T94 195T113 204T138 215T170 230T210 250L74 315Q65 324 65 338Q65 353 74 363T98 374Q106 374 116 368T171 328L229 286Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E275-MJMAIN-35"></use><use xlink:href="#E275-MJMAIN-30" x="500" y="0"></use><use xlink:href="#E275-MJMAIN-2217" x="1222" y="0"></use><g transform="translate(1944,0)"><use xlink:href="#E275-MJMAIN-35"></use><use xlink:href="#E275-MJMAIN-30" x="500" y="0"></use></g></g></svg></span><script type="math/tex">50 * 50</script> 像素的灰度图片（一个像素只有亮度一个值），选择每个像素点作为特征，则特征总量 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="9.136ex" height="1.994ex" viewBox="0 -755.9 3933.6 858.4" role="img" focusable="false" style="vertical-align: -0.238ex;"><defs><path stroke-width="0" id="E276-MJMATHI-6E" d="M21 287Q22 293 24 303T36 341T56 388T89 425T135 442Q171 442 195 424T225 390T231 369Q231 367 232 367L243 378Q304 442 382 442Q436 442 469 415T503 336T465 179T427 52Q427 26 444 26Q450 26 453 27Q482 32 505 65T540 145Q542 153 560 153Q580 153 580 145Q580 144 576 130Q568 101 554 73T508 17T439 -10Q392 -10 371 17T350 73Q350 92 386 193T423 345Q423 404 379 404H374Q288 404 229 303L222 291L189 157Q156 26 151 16Q138 -11 108 -11Q95 -11 87 -5T76 7T74 17Q74 30 112 180T152 343Q153 348 153 366Q153 405 129 405Q91 405 66 305Q60 285 60 284Q58 278 41 278H27Q21 284 21 287Z"></path><path stroke-width="0" id="E276-MJMAIN-3D" d="M56 347Q56 360 70 367H707Q722 359 722 347Q722 336 708 328L390 327H72Q56 332 56 347ZM56 153Q56 168 72 173H708Q722 163 722 153Q722 140 707 133H70Q56 140 56 153Z"></path><path stroke-width="0" id="E276-MJMAIN-32" d="M109 429Q82 429 66 447T50 491Q50 562 103 614T235 666Q326 666 387 610T449 465Q449 422 429 383T381 315T301 241Q265 210 201 149L142 93L218 92Q375 92 385 97Q392 99 409 186V189H449V186Q448 183 436 95T421 3V0H50V19V31Q50 38 56 46T86 81Q115 113 136 137Q145 147 170 174T204 211T233 244T261 278T284 308T305 340T320 369T333 401T340 431T343 464Q343 527 309 573T212 619Q179 619 154 602T119 569T109 550Q109 549 114 549Q132 549 151 535T170 489Q170 464 154 447T109 429Z"></path><path stroke-width="0" id="E276-MJMAIN-35" d="M164 157Q164 133 148 117T109 101H102Q148 22 224 22Q294 22 326 82Q345 115 345 210Q345 313 318 349Q292 382 260 382H254Q176 382 136 314Q132 307 129 306T114 304Q97 304 95 310Q93 314 93 485V614Q93 664 98 664Q100 666 102 666Q103 666 123 658T178 642T253 634Q324 634 389 662Q397 666 402 666Q410 666 410 648V635Q328 538 205 538Q174 538 149 544L139 546V374Q158 388 169 396T205 412T256 420Q337 420 393 355T449 201Q449 109 385 44T229 -22Q148 -22 99 32T50 154Q50 178 61 192T84 210T107 214Q132 214 148 197T164 157Z"></path><path stroke-width="0" id="E276-MJMAIN-30" d="M96 585Q152 666 249 666Q297 666 345 640T423 548Q460 465 460 320Q460 165 417 83Q397 41 362 16T301 -15T250 -22Q224 -22 198 -16T137 16T82 83Q39 165 39 320Q39 494 96 585ZM321 597Q291 629 250 629Q208 629 178 597Q153 571 145 525T137 333Q137 175 145 125T181 46Q209 16 250 16Q290 16 318 46Q347 76 354 130T362 333Q362 478 354 524T321 597Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E276-MJMATHI-6E" x="0" y="0"></use><use xlink:href="#E276-MJMAIN-3D" x="877" y="0"></use><g transform="translate(1933,0)"><use xlink:href="#E276-MJMAIN-32"></use><use xlink:href="#E276-MJMAIN-35" x="500" y="0"></use><use xlink:href="#E276-MJMAIN-30" x="1000" y="0"></use><use xlink:href="#E276-MJMAIN-30" x="1500" y="0"></use></g></g></svg></span><script type="math/tex">n=2500</script>（换成 RGB（一个像素有三个值），则 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="9.136ex" height="1.994ex" viewBox="0 -755.9 3933.6 858.4" role="img" focusable="false" style="vertical-align: -0.238ex;"><defs><path stroke-width="0" id="E277-MJMATHI-6E" d="M21 287Q22 293 24 303T36 341T56 388T89 425T135 442Q171 442 195 424T225 390T231 369Q231 367 232 367L243 378Q304 442 382 442Q436 442 469 415T503 336T465 179T427 52Q427 26 444 26Q450 26 453 27Q482 32 505 65T540 145Q542 153 560 153Q580 153 580 145Q580 144 576 130Q568 101 554 73T508 17T439 -10Q392 -10 371 17T350 73Q350 92 386 193T423 345Q423 404 379 404H374Q288 404 229 303L222 291L189 157Q156 26 151 16Q138 -11 108 -11Q95 -11 87 -5T76 7T74 17Q74 30 112 180T152 343Q153 348 153 366Q153 405 129 405Q91 405 66 305Q60 285 60 284Q58 278 41 278H27Q21 284 21 287Z"></path><path stroke-width="0" id="E277-MJMAIN-3D" d="M56 347Q56 360 70 367H707Q722 359 722 347Q722 336 708 328L390 327H72Q56 332 56 347ZM56 153Q56 168 72 173H708Q722 163 722 153Q722 140 707 133H70Q56 140 56 153Z"></path><path stroke-width="0" id="E277-MJMAIN-37" d="M55 458Q56 460 72 567L88 674Q88 676 108 676H128V672Q128 662 143 655T195 646T364 644H485V605L417 512Q408 500 387 472T360 435T339 403T319 367T305 330T292 284T284 230T278 162T275 80Q275 66 275 52T274 28V19Q270 2 255 -10T221 -22Q210 -22 200 -19T179 0T168 40Q168 198 265 368Q285 400 349 489L395 552H302Q128 552 119 546Q113 543 108 522T98 479L95 458V455H55V458Z"></path><path stroke-width="0" id="E277-MJMAIN-35" d="M164 157Q164 133 148 117T109 101H102Q148 22 224 22Q294 22 326 82Q345 115 345 210Q345 313 318 349Q292 382 260 382H254Q176 382 136 314Q132 307 129 306T114 304Q97 304 95 310Q93 314 93 485V614Q93 664 98 664Q100 666 102 666Q103 666 123 658T178 642T253 634Q324 634 389 662Q397 666 402 666Q410 666 410 648V635Q328 538 205 538Q174 538 149 544L139 546V374Q158 388 169 396T205 412T256 420Q337 420 393 355T449 201Q449 109 385 44T229 -22Q148 -22 99 32T50 154Q50 178 61 192T84 210T107 214Q132 214 148 197T164 157Z"></path><path stroke-width="0" id="E277-MJMAIN-30" d="M96 585Q152 666 249 666Q297 666 345 640T423 548Q460 465 460 320Q460 165 417 83Q397 41 362 16T301 -15T250 -22Q224 -22 198 -16T137 16T82 83Q39 165 39 320Q39 494 96 585ZM321 597Q291 629 250 629Q208 629 178 597Q153 571 145 525T137 333Q137 175 145 125T181 46Q209 16 250 16Q290 16 318 46Q347 76 354 130T362 333Q362 478 354 524T321 597Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E277-MJMATHI-6E" x="0" y="0"></use><use xlink:href="#E277-MJMAIN-3D" x="877" y="0"></use><g transform="translate(1933,0)"><use xlink:href="#E277-MJMAIN-37"></use><use xlink:href="#E277-MJMAIN-35" x="500" y="0"></use><use xlink:href="#E277-MJMAIN-30" x="1000" y="0"></use><use xlink:href="#E277-MJMAIN-30" x="1500" y="0"></use></g></g></svg></span><script type="math/tex">n = 7500</script>），如果将其两两组合作为新特征，则特征数量将为 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="17.563ex" height="3.044ex" viewBox="0 -906.7 7561.8 1310.7" role="img" focusable="false" style="vertical-align: -0.938ex;"><defs><path stroke-width="0" id="E278-MJMATHI-43" d="M50 252Q50 367 117 473T286 641T490 704Q580 704 633 653Q642 643 648 636T656 626L657 623Q660 623 684 649Q691 655 699 663T715 679T725 690L740 705H746Q760 705 760 698Q760 694 728 561Q692 422 692 421Q690 416 687 415T669 413H653Q647 419 647 422Q647 423 648 429T650 449T651 481Q651 552 619 605T510 659Q484 659 454 652T382 628T299 572T226 479Q194 422 175 346T156 222Q156 108 232 58Q280 24 350 24Q441 24 512 92T606 240Q610 253 612 255T628 257Q648 257 648 248Q648 243 647 239Q618 132 523 55T319 -22Q206 -22 128 53T50 252Z"></path><path stroke-width="0" id="E278-MJMAIN-32" d="M109 429Q82 429 66 447T50 491Q50 562 103 614T235 666Q326 666 387 610T449 465Q449 422 429 383T381 315T301 241Q265 210 201 149L142 93L218 92Q375 92 385 97Q392 99 409 186V189H449V186Q448 183 436 95T421 3V0H50V19V31Q50 38 56 46T86 81Q115 113 136 137Q145 147 170 174T204 211T233 244T261 278T284 308T305 340T320 369T333 401T340 431T343 464Q343 527 309 573T212 619Q179 619 154 602T119 569T109 550Q109 549 114 549Q132 549 151 535T170 489Q170 464 154 447T109 429Z"></path><path stroke-width="0" id="E278-MJMAIN-35" d="M164 157Q164 133 148 117T109 101H102Q148 22 224 22Q294 22 326 82Q345 115 345 210Q345 313 318 349Q292 382 260 382H254Q176 382 136 314Q132 307 129 306T114 304Q97 304 95 310Q93 314 93 485V614Q93 664 98 664Q100 666 102 666Q103 666 123 658T178 642T253 634Q324 634 389 662Q397 666 402 666Q410 666 410 648V635Q328 538 205 538Q174 538 149 544L139 546V374Q158 388 169 396T205 412T256 420Q337 420 393 355T449 201Q449 109 385 44T229 -22Q148 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55 468 76T388 123T308 170T224 192Q164 192 125 159T83 84Q80 55 69 55Q55 55 55 85Z"></path><path stroke-width="0" id="E278-MJMAIN-33" d="M127 463Q100 463 85 480T69 524Q69 579 117 622T233 665Q268 665 277 664Q351 652 390 611T430 522Q430 470 396 421T302 350L299 348Q299 347 308 345T337 336T375 315Q457 262 457 175Q457 96 395 37T238 -22Q158 -22 100 21T42 130Q42 158 60 175T105 193Q133 193 151 175T169 130Q169 119 166 110T159 94T148 82T136 74T126 70T118 67L114 66Q165 21 238 21Q293 21 321 74Q338 107 338 175V195Q338 290 274 322Q259 328 213 329L171 330L168 332Q166 335 166 348Q166 366 174 366Q202 366 232 371Q266 376 294 413T322 525V533Q322 590 287 612Q265 626 240 626Q208 626 181 615T143 592T132 580H135Q138 579 143 578T153 573T165 566T175 555T183 540T186 520Q186 498 172 481T127 463Z"></path><path stroke-width="0" id="E278-MJMATHI-6D" d="M21 287Q22 293 24 303T36 341T56 388T88 425T132 442T175 435T205 417T221 395T229 376L231 369Q231 367 232 367L243 378Q303 442 384 442Q401 442 415 440T441 433T460 423T475 411T485 398T493 385T497 373T500 364T502 357L510 367Q573 442 659 442Q713 442 746 415T780 336Q780 285 742 178T704 50Q705 36 709 31T724 26Q752 26 776 56T815 138Q818 149 821 151T837 153Q857 153 857 145Q857 144 853 130Q845 101 831 73T785 17T716 -10Q669 -10 648 17T627 73Q627 92 663 193T700 345Q700 404 656 404H651Q565 404 506 303L499 291L466 157Q433 26 428 16Q415 -11 385 -11Q372 -11 364 -4T353 8T350 18Q350 29 384 161L420 307Q423 322 423 345Q423 404 379 404H374Q288 404 229 303L222 291L189 157Q156 26 151 16Q138 -11 108 -11Q95 -11 87 -5T76 7T74 17Q74 30 112 181Q151 335 151 342Q154 357 154 369Q154 405 129 405Q107 405 92 377T69 316T57 280Q55 278 41 278H27Q21 284 21 287Z"></path><path stroke-width="0" id="E278-MJMATHI-69" d="M184 600Q184 624 203 642T247 661Q265 661 277 649T290 619Q290 596 270 577T226 557Q211 557 198 567T184 600ZM21 287Q21 295 30 318T54 369T98 420T158 442Q197 442 223 419T250 357Q250 340 236 301T196 196T154 83Q149 61 149 51Q149 26 166 26Q175 26 185 29T208 43T235 78T260 137Q263 149 265 151T282 153Q302 153 302 143Q302 135 293 112T268 61T223 11T161 -11Q129 -11 102 10T74 74Q74 91 79 106T122 220Q160 321 166 341T173 380Q173 404 156 404H154Q124 404 99 371T61 287Q60 286 59 284T58 281T56 279T53 278T49 278T41 278H27Q21 284 21 287Z"></path><path stroke-width="0" id="E278-MJMATHI-6C" d="M117 59Q117 26 142 26Q179 26 205 131Q211 151 215 152Q217 153 225 153H229Q238 153 241 153T246 151T248 144Q247 138 245 128T234 90T214 43T183 6T137 -11Q101 -11 70 11T38 85Q38 97 39 102L104 360Q167 615 167 623Q167 626 166 628T162 632T157 634T149 635T141 636T132 637T122 637Q112 637 109 637T101 638T95 641T94 647Q94 649 96 661Q101 680 107 682T179 688Q194 689 213 690T243 693T254 694Q266 694 266 686Q266 675 193 386T118 83Q118 81 118 75T117 65V59Z"></path><path stroke-width="0" id="E278-MJMATHI-6F" d="M201 -11Q126 -11 80 38T34 156Q34 221 64 279T146 380Q222 441 301 441Q333 441 341 440Q354 437 367 433T402 417T438 387T464 338T476 268Q476 161 390 75T201 -11ZM121 120Q121 70 147 48T206 26Q250 26 289 58T351 142Q360 163 374 216T388 308Q388 352 370 375Q346 405 306 405Q243 405 195 347Q158 303 140 230T121 120Z"></path><path stroke-width="0" id="E278-MJMATHI-6E" d="M21 287Q22 293 24 303T36 341T56 388T89 425T135 442Q171 442 195 424T225 390T231 369Q231 367 232 367L243 378Q304 442 382 442Q436 442 469 415T503 336T465 179T427 52Q427 26 444 26Q450 26 453 27Q482 32 505 65T540 145Q542 153 560 153Q580 153 580 145Q580 144 576 130Q568 101 554 73T508 17T439 -10Q392 -10 371 17T350 73Q350 92 386 193T423 345Q423 404 379 404H374Q288 404 229 303L222 291L189 157Q156 26 151 16Q138 -11 108 -11Q95 -11 87 -5T76 7T74 17Q74 30 112 180T152 343Q153 348 153 366Q153 405 129 405Q91 405 66 305Q60 285 60 284Q58 278 41 278H27Q21 284 21 287Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E278-MJMATHI-43" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E278-MJMAIN-32" x="1093" y="487"></use><g transform="translate(715,-307)"><use transform="scale(0.707)" xlink:href="#E278-MJMAIN-32"></use><use transform="scale(0.707)" xlink:href="#E278-MJMAIN-35" x="500" y="0"></use><use transform="scale(0.707)" xlink:href="#E278-MJMAIN-30" x="1000" y="0"></use><use transform="scale(0.707)" xlink:href="#E278-MJMAIN-30" x="1499" y="0"></use></g><use xlink:href="#E278-MJMAIN-2248" x="2506" y="0"></use><use xlink:href="#E278-MJMAIN-33" x="3562" y="0"></use><use xlink:href="#E278-MJMATHI-6D" x="4312" y="0"></use><use xlink:href="#E278-MJMATHI-69" x="5190" y="0"></use><use xlink:href="#E278-MJMATHI-6C" x="5535" y="0"></use><use xlink:href="#E278-MJMATHI-6C" x="5833" y="0"></use><use xlink:href="#E278-MJMATHI-69" x="6131" y="0"></use><use xlink:href="#E278-MJMATHI-6F" x="6476" y="0"></use><use xlink:href="#E278-MJMATHI-6E" x="6961" y="0"></use></g></svg></span><script type="math/tex">C_{2500}^{2} \approx 3\ million</script>。</p><h2><a name='header-n9' class='md-header-anchor '></a>8.2 神经网络和大脑(Neurons and the Brain)</h2><p>脑科学家通过对动物实验，发现大脑中专用于处理听觉信号的脑皮层也能处理其他诸如视觉等信号，即如果切断其与耳朵的联系，将其与眼睛相连，则这块负责听觉的脑皮层区域也能接受并处理视觉信号，从而学会“看”。脑科学家通过这类换源实验，就推论假设大脑的学习算法只有一种(“one learning algorithm” hypothesis)。那么如果能找出这种学习算法并应用于计算机中，那梦想中和人一样的人工智能就成真了。</p><p>神经网络就源于<strong>模拟人类大脑</strong>，但其需要的计算量很大。随着计算机硬件性能的提高，神经网络逐渐从衰落变为流行，如今已广泛地被应用在各行各业中。</p><p>下图是根据研究做的一些应用（有兴趣可回顾视频）：</p><p><img src='images/20180115_101441.png' alt='' referrerPolicy='no-referrer' /></p><p>BrainPort 系统：帮助失明人士通过摄像头以及舌尖感官“看”东西</p><p><img src='images/20180115_101442.png' alt='' referrerPolicy='no-referrer' /></p><p>触觉皮带：在朝北时蜂鸣器会发出声响，可使人拥有方向感（声音信号转换为方向信号）。</p><h2><a name='header-n17' class='md-header-anchor '></a>8.3 模型表示1(Model Representation I)</h2><p>既然神经网络模仿的是大脑神经元，那就先看一下大脑的神经元长什么样吧：</p><p><img src='images/20141213201613758.jpg' alt='来源: http://blog.csdn.net/zzwu/article/details/574931' referrerPolicy='no-referrer' /></p><p>想象一下印刷厂中流水线的工人（机器人也算哦），每个工人都有特定的任务，比如装订，塑封，贴防伪标识等等，工人们看到书本并处理完自己的任务后，就回放回传送带，紧接着传送带就传给下一个环节的工人，如此不断重复从而完成一个又一个环节，直到一本书印制完成。</p><p>那么类比一下，把上图中的<strong>细胞核(nucleus)</strong>类比成工人，<strong>轴突(axon)</strong>类比传送带，<strong>树突(dendrite)</strong>则比类比成工人的双眼。一个又一个细胞体，从树突接收需要处理的信息，对其进行处理后，再经由轴突通过电信号把处理完的信息传递出去，直到理解信息的内容。当然啦，我们大脑的实际上还要更为复杂，而且一个人的神经元数目就比地球上所有流水线的工人之和还要多呢~</p><p>人工神经网络中，树突对应<strong>输入(input)</strong>，细胞核对应<strong>激活单元(activation unit)</strong>，轴突对应<strong>输出(output)</strong>。</p><p>我们一般把神经网络划分为三部分（注意，不是只有三层！），即输入层(input layer)，隐藏层(hidden layer)和输出层(output layer)。</p><p><img src='images/20180116_001543.png' alt='' referrerPolicy='no-referrer' /></p><p>图中的一个圈表示神经网络中的一个激活单元，输入层对应输入单元，隐藏层对应中间单元，输出层则对应输出单元。中间激活单元应用<strong>激活函数</strong>(<a href='https://en.wikipedia.org/wiki/Activation_function'>activation_function</a>)处理数据。</p><p>下面列出一些已有概念在神经网络中的别称：</p><ul><li><span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="2.382ex" height="1.76ex" viewBox="0 -504.6 1025.6 757.9" role="img" focusable="false" style="vertical-align: -0.588ex;"><defs><path stroke-width="0" id="E100-MJMATHI-78" d="M52 289Q59 331 106 386T222 442Q257 442 286 424T329 379Q371 442 430 442Q467 442 494 420T522 361Q522 332 508 314T481 292T458 288Q439 288 427 299T415 328Q415 374 465 391Q454 404 425 404Q412 404 406 402Q368 386 350 336Q290 115 290 78Q290 50 306 38T341 26Q378 26 414 59T463 140Q466 150 469 151T485 153H489Q504 153 504 145Q504 144 502 134Q486 77 440 33T333 -11Q263 -11 227 52Q186 -10 133 -10H127Q78 -10 57 16T35 71Q35 103 54 123T99 143Q142 143 142 101Q142 81 130 66T107 46T94 41L91 40Q91 39 97 36T113 29T132 26Q168 26 194 71Q203 87 217 139T245 247T261 313Q266 340 266 352Q266 380 251 392T217 404Q177 404 142 372T93 290Q91 281 88 280T72 278H58Q52 284 52 289Z"></path><path stroke-width="0" id="E100-MJMAIN-30" d="M96 585Q152 666 249 666Q297 666 345 640T423 548Q460 465 460 320Q460 165 417 83Q397 41 362 16T301 -15T250 -22Q224 -22 198 -16T137 16T82 83Q39 165 39 320Q39 494 96 585ZM321 597Q291 629 250 629Q208 629 178 597Q153 571 145 525T137 333Q137 175 145 125T181 46Q209 16 250 16Q290 16 318 46Q347 76 354 130T362 333Q362 478 354 524T321 597Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E100-MJMATHI-78" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E100-MJMAIN-30" x="808" y="-213"></use></g></svg></span><script type="math/tex">x_0</script>: 偏置单元(bias unit)，<span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="2.382ex" height="1.76ex" viewBox="0 -504.6 1025.6 757.9" role="img" focusable="false" style="vertical-align: -0.588ex;"><defs><path stroke-width="0" id="E100-MJMATHI-78" d="M52 289Q59 331 106 386T222 442Q257 442 286 424T329 379Q371 442 430 442Q467 442 494 420T522 361Q522 332 508 314T481 292T458 288Q439 288 427 299T415 328Q415 374 465 391Q454 404 425 404Q412 404 406 402Q368 386 350 336Q290 115 290 78Q290 50 306 38T341 26Q378 26 414 59T463 140Q466 150 469 151T485 153H489Q504 153 504 145Q504 144 502 134Q486 77 440 33T333 -11Q263 -11 227 52Q186 -10 133 -10H127Q78 -10 57 16T35 71Q35 103 54 123T99 143Q142 143 142 101Q142 81 130 66T107 46T94 41L91 40Q91 39 97 36T113 29T132 26Q168 26 194 71Q203 87 217 139T245 247T261 313Q266 340 266 352Q266 380 251 392T217 404Q177 404 142 372T93 290Q91 281 88 280T72 278H58Q52 284 52 289Z"></path><path stroke-width="0" id="E100-MJMAIN-30" d="M96 585Q152 666 249 666Q297 666 345 640T423 548Q460 465 460 320Q460 165 417 83Q397 41 362 16T301 -15T250 -22Q224 -22 198 -16T137 16T82 83Q39 165 39 320Q39 494 96 585ZM321 597Q291 629 250 629Q208 629 178 597Q153 571 145 525T137 333Q137 175 145 125T181 46Q209 16 250 16Q290 16 318 46Q347 76 354 130T362 333Q362 478 354 524T321 597Z"></path></defs><g stroke="currentColor" fill="currentColor" 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stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E279-MJMAIN-398" x="0" y="0"></use></g></svg></span><script type="math/tex">\Theta</script>: 权重(weight)，即参数。</li><li>激活函数: <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="1.115ex" height="1.877ex" viewBox="0 -504.6 480 808.1" role="img" focusable="false" style="vertical-align: -0.705ex;"><defs><path stroke-width="0" id="E160-MJMATHI-67" d="M311 43Q296 30 267 15T206 0Q143 0 105 45T66 160Q66 265 143 353T314 442Q361 442 401 394L404 398Q406 401 409 404T418 412T431 419T447 422Q461 422 470 413T480 394Q480 379 423 152T363 -80Q345 -134 286 -169T151 -205Q10 -205 10 -137Q10 -111 28 -91T74 -71Q89 -71 102 -80T116 -111Q116 -121 114 -130T107 -144T99 -154T92 -162L90 -164H91Q101 -167 151 -167Q189 -167 211 -155Q234 -144 254 -122T282 -75Q288 -56 298 -13Q311 35 311 43ZM384 328L380 339Q377 350 375 354T369 368T359 382T346 393T328 402T306 405Q262 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style="vertical-align: -0.238ex;"><defs><path stroke-width="0" id="E286-MJMATHI-76" d="M173 380Q173 405 154 405Q130 405 104 376T61 287Q60 286 59 284T58 281T56 279T53 278T49 278T41 278H27Q21 284 21 287Q21 294 29 316T53 368T97 419T160 441Q202 441 225 417T249 361Q249 344 246 335Q246 329 231 291T200 202T182 113Q182 86 187 69Q200 26 250 26Q287 26 319 60T369 139T398 222T409 277Q409 300 401 317T383 343T365 361T357 383Q357 405 376 424T417 443Q436 443 451 425T467 367Q467 340 455 284T418 159T347 40T241 -11Q177 -11 139 22Q102 54 102 117Q102 148 110 181T151 298Q173 362 173 380Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E286-MJMATHI-76" x="0" y="0"></use></g></svg></span><script type="math/tex">v</script> 个单元的权重</p><p><span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="1.998ex" height="1.994ex" viewBox="0 -504.6 860.3 858.4" 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x="0" y="0"></use><g transform="translate(778,362)"><use transform="scale(0.707)" xlink:href="#E291-MJMAIN-28" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E291-MJMATHI-6A" x="389" y="0"></use><use transform="scale(0.707)" xlink:href="#E291-MJMAIN-29" x="801" y="0"></use></g></g></svg></span><script type="math/tex">\Theta^{(j)}</script> 是一个 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="14.742ex" height="2.694ex" viewBox="0 -806.1 6347.2 1160" role="img" focusable="false" style="vertical-align: -0.822ex;"><defs><path stroke-width="0" id="E292-MJMATHI-73" d="M131 289Q131 321 147 354T203 415T300 442Q362 442 390 415T419 355Q419 323 402 308T364 292Q351 292 340 300T328 326Q328 342 337 354T354 372T367 378Q368 378 368 379Q368 382 361 388T336 399T297 405Q249 405 227 379T204 326Q204 301 223 291T278 274T330 259Q396 230 396 163Q396 135 385 107T352 51T289 7T195 -10Q118 -10 86 19T53 87Q53 126 74 143T118 160Q133 160 146 151T160 120Q160 94 142 76T111 58Q109 57 108 57T107 55Q108 52 115 47T146 34T201 27Q237 27 263 38T301 66T318 97T323 122Q323 150 302 164T254 181T195 196T148 231Q131 256 131 289Z"></path><path stroke-width="0" id="E292-MJMATHI-6A" d="M297 596Q297 627 318 644T361 661Q378 661 389 651T403 623Q403 595 384 576T340 557Q322 557 310 567T297 596ZM288 376Q288 405 262 405Q240 405 220 393T185 362T161 325T144 293L137 279Q135 278 121 278H107Q101 284 101 286T105 299Q126 348 164 391T252 441Q253 441 260 441T272 442Q296 441 316 432Q341 418 354 401T367 348V332L318 133Q267 -67 264 -75Q246 -125 194 -164T75 -204Q25 -204 7 -183T-12 -137Q-12 -110 7 -91T53 -71Q70 -71 82 -81T95 -112Q95 -148 63 -167Q69 -168 77 -168Q111 -168 139 -140T182 -74L193 -32Q204 11 219 72T251 197T278 308T289 365Q289 372 288 376Z"></path><path stroke-width="0" id="E292-MJMAIN-2B" d="M56 237T56 250T70 270H369V420L370 570Q380 583 389 583Q402 583 409 568V270H707Q722 262 722 250T707 230H409V-68Q401 -82 391 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type="math/tex">h_\Theta(x) = a_1^{(3)} = g(\Theta_{10}^{(2)}a_0^{(2)} + \Theta_{11}^{(2)}a_1^{(2)} + \Theta_{12}^{(2)}a_2^{(2)} + \Theta_{13}^{(2)}a_3^{(2)})</script></p><p>上面的计算过程被称为<strong>前向传播(Forward propagation)</strong>，即从输入层开始，一层一层地向下计算并传递结果。</p><p> </p><p>再回顾一下逻辑回归：</p><p><span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="36.504ex" height="2.577ex" viewBox="0 -806.1 15716.7 1109.7" role="img" focusable="false" style="vertical-align: -0.705ex;"><defs><path stroke-width="0" id="E298-MJMATHI-68" d="M137 683Q138 683 209 688T282 694Q294 694 294 685Q294 674 258 534Q220 386 220 383Q220 381 227 388Q288 442 357 442Q411 442 444 415T478 336Q478 285 440 178T402 50Q403 36 407 31T422 26Q450 26 474 56T513 138Q516 149 519 151T535 153Q555 153 555 145Q555 144 551 130Q535 71 500 33Q466 -10 419 -10H414Q367 -10 346 17T325 74Q325 90 361 192T398 345Q398 404 354 404H349Q266 404 205 306L198 293L164 158Q132 28 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y="0"></use><use transform="scale(0.707)" xlink:href="#E301-MJMAIN-31" x="389" y="0"></use><use transform="scale(0.707)" xlink:href="#E301-MJMAIN-29" x="888" y="0"></use></g><g transform="translate(778,-306)"><use transform="scale(0.707)" xlink:href="#E301-MJMAIN-33"></use><use transform="scale(0.707)" xlink:href="#E301-MJMAIN-33" x="500" y="0"></use></g></g></g></g><g transform="translate(11112,2710)"><use xlink:href="#E301-MJSZ4-23A4" x="0" y="-1154"></use><g transform="translate(0,-3171.7887399286774) scale(1,2.363436445064248)"><use xlink:href="#E301-MJSZ4-23A5"></use></g><use xlink:href="#E301-MJSZ4-23A6" x="0" y="-4277"></use></g></g></g></svg></span><script type="math/tex">\Theta^{(1)}=\left[\begin{matrix}\Theta^{(1)}_{10}& \Theta^{(1)}_{11}& \Theta^{(1)}_{12}& \Theta^{(1)}_{13}\\ \Theta^{(1)}_{20}& \Theta^{(1)}_{21}& \Theta^{(1)}_{22}& \Theta^{(1)}_{23}\\ \Theta^{(1)}_{30}& \Theta^{(1)}_{31}& \Theta^{(1)}_{32} & \Theta^{(1)}_{33}\end{matrix}\right]</script>，</p><p><span 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y="-4231"></use></g></g></g></svg></span><script type="math/tex">z^{(2)}=\left[ \begin{matrix}z_1^{(2)}\\ z_1^{(2)} \\ z_1^{(2)}\end{matrix} \right]</script></p><p>则有 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="26.716ex" height="2.928ex" viewBox="0 -956.9 11502.8 1260.5" role="img" focusable="false" style="vertical-align: -0.705ex;"><defs><path stroke-width="0" id="E304-MJMATHI-61" d="M33 157Q33 258 109 349T280 441Q331 441 370 392Q386 422 416 422Q429 422 439 414T449 394Q449 381 412 234T374 68Q374 43 381 35T402 26Q411 27 422 35Q443 55 463 131Q469 151 473 152Q475 153 483 153H487Q506 153 506 144Q506 138 501 117T481 63T449 13Q436 0 417 -8Q409 -10 393 -10Q359 -10 336 5T306 36L300 51Q299 52 296 50Q294 48 292 46Q233 -10 172 -10Q117 -10 75 30T33 157ZM351 328Q351 334 346 350T323 385T277 405Q242 405 210 374T160 293Q131 214 119 129Q119 126 119 118T118 106Q118 61 136 44T179 26Q217 26 254 59T298 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transform="translate(3735,0)"><use xlink:href="#E304-MJMAIN-398" x="0" y="0"></use><g transform="translate(778,362)"><use transform="scale(0.707)" xlink:href="#E304-MJMAIN-28" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E304-MJMAIN-31" x="389" y="0"></use><use transform="scale(0.707)" xlink:href="#E304-MJMAIN-29" x="888" y="0"></use></g></g><g transform="translate(5516,0)"><use xlink:href="#E304-MJMATHI-61" x="0" y="0"></use><g transform="translate(529,362)"><use transform="scale(0.707)" xlink:href="#E304-MJMAIN-28" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E304-MJMAIN-31" x="389" y="0"></use><use transform="scale(0.707)" xlink:href="#E304-MJMAIN-29" x="888" y="0"></use></g></g><use xlink:href="#E304-MJMAIN-29" x="7049" y="0"></use><use xlink:href="#E304-MJMAIN-3D" x="7716" y="0"></use><use xlink:href="#E304-MJMATHI-67" x="8772" y="0"></use><use xlink:href="#E304-MJMAIN-28" x="9252" y="0"></use><g transform="translate(9641,0)"><use 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56T513 138Q516 149 519 151T535 153Q555 153 555 145Q555 144 551 130Q535 71 500 33Q466 -10 419 -10H414Q367 -10 346 17T325 74Q325 90 361 192T398 345Q398 404 354 404H349Q266 404 205 306L198 293L164 158Q132 28 127 16Q114 -11 83 -11Q69 -11 59 -2T48 16Q48 30 121 320L195 616Q195 629 188 632T149 637H128Q122 643 122 645T124 664Q129 683 137 683Z"></path><path stroke-width="0" id="E305-MJMAIN-398" d="M56 340Q56 423 86 494T164 610T270 680T388 705Q521 705 621 601T722 341Q722 260 693 191T617 75T510 4T388 -22T267 3T160 74T85 189T56 340ZM610 339Q610 428 590 495T535 598T463 651T384 668Q332 668 289 638T221 566Q168 485 168 339Q168 274 176 235Q189 158 228 105T324 28Q356 16 388 16Q415 16 442 24T501 54T555 111T594 205T610 339ZM223 263V422H263V388H514V422H554V263H514V297H263V263H223Z"></path><path stroke-width="0" id="E305-MJMAIN-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 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375 411 408T434 441Q435 442 449 442H462Q468 436 468 434Q468 430 463 420T449 399T432 377T418 358L411 349Q368 298 275 214T160 106L148 94L163 93Q185 93 227 82T290 71Q328 71 360 90T402 140Q406 149 409 151T424 153Q443 153 443 143Q443 138 442 134Q425 72 376 31T278 -11Q252 -11 232 6T193 40T155 57Q111 57 76 -3Q70 -11 59 -11H54H41Q35 -5 35 -2Q35 13 93 84Q132 129 225 214T340 322Q352 338 347 338Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E308-MJMATHI-61" x="0" y="0"></use><g transform="translate(529,362)"><use transform="scale(0.707)" xlink:href="#E308-MJMAIN-28" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E308-MJMATHI-6A" x="389" y="0"></use><use transform="scale(0.707)" xlink:href="#E308-MJMAIN-29" x="801" y="0"></use></g><use xlink:href="#E308-MJMAIN-3D" x="1748" y="0"></use><use xlink:href="#E308-MJMATHI-67" x="2804" y="0"></use><use xlink:href="#E308-MJMAIN-28" x="3284" y="0"></use><g transform="translate(3673,0)"><use xlink:href="#E308-MJMATHI-7A" x="0" y="0"></use><g transform="translate(468,362)"><use transform="scale(0.707)" xlink:href="#E308-MJMAIN-28" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E308-MJMATHI-6A" x="389" y="0"></use><use transform="scale(0.707)" xlink:href="#E308-MJMAIN-29" x="801" y="0"></use></g></g><use xlink:href="#E308-MJMAIN-29" x="5083" y="0"></use></g></svg></span><script type="math/tex">a^{(j)} = g(z^{(j)})</script>，通过该式即可计算神经网络中每一层的值。</p><p>扩展到所有样本实例：</p><p><span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="14.04ex" height="2.694ex" viewBox="0 -1057.4 6044.9 1160" role="img" focusable="false" style="vertical-align: -0.238ex;"><defs><path stroke-width="0" id="E309-MJMATHI-7A" d="M347 338Q337 338 294 349T231 360Q211 360 197 356T174 346T162 335T155 324L153 320Q150 317 138 317Q117 317 117 325Q117 330 120 339Q133 378 163 406T229 440Q241 442 246 442Q271 442 291 425T329 392T367 375Q389 375 411 408T434 441Q435 442 449 442H462Q468 436 468 434Q468 430 463 420T449 399T432 377T418 358L411 349Q368 298 275 214T160 106L148 94L163 93Q185 93 227 82T290 71Q328 71 360 90T402 140Q406 149 409 151T424 153Q443 153 443 143Q443 138 442 134Q425 72 376 31T278 -11Q252 -11 232 6T193 40T155 57Q111 57 76 -3Q70 -11 59 -11H54H41Q35 -5 35 -2Q35 13 93 84Q132 129 225 214T340 322Q352 338 347 338Z"></path><path stroke-width="0" id="E309-MJMAIN-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path><path stroke-width="0" id="E309-MJMAIN-32" d="M109 429Q82 429 66 447T50 491Q50 562 103 614T235 666Q326 666 387 610T449 465Q449 422 429 383T381 315T301 241Q265 210 201 149L142 93L218 92Q375 92 385 97Q392 99 409 186V189H449V186Q448 183 436 95T421 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681T738 680Q828 680 837 683H845Q852 676 852 672Q850 647 840 637H824Q790 636 763 628T722 611T698 593L687 584Q687 585 592 480L505 384Q505 383 536 304T601 142T638 56Q648 47 699 46Q734 46 734 37Q734 35 732 23Q728 7 725 4T711 1Q708 1 678 1T589 2Q528 2 496 2T461 1Q444 1 444 10Q444 11 446 25Q448 35 450 39T455 44T464 46T480 47T506 54Q523 62 523 64Q522 64 476 181L429 299Q241 95 236 84Q232 76 232 72Q232 53 261 47Q262 47 267 47T273 46Q276 46 277 46T280 45T283 42T284 35Q284 26 282 19Q279 6 276 4T261 1Q258 1 243 1T201 2T142 2Q64 2 42 0Z"></path><path stroke-width="0" id="E309-MJMATHI-54" d="M40 437Q21 437 21 445Q21 450 37 501T71 602L88 651Q93 669 101 677H569H659Q691 677 697 676T704 667Q704 661 687 553T668 444Q668 437 649 437Q640 437 637 437T631 442L629 445Q629 451 635 490T641 551Q641 586 628 604T573 629Q568 630 515 631Q469 631 457 630T439 622Q438 621 368 343T298 60Q298 48 386 46Q418 46 427 45T436 36Q436 31 433 22Q429 4 424 1L422 0Q419 0 415 0Q410 0 363 1T228 2Q99 2 64 0H49Q43 6 43 9T45 27Q49 40 55 46H83H94Q174 46 189 55Q190 56 191 56Q196 59 201 76T241 233Q258 301 269 344Q339 619 339 625Q339 630 310 630H279Q212 630 191 624Q146 614 121 583T67 467Q60 445 57 441T43 437H40Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E309-MJMATHI-7A" x="0" y="0"></use><g transform="translate(468,362)"><use transform="scale(0.707)" xlink:href="#E309-MJMAIN-28" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E309-MJMAIN-32" x="389" y="0"></use><use transform="scale(0.707)" xlink:href="#E309-MJMAIN-29" x="888" y="0"></use></g><use xlink:href="#E309-MJMAIN-3D" x="1750" y="0"></use><g transform="translate(2806,0)"><use xlink:href="#E309-MJMAIN-398" x="0" y="0"></use><g transform="translate(778,431)"><use transform="scale(0.707)" xlink:href="#E309-MJMAIN-28" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E309-MJMAIN-31" x="389" y="0"></use><use transform="scale(0.707)" xlink:href="#E309-MJMAIN-29" x="888" y="0"></use></g></g><g transform="translate(4587,0)"><use xlink:href="#E309-MJMATHI-58" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E309-MJMATHI-54" x="1215" y="579"></use></g></g></svg></span><script type="math/tex">{{z}^{\left( 2 \right)}}={{\Theta }^{\left( 1 \right)}} {{X}^{T}}</script>，这时 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="3.42ex" height="2.461ex" viewBox="0 -956.9 1472.6 1059.4" role="img" focusable="false" style="vertical-align: -0.238ex;"><defs><path stroke-width="0" id="E310-MJMATHI-7A" d="M347 338Q337 338 294 349T231 360Q211 360 197 356T174 346T162 335T155 324L153 320Q150 317 138 317Q117 317 117 325Q117 330 120 339Q133 378 163 406T229 440Q241 442 246 442Q271 442 291 425T329 392T367 375Q389 375 411 408T434 441Q435 442 449 442H462Q468 436 468 434Q468 430 463 420T449 399T432 377T418 358L411 349Q368 298 275 214T160 106L148 94L163 93Q185 93 227 82T290 71Q328 71 360 90T402 140Q406 149 409 151T424 153Q443 153 443 143Q443 138 442 134Q425 72 376 31T278 -11Q252 -11 232 6T193 40T155 57Q111 57 76 -3Q70 -11 59 -11H54H41Q35 -5 35 -2Q35 13 93 84Q132 129 225 214T340 322Q352 338 347 338Z"></path><path stroke-width="0" id="E310-MJMAIN-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path><path stroke-width="0" id="E310-MJMAIN-32" d="M109 429Q82 429 66 447T50 491Q50 562 103 614T235 666Q326 666 387 610T449 465Q449 422 429 383T381 315T301 241Q265 210 201 149L142 93L218 92Q375 92 385 97Q392 99 409 186V189H449V186Q448 183 436 95T421 3V0H50V19V31Q50 38 56 46T86 81Q115 113 136 137Q145 147 170 174T204 211T233 244T261 278T284 308T305 340T320 369T333 401T340 431T343 464Q343 527 309 573T212 619Q179 619 154 602T119 569T109 550Q109 549 114 549Q132 549 151 535T170 489Q170 464 154 447T109 429Z"></path><path stroke-width="0" id="E310-MJMAIN-29" d="M60 749L64 750Q69 750 74 750H86L114 726Q208 641 251 514T294 250Q294 182 284 119T261 12T224 -76T186 -143T145 -194T113 -227T90 -246Q87 -249 86 -250H74Q66 -250 63 -250T58 -247T55 -238Q56 -237 66 -225Q221 -64 221 250T66 725Q56 737 55 738Q55 746 60 749Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E310-MJMATHI-7A" x="0" y="0"></use><g transform="translate(468,362)"><use transform="scale(0.707)" xlink:href="#E310-MJMAIN-28" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E310-MJMAIN-32" x="389" y="0"></use><use transform="scale(0.707)" xlink:href="#E310-MJMAIN-29" x="888" y="0"></use></g></g></svg></span><script type="math/tex">z^{(2)}</script> 是一个 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="7.021ex" 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170 174T204 211T233 244T261 278T284 308T305 340T320 369T333 401T340 431T343 464Q343 527 309 573T212 619Q179 619 154 602T119 569T109 550Q109 549 114 549Q132 549 151 535T170 489Q170 464 154 447T109 429Z"></path><path stroke-width="0" id="E311-MJMAIN-D7" d="M630 29Q630 9 609 9Q604 9 587 25T493 118L389 222L284 117Q178 13 175 11Q171 9 168 9Q160 9 154 15T147 29Q147 36 161 51T255 146L359 250L255 354Q174 435 161 449T147 471Q147 480 153 485T168 490Q173 490 175 489Q178 487 284 383L389 278L493 382Q570 459 587 475T609 491Q630 491 630 471Q630 464 620 453T522 355L418 250L522 145Q606 61 618 48T630 29Z"></path><path stroke-width="0" id="E311-MJMATHI-6D" d="M21 287Q22 293 24 303T36 341T56 388T88 425T132 442T175 435T205 417T221 395T229 376L231 369Q231 367 232 367L243 378Q303 442 384 442Q401 442 415 440T441 433T460 423T475 411T485 398T493 385T497 373T500 364T502 357L510 367Q573 442 659 442Q713 442 746 415T780 336Q780 285 742 178T704 50Q705 36 709 31T724 26Q752 26 776 56T815 138Q818 149 821 151T837 153Q857 153 857 145Q857 144 853 130Q845 101 831 73T785 17T716 -10Q669 -10 648 17T627 73Q627 92 663 193T700 345Q700 404 656 404H651Q565 404 506 303L499 291L466 157Q433 26 428 16Q415 -11 385 -11Q372 -11 364 -4T353 8T350 18Q350 29 384 161L420 307Q423 322 423 345Q423 404 379 404H374Q288 404 229 303L222 291L189 157Q156 26 151 16Q138 -11 108 -11Q95 -11 87 -5T76 7T74 17Q74 30 112 181Q151 335 151 342Q154 357 154 369Q154 405 129 405Q107 405 92 377T69 316T57 280Q55 278 41 278H27Q21 284 21 287Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E311-MJMATHI-73" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E311-MJMAIN-32" x="663" y="-213"></use><use xlink:href="#E311-MJMAIN-D7" x="1144" y="0"></use><use xlink:href="#E311-MJMATHI-6D" x="2144" y="0"></use></g></svg></span><script type="math/tex">s_2 \times m</script> 维矩阵。</p><blockquote><p><span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; 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第二层神经网络中激活单元的数量</p></blockquote><p> </p><p>当然，神经网络可有多层，每层的激活单元数量也并不固定：</p><p><img src='images/20180116_105545.png' alt='' referrerPolicy='no-referrer' /></p><blockquote><p>我们习惯于将输入层称为神经网络的第 0 层，如上图的神经网络被称为三层网络。</p></blockquote><h2><a name='header-n87' class='md-header-anchor '></a>8.5 例子和直观理解1(Examples and Intuitions I)</h2><p>为了更好的理解神经网络，举例单层神经网络进行逻辑运算的例子。</p><p>下面的例子中，<span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="5.797ex" height="1.76ex" viewBox="0 -504.6 2495.8 757.9" role="img" focusable="false" style="vertical-align: -0.588ex;"><defs><path stroke-width="0" id="E313-MJMATHI-78" d="M52 289Q59 331 106 386T222 442Q257 442 286 424T329 379Q371 442 430 442Q467 442 494 420T522 361Q522 332 508 314T481 292T458 288Q439 288 427 299T415 328Q415 374 465 391Q454 404 425 404Q412 404 406 402Q368 386 350 336Q290 115 290 78Q290 50 306 38T341 26Q378 26 414 59T463 140Q466 150 469 151T485 153H489Q504 153 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为二进制数。</p><p>逻辑与(AND)运算（都为真值则结果才为真）神经网络：</p><p><img src='images/20180117_000612.png' alt='' referrerPolicy='no-referrer' /></p><p><span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="22.688ex" height="2.928ex" viewBox="0 -956.9 9768.3 1260.5" role="img" focusable="false" style="vertical-align: -0.705ex;"><defs><path stroke-width="0" id="E314-MJMAIN-398" d="M56 340Q56 423 86 494T164 610T270 680T388 705Q521 705 621 601T722 341Q722 260 693 191T617 75T510 4T388 -22T267 3T160 74T85 189T56 340ZM610 339Q610 428 590 495T535 598T463 651T384 668Q332 668 289 638T221 566Q168 485 168 339Q168 274 176 235Q189 158 228 105T324 28Q356 16 388 16Q415 16 442 24T501 54T555 111T594 205T610 339ZM223 263V422H263V388H514V422H554V263H514V297H263V263H223Z"></path><path stroke-width="0" id="E314-MJMAIN-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 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-1 0 0)"><use xlink:href="#E316-MJMATHI-68" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E316-MJMATHI-3B8" x="814" y="-218"></use><use xlink:href="#E316-MJMAIN-28" x="1007" y="0"></use><use xlink:href="#E316-MJMATHI-78" x="1396" y="0"></use><use xlink:href="#E316-MJMAIN-29" x="1968" y="0"></use><use xlink:href="#E316-MJMAIN-2248" x="2635" y="0"></use><g transform="translate(3691,0)"><use xlink:href="#E316-MJMATHI-78" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E316-MJMAIN-31" x="808" y="-213"></use></g><g transform="translate(4966,0)"><use xlink:href="#E316-MJMAIN-41"></use><use xlink:href="#E316-MJMAIN-4E" x="750" y="0"></use><use xlink:href="#E316-MJMAIN-44" x="1500" y="0"></use></g><g transform="translate(7480,0)"><use xlink:href="#E316-MJMATHI-78" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E316-MJMAIN-32" x="808" y="-213"></use></g></g></svg></span><script type="math/tex">h_\theta(x)\approx x_1\ \text{AND}\ x_2</script>。这样就实现了一个能够进行与运算的神经网络。 </p><p><img src='images/2413fbec8ff9fa1f19aaf78265b8a33b_Logistic_function.png' alt='sigmoid function' referrerPolicy='no-referrer' /></p><p> </p><p>再举一例，逻辑或(OR)运算（有一个真值则结果就为真）神经网络：</p><p><img src='images/20180117_000349.png' alt='' referrerPolicy='no-referrer' /></p><p> </p><h2><a name='header-n99' class='md-header-anchor '></a>8.6 例子和直观理解2(Examples and Intuitions II)</h2><p>下面逐步构建复杂一点的神经网络</p><p><img src='images/20180117_004820.png' alt='' referrerPolicy='no-referrer' /></p><p>如上图，我们分别构建了三个单层神经网络，将这三个网络组合起来，可得到一个新的神经网络，其可完成逻辑运算中的异或(XNOR)操作：</p><p><img src='images/20180116_235545.png' alt='' referrerPolicy='no-referrer' /></p><p>这里的组合即为 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="54.026ex" height="2.577ex" viewBox="0 -806.1 23261.1 1109.7" role="img" focusable="false" style="vertical-align: -0.705ex;"><defs><path stroke-width="0" id="E317-MJMAIN-58" 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x="0" y="0"></use><g transform="translate(389,0)"><use xlink:href="#E317-MJMAIN-28" x="0" y="0"></use><g transform="translate(389,0)"><use xlink:href="#E317-MJMAIN-4E"></use><use xlink:href="#E317-MJMAIN-4F" x="750" y="0"></use><use xlink:href="#E317-MJMAIN-54" x="1528" y="0"></use></g><g transform="translate(2805,0)"><use xlink:href="#E317-MJMAIN-78" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E317-MJMAIN-31" x="746" y="-213"></use></g><use xlink:href="#E317-MJMAIN-29" x="3787" y="0"></use></g><g transform="translate(4731,0)"><use xlink:href="#E317-MJMAIN-41"></use><use xlink:href="#E317-MJMAIN-4E" x="750" y="0"></use><use xlink:href="#E317-MJMAIN-44" x="1500" y="0"></use></g><g transform="translate(7162,0)"><use xlink:href="#E317-MJMAIN-28" x="0" y="0"></use><g transform="translate(389,0)"><use xlink:href="#E317-MJMAIN-4E"></use><use xlink:href="#E317-MJMAIN-4F" x="750" y="0"></use><use xlink:href="#E317-MJMAIN-54" x="1528" y="0"></use></g><g 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xlink:href="#E320-MJMAIN-29" x="888" y="0"></use></g></g></g></g></g></g></svg></span><script type="math/tex">\begin{align*}& a^{(2)} = g(\Theta^{(1)} \cdot x) \newline& a^{(3)} = g(\Theta^{(2)} \cdot a^{(2)}) \newline& h_\Theta(x) = a^{(3)}\end{align*}</script></p><p>可见，特征值能不断升级，并抽取出更多信息，直到计算出结果。而如此不断组合，我们就可以逐渐构造出越来越复杂、强大的神经网络，比如用于手写识别的神经网络。</p><h2><a name='header-n107' class='md-header-anchor '></a>8.7 多类别分类(Multiclass Classification)</h2><p>之前讨论的都是预测结果为单值情况下的神经网络，要实现多类别分类，其实只要修改一下输出层，让输出层包含多个输出单元即可。</p><p>举一个 4 分类问题的实例：</p><p><img src='images/20180117_010904.png' alt='' referrerPolicy='no-referrer' /></p><p>有四种分类情况，那么就让输出层包含 4 个输出单元即可，则 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="2.848ex" height="2.344ex" viewBox="0 -755.9 1226.1 1009.2" role="img" focusable="false" style="vertical-align: -0.588ex;"><defs><path stroke-width="0" id="E321-MJMATHI-68" d="M137 683Q138 683 209 688T282 694Q294 694 294 685Q294 674 258 534Q220 386 220 383Q220 381 227 388Q288 442 357 442Q411 442 444 415T478 336Q478 285 440 178T402 50Q403 36 407 31T422 26Q450 26 474 56T513 138Q516 149 519 151T535 153Q555 153 555 145Q555 144 551 130Q535 71 500 33Q466 -10 419 -10H414Q367 -10 346 17T325 74Q325 90 361 192T398 345Q398 404 354 404H349Q266 404 205 306L198 293L164 158Q132 28 127 16Q114 -11 83 -11Q69 -11 59 -2T48 16Q48 30 121 320L195 616Q195 629 188 632T149 637H128Q122 643 122 645T124 664Q129 683 137 683Z"></path><path stroke-width="0" id="E321-MJMAIN-398" d="M56 340Q56 423 86 494T164 610T270 680T388 705Q521 705 621 601T722 341Q722 260 693 191T617 75T510 4T388 -22T267 3T160 74T85 189T56 340ZM610 339Q610 428 590 495T535 598T463 651T384 668Q332 668 289 638T221 566Q168 485 168 339Q168 274 176 235Q189 158 228 105T324 28Q356 16 388 16Q415 16 442 24T501 54T555 111T594 205T610 339ZM223 263V422H263V388H514V422H554V263H514V297H263V263H223Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E321-MJMATHI-68" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E321-MJMAIN-398" x="814" y="-218"></use></g></svg></span><script type="math/tex">h_\Theta</script> 为 4 维向量。 </p><p>神经网络中的多分类算法算是对 one-vs-all 思想的扩展，定义预测结果一共有 4 种情况：</p><p><img src='images/20180117_011331.png' alt='' referrerPolicy='no-referrer' /></p><p>如果预测结果 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="14.081ex" height="12.383ex" viewBox="0 -2917 6062.8 5331.5" role="img" focusable="false" style="vertical-align: -5.608ex;"><defs><path stroke-width="0" id="E322-MJMATHI-68" d="M137 683Q138 683 209 688T282 694Q294 694 294 685Q294 674 258 534Q220 386 220 383Q220 381 227 388Q288 442 357 442Q411 442 444 415T478 336Q478 285 440 178T402 50Q403 36 407 31T422 26Q450 26 474 56T513 138Q516 149 519 151T535 153Q555 153 555 145Q555 144 551 130Q535 71 500 33Q466 -10 419 -10H414Q367 -10 346 17T325 74Q325 90 361 192T398 345Q398 404 354 404H349Q266 404 205 306L198 293L164 158Q132 28 127 16Q114 -11 83 -11Q69 -11 59 -2T48 16Q48 30 121 320L195 616Q195 629 188 632T149 637H128Q122 643 122 645T124 664Q129 683 137 683Z"></path><path stroke-width="0" id="E322-MJMAIN-398" d="M56 340Q56 423 86 494T164 610T270 680T388 705Q521 705 621 601T722 341Q722 260 693 191T617 75T510 4T388 -22T267 3T160 74T85 189T56 340ZM610 339Q610 428 590 495T535 598T463 651T384 668Q332 668 289 638T221 566Q168 485 168 339Q168 274 176 235Q189 158 228 105T324 28Q356 16 388 16Q415 16 442 24T501 54T555 111T594 205T610 339ZM223 263V422H263V388H514V422H554V263H514V297H263V263H223Z"></path><path stroke-width="0" id="E322-MJMAIN-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 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738Q55 746 60 749Z"></path><path stroke-width="0" id="E322-MJMAIN-3D" d="M56 347Q56 360 70 367H707Q722 359 722 347Q722 336 708 328L390 327H72Q56 332 56 347ZM56 153Q56 168 72 173H708Q722 163 722 153Q722 140 707 133H70Q56 140 56 153Z"></path><path stroke-width="0" id="E322-MJMAIN-5B" d="M118 -250V750H255V710H158V-210H255V-250H118Z"></path><path stroke-width="0" id="E322-MJMAIN-30" d="M96 585Q152 666 249 666Q297 666 345 640T423 548Q460 465 460 320Q460 165 417 83Q397 41 362 16T301 -15T250 -22Q224 -22 198 -16T137 16T82 83Q39 165 39 320Q39 494 96 585ZM321 597Q291 629 250 629Q208 629 178 597Q153 571 145 525T137 333Q137 175 145 125T181 46Q209 16 250 16Q290 16 318 46Q347 76 354 130T362 333Q362 478 354 524T321 597Z"></path><path stroke-width="0" id="E322-MJMAIN-31" d="M213 578L200 573Q186 568 160 563T102 556H83V602H102Q149 604 189 617T245 641T273 663Q275 666 285 666Q294 666 302 660V361L303 61Q310 54 315 52T339 48T401 46H427V0H416Q395 3 257 3Q121 3 100 0H88V46H114Q136 46 152 46T177 47T193 50T201 52T207 57T213 61V578Z"></path><path stroke-width="0" id="E322-MJMAIN-5D" d="M22 710V750H159V-250H22V-210H119V710H22Z"></path><path stroke-width="0" id="E322-MJSZ4-23A1" d="M319 -645V1154H666V1070H403V-645H319Z"></path><path stroke-width="0" id="E322-MJSZ4-23A3" d="M319 -644V1155H403V-560H666V-644H319Z"></path><path stroke-width="0" id="E322-MJSZ4-23A2" d="M319 0V602H403V0H319Z"></path><path stroke-width="0" id="E322-MJSZ4-23A4" d="M0 1070V1154H347V-645H263V1070H0Z"></path><path stroke-width="0" id="E322-MJSZ4-23A6" d="M263 -560V1155H347V-644H0V-560H263Z"></path><path stroke-width="0" id="E322-MJSZ4-23A5" d="M263 0V602H347V0H263Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E322-MJMATHI-68" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E322-MJMAIN-398" x="814" y="-218"></use><use xlink:href="#E322-MJMAIN-28" x="1226" y="0"></use><use xlink:href="#E322-MJMATHI-78" x="1615" y="0"></use><use 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type="math/tex">h_\Theta(x) =\begin{bmatrix}0 \newline 0 \newline 1 \newline 0 \newline\end{bmatrix}</script>，那么表示 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="7.037ex" height="2.577ex" viewBox="0 -806.1 3029.7 1109.7" role="img" focusable="false" style="vertical-align: -0.705ex;"><defs><path stroke-width="0" id="E323-MJMATHI-68" d="M137 683Q138 683 209 688T282 694Q294 694 294 685Q294 674 258 534Q220 386 220 383Q220 381 227 388Q288 442 357 442Q411 442 444 415T478 336Q478 285 440 178T402 50Q403 36 407 31T422 26Q450 26 474 56T513 138Q516 149 519 151T535 153Q555 153 555 145Q555 144 551 130Q535 71 500 33Q466 -10 419 -10H414Q367 -10 346 17T325 74Q325 90 361 192T398 345Q398 404 354 404H349Q266 404 205 306L198 293L164 158Q132 28 127 16Q114 -11 83 -11Q69 -11 59 -2T48 16Q48 30 121 320L195 616Q195 629 188 632T149 637H128Q122 643 122 645T124 664Q129 683 137 683Z"></path><path stroke-width="0" id="E323-MJMAIN-398" d="M56 340Q56 423 86 494T164 610T270 680T388 705Q521 705 621 601T722 341Q722 260 693 191T617 75T510 4T388 -22T267 3T160 74T85 189T56 340ZM610 339Q610 428 590 495T535 598T463 651T384 668Q332 668 289 638T221 566Q168 485 168 339Q168 274 176 235Q189 158 228 105T324 28Q356 16 388 16Q415 16 442 24T501 54T555 111T594 205T610 339ZM223 263V422H263V388H514V422H554V263H514V297H263V263H223Z"></path><path stroke-width="0" id="E323-MJMAIN-28" d="M94 250Q94 319 104 381T127 488T164 576T202 643T244 695T277 729T302 750H315H319Q333 750 333 741Q333 738 316 720T275 667T226 581T184 443T167 250T184 58T225 -81T274 -167T316 -220T333 -241Q333 -250 318 -250H315H302L274 -226Q180 -141 137 -14T94 250Z"></path><path stroke-width="0" id="E323-MJMATHI-78" d="M52 289Q59 331 106 386T222 442Q257 442 286 424T329 379Q371 442 430 442Q467 442 494 420T522 361Q522 332 508 314T481 292T458 288Q439 288 427 299T415 328Q415 374 465 391Q454 404 425 404Q412 404 406 402Q368 386 350 336Q290 115 290 78Q290 50 306 38T341 26Q378 26 414 59T463 140Q466 150 469 151T485 153H489Q504 153 504 145Q504 144 502 134Q486 77 440 33T333 -11Q263 -11 227 52Q186 -10 133 -10H127Q78 -10 57 16T35 71Q35 103 54 123T99 143Q142 143 142 101Q142 81 130 66T107 46T94 41L91 40Q91 39 97 36T113 29T132 26Q168 26 194 71Q203 87 217 139T245 247T261 313Q266 340 266 352Q266 380 251 392T217 404Q177 404 142 372T93 290Q91 281 88 280T72 278H58Q52 284 52 289Z"></path><path stroke-width="0" id="E323-MJMAIN-29" d="M60 749L64 750Q69 750 74 750H86L114 726Q208 641 251 514T294 250Q294 182 284 119T261 12T224 -76T186 -143T145 -194T113 -227T90 -246Q87 -249 86 -250H74Q66 -250 63 -250T58 -247T55 -238Q56 -237 66 -225Q221 -64 221 250T66 725Q56 737 55 738Q55 746 60 749Z"></path><path stroke-width="0" id="E323-MJMAIN-33" d="M127 463Q100 463 85 480T69 524Q69 579 117 622T233 665Q268 665 277 664Q351 652 390 611T430 522Q430 470 396 421T302 350L299 348Q299 347 308 345T337 336T375 315Q457 262 457 175Q457 96 395 37T238 -22Q158 -22 100 21T42 130Q42 158 60 175T105 193Q133 193 151 175T169 130Q169 119 166 110T159 94T148 82T136 74T126 70T118 67L114 66Q165 21 238 21Q293 21 321 74Q338 107 338 175V195Q338 290 274 322Q259 328 213 329L171 330L168 332Q166 335 166 348Q166 366 174 366Q202 366 232 371Q266 376 294 413T322 525V533Q322 590 287 612Q265 626 240 626Q208 626 181 615T143 592T132 580H135Q138 579 143 578T153 573T165 566T175 555T183 540T186 520Q186 498 172 481T127 463Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E323-MJMATHI-68" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E323-MJMAIN-398" x="814" y="-218"></use><use xlink:href="#E323-MJMAIN-28" x="1226" y="0"></use><use xlink:href="#E323-MJMATHI-78" x="1615" y="0"></use><g transform="translate(2187,0)"><use xlink:href="#E323-MJMAIN-29" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E323-MJMAIN-33" x="550" y="-213"></use></g></g></svg></span><script type="math/tex">h_\Theta(x)_3</script>，即分为第 3 类，对应于图中的摩托车(Motorcycle)。</p><p><strong>总结一下</strong></p><p>多分类问题，要分为 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="2.065ex" height="1.877ex" viewBox="0 -755.9 889 808.1" role="img" focusable="false" style="vertical-align: -0.121ex;"><defs><path stroke-width="0" id="E329-MJMATHI-4B" d="M285 628Q285 635 228 637Q205 637 198 638T191 647Q191 649 193 661Q199 681 203 682Q205 683 214 683H219Q260 681 355 681Q389 681 418 681T463 682T483 682Q500 682 500 674Q500 669 497 660Q496 658 496 654T495 648T493 644T490 641T486 639T479 638T470 637T456 637Q416 636 405 634T387 623L306 305Q307 305 490 449T678 597Q692 611 692 620Q692 635 667 637Q651 637 651 648Q651 650 654 662T659 677Q662 682 676 682Q680 682 711 681T791 680Q814 680 839 681T869 682Q889 682 889 672Q889 650 881 642Q878 637 862 637Q787 632 726 586Q710 576 656 534T556 455L509 418L518 396Q527 374 546 329T581 244Q656 67 661 61Q663 59 666 57Q680 47 717 46H738Q744 38 744 37T741 19Q737 6 731 0H720Q680 3 625 3Q503 3 488 0H478Q472 6 472 9T474 27Q478 40 480 43T491 46H494Q544 46 544 71Q544 75 517 141T485 216L427 354L359 301L291 248L268 155Q245 63 245 58Q245 51 253 49T303 46H334Q340 37 340 35Q340 19 333 5Q328 0 317 0Q314 0 280 1T180 2Q118 2 85 2T49 1Q31 1 31 11Q31 13 34 25Q38 41 42 43T65 46Q92 46 125 49Q139 52 144 61Q147 65 216 339T285 628Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E329-MJMATHI-4B" x="0" y="0"></use></g></svg></span><script type="math/tex">K</script> 类，就在输出层放置 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="2.065ex" height="1.877ex" viewBox="0 -755.9 889 808.1" role="img" focusable="false" style="vertical-align: -0.121ex;"><defs><path stroke-width="0" id="E329-MJMATHI-4B" d="M285 628Q285 635 228 637Q205 637 198 638T191 647Q191 649 193 661Q199 681 203 682Q205 683 214 683H219Q260 681 355 681Q389 681 418 681T463 682T483 682Q500 682 500 674Q500 669 497 660Q496 658 496 654T495 648T493 644T490 641T486 639T479 638T470 637T456 637Q416 636 405 634T387 623L306 305Q307 305 490 449T678 597Q692 611 692 620Q692 635 667 637Q651 637 651 648Q651 650 654 662T659 677Q662 682 676 682Q680 682 711 681T791 680Q814 680 839 681T869 682Q889 682 889 672Q889 650 881 642Q878 637 862 637Q787 632 726 586Q710 576 656 534T556 455L509 418L518 396Q527 374 546 329T581 244Q656 67 661 61Q663 59 666 57Q680 47 717 46H738Q744 38 744 37T741 19Q737 6 731 0H720Q680 3 625 3Q503 3 488 0H478Q472 6 472 9T474 27Q478 40 480 43T491 46H494Q544 46 544 71Q544 75 517 141T485 216L427 354L359 301L291 248L268 155Q245 63 245 58Q245 51 253 49T303 46H334Q340 37 340 35Q340 19 333 5Q328 0 317 0Q314 0 280 1T180 2Q118 2 85 2T49 1Q31 1 31 11Q31 13 34 25Q38 41 42 43T65 46Q92 46 125 49Q139 52 144 61Q147 65 216 339T285 628Z"></path></defs><g 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-250T58 -247T55 -238Q56 -237 66 -225Q221 -64 221 250T66 725Q56 737 55 738Q55 746 60 749Z"></path></defs><g stroke="currentColor" fill="currentColor" stroke-width="0" transform="matrix(1 0 0 -1 0 0)"><use xlink:href="#E328-MJMATHI-79" x="0" y="0"></use><g transform="translate(499,362)"><use transform="scale(0.707)" xlink:href="#E328-MJMAIN-28" x="0" y="0"></use><use transform="scale(0.707)" xlink:href="#E328-MJMATHI-69" x="389" y="0"></use><use transform="scale(0.707)" xlink:href="#E328-MJMAIN-29" x="733" y="0"></use></g></g></svg></span><script type="math/tex">y^{(i)}</script>。注意，神经网络中的预测和结果都是 <span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="2.065ex" height="1.877ex" viewBox="0 -755.9 889 808.1" role="img" focusable="false" style="vertical-align: -0.121ex;"><defs><path stroke-width="0" id="E329-MJMATHI-4B" d="M285 628Q285 635 228 637Q205 637 198 638T191 647Q191 649 193 661Q199 681 203 682Q205 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