demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  MicroGrad demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from micrograd.engine import Value\n",
    "from micrograd.nn import Neuron, Layer, MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1337)\n",
    "random.seed(1337)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10f636400>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iyt8IPBWA61Y5JpPixRf78+KL8izNaC1aFG6Q83g0rVtXbuu+CE6BuFWvDxwosH3Qt68QpVRjoCmwvMDuSKXUWqXUr0qpYQGIR4hyi4mJYMGCm4mLiyQiwkxsrI1vvhlFfLy0rIvA1DiLGkpRXK/6UcAcrbWnwL5GWusUpVQzYLlSarPWenehiyg1DhgH0KhR6K63I0JHz54NOXHiEU6ezKFGjUgZNSTyBaLGeRBoWGC7AZBSTNlRwOcFd2itU3yf9wDf4//8s2C5yVrrrlrrromJMrOQqBxKKeLjoyRpCj+BSJxrgJZKqaZKqQjykmOh1nGlVBIQB/xSYF+cUsrm+7om0Ivin40GrcOHM5g7dxtLluzG4/Ge/wAhREgr96261tqtlBoPLALMwBSt9Val1DPAWq31mSR6IzBT+w+ObwO8r5TykpfEJxZsjQ8Fa9Ycom/faUDelGedOtVh+fJbsVrN5zlSCBGqZJKPMsrOdjFjxmYee2wZqal/dlK3261MmjSAO+/sYmB0QoiykEk+KlBOjpvu3T9kz56TfhNBQN5kEAcOpBsUmRCiMkjiLINZs7awd2/hpAl5Nc4ePYrsjSWCTGqqgylTfiMjI5drrkmiWzf5uYmSkcRZBidP5uB2F24EslhMPPJITwYPbmVAVKI0jh/Pon37dzl5MgeXy8Nrr/3C7Nkj5GcnSkQSZxlccUVTv+4pERFmevVqyPz5NxMZKW9pKHj//XWkpWXjcuX9A3Q43Dz44CJJnKJEZHakMujQoTazZl1PnToxREVZ6Nu3Kf/970hJmiEkr6bpf9eQkZFrUDQi1MhfehkNGZLE4cNJRochymjYsCTee28NDkfe0s1RURauu66NwVGJUCE1ThGWLrmkMVOnDqNhw1gSEqK49daO/Oc/VxkdlggR0o+zlLZuPcaGDUdo0qQGvXrJmHkhqhLpx1kBPvroN+67bz4WiwmvVzN6dAfee+9qo8MKW1prPvhgPbNmbSE+3s6zz14u076JSiE1zhLKznYRF/ciubl/Tuxkt1v58cfb6NKlXqXGIvI8//z/eOGF/+FwuFAqbyq4TZvulomGRZmVtMYpzzhL6MSJ7EIz5FgsJg4dyjAoIjFp0i/5gxC0huxsNzNnbin3eXfvTuOtt1bz0UfrSU+XlnZRmNyql1DdujHExtrIznbn73O7vXTsWNvAqMJbUTdLXm/57qBWrTpI377T8Hi8mEwmnn76B3777a8kJNjLdV5RtUiNs4TMZhNLltxCvXrVsFhMREdbmTnzOho3lttCo4wf3z1/cTulIDLSwqhR7cp1znvumU9WloucHA8Oh4sjRzKZNOnXQIQrqhCpcRYjJSWDp5/+gZSUdIYMSeLOOzvTvn1tDh78GxkZTmJiImRyW4M99dRlJCREMXPmVuLjo3jhhSto1iyuXOcsONMVgMvl5fDhzHKdU1Q9kjiLkJaWzYUXvkdaWjZut2b58n3s2XOSiRP7oZQiNtZmdIiCvNnZ77uvB/fd1yNg5xw4sAXTpm3MfyRjt1sZPLhlwM4vqga5VS/Cl1/+TmamC7c773mZw+Fi0qRfCcUeCKJ0Jk26iiFDkrBa8x7HPP10H4YPlxFFwp/UOIvgdnsLJcnyNjqI0BAZaWHWrOuNDkMEOalxFuHqq1thtZpRvkeYdruV0aM7oJQ80xRCSOIsUv36sfz661iuvLI5HTvW5sEHe/D++zJCSAiRR27Vi9GmTSILF442OgwRBJxODx6Pl6goq9GhiCAhNU4hiqG15r775hMd/TzVqv2bwYM/Izu78HIpIvxI4hSiGO+/v44pUzbgdms8Hs3y5Xt56KHFRoclgoAkTiGKsWzZHr8F+XJy3CxfvtfAiESwkMQpRDGaNIkjIsKcv20yKRo1qm5gRCJYSOIUohiPP96bBg2qUa1aBDExEVSvbuPttwcZHZYIAtKqLkQx4uKi2Lz5HhYtSsbp9NC3bzNq1pRZkoQkTiHOyW63ypBLUYjcqgshRCkFJHEqpa5SSu1QSiUrpSYU8fptSqnjSqkNvo+/FHhtjFJql+9jTCDiEUKIilTuW3WllBl4G+gPHATWKKW+0VpvO6voLK31+LOOjQeeAroCGljnO/ZkeeMSQoQfrTW7Fy3i+LZt1GzdmhYDB1bIHBOBeMbZHUjWWu8BUErNBIYCZyfOogwAlmit03zHLgGuAj4PQFxCiDCz6MEHWf/RR3hdLkxWKx3HjGHw228H/DqBuFWvDxwosH3Qt+9s1ymlNiml5iilGpbyWJRS45RSa5VSa48fPx6AsEUgJS9cyLfjxrHk0UfJOHzY6HBEGDr9xx+smzwZV1YWHqcTV1YWG6ZM4eSePQG/ViASZ1H14LMnr/wWaKK17gAsBT4pxbF5O7WerLXuqrXumpiYWOZgReBtmDqV2dddx/oPPuDX117jvY4dyTx61OiwRJhxpKZijojw22eOiMBx4kTArxWIxHkQaFhguwGQUrCA1vqE1vrMOqsfAF1Kemwoyc52sW5dCrt2nQir2eKX/fOfuBx5a/V43W5yT59m47RpBkclwk3N1q0xWf1nsDJZLCS2CXx3skAkzjVAS6VUU6VUBDAK+KZgAaVU3QKb1wC/+75eBFyplIpTSsUBV/r2hZzdu9No3vwNLr/8Ezp2fI+bbpobNrPGu3Ny/LY9bnd+Ig01J09m8913O1mxYi9ut/e85XfvTmPs2K8ZPnwms2dvrYQIw0P2yZP8NHEiSx55hH0//FCiY6x2O7d9/z3xLVuizGbimjfn1uXLiYiJCXh85W4c0lq7lVLjyUt4ZmCK1nqrUuoZYK3W+hvgfqXUNYAbSANu8x2bppR6lrzkC/DMmYaiUHPzzf/l6NGs/GT57bc7+eyzzYwe3cHgyCpe+5tv5rcpU3D7kqU1MpLWw4YZHFXp7diRSs+eU3C7PXi90Lp1TX788bZi5+H844/TdOkymYyMXLxeWLx4D8ePO7j33m6VHHnVknPqFO916EDWsWN4nE7WvP02V0+eTIebbz7vsbXateO+nTsrPMaA9OPUWs/XWrfSWjfXWj/v2/cvX9JEa/2Y1rqt1rqj1vpyrfX2AsdO0Vq38H1MDUQ8Rtix44RfDTMry8XWrccMjKjyDHjtNbrdfTfVGzemdocO3DhvHnU6djQ6rFIbO/YbTp7MJj3dSWamky1bjvHWW6uLLT99+kayslx4fRVTh8PF88//WEnRVl0bp03DkZqKx+kEwOVwsPihhwyOyp8MuQyQpKQE1qxJyU+e0dFW2ratZXBUlcNstXLlK69w5SuvGB1Kuezbd4qCj6Zzctzs2lX8DZDT6Sn0OKYkt/fi3JyZmXhc/hNGB9ujHxlyGSCffXYddepEExtrIyrKwjXXJHHTTe2NDkuUQo8eDYiI+PNPwm630qtXw2LLjxzZjqgoi1/5ceO6FFtelEyLgQMx22z525YgfPSjQrH1t2vXrnrt2rVGh1FITo6b338/TrVqNpo3j6uyq2K6HA48TieRNWoYHUpApaVlc+WV09my5Rher2bs2At5553B5/w5rlp1kEceWcrp0zmMGtWORx7phclUNX/ulWnXggUsuO8+ck+fptWQIQx+5x0skZEVfl2l1DqtddfzlpPEKUpKa83CBx5g7bvvglLU79aNm+bPJ7J61ZncV2vN8eMOIiMtxMbazn+AqFJKmjjlVl2U2Kbp0/ltyhS8bjdel4uUdeuYd9ddRocVULNnb+Xmm//LmDFfsn69jIASRZPGIVFi+374AVdWVv62JzeXAz//bGBEgfXRR+u5//6F+esMLVmyh19+GUv79rUNjkwEmypf4/R6NWvXpvDjj/vJzHQaHU5Ii2/e3P85k1JUb+jfeOLOyWH3kiUkL1yIMzOzkiMsnxdf/NlvcbasLBcffLDewIhEsKrSNU6Xy8PAgTP49deDmM0moqIsrFw5lmbN4owOLST1eOABts6axck9e1AmE8psZsiHH+a/nnPqFB/26EHG4cMopYioVo07V6+mWr16BkZdckWN9AqX0V+idKp0jfPdd9eycuUBsrJcpKfncvy4g9tu+8rosEJWRHQ0d65Zw4g5cxj68cfct3On3zjg7596ilP79uHMyCA3PZ3Mo0dZ8MADBkZcOg891BO7/c9RQna7lb/8pbOBEYlgVaVrnNu2HSc7252/7fVqkpNDckRn0DBHRNBiwIAiX0vduTN/tAeAdrtJS06urNDK7e67uxIVZeGjj34jJsbKU0/1oVOnOkaHJYJQla5xdutWz68GYbGY6NhR/hAqSuNLLsFq/3MVSEtkJA179jQwotK77bZO/O9/t7NgwWguuqiB0eGIIFWlE+ftt1/I8OGtsdnMREdbadYsjqlTh/qVSU5O45lnfuCZZ36Q2mg59XrkEVoMGoTJasUcEUGDnj258uWXjQ5LiIALiw7wKSkZOBwumjatgdn85/+KzZuP0rPnFLKz81pSo6KsrFx5h3Q/KafstDS8Hg/2mjWr7OgpUTVJB/gC6tWrRosW8X5JE+Bf/1pBVpYTj0fj8Wiyspw8+eQKg6KsOqLi44lOTJSkKaqsKt04dD5paTl+s+FoDSdP5hR/gAhrCxcms2BBMnXqRHPPPd2oXr3ix05XNVprMg4dwuNyUaNxY5QpNOtuYZ04b765PWvXpuR3eo6OtsqMRqIQj8fL3/++iPfeW4fT6cFmM/PBB+vZtOluYmIizn8CAYDH5WLW8OHsXbYMlKJWu3bcunQptthYo0MrtdBM9wFy552defLJS6ldO5pataJ54olLGTdO+u2JP7lcHq64YhpvvLEap9MDQG6uh2PHspgzpyQrYIszfn7pJfYuX447Jwd3djZHN21i4YMPGh1WmYRljfOTTzbyn//8gtls4p//vIQjRx42OiQRpKZO3cDatYXXD/R4tAzhLaVDv/6KOzs7f9uTm8uhNWvOcUTwCrvE+emnm7jnnu/yb89vueVLbDYzgwe3MjgyEYx2707zG79+hsmkuPLK5gZEFLoS27Zl99KleHyL+5ms1gpZgbIyhN2t+jvvrPH7Q3A4XLz7rsztKYp20UUNiI72X6zNbreycOHNtGqVYFBUoenSJ54gsU0bImJiiKhWjWr16zPwjTeMDqtMwq7GGRFhLrQvMjLs3oYyObJhA1+MGMGp/fuJb9mSkf/9LzWTkowOq0ING9aae+7pxqRJv2IyKVq2TGDp0luoXTvwS85WdRExMdy5ejUpa9fidbup26UL1qgoo8Mqk7DoAF/QsmV7GDLk8/wx7Ha7lRUrxtC9e/1Ahljl5KanM6lJE3JOnszboRTRtWrx4P79WGxVf6b0zEwnDoeLxES79E8NgD9++on548eTnZZGqyFDGPDaa0Hxe1TSDvBhV9Xq27cZixffwjvvrMFiMfHAAz3o0iU0pj0z0tHNm9HeAis4ao3L4SAtOZlabdsaF1gliYmJkK5HAZK6fTufDhiQv3LlhqlTcWVmMuyTTwyOrOTCLnEC9O7diN69GxkdRkiJio/He9aSrR6nk6j4eIMiEqFq53ff+S3/687OZtucOedMnBkpKSz5xz84uXcvzfr149Inn8RstRZbvqKFZeIUpZfYpg0XjBjBtjlz8OTmYrbZ6DJuHNXq1jU6NBFirFFRmCwWv3/E5ojia/M5p08zuUsXslJT0W43RzZs4MTOnVw/c2ZlhFskSZyixIZOnUrrYcM4sXMntdq3p+XAgUaHJEJQuxtv5MfnnsPhW/TPardzxXPPFVt+z9KlOLOy0O68dokzNVR3Tk6lLBlcFEmcosSUUrQeNszoMESIi4qL4+5Nm/j19ddxHDtGqyFDaHX11cWWD8bGOEmcZeTxeDl6NIv4+CjpziREKdlr1uSKZ58tUdlm/foRERODOzsbr9uN1W4naehQw2qbEKAO8Eqpq5RSO5RSyUqpCUW8/nel1Dal1Cal1DKlVOMCr3mUUht8H98EIp6KtnnzUerXf40WLd6gRo2JfPLJBqNDEqLKssXGMm7dOtrfdBONLrmEno88wvBp0wyNqdz9OJVSZmAn0B84CKwBbtRabytQ5nJgldbaoZS6G+ijtR7pey1Ta12q3sTl6cdZXlprGjT4DykpGfn7oqIsrF39MSFPAAAgAElEQVQ7jgsuSDQkJiFEYFTmRMbdgWSt9R6ttROYCfitT6G1XqG1dvg2fwVCdjGXU6dySE3N8ttnsZjYuPGIQREJISpbIBJnfeBAge2Dvn3FGQssKLAdqZRaq5T6VSlVbMuDUmqcr9za48ePly/icqhePRKLxX/YpteradSoukERCSEqWyASZ1FNXkXe/yulRgNdgYIreDXyVY1vAiYppYqcckZrPVlr3VVr3TUx0bhbYpNJMWPGtdjtVmJjbdjtVkaP7kCvXtKhXpSN1pqjRzPz174SwS8QzcEHgYYFthsAhSYwVEr1A/4JXKa1zj2zX2ud4vu8Ryn1PXAhsDsAcVWYYcNas3XrPWzYcIQGDWLp2lWGbIqiZWU5efjhxfz88wFatkzgjTeuon79P2c8P3DgNH37TuOPP07j9WqefvpyHnust4ERi5IIROOQhbzGob7AIfIah27SWm8tUOZCYA5wldZ6V4H9cYBDa52rlKoJ/AIMLdiwVBQjG4eEKCmtNZdd9jFr1hwiJ8eDxaKoXTuG7dvH549779btA3777TAeT97fod1uZd68G7n88qZGhh62Kq1xSGvtBsYDi4Dfgdla661KqWeUUtf4ir0MxABfnNXtqA2wVim1EVgBTDxf0jTS778f57vvdrJnz0mjQxEh4NixLFavzkuaAG63JiMjl5Ur/2wS2LTpaH7SBHA6PaxefajSYxWlE5Ce21rr+cD8s/b9q8DX/Yo5biUQEqujPffcj/z73//DYjHjcnl4993BjBnTyeiwgtqGqVNZ9vjjuHNyaDtyJAPffNPQiRkqm9ls4uwbOq3zemGcUadONH/8kZ6/bbOZady4RmWFKMoo7GaAL4tdu07wwgv/w+Fwk56eS3a2m7vu+o709NzzHxymkhctYv748WQeOULOqVNsnDaNJY88YnRYlapmTTtDhrTCbs/7Z2GzmWnQIJZevf5sEvjss+uIiYkgNtZGTEwEl13WmBEjLjAqZFFCMlawBPbvP01EhDl/8mPIqzUcOZJJbKzxk68Go+1ffpk/3yLkTczw+9y5XPWf/xgYVeWbOfN6Xn55JT/99AdJSQk89dRl2Gx//tn16tWIHTvGs3r1IeLjo+jduxEmU/CNzQ5WWmuSFywgdft2Ei+4gOYDBlTK2HZJnCXQpk1NXC6P3z6TSdGwYeitB11a2usFpUr9yxgVH583dZj7z382kdXDr6+rxWI6byt5vXrVGDasdSVFVLXMHz+ejZ98gtflwmS10nnsWK56/fUKv27Y3qq73V7efHMVY8Z8xSuvrMxfM7so9evHMn36tURFWbDbrVSvbmPevBuJiqq6z+s8Tidf3nILz9lsPB8VxdIJEyhND4weDzxAZHw85ogIlNmMxW6vlF9oYTxnZia56ennL1hOJ/fsYcOUKbiysvA4nbiyslg3eTKn9u+v8GuHZY1Ta831189myZI9OBwuoqIszJ+/i6VLby32Nunaa9uQlvYoR49mUrdutSIXfatKlj/xBNvmzs2rMbrdrH7zTeJbtKDzX/5SouNjatfmni1b2DhtGi6Hg6RrrqFOx44VHLUwktft5ssxY9g2ezYAzQcM4IY5cypsFiNHairmiAjcvuWGIW9C5OwTJ6jRuPE5jiy/sKxx7tt3isWLd+cvE5yd7Wb16kNs2XLsnMdFRlpo3LhGlU+aALvmz8ednZ2/7XI42DlvXqnOEZ2YSM+HHuKyJ5+UpBkGVr7yCju++gqv243X7WbvsmUse/zxCrte4gUXYLL41/1MVisJlbDyalgmzpwcd6GapcmkZMhbATF160KB55omi4XYBiE7N4uoBPtWrPBvEMzJYd8PP1TY9SJiYhizYgVxzZujzGbiW7ZkzIoVRERHV9g1zwjLW/WWLROoXz+WvXtP4nJ5MZsVNWpE0qFDbaNDCxoDX3+djy6+OO9WXSls1apx6ZNPGh2WCGJxLVpgWrEify0hZTYT17RiR0DV7tCB+5OTK/QaRQm7ddXPOHYsizvv/IYNG47Spk1NPvzwGho0qPqt5KWRfugQyQsWYLJYSBo6lKi4OKNDEkHMceIEH3TrhiM1FYCI6GjuXLMmpO5USjrkMmwTpxAi8FwOB3uXL8fr8dCkT5+Q64JW0sQZlrfqongel4tNn35K+oED1O/RgxYDBpT5XBkpKeSmpxPXvHlYDbUMZ1a7/ZwLr1UVkjhFPq/Hw/T+/UlZswZXdjbWqCh6P/44l/7zn6U6j9aaeX/9KxunTcNktWJPSOD2H3+keiOZs1RUDWHZqi6Ktm/FCg6vW5fXMqo1LoeDH55+Gndu6cbkb509m82ffYYnNxdXZibpBw8y96abKihqISqfJE6RL+fUKb8uSAAo5dfFpCSObNiAK+vPdZm0x8OxLVsCEaIQQUESp8jXsGdPCs6DpiwWaiYlEVmjdNOc1WzdGmvBvnRKEde8yBVRhAhJkjhFvmr16nHLkiXEtWhBREwMjXr14pbFi0s9wUeH0aNp1rcvVrsdW2ws9oQErpsxo4KiFqLySXckUSG01hzdtInc9HTqdOqErVo1o0MKapMnr+P//u97nE4Pt9/eiYkT+2E2m9i//xS33fY127en0q5dLT7+eKjfmkUisKQ7kjCUUkrGp5fQ119v529/W5Q/d8I776wlOjqCRx/tRe/eUzh8OBOPR3P8eBa9e09lx47xYTFfQjCTW3UhDDZr1tb8pAngcLiYOXMLW7Yc4/Tp3Pw1iTweTWqqg507TxgVqvCRGud5pKVl8+abqzh6NItBg1py9dWtjA5JVDHx8VGYzcpv0bbq1SOx261++wA8Hm/+UhzCOFLjPIfTp3Po1Ok9XnjhJ959dy0jR87hjTdWGR2WqGIeeaQXsbE2rFYTJlPeEsGvvnolF1yQSJ8+TfITpd1uZeDAljRtKou5GU0ah87hgw/W8eCDi/xuo6pViyA9/bEKv3Yo2714MUc2biS+eXNaDx9eKWvAhLpDh9KZNm0jTqeH6667gHbtagF5KxV88ME6Nm48Spcudbnjjgsxm6W+U1GkcSgAHA4XHo/Xb9+5ltgQsOzxx1n1xht4nE7MEREkDR3KtZ9+KsnzPOrXj+Wxxy4ptN9iMXH33d0MiCiwvB4PSimUqWok/arxXVSQgQNb+q2BHRVlkUW1zsGRmsovr76KKysLr8uFKyuLHV99xdFNm4wOTVQS7fWScfgwzsxMIG/SmK/GjOH5yEies9mYf999eQsAhjipcZ5Dq1YJLFo0mnvvnU9qqoNBg1ry+utXGR1W0Mo+eRJzRAQepzN/n8lqJfuEtAKHg/RDh5jWty+n9u9Hezxc8vjjeD0ets2Zk7/a6YYpU4hv0YKLHnjA4GjLRxLnefTq1YgNG+4yOoyQUKNJE2zVq+PMyvIbulmnUycDoxKVZc4NN5CWnIz25D3OWvnKK8TUres314HL4WDX/PkhnzjlVl0EjNlq5bbvv6dWu3aYrFZqNG3KrUuXEhUfb3RoohIc2bAhP2kCuLKzMVssfs81VRVZuyogiVMpdZVSaodSKlkpNaGI121KqVm+11cppZoUeO0x3/4dSqmyz5orgkJ8ixbcvWkTTzqdPLBnD/W6nreBUlQR1erX99u2RkVx4dix2KpXxxodjTU6Gnt8PFc8+6xBEQZOuW/VlVJm4G2gP3AQWKOU+kZrva1AsbHASa11C6XUKOBFYKRS6gJgFNAWqAcsVUq10lpL03UVcGLnTubedBNpu3ZRs00brvvsM+KaNTM6LFFBrp0xg2l9+6KUQnu9NOzZk4v+9jc63noru+bPR5lMtBoypEqsXVXufpxKqYuB/9NaD/BtPwagtf53gTKLfGV+UUpZgCNAIjChYNmC5c51TZnkI/i5HA5eb9qUrOPHQWuUyURM3brcv3s3FpvN6PBEBck8epRDq1cTWaMGjXr1CrnuRyXtxxmI76o+cKDA9kHfviLLaK3dwGkgoYTHihB0bMsW3Dk5+Y1E2uslNz2dEzt2GByZqEgxtWuTNGQIjS+5JOSSZmkE4jsrqmfz2dXY4sqU5Ni8Eyg1Tim1Vim19vjx46UMUVQ2W2wsHpfLb5/X5cIWYqseClGUQCTOg0DDAtsNgJTiyvhu1asDaSU8FgCt9WStdVetddfExMQAhC0qUkJSEklDhuTPBG+NjqbtqFHUaNzY4MiCn8fj5emnv6dDh3e5/PJPWL/+sNEhibMEoh/nGqClUqopcIi8xp6zV+b6BhgD/AJcDyzXWmul1DfAZ0qp18hrHGoJrA5ATMJgSimu+/xzNn/+Oam//06tdu1oO3Kk0WGFhL//fREffvhb/hwJl146lY0b76J5c+nWFSzKnTi11m6l1HhgEWAGpmittyqlngHWaq2/AT4CpiulksmraY7yHbtVKTUb2Aa4gXulRT30eFwuTu7ZQ2SNGsTUrp2/X5lMdLj5ZgMjC01Tp27wm1jG6fTw5ZfbefjhngZGJQoKyMghrfV8YP5Z+/5V4OscYEQxxz4PPB+IOETlS9u9m48vu4zc06fxuFx0u/deBrz6qtFhhbSzZz8ymRRWa9VtaAlF8tMQ5TL7+uvJ9E3q4MnNZd3777Nz3jyjwwppEyb0yp+D02xWREdHMHJkO4OjEgXJWHVRLie2b/eb7cadk8PRTZtodfXVBkYV2h55pBf168cyd+42atWK5oknLqVOnRijwxIFSOIU5VK9USNO7NyZv22JjCS+RQsDIwp9SilGj+7A6NEdjA5FFENu1UW5XD9rFpFxcXnjke12Wl19NRdcf73RYQlRoaTGKcqlTqdOPLBnD0c2biQqLo5a7dvLbO+iypPEKcotskYNmlx2WZmP114vRzZuxOVwUKdTJyJ8neaFCFZyqw4sWpTMgAHTGTDgUxYv3m10OGHF43IxfcAApl5yCZ8NGsSbLVtycu9eo8MSBtizbBmv1q3Ls1YrH3TrRvrBg0aHVKywT5yLF+/m2mtnsXjxHhYv3s2wYTMleVaite+9x4Gff8aVlUVuejpZR4/y9e23l+jY/T/+yNIJE1j5yivknDpVwZGKinRq/35mDh1K5pEjeN1uDv/2G9MHBO/0vGF/q/7yyytxONz529nZbl59dSVXXtncwKjCx/EtW3BnZ+dva6/Xr5W+OJumT2feXXflzTIeEcHqt97iro0biZRJRELSwV9+8ZtNSXs8pO3aRc7p00H5Mw37GmdRQnCp+ZBVr1s3rHZ7/rbJYqFOx47nPW7RQw/lrWWjNZ7cXLKOHWPzjBkVGaqoQFEJCYX/8JQK2ufdYZ84H374YqKi/qx4R0VZeOihiw2MKLxceMcdJA0diiUyEmt0NDWaNmXo1KkAHPjlF1a++iqbZszIXyXxjIILgEHelHW5GRmVFnc4ObhqFV+MGMHM4cPZs3RphVyjWd++NOjZE2t0NOaICKx2OwNefRWTJThviss9A7wRAj0D/MKFybz66kpA8fDDFzNggHTgrmzpBw/icjiIa9YMk8XCusmTWfS3v+F1uzFZrdTt3JkxK1ZgMpsBmHPjjez46qu8yZIBi93O2J9/lhU1A+zQ6tV8cvnl+f+oLHY7I2bPptXgwQG/1pmlhDMOHaJ+jx406tUr4Nc4n5LOAC+JUwQdrTUv2O35SREgIiaGaz/7jKQhQ4C8FRTn3XUXu777DltsLIPfeYcWV8ma94H2xciRbJs9229fg4svZuzKlaU+17GtW8k6dozaHTpgT0gIVIgBVdLEGZz1YBHWPE4nHqfTb5/WmuwTJ/K3rVFRDP/kk8oOLeyc/YikuH3norVm3l13sfnTTzFZrWivl9ELF9KwZ+hOkxf2zzhF8LHYbNTp1AlV8PmW1jTq3du4oMJUt3vu8Wu8s9rtXPTgg6U6x54lS9g8YwYuh4Pc06dxZmQwO8SH5UriFEHppu++o0GPHpisVmLq1OGG//5XJg8xQLO+fRkxZw4Ne/akXvfuDPngA9rfdPYCD+eWlpzsN4MWkNdf0xO6c5bLrboISjF16nDHTz8ZHYYAWg4cSMuBA8t8fO0OHQrNXxDXtGl+Q18okhqnEKJCNerdm94TJmC22YiIicGemMior782OqxykVZ1IUS5aa8XrfU5a5GOEydwpKYS17Qp5oiIYsvlpqfz7V//yh8//khM3boM+eAD6l54YUWEXUhJW9WlximEKDOtNUsefZTnIiN5zmbjixtuwJ2bW2RZe0ICNZOSzpk0AWYNH872L78kIyWFw+vW8fFll5GRUuSq4YaRxCmEKLMNU6ey5q238LpcaI+HnfPmsfSRR8p8PndODvt++AFPweSrNXtXrAhAtIEjiVMIUWa7FizwG/7qzs4medGiMp/PZLUWORF2wS5RwUASpxCizKo3bIjJav1zh1JUq1evzOczmc30fuyx/ERpttmIbdiwXK36FUEah4QQZeZITeX9Cy8k+9Qp8DUOjf3lFxIvuKDM59Ras23OHPYuW0aNJk3oPn48ETGVs8qnjFUXQc/lcLD4H//gj59+Ir55cwa+8QaxDRoYHZYopdz0dHZ8+y0ep5MWAwaUq8ZpNEmcIqhprZnevz8Hfv4Zd04OymwmulYtxu/Yga1aNaPDE2FKuiOJoJadlsYf//tf/gxI2uPBmZnJH//7n8GRhTbt9frNKiUqhiROYQh3dnaRY5VVCA/DM9r6Dz/khehoXoiJ4f3Onck8csTokKqsciVOpVS8UmqJUmqX73NcEWU6KaV+UUptVUptUkqNLPDax0qpvUqpDb4PmYU2DGSfPMnUSy/122eyWqlWt265lhkOZwd//ZWFDzyAOycH7fFwdPNmZl17rdFhVVnlrXFOAJZprVsCy3zbZ3MAt2qt2wJXAZOUUjUKvP4PrXUn38eGcsYjQsBPEyeScegQukCNMzoxkb+sWoUlMtLAyELXgZUr/ebJ1G43KWvWGBhR1VbexDkUODOb7CfAsLMLaK13aq13+b5OAY4BieW8rghh6QcOFJqo2BIVRWSNGsUcIc4npk4d//6U+BZAExWivImzttb6MIDvc61zFVZKdQcigIILlz/vu4X/j1LKdo5jxyml1iql1h4/frycYQsjNb/ySqwFVi+0REbSrF8/AyMKfW1vuIF6XboQERODNToaq93OMJkhv8KctzuSUmopUKeIl/4JfKK1rlGg7EmtdaHnnL7X6gLfA2O01r8W2HeEvGQ6GdittX7mfEFLd6TQprVm6YQJ/Praa2itaTloENfPmoU1Ksro0EKa1+Nh1/z5ZJ84QcNevUho2dLokEJOpfTjVErtAPporQ+fSYxa66QiysWSlzT/rbX+ophz9QEe1lpffb7rSuKsGrweD9rrxXzWLaYQRqmsfpzfAGN8X48BCs1OqpSKAL4Epp2dNH3JFpU3qn8YsKWc8YgQYjKbJWmKkFTexDkR6K+U2gX0922jlOqqlPrQV+YG4FLgtiK6Hc1QSm0GNgM1gefKGY8QQlQ4GXIphBA+MuRSCCEqiCROIYQoJUmcosrKPHKEz4YMYVLjxswYOJD0Q4eMDilk5WZkcGLnTr/Z3sOZrKsuqiSPy8XUSy7h1L59eN1u0g8dYkqvXozfvl2GdZbSlpkz+fqOO/JWsFSKkV9+SbO+fY0Oy1BS4xRV0okdO8g8ciR//Lb2eMhOS+Po5s0GRxZa0g8d4us77sCdnY0zMxNnRgazhg8P+5qnJE5RJVkiIwtNW6e9XhmdVEqp27cXXs5Xa07/8YcxAQUJSZyiSopr3pymffvmL/pliYqiYc+eJLZta3BkoSWuadNCE7K4cnLAFN6pI7y/e1FlKaUY9eWX9P33v+l4661c8fzz3PTdd0UuPSuKF9esGVc8+6zfzEva42Fq795khfFkO9IBXogQpr1e0nbvBq2Jb9ECr8eD1+UK+DrkE+PiyD11Kn/bbLPR78UXueiBBwJ6HaOVtAO8tKoLUQbu3Ny8iYKVon63boWfA1YCl8PB9P79ObJhA1prImvUwOGrBTa4+GJu/PZbIqtXB/JmpDq0ejVZx45Rr0uXUq9Eqc9+Xuzx4MnNDcw3EoIkcQpRStlpaXzUsycZKSkAxNavzx0rVxIVV+SMihVm+ZNPcnj9+vzF2TKzs/NfO7RqFd/85S/c8MUXaK357+jR7Pj6a0xmM16Phxu//Zaml19e4mu1v/lmNk6bhtvXmm622Ui65prAfkMhRJ5xClFKSx59lFN79+LMyMCZkcHJPXtYNqGoVWMqVsqaNcWuaOlxOvNXDE1esIAdX3+NKyuL3PR0XFlZzLnhhlJda9Cbb9L93nuJb9GCet27c8uSJdRs3brc30OokhqnEKWUum2bX0uzx+nk+LZtlR5H7Q4dOLR6dbG3zGdux0/t24f2ev1ec5w4gdfjyevUXgImi4X+L71E/5deKl/QVYTUOIXw8Xo87F2xgu1ffUXm0aPFlqvfo4ff6CNLZCT1L7ronOc+tX8/8/76V2aPGMG2OXMCEm/fF16gZlJS/nIZZpsNa0wMEdWqYYuN5ZqPPgKgbpcu/r0JlCKhZcsSJ01RmLSqC0HeEM3p/ftzeN06lMmE1poxy5dTr2vhBlaXw8GMQYM4tGoVkJdIb16woNjO9emHDvFuu3bkpqfndcK32+n34ot0Hz++3HF73W6ObNwIWpPQujX7li/HmZVFkz59qFa3bn65XydNYumjj6LMZuwJCdy6fLksrVGESlk6wyiSOEWgrf/oIxbef7/fUMKE1q0Z//vvRZbXvtEzSiliGzY8Z//Qn158kRVPPonX5crfF127Ng8fOeJ/Tq8XVYEdy10OB9knT1Ktbt0KvU4ok/k4hSiF0/v3Fxp/nXGO2ZSUUtRo3JjqjRqdt1O9x+ks9IyxYBI9tW8f77Zvz7NWKy8nJrJ78eIyfAfnZ7Xbia1fX5JmAMg7KKqc1W++yWsNGvBq3br8+NxzlOSuqn6PHn5LFiuLhXpduuB1u1n8j3/wSp06TGrShM2ffQbA5s8+4+XERF6Ijmb29deTdfw4h9asIXXHjkLXu+D66/2eiVrtdi684w4gr+Y6rV8/jm/bhvZ6caSmMmv4cE7t2xeAd0JUFGlVF1XKpk8/ZemECfm1x58mTsRWvTo97rvvnMe1GjyYix96iJ/+/W+UyUR8ixZc9/nnrPjXv1j7zjv55/v2zjvJOnaMZf/8Z36fxp3ffsvOefOw2Gx4XC5aDxvGtZ9+ml+zS2zThluXLmXxww+Tc+oUbUeO5JLHHwfy+oSmHzjgVyM1WSwcWrOGGk2aBPrtEQEizzhFlTJj0CCSFyzw21eve3fu9DXknI/L4cCZlYW9Zk2UUkxq2pTTZ9X+6nTuzJHffoNi/nas0dEMmTyZ9jfddN7reZxOXqhWDW+B7k3W6GhumjePJn36lChmETjyjFOEJVv16nDWM8czww5Lwmq3E52YmP/c0hYT4/e6MpuJrFEDi81W7DlcWVkcL6ZR6WzmiAiumjQJq92OJTKSiJgYmvXrR+PLLitxzKLySeIUVUqfp54iIiYG5Zut3BodTd8XXijz+a589VUsvm5GJouFyOrVGfTWW8TUq4clKgqTxZKXqAska2t0NLVKMX1dt7vvZsyKFfR/5RWGf/opI//7X5nFKcjJrbqock7u2cOGTz5Bezy0v/lmEtu0Kdf5Dq1Zw+9z52KNjqbz2LFUq1eP3IwMNs+YQc7p0yRecAHz7roLZ0YGXpeLtiNHMnTq1DIlP601rqwsrNHRkjwNIP04hahE7pwcUrdvx1a9OnFNm5bpHEc2buSzQYPIOnYMq93OiDlzaN6/f4AjFeciiVOIEOJxOnmtfn0cqan5+6zR0dyfnExMnTrlPr/2etk0Ywapv/9OrfbtaTdqlNRoiyDzcQoRQtIPHsRVYFo4yHumenTTpnInTq01c0aNYtf8+fmPAfYsWcLQKVPKdd5wJo1DQgQBe82a+StynuFxOks94XBRUn//nV3ffYcrKwvIa/Xf8vnnnNq/v9znDleSOIUIArbYWK58+WWsdnv+bEed//IXarVrV+5z56an57X+F2CyWslNTy/3ucNVuW7VlVLxwCygCbAPuEFrfbKIch7gzILWf2itr/HtbwrMBOKB9cAtWmvn2ccLEQ6633cfjS65hCMbNxLfvDmNevcOyHlrtW+PJTKS3IwM0BplMmGrXp2EVq0Ccv5wVN4a5wRgmda6JbDMt12UbK11J99Hwfn2XwT+4zv+JDC2nPEIEdLqdOpEpzFjApY0ASKio7n9f/+jbufO2GJjqdetG7f/+OM5O/GLcytXq7pSagfQR2t9WClVF/hea51URLlMrXXMWfsUcByoo7V2K6UuBv5Paz3gfNeVVnUhREWorCGXtbXWhwF8n2sVUy5SKbVWKfWrUmqYb18CcEprfeaJ+EGgfnEXUkqN851j7fEwXs9ZCGG88z7jVEotBYrqD/HPUlynkdY6RSnVDFiulNoMFPVkutjqr9Z6MjAZ8mqcpbi2EAGnvV7SkpMBiG/RQua4DDPnTZxa637FvaaUOqqUqlvgVv1YMedI8X3eo5T6HrgQmAvUUEpZfLXOBkBKGb4HISqVMyuL6f37c3TjRiCv8eXWpUuJOGtCEFF1lfff5DfAGN/XY4Cvzy6glIpTStl8X9cEegHbdN7D1RXA9ec6Xohgs+KJJzjy22+4HA5cDgdHNm5kmW9+zZJwZWeTc+pUBUYoKlp5E+dEoL9SahfQ37eNUqqrUupDX5k2wFql1EbyEuVErfWZtVQfBf6ulEom75nnR+WMR4gKl7Jund965p6cHFLO01jp9XjYNGMGU3r35t8xMbxcqxYf9ewpCTRElasfp9b6BNC3iP1rgb/4vl4JtC/m+D1A9/LEIERlO3s9c7PNRu0OHYotr71ePrv6avatWJF/jPZ6ObxuHd/eeScjvviiUuIWgSNPtIUopYLrmUfExJDQsiX9Xnyx2PL7f/yRA6Ylhc0AAAVQSURBVD/9lJ80z/A4nRxYubKiwxUVQCb5EKKUbLGxjFu3Ln8989odO2K2Wostn33yZLGt7rENG1ZUmKICSeIUogxMvlUwS6JBjx6FlgdGKWyxsVzzkTzWD0Vyqy5EBatWrx6jFy2ieuPGmG02EpKSuPr997lv165SLbEhgofUOIWoBA179uRBWSu9ypAapxBClJIkTiGEKCVJnEIIUUqSOIUQopQkcQohRClJ4hRCiFKSxCmEEKUkiVMIIUpJEqcQQpSSJE4hhCilcq1yaRSl1HFgfxkPrwmkBjCcQAvm+II5Ngju+II5NpD4zmistU48X6GQTJzloZRaW5LlP40SzPEFc2wQ3PEFc2wg8ZWW3KoLIUQpSeIUQohSCsfEOdnoAM4jmOML5tgguOML5thA4iuVsHvGKYQQ5RWONU4hhCgXSZxCCFFKVT5xKqVGKKW2KqW8SqliuzMopa5SSu1QSiUrpSZUYnzxSqklSqldvs9xxZTzKKU2+D6+qeCYzvleKKVsSqlZvtdXKaWaVGQ8pYztNqXU8QLv1V8qKzbf9acopY4ppbYU87pSSr3hi3+TUqpzEMXWRyl1usB7969KjK2hUmqFUup339/rA0WUMey9K0RrXaU/gDZAEvA90LWYMmZgN9AMiAA2AhdUUnwvARN8X08AXiymXGYlxXPe9wK4B3jP9/UoYFYQxXYb8JaBv2+XAp2BLcW8PghYACjgImBVEMXWB5hn0PtWF+js+7oasLOIn61h793ZH1W+xqm1/l1rveM8xboDyVrrPVprJzATGFrx0YHvOp/4vv4EGFZJ1y1OSd6LgjHPAfoqpVSQxGYorfWPQNo5igwFpuk8vwI1lFJ1gyQ2w2itD2ut1/u+zgB+B+qfVcyw9+5sVT5xllB94ECB7YMU/qFVlNpa68OQ98sD1CqmXKRSaq1S6lelVEUm15K8F/lltNZu4DSQUIExlSY2gOt8t3JzlFINKyGu0jDyd60kLlZKbVRKLVBKGbJ2se/Rz4XAqrNeCpr3rkosD6yUWgrUKeKlf2qtvy7JKYrYF7B+WueKrxSnaaS1TlFKNQOWK6U2a613ByZCPyV5Lyr0/TqHklz3W+BzrXWuUuou8mrGV1R4ZCVn1HtXEuvJG6udqZQaBHwFtKzMAJRSMcBc4EGtdfrZLxdxiCHvXZVInFrrfuU8xUGgYM2kAZBSznPmO1d8SqmjSqm6WuvDvtuOY8WcI8X3eY9S6nvy/iNXROIsyXtxpsxBpZQFqE7l3AKeNzat9YkCmx8AL1ZCXKVRob9r5VEwUWmt5yul3lFK1dRaV8rkH0opK3lJc4bW+r9FFAma905u1fOsAVoqpZoqpSLIa/Co0JbrAr75//btX6WBIIjj+Hcqbf1TiKWVDyAi0SdIERCsTZEmhU9hY5fOLtYWdhaChUkrVuKhFmotFhaWYnEpdgJHQowL3t4Vvw8sWe4uZBiOuexsArR93gamviGb2ZKZLfh8FdgFnkqK5y+5KMZ8AAxy796XbG5sEz2vFqFXVieXwKHvEO8AX+NWTdXMbG3cqzazbUJ9+Pz9Xf/22QacAc95nvdmXFaf3FW1K5VqAPuEJ9U38AFc+/F14KpwXZOwk/dGWOKnim8FuAFe/HXZj28BfZ83gIywi5wBnZJjmsoFcAy0fL4IXACvwB2wkTBf82I7AR49V0NgM/H9dg68Az9+33WALtD18wacevwZM37pUVFsR4Xc3QKNhLHtEZbdD8C9j2Zdcjc59JdLEZFIWqqLiERS4RQRiaTCKSISSYVTRCSSCqeISCQVThGRSCqcIiKRRqEh9G3ntFKpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make up a dataset\n",
    "\n",
    "from sklearn.datasets import make_moons, make_blobs\n",
    "X, y = make_moons(n_samples=100, noise=0.1)\n",
    "\n",
    "y = y*2 - 1 # make y be -1 or 1\n",
    "# visualize in 2D\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.scatter(X[:,0], X[:,1], c=y, s=20, cmap='jet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP of [Layer of [ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2), ReLUNeuron(2)], Layer of [ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16), ReLUNeuron(16)], Layer of [LinearNeuron(16)]]\n",
      "number of parameters 337\n"
     ]
    }
   ],
   "source": [
    "# initialize a model \n",
    "model = MLP(2, [16, 16, 1]) # 2-layer neural network\n",
    "print(model)\n",
    "print(\"number of parameters\", len(model.parameters()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value(data=0.8958441028683222, grad=0) 0.5\n"
     ]
    }
   ],
   "source": [
    "# loss function\n",
    "def loss(batch_size=None):\n",
    "    \n",
    "    # inline DataLoader :)\n",
    "    if batch_size is None:\n",
    "        Xb, yb = X, y\n",
    "    else:\n",
    "        ri = np.random.permutation(X.shape[0])[:batch_size]\n",
    "        Xb, yb = X[ri], y[ri]\n",
    "    inputs = [list(map(Value, xrow)) for xrow in Xb]\n",
    "    \n",
    "    # forward the model to get scores\n",
    "    scores = list(map(model, inputs))\n",
    "    \n",
    "    # svm \"max-margin\" loss\n",
    "    losses = [(1 + -yi*scorei).relu() for yi, scorei in zip(yb, scores)]\n",
    "    data_loss = sum(losses) * (1.0 / len(losses))\n",
    "    # L2 regularization\n",
    "    alpha = 1e-4\n",
    "    reg_loss = alpha * sum((p*p for p in model.parameters()))\n",
    "    total_loss = data_loss + reg_loss\n",
    "    \n",
    "    # also get accuracy\n",
    "    accuracy = [(yi > 0) == (scorei.data > 0) for yi, scorei in zip(yb, scores)]\n",
    "    return total_loss, sum(accuracy) / len(accuracy)\n",
    "\n",
    "total_loss, acc = loss()\n",
    "print(total_loss, acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step 0 loss 0.8958441028683222, accuracy 50.0%\n",
      "step 1 loss 1.7235905336972022, accuracy 81.0%\n",
      "step 2 loss 0.7429006313851131, accuracy 77.0%\n",
      "step 3 loss 0.7705641260584198, accuracy 82.0%\n",
      "step 4 loss 0.3692793385976538, accuracy 84.0%\n",
      "step 5 loss 0.313545481918522, accuracy 86.0%\n",
      "step 6 loss 0.2814234349772435, accuracy 89.0%\n",
      "step 7 loss 0.26888733313983904, accuracy 91.0%\n",
      "step 8 loss 0.2567147286057417, accuracy 91.0%\n",
      "step 9 loss 0.2704862551637922, accuracy 91.0%\n",
      "step 10 loss 0.24507023853658053, accuracy 91.0%\n",
      "step 11 loss 0.2509905529791503, accuracy 92.0%\n",
      "step 12 loss 0.21560951851922952, accuracy 91.0%\n",
      "step 13 loss 0.23090378446402726, accuracy 93.0%\n",
      "step 14 loss 0.20152151227899445, accuracy 92.0%\n",
      "step 15 loss 0.22574506279282217, accuracy 93.0%\n",
      "step 16 loss 0.19447987596204114, accuracy 92.0%\n",
      "step 17 loss 0.21089496199246363, accuracy 93.0%\n",
      "step 18 loss 0.159830773563036, accuracy 94.0%\n",
      "step 19 loss 0.1845374874688392, accuracy 93.0%\n",
      "step 20 loss 0.18977522856087634, accuracy 91.0%\n",
      "step 21 loss 0.19072704042579647, accuracy 93.0%\n",
      "step 22 loss 0.11733695088756485, accuracy 97.0%\n",
      "step 23 loss 0.12173524408232454, accuracy 95.0%\n",
      "step 24 loss 0.1261571261277045, accuracy 95.0%\n",
      "step 25 loss 0.16049097780801674, accuracy 95.0%\n",
      "step 26 loss 0.18747197705245805, accuracy 92.0%\n",
      "step 27 loss 0.16741837891059408, accuracy 95.0%\n",
      "step 28 loss 0.09586583491455399, accuracy 97.0%\n",
      "step 29 loss 0.0877878370742091, accuracy 96.0%\n",
      "step 30 loss 0.11731297569011848, accuracy 95.0%\n",
      "step 31 loss 0.09340146460619836, accuracy 97.0%\n",
      "step 32 loss 0.12454454903103446, accuracy 95.0%\n",
      "step 33 loss 0.07984002652777272, accuracy 97.0%\n",
      "step 34 loss 0.07727519232921673, accuracy 97.0%\n",
      "step 35 loss 0.07661250143094483, accuracy 98.0%\n",
      "step 36 loss 0.10610492379198365, accuracy 96.0%\n",
      "step 37 loss 0.09062808429265976, accuracy 99.0%\n",
      "step 38 loss 0.10671887043036932, accuracy 95.0%\n",
      "step 39 loss 0.05225659921975849, accuracy 98.0%\n",
      "step 40 loss 0.06016009895234464, accuracy 100.0%\n",
      "step 41 loss 0.08596724533333942, accuracy 96.0%\n",
      "step 42 loss 0.051121079431796, accuracy 99.0%\n",
      "step 43 loss 0.052401424016428284, accuracy 97.0%\n",
      "step 44 loss 0.045306841790015734, accuracy 100.0%\n",
      "step 45 loss 0.07211073370655095, accuracy 97.0%\n",
      "step 46 loss 0.03334238651310234, accuracy 99.0%\n",
      "step 47 loss 0.03143222795751122, accuracy 100.0%\n",
      "step 48 loss 0.03658536747111507, accuracy 99.0%\n",
      "step 49 loss 0.04829139382390309, accuracy 99.0%\n",
      "step 50 loss 0.09875114765619622, accuracy 96.0%\n",
      "step 51 loss 0.05449063965875453, accuracy 99.0%\n",
      "step 52 loss 0.03392679435708309, accuracy 100.0%\n",
      "step 53 loss 0.05261517263568441, accuracy 97.0%\n",
      "step 54 loss 0.03250295251424923, accuracy 99.0%\n",
      "step 55 loss 0.02888327387207822, accuracy 100.0%\n",
      "step 56 loss 0.04139151104027239, accuracy 98.0%\n",
      "step 57 loss 0.018987407426128502, accuracy 100.0%\n",
      "step 58 loss 0.0252383352388374, accuracy 100.0%\n",
      "step 59 loss 0.02079656521341895, accuracy 100.0%\n",
      "step 60 loss 0.0325971115781023, accuracy 99.0%\n",
      "step 61 loss 0.017863351693480307, accuracy 100.0%\n",
      "step 62 loss 0.023008717832211683, accuracy 100.0%\n",
      "step 63 loss 0.022079325463581503, accuracy 100.0%\n",
      "step 64 loss 0.029432917853529684, accuracy 99.0%\n",
      "step 65 loss 0.01625151464409193, accuracy 100.0%\n",
      "step 66 loss 0.02846853448326446, accuracy 99.0%\n",
      "step 67 loss 0.013994365546208731, accuracy 100.0%\n",
      "step 68 loss 0.015552344843651405, accuracy 100.0%\n",
      "step 69 loss 0.0338911994616017, accuracy 99.0%\n",
      "step 70 loss 0.014229870065926908, accuracy 100.0%\n",
      "step 71 loss 0.013255281583285504, accuracy 100.0%\n",
      "step 72 loss 0.012300277590022063, accuracy 100.0%\n",
      "step 73 loss 0.012676052498355976, accuracy 100.0%\n",
      "step 74 loss 0.020593811955954763, accuracy 100.0%\n",
      "step 75 loss 0.011845398205364453, accuracy 100.0%\n",
      "step 76 loss 0.016012697472883086, accuracy 100.0%\n",
      "step 77 loss 0.025458360239222128, accuracy 100.0%\n",
      "step 78 loss 0.014382930289661911, accuracy 100.0%\n",
      "step 79 loss 0.011698962425817985, accuracy 100.0%\n",
      "step 80 loss 0.012318500800515763, accuracy 100.0%\n",
      "step 81 loss 0.014121117031464233, accuracy 100.0%\n",
      "step 82 loss 0.011664591962446225, accuracy 100.0%\n",
      "step 83 loss 0.011589314549188726, accuracy 100.0%\n",
      "step 84 loss 0.010990299347735226, accuracy 100.0%\n",
      "step 85 loss 0.01098922672069161, accuracy 100.0%\n",
      "step 86 loss 0.010988193757655071, accuracy 100.0%\n",
      "step 87 loss 0.010987200447388707, accuracy 100.0%\n",
      "step 88 loss 0.010986246779084925, accuracy 100.0%\n",
      "step 89 loss 0.010985332742365272, accuracy 100.0%\n",
      "step 90 loss 0.010984458327280174, accuracy 100.0%\n",
      "step 91 loss 0.010983623524308862, accuracy 100.0%\n",
      "step 92 loss 0.010982828324359073, accuracy 100.0%\n",
      "step 93 loss 0.010982072718767003, accuracy 100.0%\n",
      "step 94 loss 0.010981356699297042, accuracy 100.0%\n",
      "step 95 loss 0.010980680258141723, accuracy 100.0%\n",
      "step 96 loss 0.010980043387921506, accuracy 100.0%\n",
      "step 97 loss 0.010979446081684675, accuracy 100.0%\n",
      "step 98 loss 0.010978888332907229, accuracy 100.0%\n",
      "step 99 loss 0.010978370135492717, accuracy 100.0%\n"
     ]
    }
   ],
   "source": [
    "# optimization\n",
    "for k in range(100):\n",
    "    \n",
    "    # forward\n",
    "    total_loss, acc = loss()\n",
    "    \n",
    "    # backward\n",
    "    model.zero_grad()\n",
    "    total_loss.backward()\n",
    "    \n",
    "    # update (sgd)\n",
    "    learning_rate = 1.0 - 0.9*k/100\n",
    "    for p in model.parameters():\n",
    "        p.data -= learning_rate * p.grad\n",
    "    \n",
    "    if k % 1 == 0:\n",
    "        print(f\"step {k} loss {total_loss.data}, accuracy {acc*100}%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.548639298268643, 1.951360701731357)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize decision boundary\n",
    "\n",
    "h = 0.25\n",
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                     np.arange(y_min, y_max, h))\n",
    "Xmesh = np.c_[xx.ravel(), yy.ravel()]\n",
    "inputs = [list(map(Value, xrow)) for xrow in Xmesh]\n",
    "scores = list(map(model, inputs))\n",
    "Z = np.array([s.data > 0 for s in scores])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}