{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

Week 5: Introduction to neural Networks

\n", "\n", "

CSCI-UA 9473 - Introduction to Machine Learning

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Solutions

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Perceptron learning rule" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This week, we will start working with neural networks. For each of the exercises below you can use the method of your choice but you should display the final boundary of your classifier." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 1. \n", "As a first exercise, load the binary dataset below and code a few steps of the perceptron learning rule. " ] }, { "cell_type": "code", "execution_count": 387, "metadata": {}, "outputs": [ { "data": { "image/png": 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ga9ekhKHJThFJnIED7SNdLVwYe1w8KwtWrUpaIm+KhlZERBqy//6xa02UlcEeeyQ/ngYokYuINGTiRJsHqCkSsTHyzp2DiSkGJXIRkYbsvbetWjn4YBtO6dHDdoTedlvQkdXS5Bi5cy4beAvoEP3zT3jvf+ec6wpMBXoDK4HTvPffJS5UEZEADB6c8iVvm3NHXgoc6b0fCAwCRjjnhgBXAjO8932BGdHnIiKSZE0mcm+qesC0j354YDQwJXp8CnBiIgIUEZHGNWuM3DnX1jn3EbAWmO69/wDo4b3/BiD6uXvCohQRkQY1K5F77yu894OAXYEDnXM/aO4JnHPjnXNznHNz1q1b18owM5f38Mor8NOfWs2f994LOiIRSTUtWrXivd8IzARGAGuccz0Bop/XNvB37vHeF3jvC/Lz87cv2gBVVMAf/gDdutkmtgMPhP/+N7Hn9B7GjrWyFg8+CP/8Jxx9NPz2t4k9r4iES5OJ3DmX75zbIfo4BzgKWAo8C4yN/rGxwLQExZgSfvUrmDQJNmywpD57NgwbZhu/EuWdd2x3cGGhPffe+sbeeiusWJG484pIuDTnjrwn8IZzbj4wGxsjfx6YBBztnFsGHB19npa+/daabhcV1T5eUgI33pi48z77bP1zgjU6efnlxJ1XRMKlyXXk3vv5wH4xjm8AhiUiqFTz2WdWsbO0tPbxykqrGZQoeXk2jFO3fn/bttXNjkVCrbIS7rsPJk+2u5ZTT4XLL0+pXZNhoJ2dzdC7t5VWqKtNG2s2nCg//nHsRt+VlTB6dOLOK5I0P/2plcP9+GPr4n3bbTYBVbeFnDRKibwZdtwRfvITK7FQU3a2lWJIlD594M477TwdO9pHbq6Nm++wQ+LOK5IUy5bBY4/VHj8sLbXysI88ElxcIaRE3kyTJ8Nll1kCdc7q6r/6amLvyAHGjYOvv4Z774UpU2DNGjjmmMSeUyQpPvgg9lvOwkKYMSP58YSY6pE3U7t2cN119uG9JfNk6dLFavaLpJWdd479QsrKsvFMaTbdkbdCMpO4SNoaOtTGLevW+27fHsaPDySksFIiF5FgtGkDM2fCAQfYRFBuLuyyi6277dUr6OhCRUMrIhKcXr1g1iz48kub9OzTJ3ZHHmmUErmIBG/XXYOOINT0q09EJOSUyEVEQk6JXEQk5JTIRURCTolcRCTklMhFRGJ58kkYNAjy82HkSJg/P+iIGqRELiJS1+TJVinv449h/Xp46SU4+GBYsCDoyGJSIk9hn3wCU6fafgnvg45GJEOUl1tZ05pVGavac6Von0VtCEpB27bBmDHw/PNWrKuyEvr2hddes9IUqaSkxCqPqg+ApI2vvqrfzQUsmc+alfx4mkF35CnottvghRestv6WLVbVc9EiK2mbKjZtgtNPtwSenw977QXvvRd0VBJK331nvRT//ndYvjzoaOwHurIy9tdStAaMEnkKuuuu+r06y8ut/vnWrcHEVNfxx8Mzz1jnpPJyWLoUhg9XU+iU9/77NnH3/e9bbeREdg9vjhdftO35F15oLd722QeuuSbYmHJzYexYyMmpfTwSgWuvDSamJiiRp6DCwoa/VrdvaBAWLoR58+q3vysrgzvuCCYmaYYXX4SjjrLPK1bYqowhQ2Du3GDi2brVfpkUFdkPfXGxjdX95S/Bv73729+sDV1Ojn1062YToMceG2xcDVAiT0GjRsVunPL976fGGPmKFVYyuq7ycli8OPnxhNbmzdYJZ968xM9mew+//GXtt3qVlZZAL7ssseduyCuvxK50WFwM//pX8uOpKSvLEveGDfDpp7B6dWqNbdahRJ6C/vhHuwGoemeXlQV5eTaMmAoGDoz9ziA7Gw49NPnxBKahcdTm+PvfYaed4OST4fDDoX9/+Oyz+MVWV3ExfP557K8FNYG3bVvs497HnmwMQk4O9OwJbdsGHUmjlMhTUM+eNuZ8ww3wox/BJZfYZOeQIUFHZnr1svxTsxl1mzb2y2bChODiSpoXXoB+/ezF3a0b3HJLy+6o334brrjCkuumTXZXvHw5jBiRuDvzDh3sN20s+fmJOWdThg+PnbBzc+GMM5IfT5h575P2ccABB3hJD+Xl3k+a5P1uu3nfpYv3Z5zh/cqVQUeVBK+/7n1OjveWcu0jEvH+979v/vc47TTvnav9PcD7vDzv585NXOyXXRY79smTE3fOpjz4oMXUvr1dk9xc78eN876yMriYUhAwxzeSW51P4k6TgoICP2fOnKSdTyTuDj0U3n23/vG8PBtPzcpq+nsccYS1OKurc2d44gmbkEyE8nIbJ//Xv2ySY9s2uPRSuP76YBvRrlgBjzxia21POAF++EM1xq3DOTfXe1/Q4NeVyEVaID/ftmzXlZMDy5ZZz8mm3H47XHWVDa3U/R6rV0OnTvGJtSEbN9qml969bRhDUl5TiVxj5CItMWBA7OPt2jV/rPncc22ioWo22zmbcJg0KfFJHGCHHWDvvZXE04i26Iu0xA03wDHH1L6bjkTsDrs5wypgCXT2bLjvPnjqKejRw4Y8Dj88MTFL2tPQikhLvfaajS0vWmRJ+Oqr4fzzNa4rCdPU0IruyEVa6qijrLypSIrQGLmISMgpkYuIhJwSuYhIyCmRi4iEXJOJ3Dm3m3PuDefcEufcIufcRdHjXZ1z051zy6KfuyQ+XBERqas5d+TbgEu993sBQ4ALnHMDgCuBGd77vsCM6HMREUmyJhO59/4b7/286OMtwBJgF2A0MCX6x6YAJyYoRhERaUSLxsidc72B/YAPgB7e+2/Akj3QPe7RibTE+vWwZEn91kUiaa7Zidw5lwc8CVzsvd/cgr833jk3xzk3Z926da2JUaRxW7bA6NHW+/Ggg6B7d/jnP4OOSiRpmpXInXPtsST+sPf+qejhNc65ntGv9wTWxvq73vt7vPcF3vuC/KAK2Et6GzPG2oaVllpS37QJLr7YttKLZIDmrFpxwH3AEu/9n2t86VlgbPTxWGBa/MMTacLq1Zaw6/aeKyqCm28OJiaRJGtOrZVDgLOBBc65j6LHrgYmAY85584BPgdOTUiEIo1Zu9aaJJSU1P/aF18kPx5pnPfw3/9aa7uBA2HffYOOKC00mci99+8ADZV1GxbfcERaqG/f2H0u27WzTjySOr791gqOffKJVYqsrLSOS9OmNdxPVJol5Xd2fvst3HMP3HYbLFgQdDQNKyyEhx6yPrzvvpu4HrpSR04O/PGPtTtBt2sHHTtaeVlJHeedBwsX2otl61Yb/nrrLbjuuqAjC7/GGnrG+6OlzZdffdV6w0Yi3mdlWY/WCRNSry/r/PnWgDgvz3rI5uZ6f8wx3peVBR1ZBnn+ee8PO8z7Pfbw/uc/937VqmDjKSz0/vrrve/b1/t+/by/6SbvS0qCjSlIZWX24qjbcBq8z8+P33mWLLHm1rvt5v2hh3r/0kvx+94BIqzNl0tKrGb/5joLHXNzrT/tiBEJCLAVvIc997R2jTVFInDTTXDhhcHEJQGqqICDD4b586vH7nNyYPBga7qciQ0oiovtXVJFRf2vdexY/4XeGkuWwIEH2p1+ZaUdi0Rg8mQYN277v3+AQtuzM1aTcbB3ZQ8+mMxIGvfpp9bHtq6iIuvkJRnolVdg8eLaE7DFxTBvHrz5ZnBxxcs771in+9xcm6P497+b/js5ObDffvWPt20Lxx0Xn7iuucYSRFUSB3shXnopbNsWn3OkqJRN5DX/L+qK9Us9KGGJU5Logw9sDLiukhL7Wpi9/771LP3vfy1JLl8OEybA7bc3/Xfvu8+aS1dNbEYi0K0b3HprfGJ7773Yk1MlJfD11/E5R4pK2UQ+dGjsRJibCz/5SdLDaVDfvraRsK6cnNC/m5PW2nXX2B3qs7Nhl12SH088TZxoCbymoiL4/e+bvuvdd19bsXLNNXD66XDjjfC//9n1ioedd459vLISunaNzzlSVMom8kgEHn7YEmKHDjasmJsLJ54Ixx8fdHTVnIPHHrMbjaqFE3l59i7yl78MNjYJyOmn28qZmpyzH+STTgompnhpaOlYaSk0pwRHjx72y+DRR+Gii6Bz5/jFNnFi7dVLYAlkzBh7UaaxlG6+PHq0vXN79FHbdT1iBAwZknpzRYMHw8qVFufXX8Mhh8Dw4dAmZX9NSkJ16mRj4WecYT8YYG/dHn20fqIJm+99z4qT1dWmTfB3vSedZBNWEyfaEEt5OZx2Gtx5Z7BxJUHKrloRSQtffGF3HvEaPqjywgtw/fWwahUUFMANN8CgQfE9Rywvvwwnn1x7eCUSsbvrG29M/Pmbo6wMPv8c8vPje8cfoNCuWhFJC7vtFv8kPmWK3WnOmgVr1sCLL9rbwHnz4nueWEaMgPvvt7H+qo1Xv/mN/SJJFVlZ0KdP2iTx5tAduUiYVFTATjvFHt445hi7Y04G722pXySiMcQkaOqOPKXHyEWkjnXrYi9tBEjmTZJzaT+BGCb6VSoSJjvs0PBsf9iXNkqrKZGLhEl2NowfX3/1SyQC114bTEwSOA2tiITNrbfaWPl999ndeVaWrRg5+eSgI5OAaLJTJKyKimDDBpv8bN8+6GgkgTTZKZKuIpHwbzCSuFAil8yzbRu8/rrdzR5+uCYJJfTScrJz8WI45xzbzn/xxbbJSwSARYtsg86pp1rHmj594Mor1dJJQi3txsjfeguOPdZq+FRU2NBhTo5VD+3fP6GnllTnPfTuXf83e24uTJ0KI0cGEpZIUzJui/748TYHVFUCt7wctmyByy4LNi5JAXPnWhPYugoL4a67kh+PSJykVSIvLLSOPXV5nx6NWWQ7FRU1vJ18y5bkxiISR2mVyLOyrHNULBlUP0cacuCBscfCIxGrIS4SUmmVyNu3hx//uLqTVJWqKpuS4bKz4Z57bNKkqvFDXh7svTf87GfBxpapPvnEXpyjRsFf/tJ4E+Zt28I3Kb12LVx3nXXDufrq2A1+48F7n7SPAw44wCdaYaH3o0Z5n53tfefO3nfo4P348d5v25bwU0tYLF7s/cUXe3/mmd4//LD3paVBR5SZXnnF+0jE+3btvAd7vNtu3q9bV/vPvf669wMGeO+c93l53l9+ufdlZcHE3BKffOJ9ly6WjMCSUceO3n/0UYu/FTDHN5Jb027VSpVVq2DFChgwwLpLSQgsWgSTJsH8+dYs4YoroF+/oKOSRKistFrtdZsiZ2XB+efb3TnAhx/CoYfWb2Rx5plw773Ji7c1Ro6El16q/y5iyBBrYt0CTa1aSdtELiHz7rtWT7u42F7k7drZUMibb8L++wcdncTbihWwzz71GzmDLRH97DN7fMop8NRT9ZNhdjZ8+SXsuGPCQ2217GxbB12Xc9bFqG5f10Zk3PJDCanzz7dlR5WV9nzbNqu7rcmN9JSXV71GuK5OnaofL1oUe1w8K8vedqeyhsonNLYqo5WUyCV4FRUNd2efNSu5sUhydO8OBx1U/640EoFf/ar6+f77x14yWlYGe+yR2Bi317nn1l950aEDnHVW3DvIK5FL8Nq0afjuRetG09fUqbbdOi/P7sKzsy3J1VxBNHGirTKqKRKBCROsyUYq+8MfYNgwi79TJ4v7hz+Ev/417qdS0SwJnnPwi1/A5Mk2Rl5F60bT20472cT27Nm2LO+AA2D33Wv/mQEDYOZMK5o0Zw506QKXXAKXXhpExC3ToQM8/zz87382RNSvH/zgBwk5lSY7JTWUldlb0ccftxdAaSmMHWvJPc7jiSJho3rkEg5ZWfCvf8Ett9iKhr59oVu3oKMSCQUlckktPXpo4b9IC2myU0Qk5JpM5M65+51za51zC2sc6+qcm+6cWxb93CWxYYqISEOac0f+IDCizrErgRne+77AjOhzkfRXVATPPgtPPgmbNgUdTbWNG23ZXiRi8w2jRqk1VgZpcozce/+Wc653ncOjgaHRx1OAmcAV8QxMJOVMnw4nnWTr3r233af/+AecfXawcXkPRxxhPQ7LyuzYSy/B4MGwfDl07BhsfJJwrR0j7+G9/wYg+rl7/EISSUEbN8KPfmRlAzZvtkYUxcXW93PFimBje+stS9hVSRxst2xhITz8cHBxSdIkfLLTOTfeOTfHOTdn3bp1iT6dSGJMmxZ7W/W2bfDQQ8mPp6bFi2PXLSkshI8/Tn48knStTeRrnHM9AaKf1zb0B73393jvC7z3Bfn5+a08nUjACgtjJ8uqprBB6t8/9qap3FzYd9/kxyNJ19pE/iwwNvp4LDAtPuGIpKhjjoldhS83F044Ifnx1DR0qBWQysqqPtamjcV21lmBhSXJ05zlh48A7wN7Oue+dM6dA0wCjnbOLQOOjj4XSV/f/z78+te2KqRqiCUvD0aPtsYHQXLO6rafcYYVnmrXDkaMgA8+CH6ic906uOEG+2X329/WbyQhcaFaKyIt8fbb8OCDNrF45plw7LFxL0m63bxPjZg+/dQaXhcVQUmJ1dDp0MEmZwcODDq6UFGtlbBav96aLHTXgqCUcthh9pHKUiGJg1Us3LixullIaal9TJjQ4lZn0jht0U81K1ZYzeJdd7WSngMHwsKFTf+97eW9vT2/6SZ44IHgJ/Ak/GbMqE7iNc2aZat9JG50R55KSkvhkENg7drqF8D8+XYHuHJl4poslJVZo9j337e3wNnZNh78xhuw336JOaekv5yc2vXlq7RvH7vrj7SarmYqee652n0rq5SVwSOPJO68d98N771XvcSusNC2n59ySuyVGiLNcc45sVudjRmjRB5nupqp5PPPY3fdLipK7O7B+++P3c189WpYtixx55X0FqvV2YEHwu23Bx1Z2tHQSiopKLC1wDW3WoMtcxsyJHHnbeyuO5l35N7bW/EOHdQVKB1UtTpbutTmefr1q79BqbjYhlrqNmGWFtEdeSo57DAbk675drRDB+jd26rZJcq4cfUb3IKtmOnXL3Hnrenll6FPH7tz69TJ+jKWlyfn3JJY/fvbMF3NJP7uu9a/smNHu1E599zY7wqlWZTIU4lz8OqrcNVV0KuXrVy56CL7oW/fPnHnveACOOgge0E5ZzsCO3Wy/pnJWMo2axacfLINH1VU2Av67rttmZqkn08+sZ2yixbZ/3dpqRX3OvXUoCMLLW0IEuO9rVJ55x3o2RNOOy1xq2TqOuEEewte92cxO9u6q3ftmpw4JDnOO8/mZeouQczOtgJg3/teMHGlMG0IkuZxDo480j6SbenS2GPxWVnw5ZdK5Olm0aLY68g7dLDdoErkLaahFQleQUHs5Wjl5XpRp6MhQ2oX+KpSWgoDBiQ/njSgRC7Bu+aa+pOtkYjNDwRd9Eni7+KL7f+75vxLJGJFv3beObCwwkyJXII3YIAVUho61F7Qu+8Of/oT3Hhj0JFJIuy6q1VmPPZYm1jv2RMmToR77w06stDSZKeISIprarJTd+QiIiGnVSsi6WbzZvjPf2y9dkGBrdHv0CHoqCSBlMhF0snSpVZBs7TUip/l5cG119qY9I47Bh2dJIiGVkTSybhx8N13lsQBtm61YmwTJwYaliSWErlIuti6FebOrb+5qrwcnngimJgkKZTIRdJFmzYN18ZRNcm0pkQuki4iEVuLXzdpZ2fD2LGBhCTJoUQu0hLew5Il1oIvVj/KoD3wgG246djRVqrk5sKgQfC73wUdmSSQVq1I08rLYdo0eO01SxLjxtnnTLNgAZx4IqxZY0MYeXkwdSocfnjQkVXbZRdYvhxeegk++8yadx9+eHLKEUtgtLNTGldcbIlg6VKbTKvq3jNtGhx1VNDRJU9JiSXJb7+tfTw31yr29egRTFySEbSzU7bPnXda2dGtW+15aak1fhgzxpoCZIpnn43dsaiiAv797+THI1KDErk07uGH7a68ruJiG2rIFGvWxE7kJSXw9dfJj0ekBiVyaVzN/qE1VVZm1rbvww6LXTM9Lw+OOCL58YjUoEQujZswwcaBa3LOxov79w8mpiAMGgQjR9a+Fjk51lD4uOMCC0sElMilKWedBSedZEkrErFlbfn58MwzmbcS4pFH4G9/s0bV++1n9dJnzNBmGwmcVq1I8yxebI2Ze/SwhgCxWnWJSEKo+bLEx4AB6qcokqI0tCIiEnJK5CIiIadELiISckrkIiIhp0QuIhJySV1+6JxbB6xK2gmD1Q1YH3QQKUTXo5quRW26HrXFuh69vPf5Df2FpCbyTOKcm9PYus9Mo+tRTdeiNl2P2lpzPTS0IiISckrkIiIhp0SeOPcEHUCK0fWopmtRm65HbS2+HhojFxEJOd2Ri4iEnBJ5HDjn7nfOrXXOLaxxrKtzbrpzbln0c5cgY0wW59xuzrk3nHNLnHOLnHMXRY9n6vXIds7Ncs59HL0e10WPZ+T1AHDOtXXOfeicez76PJOvxUrn3ALn3EfOuTnRYy2+Hkrk8fEgMKLOsSuBGd77vsCM6PNMsA241Hu/FzAEuMA5N4DMvR6lwJHe+4HAIGCEc24ImXs9AC4CltR4nsnXAuAI7/2gGksOW3w9lMjjwHv/FlCnvTqjgSnRx1OAE5MZU1C899947+dFH2/BXrC7kLnXw3vvo52raR/98GTo9XDO7QqMBO6tcTgjr0UjWnw9lMgTp4f3/huw5AZ0DziepHPO9Qb2Az4gg69HdCjhI2AtMN17n8nX46/A5UBljWOZei3Afqm/6pyb65wbHz3W4uuhxhKSEM65POBJ4GLv/WaXaW3havDeVwCDnHM7AE87534QcEiBcM4dD6z13s91zg0NOJxUcYj3/mvnXHdgunNuaWu+ie7IE2eNc64nQPTz2oDjSRrnXHssiT/svX8qejhjr0cV7/1GYCY2n5KJ1+MQ4ATn3ErgUeBI59xDZOa1AMB7/3X081rgaeBAWnE9lMgT51lgbPTxWGBagLEkjbNb7/uAJd77P9f4UqZej/zonTjOuRzgKGApGXg9vPdXee939d73Bs4AXvfen0UGXgsA51yuc65j1WNgOLCQVlwPbQiKA+fcI8BQrGrZGuB3wDPAY8DuwOfAqd77uhOiacc5dyjwNrCA6nHQq7Fx8ky8HvtiE1ZtsRunx7z31zvndiQDr0eV6NDKZd774zP1Wjjn9sDuwsGGuf/jvf9ja66HErmISMhpaEVEJOSUyEVEQk6JXEQk5JTIRURCTolcRCTklMhFREJOiVxEJOSUyEVEQu7/ARsLKHHqS3mKAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import scipy.io as sio\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.colors import ListedColormap\n", "cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n", "\n", "data1 = sio.loadmat('perceptron_data_class1.mat')\n", "data2 = sio.loadmat('perceptron_data_class2.mat')\n", "\n", "data1 = data1['perceptron_data_class1']\n", "data2 = data2['perceptron_data_class2']\n", "\n", "\n", "data = np.vstack((data1,data2))\n", "\n", "target1 = np.ones((np.shape(data1)[0], 1))\n", "target2 = -np.ones((np.shape(data2)[0], 1))\n", "targets = np.vstack((target1, target2))\n", "\n", "\n", "plt.scatter(data[:,0], data[:,1], c = targets, cmap = cm_bright)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The code below fits a simple perceptron to the linearly separable dataset given above by repeatedly applying the perceptron learning rule until all the points are correctly classified." ] }, { "cell_type": "code", "execution_count": 388, "metadata": {}, "outputs": [], "source": [ "beta = np.random.normal(0,1,(3,))\n", "\n", "\n", "# looking for the points sign(beta^T x^i) != t^i\n", "\n", "\n", "Xtilde = np.hstack((np.ones((np.shape(data)[0], 1)) , data))\n", "\n", "prediction = np.matmul(Xtilde, beta.reshape(-1,1))\n", "\n", "indices_misclassified = np.squeeze(np.argwhere(np.squeeze(np.sign(prediction))\\\n", " !=np.squeeze(targets)))\n", "\n", "# perceptron learning rule, beta <--- beta + eta* x^i * t^i\n", "\n", "eta = .1\n", "\n", "while indices_misclassified.size>0:\n", " \n", " \n", " beta += np.squeeze(eta*Xtilde[indices_misclassified[0]] * \\\n", " targets[indices_misclassified[0]])\n", " \n", " # re-evaluate the misclassified samples\n", " \n", " prediction = np.matmul(Xtilde, beta.reshape(-1,1))\n", " \n", " indices_misclassified = np.squeeze(np.argwhere(np.squeeze(np.sign(prediction))\\\n", " !=np.squeeze(targets)))\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then display the result using meshgrid and contourf." ] }, { "cell_type": "code", "execution_count": 389, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x1min = np.min(data[:,0]) \n", "x1max = np.max(data[:,0]) \n", "x2min = np.min(data[:,1])\n", "x2max = np.max(data[:,1])\n", "\n", "x1 = np.linspace(x1min, x1max, 500)\n", "x2 = np.linspace(x2min, x2max, 500)\n", "\n", "xx1, xx2 = np.meshgrid(x1, x2)\n", "\n", "tmp = np.vstack((xx1.flatten(), xx2.flatten())).T\n", "\n", "gridPoints = np.hstack((np.ones((len(xx1.flatten()), 1)), tmp))\n", "\n", "prediction = np.sign(np.matmul(gridPoints, beta.reshape(-1,1)))\n", "\n", "plt.contourf(xx1, xx2, prediction.reshape(np.shape(xx1)), alpha=.2, cmap = cm_bright)\n", "plt.scatter(data[:,0], data[:,1], c = targets, cmap = cm_bright)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 2.\n", "\n", "__2a.__ Load the data below. Using the neural_network module from scikit-learn and its MLPClassifier model, learn a classifier, for the dataset below using \n", "\n", "- One hidden layer with a linear activation function and \n", " - One neuron\n", " - Two neurons\n", " \n", " \n", " \n", "- One hidden layer with a non linear activation function (take Relu for example or a binary step)\n", " - One neuron\n", " - Two neurons\n", "\n", "How many neurons, hidden layers do you need to learn the distribution of the data? Do you have an idea why?\n", "\n", "Try increasing the number of neurons and hidden layers. Then try different values of the learning rate. \n" ] }, { "cell_type": "code", "execution_count": 381, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "from sklearn.neural_network import MLPClassifier\n", "import matplotlib.pyplot as plt\n", "import scipy.io as sio\n", "import numpy as np\n", "\n", "data1 = sio.loadmat('data_Week6_class1_XOR.mat')\n", "data2 = sio.loadmat('data_Week6_class2_XOR.mat')\n", "\n", "data1 = data1['data_Week6_class1_XOR']\n", "data2 = data2['data_Week6_class2_XOR']\n", "\n", "\n", "plt.scatter(data1[:,0], data1[:,1], c= 'r')\n", "plt.scatter(data2[:,0], data2[:,1], c= 'b')\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start by learning a simple 1 hidden layer classifier with a single (linear) unit in the hidden layer classifier. We can learn this classifier with SGD (default solver provided by scikit learn) as shown below. " ] }, { "cell_type": "code", "execution_count": 400, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data = np.vstack((data1, data2))\n", "target1 = np.ones((np.shape(data1)[0], 1))\n", "target2 = 0*np.ones((np.shape(data2)[0], 1))\n", "targets = np.vstack((target1, target2))\n", "\n", "from sklearn.neural_network import MLPClassifier\n", "import warnings\n", "from sklearn.exceptions import DataConversionWarning\n", "warnings.filterwarnings(action='ignore')\n", "\n", "# generating the grid\n", "x1min = np.min(data[:,0])\n", "x1max = np.max(data[:,0])\n", "x2min = np.min(data[:,1])\n", "x2max = np.max(data[:,1])\n", "x1 = np.linspace(x1min, x1max, 500)\n", "x2 = np.linspace(x2min, x2max, 500)\n", "xx1, xx2 = np.meshgrid(x1, x2)\n", "\n", "data_grid = np.vstack((xx1.flatten(), xx2.flatten())).T\n", "\n", "# single neuron (on top of the sigmoid activation of the output unit)\n", "\n", "my_model = MLPClassifier(hidden_layer_sizes = (1,), \\\n", " activation = 'identity')\n", "\n", "my_model.fit(data, np.squeeze(targets.reshape(-1,1)))\n", "prediction = my_model.predict(data_grid)\n", "\n", "plt.contourf(xx1, xx2, prediction.reshape(np.shape(xx1)), \\\n", " alpha = 0.2, cmap =cm_bright)\n", "\n", "\n", "plt.scatter(data1[:,0], data1[:,1], c= 'r')\n", "plt.scatter(data2[:,0], data2[:,1], c= 'b')\n", "plt.title('Solution found through a couple of SGD steps')\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then look at the value of the loss returned for this first classifier by using the 'loss_' attribute provided by scikit's MLPClassifier implementation. " ] }, { "cell_type": "code", "execution_count": 390, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.7508727688086991" ] }, "execution_count": 390, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_model.loss_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this simple framework (2D dataset with a very small dataset), we can in fact improve the search by relying on more powerful (quasi second order) solvers such as LBFGS. Although we might not be able to escape some of the local minimizers (see spiral example with the sigmoid activation), if the landscape is relatively smooth, we should be able to reach a better minimizer. " ] }, { "cell_type": "code", "execution_count": 403, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "from sklearn.neural_network import MLPClassifier\n", "import matplotlib.pyplot as plt\n", "import scipy.io as sio\n", "import numpy as np\n", "\n", "data1 = sio.loadmat('data_Week6_class1_XOR.mat')\n", "data2 = sio.loadmat('data_Week6_class2_XOR.mat')\n", "\n", "data1 = data1['data_Week6_class1_XOR']\n", "data2 = data2['data_Week6_class2_XOR']\n", "data = np.vstack((data1,data2))\n", "\n", "target1 = np.ones((np.shape(data1)[0], 1))\n", "target2 = 0*np.ones((np.shape(data2)[0], 1))\n", "targets = np.vstack((target1, target2))\n", "\n", "\n", "\n", "my_model = MLPClassifier(hidden_layer_sizes = (1,), \\\n", " activation = 'identity', solver = 'lbfgs')\n", "\n", "my_model.fit(data, np.squeeze(targets.reshape(-1,1)))\n", "\n", "x1min = np.min(data[:,0])\n", "x1max = np.max(data[:,0])\n", "x2min = np.min(data[:,1])\n", "x2max = np.max(data[:,1])\n", "x1 = np.linspace(x1min, x1max, 100)\n", "x2 = np.linspace(x2min, x2max, 100)\n", "xx1, xx2 = np.meshgrid(x1, x2)\n", "\n", "data_grid = np.vstack((xx1.flatten(), xx2.flatten())).T\n", "\n", "prediction = my_model.predict(data_grid)\n", "\n", "plt.contourf(xx1, xx2, prediction.reshape(np.shape(xx1)), \\\n", " alpha = 0.2, cmap =cm_bright)\n", "\n", "plt.scatter(data[:,0], data[:,1], c = targets, cmap = cm_bright)\n", "plt.title('Solution found through LBFGS')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To compare our LBFGS solution with the solution returned by the Stochastic gradient steps, we can again check the final value of the loss (bianry cross entropy in this case) using the 'loss_' attribute." ] }, { "cell_type": "code", "execution_count": 404, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.6854995827167638\n" ] } ], "source": [ "print(my_model.loss_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In turns out that in this case, the best solution puts all the sample in a single class. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then increase the number of neurons and consider non linear activation functions" ] }, { "cell_type": "code", "execution_count": 407, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# two neurons with linear activation\n", "\n", "my_model = MLPClassifier(hidden_layer_sizes = (2,), activation = 'identity')\n", "my_model.fit(data, np.squeeze(targets.reshape(-1,1)))\n", "prediction = my_model.predict(data_grid)\n", "\n", "plt.contourf(xx1, xx2, prediction.reshape(np.shape(xx1)), \\\n", " alpha = 0.2, cmap =cm_bright)\n", "\n", "\n", "plt.scatter(data1[:,0], data1[:,1], c= 'r')\n", "plt.scatter(data2[:,0], data2[:,1], c= 'b')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Considering the Sigmoid function first.." ] }, { "cell_type": "code", "execution_count": 409, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "my_model = MLPClassifier(hidden_layer_sizes = (10,10), \\\n", " activation = 'logistic', \n", " solver = 'lbfgs', max_iter=100000, alpha=.1)\n", "\n", "my_model.fit(data, np.squeeze(targets.reshape(-1,1)))\n", "prediction = my_model.predict(data_grid)\n", "\n", "plt.contourf(xx1, xx2, prediction.reshape(np.shape(xx1)), \\\n", " alpha = 0.2, cmap =cm_bright)\n", "\n", "\n", "plt.scatter(data1[:,0], data1[:,1], c= 'b')\n", "plt.scatter(data2[:,0], data2[:,1], c= 'r')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then the relu" ] }, { "cell_type": "code", "execution_count": 408, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "my_model = MLPClassifier(hidden_layer_sizes = (10,10), \\\n", " activation = 'relu', \n", " solver = 'lbfgs', max_iter=100000, alpha=.1)\n", "\n", "my_model.fit(data, np.squeeze(targets.reshape(-1,1)))\n", "prediction = my_model.predict(data_grid)\n", "\n", "plt.contourf(xx1, xx2, prediction.reshape(np.shape(xx1)), \\\n", " alpha = 0.2, cmap =cm_bright)\n", "\n", "\n", "plt.scatter(data1[:,0], data1[:,1], c= 'b')\n", "plt.scatter(data2[:,0], data2[:,1], c= 'r')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 3. \n", "\n", "__3a.__Load the data below. Try to build the best neural network you can for this dataset. Split the data between a training and a test set and evaluate the models you built. What is the best validation error you can get?" ] }, { "cell_type": "code", "execution_count": 410, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.neural_network import MLPClassifier\n", "import matplotlib.pyplot as plt\n", "import scipy.io as sio\n", "import numpy as np\n", "\n", "data1 = sio.loadmat('neural_net_ex2_class1.mat')\n", "data2 = sio.loadmat('neural_net_ex2_class2.mat')\n", "\n", "data1 = data1['neural_net_ex2_class1']\n", "data2 = data2['neural_net_ex2_class2']\n", "data = np.vstack((data1, data2))\n", "\n", "target1 = np.ones((np.shape(data1)[0], 1))\n", "target2 = np.zeros((np.shape(data2)[0], 1))\n", "\n", "targets = np.vstack((target1, target2))\n", "\n", "# scaling the data (centering and dividig by the variance)\n", "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", "scaler.fit(data)\n", "scaled_data = scaler.transform(data)\n", "plt.scatter(scaled_data[:,0], scaled_data[:,1], c= targets, cmap = cm_bright)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will successively train three neural networks with activations given by (1) the relu (2) the hypebolic tangent and (3) the sigmoid. For each of those MLP architecture, one can get a sense of the complexity of the landscape by playing with the 'random_state' parameter which controls weights and bias initialization. The quality of the solution also illustrates a difference in the convergence to local vs global solutions" ] }, { "cell_type": "code", "execution_count": 413, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# learning a neural network with the relu activation function\n", "\n", "my_neural_net = MLPClassifier(hidden_layer_sizes = (50,50),\\\n", " activation = 'relu', \\\n", " solver = 'lbfgs', max_iter = 15000, \\\n", " alpha=.005,random_state = 100)\n", "\n", "my_neural_net.fit(scaled_data, np.squeeze(targets))\n", "\n", "x1min=np.min(scaled_data[:,0])\n", "x1max=np.max(scaled_data[:,0])\n", "x2min=np.min(scaled_data[:,1]) \n", "x2max=np.max(scaled_data[:,1])\n", "\n", "x1 = np.linspace(x1min, x1max)\n", "x2 = np.linspace(x2min, x2max)\n", "\n", "xx1, xx2 = np.meshgrid(x1, x2)\n", "\n", "data_grid = np.vstack((xx1.flatten(), xx2.flatten())).T\n", "\n", "prediction = my_neural_net.predict(data_grid)\n", "\n", "plt.contourf(xx1, xx2, prediction.reshape(np.shape(xx1)), alpha =.2, cmap = cm_bright)\n", "plt.scatter(scaled_data[:,0], scaled_data[:,1], c= targets, cmap = cm_bright)\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 412, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# learning the network with tanh\n", "my_neural_net = MLPClassifier(hidden_layer_sizes = (30,30),max_iter=20000,\\\n", " learning_rate_init=.0001, alpha = .1, solver = 'lbfgs',\\\n", " activation = 'tanh', random_state = 100)\n", "\n", "\n", "my_neural_net.fit(scaled_data, np.squeeze(targets))\n", "\n", "x1min=np.min(scaled_data[:,0])\n", "x1max=np.max(scaled_data[:,0])\n", "x2min=np.min(scaled_data[:,1]) \n", "x2max=np.max(scaled_data[:,1])\n", "\n", "x1 = np.linspace(x1min, x1max)\n", "x2 = np.linspace(x2min, x2max)\n", "\n", "xx1, xx2 = np.meshgrid(x1, x2)\n", "\n", "data_grid = np.vstack((xx1.flatten(), xx2.flatten())).T\n", "\n", "prediction = my_neural_net.predict(data_grid)\n", "\n", "plt.contourf(xx1, xx2, prediction.reshape(np.shape(xx1)), alpha =.2, cmap = cm_bright)\n", "plt.scatter(scaled_data[:,0], scaled_data[:,1], c= targets, cmap = cm_bright)\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 411, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# learning the network with a logistic activation\n", "\n", "my_neural_net = MLPClassifier(hidden_layer_sizes = (10,10),max_iter=100000,\\\n", " alpha=1e-15, solver = 'lbfgs',\\\n", " activation = 'logistic', random_state = 100,max_fun = 1e7)\n", "\n", "my_neural_net.fit(scaled_data, np.squeeze(targets))\n", "\n", "x1min=np.min(scaled_data[:,0])\n", "x1max=np.max(scaled_data[:,0])\n", "x2min=np.min(scaled_data[:,1]) \n", "x2max=np.max(scaled_data[:,1])\n", "\n", "x1 = np.linspace(x1min, x1max)\n", "x2 = np.linspace(x2min, x2max)\n", "\n", "xx1, xx2 = np.meshgrid(x1, x2)\n", "\n", "data_grid = np.vstack((xx1.flatten(), xx2.flatten())).T\n", "\n", "prediction = my_neural_net.predict(data_grid)\n", "\n", "plt.contourf(xx1, xx2, prediction.reshape(np.shape(xx1)), alpha =.2, cmap = cm_bright)\n", "plt.scatter(scaled_data[:,0], scaled_data[:,1], c= targets, cmap = cm_bright)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__3b.__ With the same dataset, add additional features to your model, e.g. $\\sin(x), \\sin(y)$ or other monomials. Can you improve your classifier ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 4.\n", "\n", "Let us go back briefly to a simple dataset to make sure we understand how things work. As a first exercise, we will code a one hidden layer neural network that outputs a binary 0/1 variable indicating the class of our data. Throughout this exercise, we will use the notation $z^{\\ell+1} = \\sigma(a^{\\ell+1})$ to denote the output of any neuron from the $(\\ell+1)^{th}$ layer and where $a^{\\ell+1} = \\sum_{k} w_{\\ell+1,k} z_k$ is the combination from the previous layer that is fed to the neuron. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\"Drawing\"\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use the function ['minimize'](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html) from scipy. Check the documentation of that function. We will set the 'jac' parameter of the optimizer to 'True', which implies that the (objective) function that we provide as an input should return both the value of the loss and the value of its gradient. \n", "\n", "For this first exercise, you are asked to write a function $f(W)$ wich takes as arguments a vector $W$ containing all the parameters of your network (as a first step consider building a network with only a few hidden units, in order to make sure the model is working). As indicated above, the function should return (1) the value of the binary cross entropy for the given set of weights and (2) the value of the gradient derived through Backpropagation (as we set the value of 'jac' to True). \n", "\n", " We will split the writing of your function into several steps. Once you have coded each step, you should gather them together in a single $fun(W)$ body that you will then pass as input to the minimize function. \n", "\n", "### Exercise 4.a.\n", "\n", "Start Load the data using the lines below and plot it using scatter()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.optimize import minimize\n", "\n", "import scipy.io as sio\n", "data1 = sio.loadmat('pointsClass1Week6.mat')\n", "data2 = sio.loadmat('pointsClass2Week6.mat')\n", "\n", "from numpy import linalg as LA\n", "\n", "data1 = data1['pointsClass1Week6']\n", "data2 = data2['pointsClass2Week6']\n", "\n", "\n", "# put your code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 4.b \n", "\n", "As indicated above, we want to apply the network to the simple binary dataset that you loaded above. We want to build an architecture similar to the one shown above, except that, since we only consider a 2D dataset, we only need 2 inputs. we want our activation function to be all sigmoid. Start by defining the function sigmoid and the gradient of this function. Once you are done, check your derivative. Also make sure you can compute the entrywise sigmoid on any numpy array if the input to your function is a numpy array. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 4.c\n", "\n", "Now that we have the sigmoid and its gradient, we will code the loss. As you might remember from previous labs, the MLPClassifier from scikit-learn optimizes the 'log-loss function' (a.k.a Binary cross entropy) which reads for a set of $N$ prototypes $x_i$ with binary $0/1$ targets, \n", "\n", "$$−\\frac{1}{N}\\sum_{i=1}^N (y_i \\log(p_{W}(x_i))+(1−y_i)\\log(1−p_{W}(x_i))) $$\n", "\n", "Here the probability $p_{W}(x_i)$ is the output of your network. Instead of minimizing this function directly, we will consider its $\\ell_2$ regularized version\n", "\n", "$$−\\frac{1}{N}\\sum_{i=1}^N (y_i \\log(p_{W}(x_i))+(1−y_i)\\log(1−p_{W}(x_i))) + \\lambda \\sum_{j\\in\\text{weights}\\setminus \\text{bias}} W^2_j$$\n", "\n", "Where the $W_j$ encode the weights. Code that function for a given labeled dataset $X,t$ such as given above, a set of weights stored in the vector $W$ (you can use a list or a dictionnary if you want but ultimately, you will need to store them in a numpy vector for use with the optimization routine). Note that one typically does not regularize the bias terms. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 4.d. \n", "\n", "Towards Backpropagation. The example above is relatively simple so that backpropagation is not really needed. Compute the gradient of the Binary cross entropy loss with respect to the weights. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 4.e. \n", "\n", "Combine all your previous steps into a single function and pass this function to the 'minimize' routine. To choose the initial value for the weights, a common heuristic is for the n^th layer to be initialized unformly at random in the interval $[-\\varepsilon_n, \\varepsilon_n]$ with $\\epsilon_n$ defined as $\\sqrt{\\frac{2}{\\text{size layer}_{\\ell-1} + \\text{size layer}_{\\ell} }}$\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bonus 1 \n", "\n", "Change the activation to the Relu and reapeat the steps above. How do you compute the gradient in this case?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 5. \n", "\n", "Coding a One hidden layer neural network is good to warm up but to really understand backpropagation, we will not add a few hidden layers. Still relying on the code that you developed above and using backpropagation, train a depth 4 neural network with 10 neurons in each layer on the binary dataset. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 5a. \n", "\n", "In this exercise, because of the multiple layers of the network, you will get to really code backpropagation. To do this, follow the steps below " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(see [Bishop Pattern Recognition and Machine Learning](http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf)) for more details.\n", "\n", "- Take any of the sample (Once you have completed one step of backprop, you should re-apply it to the next sample to get the gradient contribution for that next sample and so on). Forward propagate that sample through the network and compute all the activations $z = \\sigma(a_i)$ and input $a_i$ for each unit. \n", "\n", "- Evaluate the $\\delta_k = y_k(x_n) - t_{n,k}$ for all the output units.\n", "\n", "- Backpropagate the $\\delta_k$ using the formula\n", "\n", "$$\\delta_j = \\sigma'(a_j) \\sum_{k} w_{k,j} \\delta_k$$\n", "\n", "- Finally, compute the derivatives as \n", "\n", "$$\\frac{\\partial \\text{loss}}{\\partial w_{ij}} = \\delta_j z_i$$\n", "\n", "Note that when coding the backpropagation algorithm, you don't need to account for the regularization part of the loss as you can just add the gradient of the regularization to the result of the backpropagation algorithm. \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.optimize import minimize\n", "\n", "\n", "# put your code here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise 6. \n", "\n", "Extending to multiple classes. Load the data using the lines given below, visualize it with scatter. How can you extend the Binary cross entropy function to the multiclass problem?" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "\n", "import scipy.io as sio\n", "data1 = sio.loadmat('xWeek6Ex3PointsClass1.mat')\n", "data2 = sio.loadmat('xWeek6Ex3PointsClass2.mat')\n", "data3 = sio.loadmat('xWeek6Ex3PointsClass3.mat')\n", "\n", "from numpy import linalg as LA\n", "\n", "data1 = data1['xWeek6Ex3PointsClass1']\n", "data2 = data2['xWeek6Ex3PointsClass2']\n", "data3 = data3['xWeek6Ex3PointsClass3']\n", "\n", "\n", "# put your code here\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }