2360 lines
170 KiB
Plaintext
2360 lines
170 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 142,
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"id": "672d74cf-2fad-4493-9e3d-7ac2286dbfee",
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"metadata": {},
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"outputs": [],
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"source": [
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"import math\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e7295cd8-1f33-40d2-aca5-e226e5b945ba",
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"metadata": {},
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"source": [
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"Let's define a function f"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 143,
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"id": "07a0fdd9-e9c4-4a28-9201-99429a638573",
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"metadata": {},
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"outputs": [],
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"source": [
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"def f(x):\n",
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" return 3*x**2 - 4*x + 5"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 144,
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"id": "67093e12-40d4-4abd-a81d-2f7f63881e19",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"20.0"
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]
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},
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"execution_count": 144,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"f(3.0)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ec1297be-8d68-4d59-af98-39e45eb9857b",
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"metadata": {},
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"source": [
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"We can also plot it for a range of values"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 145,
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"id": "0a3f756f-886b-455d-b1f2-53060088dce9",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x13257cd0250>]"
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]
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},
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"execution_count": 145,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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Pi6Vf2xbU1LmYrU0ERUTEz6mg+JA7zq8fRXkrex/l1XUmpxEREWk6Kig+JLNLPO1aR1BaVcc8bSIoIiJ+TAXFh1gsRsMoyt+X79UmgiIi4rdUUHzMqD5taB0VwiFHFR9pE0EREfFTKig+JsRm5bah9ZsIvrJsN263NhEUERH/o4LigxptIri92Ow4IiIiHqeC4oPsYUHc0LCJ4G6T04iIiHieCoqPGj80jSCrwaq9JXyjTQRFRMTPqKD4qER7GFd9t4ngMm0iKCIi/kUFxYdNPHHL8aLNBew9XGFyGhEREc9RQfFhneKjuDgj7sQmghpFERER/6GC4uPuuKA9AO/lHKC4rNrkNCIiIp6hguLjBqS1oE9qjDYRFBERv6KC4uMMw+CO8+tHUd7MzqW0qtbkRCIiImdPBcUPDO8aT4e4SEqr6ngre5/ZcURERM6aCoofsFgMJl1YP4ry9+V7qaypMzmRiIjI2VFB8RNX9EyibctwSipqmLtqv9lxREREzooKip+wWS3cNax+FOWVZXuoqnWanEhEROTMqaD4kav7JNMmJozismreXZtndhwREZEzpoLiR4JtFu68oH512Vlf7aamzmVyIhERkTOjguJnxvRPIS4qhHxHFe+vO2B2HBERkTOiguJnQoOsDXv0vPzVbuqcGkURERHfo4Lih24YlErLiGD2l1Ty4YZ8s+OIiIicNhUUPxQebOP289IBePHLXThdbpMTiYiInB4VFD910+C22MOC2FNcwaebDpkdR0RE5LSooPipqNAgxg9NA+DFL3bh0iiKiIj4EBUUPzb+nHQiQ2xsKyjj862FZscRERE5ZSoofsweHsTNQ9oC8MIXu3C7NYoiIiK+QQXFz91+bjphQVY2HnTw1Y5is+OIiIicEhUUP9cyMoRxg1IBeGHJTo2iiIiIT1BBCQATz29HsM3Cuv3HyN59xOw4IiIiP0sFJQDERYcydkAKUD8XRURExNupoASIOy5oT5DVIHvPEdbmlpgdR0RE5CepoASINjFhjO6bDGgURUREvJ8KSgC5a1gHrBaDpTuK2ZB3zOw4IiIiP0oFJYCktgznql5JQP0ePSIiIt5KBSXA3HVhBwwDFm8pZEt+qdlxRERETkoFJcB0iIvksh6JADy/ZKfJaURERE5OBSUATbm4I4YBizYXsPGAw+w4IiIiP6CCEoA6xkcxqncbAJ5ZvN3kNCIiIj+kghKg7rm4I1aLwZfbi8nZd9TsOCIiIo00SUE5ePAgN954Iy1btiQsLIwePXqwdu3ahsfdbjczZswgMTGRsLAwMjMz2blT8yGaU1qrCK49sS6KRlFERMTbeLygHD16lKFDhxIUFMSnn37Kli1b+Mtf/kKLFi0anvPkk0/y/PPPM2vWLFatWkVERAQjRoygqqrK03HkJ9x9cQeCrAZf7zrCit2HzY4jIiLSwHB7eHvbadOm8fXXX/Of//znpI+73W6SkpK47777uP/++wFwOBzEx8fzxhtvMHbs2J/9HqWlpdjtdhwOB9HR0Z6MH3BmLNzEm9n76N+2BfPvHIJhGGZHEhERP3U6n98eH0H58MMP6d+/P2PGjCEuLo4+ffrw6quvNjy+d+9eCgoKyMzMbDhnt9sZNGgQ2dnZJ33N6upqSktLGx3iGZMu7ECIzcLafUdZuqPY7DgiIiJAExSUPXv2MHPmTDp27Mhnn33Gr3/9a37zm98we/ZsAAoKCgCIj49v9HXx8fENj31fVlYWdru94UhJSfF07IAVHx3KTYPbAvDM4h14eEBNRETkjHi8oLhcLvr27ctjjz1Gnz59mDhxIhMmTGDWrFln/JrTp0/H4XA0HHl5eR5MLHcOa094sJVvDzhYvKXQ7DgiIiKeLyiJiYl07dq10bkuXbqwf/9+ABISEgAoLGz8QVhYWNjw2PeFhIQQHR3d6BDPaRUZwvihaUD9KIrLpVEUERExl8cLytChQ9m+vfFtqzt27KBt2/rLCOnp6SQkJLBkyZKGx0tLS1m1ahVDhgzxdBw5RRPPa09UqI1tBWV8vPGQ2XFERCTAebyg3HvvvaxcuZLHHnuMXbt2MXfuXP76178yadIkAAzDYMqUKTz66KN8+OGHbNy4kZtvvpmkpCRGjRrl6ThyiuzhQUw4rx0Az36+gzqny+REIiISyDxeUAYMGMCCBQt4++236d69O4888gjPPfcc48aNa3jOgw8+yN13383EiRMZMGAA5eXlLFq0iNDQUE/HkdMwfmgaMeFB7Cmu4IP1+WbHERGRAObxdVCag9ZBaTqzlu7m8U+3kRIbxhf3DSPIqt0QRETEM0xdB0V8281D2tIqMoS8kuPMX3vA7DgiIhKgVFCkkfBgG5MubA/AC1/spKrWaXIiEREJRCoo8gO/HJhKoj2UQ44q3l693+w4IiISgFRQ5AdCg6xMvqgDAC99uZvjNRpFERGR5qWCIic1pl8KKbFhHC6v5s3sXLPjiIhIgFFBkZMKtlm45+JOQP2dPWVVtSYnEhGRQKKCIj9qVO8k2rWO4GhlLa9/nWt2HBERCSAqKPKjbFYL92bWj6K8umwPRytqTE4kIiKBQgVFftLIHol0SYymrLqOF7/cZXYcEREJECoo8pMsFoPpl2YA8GZ2LnkllSYnEhGRQKCCIj/r/E6tOa9jK2qdbp7+9/af/wIREZGzpIIip+S3l2RgGLBwfT4bDzjMjiMiIn5OBUVOSfc2dq7u3QaAxz7Zig/uMSkiIj5EBUVO2dThnQi2Wcjec4SvdhSbHUdERJqA2+1mV1GZ2TFUUOTUJbcIZ/w5aQA8/sk2nC6NooiI+Jt/fXuIXzy7jIcXbjI1hwqKnJa7hnXAHhbE9sIy/rnugNlxRETEg6pqnTyxaBtuN8RGhJiaRQVFTos9PIjJF9ZvJPjMv3doI0ERET8ye0UuB44eJz46hAnnp5uaRQVFTttNQ9rSJiaMgtIqXvt6r9lxRETEA0oqahoW5Lx/eGfCg22m5lFBkdMWGmTlgRGdAZj51W6OlFebnEhERM7W//t8B2VVdXRNjGZ032Sz46igyJm5slcS3ZKiKa+u44UvtAS+iIgv211czpxV+wH4w8guWCyGyYlUUOQMWSwGv7usCwD/WLmP3MMVJicSEZEzlfXJNupcbi7OiOOcDq3MjgOooMhZGNqhFRd0ak2dy81TWgJfRMQnZe8+wudbC7FaDKaf+B9Pb6CCImdl2qX1S+B//O0hvtl/1Ow4IiJyGlwuN3/+ZAsANwxMpUNcpMmJ/ksFRc5Kl/+ZTJX16TYtgS8i4kMWfHOQTQdLiQyxMSWzo9lxGlFBkbM29RedCLFZWL23hC+2FZkdR0RETsHxGmfDDvV3XdielpHmLsz2fSooctaSYsK47dz6BX0e/3QbdU6XyYlEROTn/H35Hg45qmgTE8ZtQ81dlO1kVFDEI349rD0twoPYWVTOezlaAl9ExJsVlVUx86vdADx4SWdCg6wmJ/ohFRTxiOjQIO6+qP765TOLd1BZU2dyIhER+THPLt5JRY2TXsl2ruiZZHack1JBEY+5cXBbUmPDKSqr5tVlWgJfRMQbbS8o4501JxZlu7yrVyzKdjIqKOIxwTYLD15yYgn8pbvIP3bc5EQiIvJ9j32yFZcbLumWwIC0WLPj/CgVFPGokT0SGZgWS1Wti6xPt5kdR0RE/seyHcUs3VFMkNVg2qUZZsf5SSoo4lGGYfDwlV2xGPDRhnxW7y0xO5KIiABOl5vHPtkKwE2D00hrFWFyop+mgiIe1y3JztiBqQD88cPNOF1avE1ExGzz1+axraAMe1gQv7m4g9lxfpYKijSJ+4d3JjrUxpZDpcw7MRlLRETMUVFdx18W7wDg7os6EBMebHKin6eCIk0iNiKYe3/RCYCnP9uOo7LW5EQiIoHrlWV7KC6rJjU2nJuGtDU7zilRQZEmc+PgtnSMi+RoZS3Pfr7D7DgiIgHp4LHj/HVZ/aJs0y7NIMTmfYuynYwKijSZIKuFh6/oBsBbK/exo7DM5EQiIoHnzx9voarWxcC0WC7tnmB2nFOmgiJN6tyOrRjRLR6ny83/fbRFux2LiDSj5TsP88nGAqwWgz9d1Q3D8M5F2U5GBUWa3B9GdiXYZmH5rsP8e0uh2XFERAJCTZ2Lhz/cBMBNg9vSJTHa5ESnRwVFmlxKbDgTz2sHwKMfb6Gq1mlyIhER//f613vZXVxBq8j/3rTgS1RQpFncdWF7EqJDySs5zt/+s8fsOCIifq3AUcXzS3YC8NtLMrCHBZmc6PSpoEizCA+2Mf2y+mWVX/pyN4cc2qdHRKSpPPbJVipqnPRJjWF032Sz45wRFRRpNlf2SmJAWguO1zp5XPv0iIg0iZV7jvDhhnwMAx65qrvX7lb8c1RQpNkYhsHDV3TDMGDh+nzW5mqfHhERT6p1unh44WYAbhiYSvc2dpMTnTkVFGlW3dvYGTsgBYA/fqR9ekREPOmt7H1sLyyjRXgQD4zobHacs6KCIs3u/uGdiQq1selgKe+uzTM7joiIXygqq+LZE/vtPDAiwyf22/kpKijS7FpGhnBvZv0tb099th3Hce3TIyJytp74dDtl1XX0TLZz/YmRal+mgiKmuGlI/T49JRU1/L/Pd5odR0TEp+XsK+Gf6w4A8Kcru2H10Ymx/0sFRUwRZLUw44quALyZncv2Au3TIyJyJpwuNzNOTIy9vn8KfVJbmJzIM1RQxDTndWzNiG7x1Lnc/G7BRlyaMCsictrmrt7P5vxSokNtPHiJb0+M/V8qKGKqP17ZjYhgKzn7jvKOJsyKiJyWkooanv5sOwD3j+hMy8gQkxN5jgqKmCrRHsZ9w+sbf9YnWykuqzY5kYiI73jqs204jtfSJTGaGwammh3Ho1RQxHS3nJNGjzZ2SqvqePTjLWbHERHxCRvyjjFvTf3I8yNXdcNm9a+PdP/6acQnWS0Gj13dA8uJFWaX7Sg2O5KIiFdzudzMWLgJtxuu6dOG/mmxZkfyOBUU8Qo9ku3cck4aAA8t3ERVrdPcQCIiXuydtXlsOOAgMsTGtBMbsfobFRTxGvcN70xCdCj7jlTy4he7zI4jIuKVisqqyPpkKwBTMjsSFxVqcqKmoYIiXiMyxMYfr+wGwCvLdrOjUGujiIh8358+3EJpVR092ti59cTIsz9SQRGvMqJbPJld4ql1uvm91kYREWlk8ZZCPt54CKvFIOuaHn43MfZ/+e9PJj7JMAz+dFU3woOtrMk9yvwcrY0iIgJQVlXLQx9sAuBX56XTvY3d5ERNq8kLyuOPP45hGEyZMqXhXFVVFZMmTaJly5ZERkYyevRoCgsLmzqK+Ig2MWFM/UX9ZoKPfbKNw+VaG0VE5KnPtlNQWkXbluFMubiT2XGaXJMWlDVr1vDKK6/Qs2fPRufvvfdePvroI+bPn8/SpUvJz8/nmmuuacoo4mNuPSeNronROI7X8uePt5odR0TEVDn7Snhr5T4Asq7uQViw1eRETa/JCkp5eTnjxo3j1VdfpUWL/25c5HA4+Pvf/84zzzzDRRddRL9+/Xj99ddZsWIFK1eubKo44mNsVgtZ1/TAMGDBNwdZvvOw2ZFERExRXefkt//ciNsNY/olc06HVmZHahZNVlAmTZrEyJEjyczMbHQ+JyeH2traRuczMjJITU0lOzv7pK9VXV1NaWlpo0P8X6+UGG4e3BaAP3ywUWujiEhAmvnVbnYVldMqMpjfj+xidpxm0yQFZd68eaxbt46srKwfPFZQUEBwcDAxMTGNzsfHx1NQUHDS18vKysJutzccKSkpTRFbvNB9IzoTHx1C7pFKXv5Sa6OISGDZWVjGSyd+9z18RTdiwoNNTtR8PF5Q8vLyuOeee5gzZw6hoZ5ZPGb69Ok4HI6GIy9Pd3YEiujQIP54Rf3aKDOX7mZXkdZGEZHA4HK5mfb+Rmqdbi7OiOPynolmR2pWHi8oOTk5FBUV0bdvX2w2GzabjaVLl/L8889js9mIj4+npqaGY8eONfq6wsJCEhISTvqaISEhREdHNzokcFzSPYGLM+Kodbr53YJNuN1aG0VE/N+c1fvJ2XeUiGArj4zqjmEYZkdqVh4vKBdffDEbN25k/fr1DUf//v0ZN25cw78HBQWxZMmShq/Zvn07+/fvZ8iQIZ6OI37gu7VRwoKsrN5bwvy1B8yOJCLSpA45jvPEp9sAePCSDJJiwkxO1Pxsnn7BqKgounfv3uhcREQELVu2bDh/++23M3XqVGJjY4mOjubuu+9myJAhDB482NNxxE8ktwjn3l905LFPtvHox1s4v1NrEuz+uf+EiAQ2t9vNQx9spry6jj6pMdx44maBQGPKSrLPPvssl19+OaNHj+b8888nISGB999/34wo4kNuG5pOr2Q7pVV1TH//W13qERG/9OmmAj7fWkiQ1eCJ0T2xWgLr0s53DLcP/pYvLS3FbrfjcDg0HyXA7CwsY+Tzy6lxunjy2p5c1193dImI/3BU1pL57FKKy6r5zUUdmDq8s9mRPOp0Pr+1F4/4lI7xUUwdXr/E8yMfbSH/2HGTE4mIeE7Wp1spLqumfesIJl3Uwew4plJBEZ8z4bx29EmNoay6jmnvb9SlHhHxC9m7jzBvTf0yGo+P7kmIzf+Xs/8pKijic6wWg6fH9CLEZmHZjmLeWaN1cUTEt1XVOvndgo0AjBuUyoC0WJMTmU8FRXxS+9aRPDCi/trsox9v5cDRSpMTiYicuScWbWPv4Qrio0P47aUZZsfxCioo4rPGD02nf9sWlFfX8dt/6q4eEfFNK3Yd5vWvcwF4YnRPokODzA3kJVRQxGdZLQZPXtuT0CALX+86wpxV+82OJCJyWhzHa7l//gag/tLOsM5xJifyHioo4tPatY7kwRH1w6GPfbKVvBJd6hER3/GnjzaT76iibctwfndZ4OxUfCpUUMTn3XpOGgPTYqmscfLge9/iculSj4h4v0WbDvH+uoNYDHjmul5EhHh8cXefpoIiPs9iMXhqTE/Cgqxk7znCP1btMzuSiMhPKiqr4ncLNgFw5wXt6ddWd+18nwqK+IW2LSOYfln9pZ6sT7ax70iFyYlERE7O7Xbzu/c3UlJRQ5fEaKZkdjI7kldSQRG/ceOgtgxp15LjtU4e0KUeEfFS89ce4POtRQRbLTx7fS+CbfooPhm9K+I3LCfu6gkPtrJ6bwmzs3PNjiQi0kheSSV/+mgzAPcN70RGgvaT+zEqKOJXUmL/OxP+u4WPRES8gdPl5r53N1BR42RAWgt+dV47syN5NRUU8TvjBqVybodWVNW6eGD+Bpy61CMiXuC15XtZnVtCeLCVv4zpjdVimB3Jq6mgiN8xDIPHR/cgMsTG2n1HmbV0t9mRRCTAbS8o46nPtgPw0OVdSW0ZbnIi76eCIn4puUU4D1/RFYBnFu9g3f6jJicSkUBVU+fi3nfWU+N0cVFGHGMHpJgdySeooIjfurZfMlf2SsLpcvObt7+htKrW7EgiEoCeX7KTLYdKaREexOOje2AYurRzKlRQxG8ZhsGjV3cnJTaMA0eP87v3N2pDQRFpVjn7jvLyV7sA+PPVPYiLCjU5ke9QQRG/Fh0axPNj+2CzGPzr20PMzzlgdiQRCRCVNXXc9+56XG64uk8bLuuRaHYkn6KCIn6vT2oLpg6vX6nx4YWb2V1cbnIiEQkEj368ldwjlSREh/LHK7uZHcfnqKBIQLjz/PYM7VC/yuzdc7+hus5pdiQR8WP/+jafuav2A/D0mF7Yw4JMTuR7VFAkIFgsBs9c15vYiGC2HCrliU+3mx1JRPxU7uEKpv1zIwB3DWvPuR1bmZzIN6mgSMCIjw7l6TE9AXjt6718sa3Q5EQi4m+q65xMfnsd5dV1DEhrwdRfaCPAM6WCIgHloox4bj0nDYD7539LUWmVuYFExK889vFWNh2sv6X4+V/2wWbVx+yZ0jsnAWfapRl0SYympKKGe99dr12PRcQjPt14iNnZ+wB45rreJNrDTE7k21RQJOCEBll54Zd9CAuy8vWuI7yybI/ZkUTEx+0/UsmD730LwB0XtOPCjDiTE/k+FRQJSB3iIvnjlfVL4f/l39v5Rkvhi8gZ+m7eSVl1Hf3atuD+4Z3NjuQXVFAkYF3XP4WRPROpc7n5zTwthS8iZ+bxT7fx7QEH9rD6eSdBmnfiEXoXJWAZhsFjV/egTUwYeSXH+cOCTVoKX0ROy2ebC3j961wA/jKmF21iNO/EU1RQJKDV/x9Pb6wWgw835POelsIXkVOUV1LJA/M3ADDhvHQyu8abnMi/qKBIwOvXNpZ7MzsC8NDCTWw9VGpyIhHxdjV1Lu5++xtKq+ronRLDg5dkmB3J76igiAC/HtaB8zq2oqrWxR1v5XCsssbsSCLixZ5ctI31eceIDrXx4g2ad9IU9I6KAFaLwfNj+5DcIoz9JZXcM289Tq2PIiIn8fmWQv62fC9Qv89OcotwkxP5JxUUkRNaRATzyk39CLFZWLqjmOc+32F2JBHxMgePHee+E/NObhuazvBuCSYn8l8qKCL/o1uSncdH9wDghS928dnmApMTiYi3qHW6uHvuOhzHa+mVbGfapZp30pRUUES+5+o+yQ379dz37gZ2F5ebG0hEvMIj/9rCuv3HiAq18eINfQm26SO0KendFTmJ34/swsC0WMqr67jjrRzKq+vMjiQiJnp79X7ezN6HYcCz1/UmJVbzTpqaCorISQRZLbw4rg/x0SHsKirn/nc3aBE3kQC1JreEGQs3AXDfLzppvZNmooIi8iPiokKZeWM/gqwGizYXMHPpbrMjiUgzO3jsOHe+lUOt083InolMurCD2ZEChgqKyE/om9qCP17ZDYCnP9vOsh3FJicSkeZyvMbJxDfXcqSihq6J0Tx1bU8MwzA7VsBQQRH5GTcMTOX6/im43PCbed+QV1JpdiQRaWJut5sH3tvA5vxSWkYE8+ot/QkPtpkdK6CooIj8DMMw+NNV3eiVbOdYZS13vJXD8Rqn2bFEpAm9/NVu/vXtIWwWg5k39tMmgCZQQRE5BaFBVmbe2I+WEcFsOVTK7xds1KRZET/1+ZZCnv73dgD+dFU3BqbHmpwoMKmgiJyipJgwXrihD1aLwfvfHGT2ilyzI4mIh+0sLGPKO+txu+HGwamMG9TW7EgBSwVF5DSc074V00+sHvnox1vJ3n3E5EQi4imOylomvLmW8uo6BqXH8vAV3cyOFNBUUERO0+3npnNlryTqXG7ueGstu4rKzI4kImepzuli8tvryD1SSZuYMF4e11c7FJtM777IaTIMgyev7Unf1BhKq+q49fU1FJdVmx1LRM7C459u4z87DxMWZOXVm/vTMjLE7EgBTwVF5AyEnvgl1rZlOAeOHudXs9dQWaPl8EV80T9zDvC35XsB+Mt1veiaFG1yIgEVFJEz1jIyhDfGD6RFeBAbDjj4zdvrcbp0Z4+IL/lm/1GmL9gIwG8u6sBlPRJNTiTfUUEROQvprSJ49eb+BNssfL61kEf+tcXsSCJyivYfqWTCmznU1LkY3jWeKZmdzI4k/0MFReQs9U+L5ZnregHwxopc/n5iqFhEvNfh8mpufm0Vh8ur6ZIYzTPX98Zi0TL23kQFRcQDLu+ZxLSG24+3sGhTgcmJROTHVFTXcfsba8g9UklyizBmjx9AZIiWsfc2KigiHnLH+e0YNygVtxvumfcN3+w/anYkEfmeWqeLX89Zx4YDDmIjgnnztoHERYeaHUtOQgVFxEMMw+BPV3bjws6tqa5z8avZa9l/RBsLingLt9vNb9/7lmU7igkLsvL3W/rTrnWk2bHkR6igiHiQzWrhxRv60i0pmiMVNdz6xmqOVdaYHUtEgCcWbef9bw5itRi8PK4vfVJbmB1JfoIKioiHRYTYeO3WASTZQ9lTXMHEN3OortPuxyJmem35XmYt3Q3A49f04MKMOJMTyc9RQRFpAvHRobw+fiBRITZW55bwwPxvcWmNFBFTfLQhn0c+rl8C4IERnRnTP8XkRHIqVFBEmkjnhChm3tgPm8Xgww35PHVi+3YRaT4rdh3mvnc34HbDLUPactew9mZHklPk8YKSlZXFgAEDiIqKIi4ujlGjRrF9e+NfzFVVVUyaNImWLVsSGRnJ6NGjKSws9HQUEdOd27EVWdf0AGDmV7uZ+dVukxOJBI7N+Q4mvpVDjdPFZT0SmHFFNwxDa534Co8XlKVLlzJp0iRWrlzJ4sWLqa2tZfjw4VRUVDQ859577+Wjjz5i/vz5LF26lPz8fK655hpPRxHxCmP6p/DgJZ0BeGLRNl7TQm4iTS6vpJJbX19DeXUdg9Jjeea63li1EJtPMdxud5NeGC8uLiYuLo6lS5dy/vnn43A4aN26NXPnzuXaa68FYNu2bXTp0oXs7GwGDx78s69ZWlqK3W7H4XAQHa1NncQ3PPPv7Tz/xS4AHru6BzcMSjU5kYh/OlJezZhZ2ew5XEFGQhTv3jmE6NAgs2MJp/f53eRzUBwOBwCxsbEA5OTkUFtbS2ZmZsNzMjIySE1NJTs7u6njiJjm3l90YuL57QD4/QcbeX/dAZMTififypo6bpu9lj2HK2gTE8bs2waqnPioJl3b1+VyMWXKFIYOHUr37t0BKCgoIDg4mJiYmEbPjY+Pp6Dg5MuDV1dXU11d3fDn0tLSJsss0lQMw2D6pRlU1zqZnb2P++dvINhm4fKeSWZHE/ELlTV1jH99DRvyjhETHsTs2wYSr1VifVaTjqBMmjSJTZs2MW/evLN6naysLOx2e8ORkqJbxMQ3GYbBw1d0Y+yAFFxumDJvPf/erH17RM7Wd+Vk1d4SokJsvH7rADrEaZVYX9ZkBWXy5Mn861//4ssvvyQ5ObnhfEJCAjU1NRw7dqzR8wsLC0lISDjpa02fPh2Hw9Fw5OXlNVVskSZnsRj8+eoejOqdRJ3LzeS537B0R7HZsUR81vfLyezbB2qVWD/g8YLidruZPHkyCxYs4IsvviA9Pb3R4/369SMoKIglS5Y0nNu+fTv79+9nyJAhJ33NkJAQoqOjGx0ivsxqMXh6TC8u7Z5AjdPFxDfXkr37iNmxRHzOycpJX5UTv+DxgjJp0iT+8Y9/MHfuXKKioigoKKCgoIDjx48DYLfbuf3225k6dSpffvklOTk5jB8/niFDhpzSHTwi/sJmtfD/xvbh4ow4qutc3D57DTn7SsyOJeIzVE78m8dvM/6xRXBef/11br31VqB+obb77ruPt99+m+rqakaMGMHLL7/8o5d4vk+3GYs/qap1MuHNtfxn52GiQmzMmTCInskxZscS8WoqJ77pdD6/m3wdlKaggiL+5niNk1teX83qvSXYw4KYN3EwXRL1d1vkZFROfJdXrYMiIj8vLNjKa7cOoHdKDI7jtdz4t1XsLCwzO5aI11E5CRwqKCJeIjLExuzbBtItKZojFTVc90o26/OOmR1LxGuonAQWFRQRL2IPC+Iftw+iV0oMRytrueHVlXy967DZsURMp3ISeFRQRLxMi4hg5vxqEEM7tKSyxsn419ewaNMhs2OJmEblJDCpoIh4ocgQG6/dOqBhnZS75qxj3ur9ZscSaXaO47Xc+prKSSBSQRHxUiE2Ky/e0LdhWfxp729k1tLdZscSaTb5x44zZtYKVueqnAQiFRQRL2a1GGRd04M7L2gPwOOfbiPrk6344OoAIqdlW0Ep17y8gh2F5cRHh/DOHUNUTgKMCoqIlzMMg2mXZjD90gwAXlm2h2n/3Eid02VyMpGmsWLXYcbMzKagtIqOcZG8f9dQuiZpXaBAo4Ii4iPuuKA9T47uicWAd9bmMXnuN1TVOs2OJeJRC9cf5JbXV1NWXcfA9Fjeu/Mc2sSEmR1LTKCCIuJDrhuQwsvj+hFstbBocwG3vbGG8uo6s2OJnDW3280rS3dzz7z11DrdjOyZyJu3DcQeHmR2NDGJCoqIj7mkewJv3DaAiGArK3Yf4YZXV1JSUWN2LJEz5nS5+dNHW8j6dBsAt5+bzgtj+xAaZDU5mZhJBUXEB53TvhVvTxxMbEQw3x5wcO2sFew9XGF2LJHTVlXrZNKcdbyxIheAP4zswkOXd8ViOfnGsxI4VFBEfFTP5BjevWMISfZQ9hRXcNWLy1m6o9jsWCKn7GhFDTf+bRWLNhcQbLXw4g19+NV57cyOJV5CBUXEh3WIi+SDyUPp17YFpVV1jH99Na8s3a3bkMXr5ZVUMnrWCtbuO0p0qI03bx/I5T2TzI4lXkQFRcTHxUWFMnfCIK7vX7+gW9an25jyznrd4SNea0PeMa6ZuYI9xRUk2UN579fnMLhdS7NjiZdRQRHxAyE2K4+P7sH/XdUNm8Vg4fp8rp21goPHjpsdTaSB2+1mzqp9jJmVTXFZNRkJUbx/11A6xUeZHU28kAqKiJ8wDIObh6Tx1u2DiI0IZtPBUq56cTlrckvMjibC8Ron983fwO8XbKLG6WJ413jevXMICfZQs6OJl1JBEfEzQ9q35MPJQ+mSGM3h8hpueHUlc1btMzuWBLC9hyu4+uWveX/dQawWg+mXZvDKTf2IDtUaJ/LjVFBE/FByi3D++eshjOyZSK3Tze8XbOL3CzZSU6fl8aV5fba5gCtfWM62gjJaRYYw51eDuOOC9hiGbiOWn6aCIuKnwoNtvPjLPjwwojOGAXNW7Wfc31ZSXFZtdjQJAHVOF1mfbuWOt3Ioq65jQFoLPvnNuZoMK6dMBUXEjxmGwaQLO/D3W/oTFWJjTe5RrnxxOevzjpkdTfxYUVkV4/62ileW7gFgwnnpzJ0wmLhozTeRU6eCIhIALsqIZ8GkobRrFcEhRxWjZ67g/32+Uzsii8etyS3h8ueXs2pvCZEhNl4e15ffj+xKkFUfN3J69DdGJEB0iItkwaShjOyZiNPl5tnPdzDmlWxytUS+eIDb7eZv/9nD2L+upKismk7xkSycPJTLeiSaHU18lAqKSACxhwXx4i/78Nz1vYkKtfHN/mNc9vx/eHv1fq0+K2espKKGX/9jHY9+vBWny81VvZP4YNJQ2reONDua+DDD7YO/lUpLS7Hb7TgcDqKjo82OI+KTDh47zn3vrmflnvp1UjK7xJF1TU9aR4WYnEx8ycffHmLGwk0cqaghyGow4/Ku3Di4re7SkZM6nc9vFRSRAOZyufn78r089dl2apwuWkYE8/jonvyia7zZ0cTLFZdVM2PhJj7dVABA5/gonh7Tix7JdpOTiTdTQRGR07L1UCn3vrOebQVlAIwdkMJDl3clIsRmcjLxNm63m4Xr8/njR5s5VlmLzWJw14UdmHxhB4JtmjUgP00FRUROW1Wtk2cW7+DV/+zB7Ya2LcN55rre9Gvbwuxo4iWKSqv43YJNfL61EICuidE8NaYn3ZI0aiKnRgVFRM5Y9u4j3PfuevIdVVgMuGtYByZf1IHQIKvZ0cQkbrebf647yP99tJnSqjqCrAa/uagjdw5rr9uH5bSooIjIWXEcr+WPH25mwTcHAUiJDeMPI7syvGu8Jj8GmEOO40x/fyNfbS8GoGeynaeu7UXnBO1ALKdPBUVEPOKTjYf4v4+2UFBaBcC5HVrx8BVd6RivDyd/53a7mbcmj8c+3kpZdR3BNgv3ZnZiwnnp2DRqImdIBUVEPKaiuo6ZX+3mr8v2UON0YbUY3DIkjXsyO2IP0260/ihnXwmPfbKNnH1HAeiTGsNT1/akQ5yKqZwdFRQR8bh9Ryp49OOtLN5SP0GyZUQwD4zozJj+KVgtuuzjD/YUl/Pkou0s2lx/63BokIX7h3dm/NB0/TcWj1BBEZEms2xHMX/6aDO7i+uXyO/Rxs4fr+xKv7axJieTM1VcVs3zS3Yyd/V+nC43FgOu65/Cvb/oRLw2+BMPUkERkSZV63TxZvY+nlu8g7LqOgCu7tOGaZdm6APNh1TW1PG3/+zllaW7qahxAnBxRhy/vTSDTppnJE1ABUVEmsXh8mqeWrSdd3PycLshPNjKhPPaces5abSICDY7nvyIOqeL+TkHeHbxDorKqgHolWxn+mVdGNyupcnpxJ+poIhIs/r2wDH++OFm1u0/BtQXlV8OTOVX56WTaA8zN5w0cLvdLNlaxOOLtrGrqByA1NhwHrykMyN7JOoWcmlyKigi0uzcbjefbCzgpS93seVQKQBBVoNr+iRzxwXtaKedbU3jdLn5YlsRry7bw+rc+s0hW4QHcfdFHRk3OJUQmxbhk+ahgiIipnG73SzdUczMr3azam/9h6FhwKXdE/j1BR20mVwzKquq5d21B5i9Ipf9JZUAhNgs3HZuOnde0F63iUuzU0EREa+Qs+8oM7/axedbixrOndexFb8e1p4h7VrqkkIT2Xu4gtkrcpm/Nq9h8mt0qI1fDkzl1qFpuuwmplFBERGvsq2glFeW7uHDDfk4XfW/cnqnxHDnBe3J7BKnlUk9wO12s3zXYV7/Opcvtxfx3W/2DnGR3HpOGtf0bUN4sHanFnOpoIiIV8orqeSvy/bw7to8qutcQP2Cb1f0SmJUnzb0SrZrVOU0Ha9x8v43B3jj61x2npj4CnBh59aMH5rOeR1b6T0Vr6GCIiJerbismte/3ss7a/I4UlHTcD69VQRX9U5iVO82pLWKMDGhd6tzulidW8KiTQUsXJ+P43gtABHBVq7tl8wt56RpUrJ4JRUUEfEJtU4Xy3cd5oNvDvLZ5gKqal0Nj/VJjWFU7zZc3jORlpEhJqb0DtV1Tr7edZhFmwpYvKWQo5W1DY+lxIZxy5A0rhuQQnSoJr6K91JBERGfU15dx783F/DB+nyW7yzmxFQVrBaD8zu2YlSfNmR2iSciJHDmUVRU1/HV9mIWbS7gy21FlJ9YtRfqbxP+Rdd4Lu2eyPmdWmuvHPEJKigi4tOKyqr4aMMhFq4/yLcHHA3nbRaD7m3sDGoXy+D0lvRLa+F3IwbHKmtYsrWIRZsLWLajuGGuDkB8dAiXdEtgRPcEBqbFanKx+BwVFBHxG7uKylm4/iAfbshn35HKRo9ZDOiaFM2g9JYMSo9lYHosMeG+s8R+dZ2TrYfK+PbAMTbkOfj2wDF2FZfzv7+V27YM55LuCVzSLYFeyTFYNFIiPkwFRUT8Ul5JJav2lrB67xFW7S35QWEByEiIYlB6LAPSY2nfOpKU2HAiveCykNPlZldRORvyjrHhwDG+PeBgW0Eptc4f/gruHB/FJd0TuLRHAp3jo3QXjvgNFRQRCQgFjipWnSgrq/YcYXdxxUmfFxsRTEpsOKmx4aTGhpEaG05Ki3BSYsNJtId65FLJ8RonxWXVFJdX1f/zu6O8mt1FFWzKd1B5YtG072frmWynZ3IMvU78s3WUJgWLf1JBEZGAVFxWzeoTIyzr846xv6Sy0d0uJ2OzGLRpEUZsRDBBFgs2q4HNasFmMbBZDIKsJ85ZTpyzGlgMg5LKGorLqjl8ooiU/c8E1h8TEWylexs7vVJi6Jlsp1dyDMktwjRCIgFDBUVE5ITSqlrySirJKzlOXkkl+08ceSWVHDh6nBqn6+df5BSF2CzERYfQOjKE1lEnjshQkluE0TPZTrvWkbrbRgLa6Xx+m39hVkSkCUWHBtEtyU63pB9uUuhyuSksq2LfkUpKj9dS53JT63RR53RT53JR63Tj/O6cy02ds/6cy+2mRXjwf0vIiSMqxKbREBEPUUERkYBlsRgk2sO0eZ6IF9JN9CIiIuJ1VFBERETE66igiIiIiNdRQRERERGvo4IiIiIiXkcFRURERLyOqQXlpZdeIi0tjdDQUAYNGsTq1avNjCMiIiJewrSC8s477zB16lQefvhh1q1bR69evRgxYgRFRUVmRRIREREvYVpBeeaZZ5gwYQLjx4+na9euzJo1i/DwcF577TWzIomIiIiXMKWg1NTUkJOTQ2Zm5n+DWCxkZmaSnZ39g+dXV1dTWlra6BARERH/ZUpBOXz4ME6nk/j4+Ebn4+PjKSgo+MHzs7KysNvtDUdKSkpzRRURERET+MRdPNOnT8fhcDQceXl5ZkcSERGRJmTKZoGtWrXCarVSWFjY6HxhYSEJCQk/eH5ISAghISHNFU9ERERMZkpBCQ4Opl+/fixZsoRRo0YB4HK5WLJkCZMnT/7Zr3e73QCaiyIiIuJDvvvc/u5z/KeYUlAApk6dyi233EL//v0ZOHAgzz33HBUVFYwfP/5nv7asrAxAc1FERER8UFlZGXa7/SefY1pBuf766ykuLmbGjBkUFBTQu3dvFi1a9IOJsyeTlJREXl4eUVFRGIbRDGm9X2lpKSkpKeTl5REdHW12HL+n97v56T1vXnq/m18gvOdut5uysjKSkpJ+9rmG+1TGWcTrlZaWYrfbcTgcfvsX25vo/W5+es+bl97v5qf3vDGfuItHREREAosKioiIiHgdFRQ/ERISwsMPP6zbsZuJ3u/mp/e8een9bn56zxvTHBQRERHxOhpBEREREa+jgiIiIiJeRwVFREREvI4KioiIiHgdFRQ/Vl1dTe/evTEMg/Xr15sdx2/l5uZy++23k56eTlhYGO3bt+fhhx+mpqbG7Gh+46WXXiItLY3Q0FAGDRrE6tWrzY7kt7KyshgwYABRUVHExcUxatQotm/fbnasgPH4449jGAZTpkwxO4rpVFD82IMPPnhKywnL2dm2bRsul4tXXnmFzZs38+yzzzJr1ix+97vfmR3NL7zzzjtMnTqVhx9+mHXr1tGrVy9GjBhBUVGR2dH80tKlS5k0aRIrV65k8eLF1NbWMnz4cCoqKsyO5vfWrFnDK6+8Qs+ePc2O4h3c4pc++eQTd0ZGhnvz5s1uwP3NN9+YHSmgPPnkk+709HSzY/iFgQMHuidNmtTwZ6fT6U5KSnJnZWWZmCpwFBUVuQH30qVLzY7i18rKytwdO3Z0L1682H3BBRe477nnHrMjmU4jKH6osLCQCRMm8NZbbxEeHm52nIDkcDiIjY01O4bPq6mpIScnh8zMzIZzFouFzMxMsrOzTUwWOBwOB4D+PjexSZMmMXLkyEZ/1wOdabsZS9Nwu93ceuut3HnnnfTv35/c3FyzIwWcXbt28cILL/D000+bHcXnHT58GKfT+YNdzuPj49m2bZtJqQKHy+ViypQpDB06lO7du5sdx2/NmzePdevWsWbNGrOjeBWNoPiIadOmYRjGTx7btm3jhRdeoKysjOnTp5sd2eed6nv+vw4ePMgll1zCmDFjmDBhgknJRTxj0qRJbNq0iXnz5pkdxW/l5eVxzz33MGfOHEJDQ82O41W01L2PKC4u5siRIz/5nHbt2nHdddfx0UcfYRhGw3mn04nVamXcuHHMnj27qaP6jVN9z4ODgwHIz89n2LBhDB48mDfeeAOLRf3/bNXU1BAeHs57773HqFGjGs7fcsstHDt2jIULF5oXzs9NnjyZhQsXsmzZMtLT082O47c++OADrr76aqxWa8M5p9OJYRhYLBaqq6sbPRZIVFD8zP79+yktLW34c35+PiNGjOC9995j0KBBJCcnm5jOfx08eJALL7yQfv368Y9//CNgf6E0hUGDBjFw4EBeeOEFoP6yQ2pqKpMnT2batGkmp/M/brebu+++mwULFvDVV1/RsWNHsyP5tbKyMvbt29fo3Pjx48nIyOC3v/1tQF9a0xwUP5Oamtroz5GRkQC0b99e5aSJHDx4kGHDhtG2bVuefvppiouLGx5LSEgwMZl/mDp1Krfccgv9+/dn4MCBPPfcc1RUVDB+/Hizo/mlSZMmMXfuXBYuXEhUVBQFBQUA2O12wsLCTE7nf6Kion5QQiIiImjZsmVAlxNQQRE5a4sXL2bXrl3s2rXrByVQA5Rn7/rrr6e4uJgZM2ZQUFBA7969WbRo0Q8mzopnzJw5E4Bhw4Y1Ov/6669z6623Nn8gCVi6xCMiIiJeR7P4RERExOuooIiIiIjXUUERERERr6OCIiIiIl5HBUVERES8jgqKiIiIeB0VFBEREfE6KigiIiLidVRQRERExOuooIiIiIjXUUERERERr6OCIiIiIl7n/wOmIpCi+M1VdAAAAABJRU5ErkJggg==",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"xs = np.arange(-5, 5, 0.25)\n",
|
|
"ys = f(xs)\n",
|
|
"plt.plot(xs, ys)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5c9cb820-159e-420d-b0d0-a8706e19f9d5",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now, what's a derivate?\n",
|
|
"> It is sensitivity of the function to the change of the output with respect to the input.\n",
|
|
"\n",
|
|
"In simpler terms, (f(x+h) - f(x))/h, where h tends to zero. This gives us the slope."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 146,
|
|
"id": "7866b622-e10c-4003-844f-9044e7bdc080",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"3.0000002482211127e-05"
|
|
]
|
|
},
|
|
"execution_count": 146,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"h = 0.00001\n",
|
|
"x = 2/3\n",
|
|
"(f(x+h) - f(x))/h"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4f027e6d-d889-4827-867e-335a96c20045",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let's define a function with mupltiple inputs"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 147,
|
|
"id": "a3bcd24a-4a48-4d85-8d49-3fb751589b40",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"4.0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"a = 2.0\n",
|
|
"b = -3.0\n",
|
|
"c = 10.0\n",
|
|
"\n",
|
|
"d = a*b + c\n",
|
|
"print(d)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 148,
|
|
"id": "78dcb378-190f-4421-8d95-af79cdf05b18",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"d1 4.0\n",
|
|
"d2 3.999699999999999\n",
|
|
"slope -3.000000000010772\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"h = 0.0001\n",
|
|
"a = 2.0\n",
|
|
"b = -3.0\n",
|
|
"c = 10.0\n",
|
|
"\n",
|
|
"d1 = a*b + c\n",
|
|
"a += h\n",
|
|
"d2 = a*b + c\n",
|
|
"\n",
|
|
"print('d1', d1)\n",
|
|
"print('d2', d2)\n",
|
|
"print('slope', (d2 - d1)/h) # By the good old derivation (wrt to a), we know that this will be b"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 149,
|
|
"id": "9fb1208f-1a1e-4fdb-a290-a7d0e8a5050a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"d1 4.0\n",
|
|
"d2 4.0002\n",
|
|
"slope 2.0000000000042206\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"h = 0.0001\n",
|
|
"a = 2.0\n",
|
|
"b = -3.0\n",
|
|
"c = 10.0\n",
|
|
"\n",
|
|
"d1 = a*b + c\n",
|
|
"b += h\n",
|
|
"d2 = a*b + c\n",
|
|
"\n",
|
|
"print('d1', d1)\n",
|
|
"print('d2', d2)\n",
|
|
"print('slope', (d2 - d1)/h) # By the good old derivation (wrt to b), we know that this will be a"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 150,
|
|
"id": "e7bfb7ef-4c16-44c6-afce-ac20ab858b4f",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
|
|
"d1 4.0\n",
|
|
"d2 4.0001\n",
|
|
"slope 0.9999999999976694\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"h = 0.0001\n",
|
|
"a = 2.0\n",
|
|
"b = -3.0\n",
|
|
"c = 10.0\n",
|
|
"\n",
|
|
"d1 = a*b + c\n",
|
|
"c += h\n",
|
|
"d2 = a*b + c\n",
|
|
"\n",
|
|
"print('d1', d1)\n",
|
|
"print('d2', d2)\n",
|
|
"print('slope', (d2 - d1)/h) # By the good old derivation (wrt to c), we know that this will be 1"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "ad332637-3dbe-4630-bb5a-d5a76e935a9b",
|
|
"metadata": {},
|
|
"source": [
|
|
"The NN will be mathematically very large expressions. We now start by building the data structures for this. Let's start by making the `Value` object from mircograd"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 167,
|
|
"id": "e11f7575-00be-44a4-9bde-ae9288b1922a",
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"class Value:\n",
|
|
"\n",
|
|
" def __init__(self, data, _children=(), _op='', label=''): # Define a empty tuple `children` to keep the pointers of other Value objects\n",
|
|
" # Define a empty set `op` to keep track of what created that Value object\n",
|
|
" self.data = data\n",
|
|
" self.grad = 0.0 # this is the derivative of Value wrt to its nodes; initalized as 0 (we assume at the beginning that every Value doesn't impact the output)\n",
|
|
" self._backward = lambda: None # for a leaf node, this should be nothing.\n",
|
|
" self._prev = set(_children) # This will be a empty set when we define a new Value object (a, b, c)\n",
|
|
" self._op = _op\n",
|
|
" self.label = label\n",
|
|
"\n",
|
|
" def __repr__(self):\n",
|
|
" return f\"Value(data={self.data})\"\n",
|
|
"\n",
|
|
" def __add__(self, other): # a.__add__(b)\n",
|
|
" other = other if isinstance(other, Value) else Value(other) # if other is NOT a Value obj, just make one\n",
|
|
" out = Value(self.data + other.data, (self, other), '+') # Since self.data and other.data is python floating point number,\n",
|
|
" # the addition is according to whatever is defined in the python kernel\n",
|
|
" def _backward():\n",
|
|
" self.grad += 1.0 * out.grad\n",
|
|
" other.grad += 1.0 * out.grad\n",
|
|
" out._backward = _backward\n",
|
|
" \n",
|
|
" return out\n",
|
|
"\n",
|
|
" def __radd__(self, other): # other + self\n",
|
|
" return self + other\n",
|
|
" \n",
|
|
" def __mul__(self, other): # a.__mul__(b) # You can't name it mult or multi, because we are defining magic methods for the Value object\n",
|
|
" other = other if isinstance(other, Value) else Value(other) # if other is NOT a Value obj, just make one\n",
|
|
" out = Value(self.data * other.data, (self, other), '*')\n",
|
|
"\n",
|
|
" def _backward():\n",
|
|
" self.grad += other.data * out.grad\n",
|
|
" other.grad += self.data * out.grad\n",
|
|
" out._backward = _backward\n",
|
|
" \n",
|
|
" return out\n",
|
|
"\n",
|
|
" def __pow__(self, other):\n",
|
|
" assert isinstance(other, (int, float)) # other is only int or float\n",
|
|
" out = Value(self.data**other, (self, ), f'**{other}')\n",
|
|
"\n",
|
|
" def _backward():\n",
|
|
" self.grad += other * self.data**(other-1) * out.grad\n",
|
|
" out._backward = _backward\n",
|
|
"\n",
|
|
" return out\n",
|
|
"\n",
|
|
" def __rmul__(self, other): # other * self\n",
|
|
" return self*other\n",
|
|
"\n",
|
|
" def __truediv__(self, other): # self / other\n",
|
|
" return self * other**-1\n",
|
|
"\n",
|
|
" def __neg__(self): # -self\n",
|
|
" return self * -1\n",
|
|
"\n",
|
|
" def __sub__(self, other): # self - other\n",
|
|
" return self + (-other)\n",
|
|
" \n",
|
|
" def tanh(self):\n",
|
|
" x = self.data\n",
|
|
" t = (math.exp(2*x) - 1)/(math.exp(2*x) + 1)\n",
|
|
" out = Value(t, (self, ), 'tanh')\n",
|
|
"\n",
|
|
" def _backward():\n",
|
|
" self.grad += (1 - t**2) * out.grad\n",
|
|
" out._backward = _backward\n",
|
|
" \n",
|
|
" return out\n",
|
|
"\n",
|
|
" def exp(self):\n",
|
|
" x = self.data\n",
|
|
" out = Value(math.exp(x), (self, ), 'exp')\n",
|
|
"\n",
|
|
" def _backward():\n",
|
|
" self.grad += out.data * out.grad\n",
|
|
" out._backward = _backward\n",
|
|
" \n",
|
|
" return out\n",
|
|
" \n",
|
|
" def backward(self):\n",
|
|
" \n",
|
|
" topo = []\n",
|
|
" visited = set()\n",
|
|
" def build_topo(v):\n",
|
|
" if v not in visited:\n",
|
|
" visited.add(v)\n",
|
|
" for child in v._prev:\n",
|
|
" build_topo(child)\n",
|
|
" topo.append(v)\n",
|
|
" build_topo(self)\n",
|
|
" \n",
|
|
" self.grad = 1.0\n",
|
|
" \n",
|
|
" for node in reversed(topo):\n",
|
|
" node._backward()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 152,
|
|
"id": "cdf24235-ee48-4675-bb65-bd78d1ae201e",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# imported from mircograd's codebase\n",
|
|
"\n",
|
|
"from graphviz import Digraph\n",
|
|
"\n",
|
|
"def trace(root):\n",
|
|
" # builds a set of all nodes and edges in a graph\n",
|
|
" nodes, edges = set(), set()\n",
|
|
" def build(v):\n",
|
|
" if v not in nodes:\n",
|
|
" nodes.add(v)\n",
|
|
" for child in v._prev:\n",
|
|
" edges.add((child, v))\n",
|
|
" build(child)\n",
|
|
" build(root)\n",
|
|
" return nodes, edges\n",
|
|
"\n",
|
|
"def draw_dot(root):\n",
|
|
" dot = Digraph(format='svg', graph_attr={'rankdir': 'LR'}) # LR = left to right\n",
|
|
" \n",
|
|
" nodes, edges = trace(root)\n",
|
|
" for n in nodes:\n",
|
|
" uid = str(id(n))\n",
|
|
" # for any value in the graph, create a rectangular ('record') node for it\n",
|
|
" dot.node(name = uid, label = \"{%s | data %.4f | grad %.4f}\" % (n.label, n.data, n.grad), shape='record')\n",
|
|
" if n._op:\n",
|
|
" # if this value is a result of some operation, create an op node for it\n",
|
|
" dot.node(name = uid + n._op, label = n._op)\n",
|
|
" # and connect this node to it\n",
|
|
" dot.edge(uid + n._op, uid)\n",
|
|
"\n",
|
|
" for n1, n2 in edges:\n",
|
|
" # connect n1 to the op node of n2\n",
|
|
" dot.edge(str(id(n1)), str(id(n2)) + n2._op)\n",
|
|
"\n",
|
|
" return dot"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {
|
|
"34f0ebc5-cb10-4db9-b7a9-9f501a3732da.webp": {
|
|
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"
|
|
}
|
|
},
|
|
"cell_type": "markdown",
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"id": "4eff7563-3a93-493d-8788-bb055a23d07a",
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"metadata": {},
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"source": [
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"Let's look at another backpropagation using a neuron\n",
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"\n",
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"\n",
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"\n",
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"activation function is usually sigmoid or tanh [[more here]](https://en.wikipedia.org/wiki/Activation_function)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 153,
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"id": "29f8a9d0-16a6-49e3-b1db-52084db2ce24",
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"metadata": {},
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"outputs": [],
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"source": [
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"# inputs x1,x2\n",
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"x1 = Value(2.0, label='x1')\n",
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"x2 = Value(0.0, label='x2')\n",
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"# weights w1,w2\n",
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"w1 = Value(-3.0, label='w1')\n",
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"w2 = Value(1.0, label='w2')\n",
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"# bias of the neuron\n",
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"b = Value(6.8813735870195432, label='b')\n",
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"# x1*w1 + x2*w2 + b\n",
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"x1w1 = x1*w1; x1w1.label = 'x1*w1'\n",
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"x2w2 = x2*w2; x2w2.label = 'x2*w2'\n",
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"x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = 'x1*w1 + x2*w2'\n",
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"n = x1w1x2w2 + b; n.label = 'n'\n",
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"o = n.tanh(); o.label = 'output'\n",
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"o.backward()"
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]
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},
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"metadata": {
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"scrolled": true
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"outputs": [
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},
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"execution_count": 154,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"draw_dot(o)"
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]
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},
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{
|
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"cell_type": "code",
|
|
"execution_count": 155,
|
|
"id": "7d33e845-75ed-4aec-9310-0f55e866e065",
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|
"metadata": {},
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"outputs": [
|
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{
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|
"data": {
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"text/plain": [
|
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"Value(data=3.0)"
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]
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},
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"execution_count": 155,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"a = Value(2.0)\n",
|
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"a + 1"
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]
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},
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{
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"cell_type": "markdown",
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"id": "59924a5f-ae4c-4d0f-b45d-611b32ba6df7",
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"metadata": {},
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|
"source": [
|
|
"Let's add this QoL improvement."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 156,
|
|
"id": "5ec7a25c-1540-47d3-aefc-eea6cfac8e8a",
|
|
"metadata": {},
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"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Value(data=4.0)\n",
|
|
"Value(data=4.0)\n"
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]
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|
}
|
|
],
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|
"source": [
|
|
"print(a * 2)\n",
|
|
"print(2 * a)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 157,
|
|
"id": "048d58a9-e70d-45b7-885f-e34317323283",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
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"text/plain": [
|
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"Value(data=54.598150033144236)"
|
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]
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},
|
|
"execution_count": 157,
|
|
"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
|
],
|
|
"source": [
|
|
"a = Value(4.0)\n",
|
|
"a.exp()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8830493e-47d3-44dd-957f-9c84a9fafa99",
|
|
"metadata": {},
|
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"source": [
|
|
"a / b\n",
|
|
"a * 1/b\n",
|
|
"a * b**-1\n",
|
|
"\n",
|
|
"so, let's implement n**k and have a spl case for division"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 158,
|
|
"id": "c75d090e-6f68-41d8-b874-fd72a5c2303a",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
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"text/plain": [
|
|
"Value(data=125.0)"
|
|
]
|
|
},
|
|
"execution_count": 158,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"z = Value(5.0)\n",
|
|
"z**3"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 159,
|
|
"id": "ff12ee1b-b022-4790-81dd-b88527719e29",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
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"Value(data=0.5)"
|
|
]
|
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},
|
|
"execution_count": 159,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"a = Value(4.0)\n",
|
|
"b = Value(8.0)\n",
|
|
"a / b"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 160,
|
|
"id": "b313cee1-dd94-40bc-a414-ac6475443585",
|
|
"metadata": {},
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|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
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"Value(data=-4.0)"
|
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]
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},
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|
"execution_count": 160,
|
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"metadata": {},
|
|
"output_type": "execute_result"
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}
|
|
],
|
|
"source": [
|
|
"a = Value(4.0)\n",
|
|
"b = Value(8.0)\n",
|
|
"a - b"
|
|
]
|
|
},
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{
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"cell_type": "code",
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"execution_count": 161,
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"id": "7079d013-6e83-4b81-b1d1-d4860d0bf3e7",
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"metadata": {},
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"outputs": [
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{
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},
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"execution_count": 161,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# inputs x1,x2\n",
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"x1 = Value(2.0, label='x1')\n",
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"x2 = Value(0.0, label='x2')\n",
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"# weights w1,w2\n",
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"w1 = Value(-3.0, label='w1')\n",
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"w2 = Value(1.0, label='w2')\n",
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"# bias of the neuron\n",
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"b = Value(6.8813735870195432, label='b')\n",
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"# x1*w1 + x2*w2 + b\n",
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"x1w1 = x1*w1; x1w1.label = 'x1*w1'\n",
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"x2w2 = x2*w2; x2w2.label = 'x2*w2'\n",
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"x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = 'x1*w1 + x2*w2'\n",
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"n = x1w1x2w2 + b; n.label = 'n'\n",
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"# Let's define tanh using exp and divide and subtract\n",
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"e = (2*n).exp()\n",
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"o = (e - 1) / (e + 1) \n",
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"#---\n",
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"o.label = 'output'\n",
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"o.backward()\n",
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"draw_dot(o)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c817b8f7-a9e5-4193-a759-27973d83868d",
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"metadata": {},
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"source": [
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"Now let's look at the same thing in pytorch"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 162,
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"id": "961afc05-1958-41e0-b434-40d8b0a148e3",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 163,
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"id": "67c41ab6-8b5d-4908-a868-0d63c196ae5f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.7071066904050358\n",
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|
"---\n",
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"x2 0.5000001283844369\n",
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"w2 0.0\n",
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"x1 -1.5000003851533106\n",
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"w1 1.0000002567688737\n"
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]
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}
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],
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"source": [
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|
"x1 = torch.Tensor([2.0]).double() ; x1.requires_grad = True # This is false by default because no one needs to see gradients on the input layer\n",
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|
"x2 = torch.Tensor([0.0]).double() ; x2.requires_grad = True\n",
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"w1 = torch.Tensor([-3.0]).double() ; w1.requires_grad = True\n",
|
|
"w2 = torch.Tensor([1.0]).double() ; w2.requires_grad = True\n",
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"b = torch.Tensor([6.8813735870195432]).double() ; b.requires_grad = True\n",
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"n = x1*w1 + x2*w2 + b\n",
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"o = torch.tanh(n)\n",
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|
"\n",
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|
"print(o.data.item())\n",
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"o.backward()\n",
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|
"\n",
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|
"print('---')\n",
|
|
"print('x2', x2.grad.item())\n",
|
|
"print('w2', w2.grad.item())\n",
|
|
"print('x1', x1.grad.item())\n",
|
|
"print('w1', w1.grad.item())"
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|
]
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},
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{
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"cell_type": "markdown",
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|
"id": "43f61380-db69-4b53-9a87-e6121fe2eae1",
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|
"metadata": {},
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|
"source": [
|
|
"Let's now build the a neural net library (multi-layer perceptron) in mircograd"
|
|
]
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|
},
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|
{
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|
"cell_type": "code",
|
|
"execution_count": 175,
|
|
"id": "9399d558-a3c6-44ae-8b3c-a995d750f20e",
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"metadata": {},
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"outputs": [],
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"source": [
|
|
"import random\n",
|
|
"class Neuron:\n",
|
|
"\n",
|
|
" def __init__(self, nin): # nin is number of inputs\n",
|
|
" self.w = [Value(random.uniform(-1,1)) for _ in range(nin)]\n",
|
|
" self.b = Value(random.uniform(-1,1))\n",
|
|
"\n",
|
|
" def __call__(self, x):\n",
|
|
" # w * x + b\n",
|
|
" act = sum((wi * xi for wi, xi in zip(self.w, x)), self.b)\n",
|
|
" out = act.tanh()\n",
|
|
" return out\n",
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|
"\n",
|
|
" def parameters(self):\n",
|
|
" return self.w + [self.b]\n",
|
|
"\n",
|
|
"class Layer:\n",
|
|
"\n",
|
|
" def __init__(self, nin, nout): # nin is the number of inputs on each neuron, nout is the number of neurons in the layer\n",
|
|
" self.neurons=[Neuron(nin) for _ in range(nout)]\n",
|
|
"\n",
|
|
" def __call__(self, x):\n",
|
|
" outs = [n(x) for n in self.neurons]\n",
|
|
" return outs[0] if len(outs) == 1 else outs # QoL \n",
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|
"\n",
|
|
" def parameters(self):\n",
|
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" return [p for neuron in self.neurons for p in neuron.parameters()]\n",
|
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" # params = []\n",
|
|
" # for neuron in self.neurons:\n",
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" # ps = neurons.parameters()\n",
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" # params.extend(ps)\n",
|
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" # return params\n",
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|
"\n",
|
|
"class MLP:\n",
|
|
"\n",
|
|
" def __init__(self, nin, nouts): # nout is the list of the numbers of neurons in each layer\n",
|
|
" sz = [nin] + nouts\n",
|
|
" self.layers = [Layer(sz[i], sz[i+1]) for i in range(len(nouts))]\n",
|
|
"\n",
|
|
" def __call__(self, x):\n",
|
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" for layer in self.layers:\n",
|
|
" x = layer(x)\n",
|
|
" return x\n",
|
|
"\n",
|
|
" def parameters(self):\n",
|
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" return [p for layer in self.layers for p in layer.parameters()]\n",
|
|
"\n",
|
|
"# x = [2.0, 3.0,]\n",
|
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"# n = Layer(2, 3) # three 2-dimensional neutrons in this Layer\n",
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"# n(x)"
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]
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},
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"
|
|
}
|
|
},
|
|
"cell_type": "markdown",
|
|
"id": "b7f37088-3ee3-466b-974b-bdf7a1df0d25",
|
|
"metadata": {},
|
|
"source": [
|
|
"\n",
|
|
"\n",
|
|
"Now let's define a layer and a MLP (multi-layer perceptron)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "7e885e5c-3de4-4d81-89c5-02423da45778",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let's train a NN! (finally!) This is a simple binary classifer NN"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 240,
|
|
"id": "58ab19a9-8f0f-41a5-b270-95f31a1127e4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Value(data=0.5295324547878887)"
|
|
]
|
|
},
|
|
"execution_count": 240,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"x = [2.0, 3.0, -1.0]\n",
|
|
"n = MLP(3, [4, 4, 1]) # implementing the example image\n",
|
|
"n(x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 184,
|
|
"id": "78f114fb-4736-458e-9eb0-3e03405dc63c",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"41"
|
|
]
|
|
},
|
|
"execution_count": 184,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"len(n.parameters())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 177,
|
|
"id": "e095ac0b-5aa5-4224-a2d7-67a5af1c9742",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[Value(data=0.07700064719309661),\n",
|
|
" Value(data=0.6874724332729629),\n",
|
|
" Value(data=-0.7191542943909137),\n",
|
|
" Value(data=-0.12016611563222777)]"
|
|
]
|
|
},
|
|
"execution_count": 177,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# when we feed the first inputs, we should get the first output (1.0) and so on.\n",
|
|
"\n",
|
|
"xs = [\n",
|
|
" [2.0, 3.0, -1.0],\n",
|
|
" [3.0, -1.0, 0.5],\n",
|
|
" [0.5, 1.0, 1.0],\n",
|
|
" [1.0, 1.0, -1.0],\n",
|
|
"]\n",
|
|
"ys = [1.0, -1.0, -1.0, 1.0] # desired targets\n",
|
|
"ypred = [n(x) for x in xs]\n",
|
|
"ypred"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c9676e56-fff4-4d14-8120-955a9a9622ee",
|
|
"metadata": {},
|
|
"source": [
|
|
"We want `ypred` to go as close to as `ys`. We do this (and in deep learning) by calculating a single number for perfomance called the *loss*. We are going to implement a mean-square loss"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 178,
|
|
"id": "584c98fa-5fef-4310-88b1-3638b41821e8",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Value(data=5.033137455307795)"
|
|
]
|
|
},
|
|
"execution_count": 178,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"loss = sum((yout - ygt)**2 for ygt, yout in zip(ys, ypred))\n",
|
|
"loss"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 179,
|
|
"id": "7e1b37ad-e268-4cbb-8f98-fa7e5daf44c8",
|
|
"metadata": {},
|
|
"outputs": [],
|
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"source": [
|
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"loss.backward()"
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]
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},
|
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{
|
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"cell_type": "code",
|
|
"execution_count": 180,
|
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"id": "4768716a-2ff9-4e50-b7c1-fff9691b2ef5",
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"data": {
|
|
"text/plain": [
|
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"0.7368026977666375"
|
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]
|
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},
|
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"execution_count": 180,
|
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"n.layers[0].neurons[0].w[0].grad # this is now NOT zero!"
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]
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},
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{
|
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"cell_type": "code",
|
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"execution_count": 185,
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"id": "1eb237df-ca08-4e5f-b22f-1c6d4d10341e",
|
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"metadata": {},
|
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"outputs": [
|
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{
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"data": {
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"text/plain": [
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"-0.32973208920024066"
|
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]
|
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},
|
|
"execution_count": 185,
|
|
"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
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],
|
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"source": [
|
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"n.layers[0].neurons[0].w[0].data"
|
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]
|
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},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 187,
|
|
"id": "fcfcb22d-b973-45b5-aee4-78d7d22e6d5b",
|
|
"metadata": {},
|
|
"outputs": [],
|
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"source": [
|
|
"for p in n.parameters():\n",
|
|
" p.data += -0.01 * p.grad # negative sign because we want to minimise the loss. We think of grad as a vector pointing towards increased loss. This is gradient descent.\n",
|
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" # https://en.wikipedia.org/wiki/Gradient_descent#An_analogy_for_understanding_gradient_descent"
|
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]
|
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},
|
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{
|
|
"cell_type": "markdown",
|
|
"id": "033750e6-d9ca-4ce7-9582-e5a2841f953a",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let's look at the new loss (It has decreased)"
|
|
]
|
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 189,
|
|
"id": "a78061fa-fca6-4a04-9a15-ebe3908dc021",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Value(data=3.7015356808242585)"
|
|
]
|
|
},
|
|
"execution_count": 189,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"ypred = [n(x) for x in xs]\n",
|
|
"loss = sum((yout - ygt)**2 for ygt, yout in zip(ys, ypred))\n",
|
|
"loss"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5922cb79-0965-4f6b-b6c4-e30253cc57cb",
|
|
"metadata": {},
|
|
"source": [
|
|
"Let backprop this loss and update the weights"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 190,
|
|
"id": "3bd72e52-5dec-4e1b-84d5-b7435f90d19f",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"loss.backward()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 191,
|
|
"id": "3dd28be9-dc33-415b-bf2c-3da6c8d59160",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for p in n.parameters():\n",
|
|
" p.data += -0.01 * p.grad # negative sign because we want to minimise the loss. We think of grad as a vector pointing towards increased loss. This is gradient descent.\n",
|
|
" # https://en.wikipedia.org/wiki/Gradient_descent#An_analogy_for_understanding_gradient_descent"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c2b0b435-a7c0-4ab2-b907-55709bfb7342",
|
|
"metadata": {},
|
|
"source": [
|
|
"Now let's find the new loss"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 192,
|
|
"id": "8cc95aa5-ecb4-4a24-b631-1daf8e9f7b78",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Value(data=3.193172494954373)"
|
|
]
|
|
},
|
|
"execution_count": 192,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"ypred = [n(x) for x in xs]\n",
|
|
"loss = sum((yout - ygt)**2 for ygt, yout in zip(ys, ypred))\n",
|
|
"loss"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3faa653d-3787-4ab3-9abc-ea8df46153a1",
|
|
"metadata": {},
|
|
"source": [
|
|
"So the flow is forward pass -> backward pass -> update"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 241,
|
|
"id": "f530b51e-9e4b-47b2-9fb0-335c48c3c424",
|
|
"metadata": {},
|
|
"outputs": [
|
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{
|
|
"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
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"Step # 0 6.063052689312497\n",
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]
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}
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|
],
|
|
"source": [
|
|
"xs = [\n",
|
|
" [2.0, 3.0, -1.0],\n",
|
|
" [3.0, -1.0, 0.5],\n",
|
|
" [0.5, 1.0, 1.0],\n",
|
|
" [1.0, 1.0, -1.0],\n",
|
|
"]\n",
|
|
"ys = [1.0, -1.0, -1.0, 1.0] # desired targets\n",
|
|
"ls = []\n",
|
|
"\n",
|
|
"for k in range(500):\n",
|
|
" # forward pass\n",
|
|
" ypred = [n(x) for x in xs]\n",
|
|
" loss = sum((yout - ygt)**2 for ygt, yout in zip(ys, ypred))\n",
|
|
"\n",
|
|
" # backward pass\n",
|
|
" for p in n.parameters():\n",
|
|
" p.grad = 0.0 # YOU NEED TO clear out your gradient before every backward pass or it will just accumulate\n",
|
|
" loss.backward()\n",
|
|
"\n",
|
|
" # update (the gradient descent)\n",
|
|
" for p in n.parameters():\n",
|
|
" p.data += -0.05 * p.grad\n",
|
|
"\n",
|
|
" print('Step #', k, ' ', loss.data)\n",
|
|
" ls += [loss.data]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 245,
|
|
"id": "8615e709-2365-4988-bad8-473590ff22b9",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[Value(data=0.9868926891382584),\n",
|
|
" Value(data=-0.9822284602914287),\n",
|
|
" Value(data=-0.9834981983414189),\n",
|
|
" Value(data=0.9831334891456814)]"
|
|
]
|
|
},
|
|
"execution_count": 245,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"ypred = [n(x) for x in xs]\n",
|
|
"ypred"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 246,
|
|
"id": "046c7e20-5384-476f-abb7-d6d313561ebb",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x1325b21b3d0>]"
|
|
]
|
|
},
|
|
"execution_count": 246,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.plot(np.arange(0, 500, 1), ls)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.11.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|