ch04-神经网络的学习.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 梯度：1维\n",
    "\n",
    "https://github.com/oreilly-japan/deep-learning-from-scratch/blob/master/ch04/gradient_1d.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1999999999990898\n"
     ]
    },
    {
     "data": {
      "image/png": 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w2+v6EBUZ7nVZ1ZefXaYJmwL1msL5P3FWwoZoE7a6qhT4xphWwAVAOyAP2ASsttaWVnYfa60Fcn1fRvr+2XOqVkSqZdm2dB6at478ohKeuW0Qowe197qk6juyxTmaXz9PTdhzdNrAN8ZcBjwCNAPWAmlAFHAj0NUYMx942lqbXcn9w4EkoBvwvLU2sYJtpgBTAGJiYs7+fyIi3yoptfzr8208t3QH3Vs15IVxQ+jWqqHXZVVdcSFs/dBpwu792mnC9vshxE9SE/YcnOkI/xpgsrV2X/kbjDERwHXA5cDbFd3ZWlsCDDLGNAHeNcb0s9ZuKrfNVGAqQFxcnP4CEDlHaTn5PDh3Hf/ddZRbhnTgidH9qFcnSKZwsg86TdikV9SEdcFpA99a+/BpbisG3qvKINba48aYL4CrcKaDRMQFy7dn8NC8deQWFPHUzQO4Na6j1yWdmbWw5yvn3PmtH/masJeXacIGyS+rIFDVOfxZwP3W2izf17HADGvtyNPcpyVQ5Av7esAo4K/nXLGIfE9RSSlPf7qNl5btpEuLBrw+KZ5ebRp5XdbpVdiEvc/XhO3idXW1UlXP0lkOJBpjfga0Bx4Gfn6G+7QFXvXN44cBb1prF5x1pSJSoX1HT/LAG85CqjHxHXnsuj6BfeGzI1uckN8wDwpznbcIHP0C9PuBmrAuq9Kzwlr7kjFmM7AUyADOs9YePsN9NgDB82aPIkHo/XUHePTdTRgDz48dzLUDAnQhVUkRJH/oBH3ZJuzQSdBBTVh/qeqUzh3AY8CdwABgoTHmLmvtejeLE5GKnSgo5vEPNjM/aT9DOjXlmdsG0aFpfa/L+r7sg86liJNegdzD0CQGRv3eacI2aO51dSGnqn/3/RC40FqbBsw1xrwLvIKO4EX8btOBLH46dy27j57ggRHdeHBkdyLCA2h1qbWwZ7lz7nzyAqcJ220UxD+rJqzHqjqlc2O5r1caYxLcKUlEKmKtZebXe/jrx1tp2iCSOZOGcX7XADpKzs925uVXTYf0rRDVBIbd61zATE3YgHCmhVe/AV6w1h4rf5u1ttAYMwKor2asiLuO5hbwi7fWszQlnVG9W/PUzQNo1qCO12U50pKdkF//htOEbTsIRj/vzNGrCRtQznSEvxH40BiTD6wB0nFW2nYHBgGfA392tUKRELc0JY1fzt9AVl4RT4zuyx3DOnn/jlQlRbB1AaycDnuX+5qwP4Chk533hPW6PqnQmQL/ZmvtBcaYX+JcVqEtkA28Dkyx1ua5XaBIqMorLOHPC5OZtWIvPVtH89rEeHq39fjc+uxDZVbCHobGMTDqd3DenWrCBoEzBf4QY0wnYBxwWbnb6uFcSE1Eatj61OP8v3nr2JVxgkkXduYXV/b07gqX32vCljjN16HPOCti1YQNGmcK/BeBRUAXYHWZ7xucK1+qEyNSg4pLSnnhi508u3g7LaPrMmdSAsO7tfCmmIIc30rYGZCe/F0TNm4iNO/qTU1yTs50LZ1ngWeNMf+x1t7rp5pEQtLeoyd4aN461u47zuhB7Xjihn40rh/p/0K+14QdCDc85zRh6wTguf5SZVU9LVNhL+ISay1vrErlDwu2EBFmeHbMedwwsJ1/izjVhF01w7mQWXgd6PsDiJ/sXI5YTdhaIYAvuCFS+2XkFvDI2xv5PPkIw7s25++3DKRdEz+eyphz+LsmbM4hpwk78nEYfCc08GgqSVyjwBfxyGdbjvCrdzaQnV/MY9f14a7hsYSF+eFI2lrnejYrpzlH9aXF0HUkXPdP571h1YSttRT4In6WdbKI33+4mXfWHqB320bMnjSInm2i3R/4e03YxpBwj5qwIUSBL+JHS7Ye4VfvbCQjt5CfjuzO/Zd1o06Ey9fBSdtapgmbA20GwA3/hn43qwkbYhT4In6QlVfEHxds4a2k/fRsHc2M8UPp176xewOWFDnvHrVqepkm7E3OStgOcWrChigFvojLvtyWziNvb+BIdj4/uawrPx3ZnboRLs2T5xz2XY74ZV8TtqPThD3vDmjY0p0xJWgo8EVckpNfxJ8+SuaNVal0b9WQF++7gIEdm9T8QNbC3m98K2E//K4Je+0/oMeVasLKtxT4Ii74ans6/zd/A4ez87nnkq48NKp7zV8aoSDHdzniGZC2xWnCxv/YuRyxmrBSAQW+SA3KOlnEHz9y5uq7tGzA/HuHMzimac0Okp7izM2vm6smrFSLAl+khny88RCPvb+ZzJOF3HepM1dfY0f1JcWQ8pFz7vz/NGEnQYehasJKlSjwRc5RWnY+j72/iU82H6Fvu0a8clcNnoHzbRP2Fcg56GvC/ta5HLGasFJNCnyRs2St5c3Vqfzxo2QKi0v5v6t6Mfmizuf+/rLWwr7/OkfzyR/4mrAj4Nqn1YSVc6LAFzkLe4+e4FfvbOSbnUdJ6NyMv/xwAJ1bNDi3H1qQW6YJu/m7JmzcRGjRrWYKl5DmWuAbYzoCrwFtgFJgqrX2GbfGE/GH4pJSXv56D09/lkJkWBh/uqkfY4bGnNs1cNJTnJBfPxcKsqFNf7j+Weh/M9Q5x18iImW4eYRfDPzcWrvGGBMNJBljPrPWbnFxTBHXrN2Xya/f3UTyoWxG9W7FH27sR9vGZ3lly5JiSFnonDu/e5nThO1zo3M5YjVhxSWuBb619hBwyPd5jjEmGWgPKPAlqGTnF/G3RSm8nriXVtF1+c+4wVzVr83ZvZF4zhFY8yqsftlpwjbqACMeg8Hj1YQV1/llDt8YEwucByRWcNsUYApATEyMP8oRqRJrLQs2HOKJBVs4mlvA+PNj+fkVPYiOqua7UFkL+1Y4R/NbPoDSIuhyGVz7d+h+JYSrlSb+4fozzRjTEHgbeMham13+dmvtVGAqQFxcnHW7HpGq2Hf0JL95fxPLtqXTv31jZo4fSv8O1TzVsiAXNr7pzM8f2QR1GztTNnF3qwkrnnA18I0xkThhP9ta+46bY4nUhMLiUqZ9tYtnF28nMjyMx6/vw53nxxJenaZs+jbf5Yh9TdjW/eH6Z6D/LWrCiqfcPEvHADOAZGvtP9waR6SmfLMzg8ff38z2tFyu7teGx6/vS5vGUVW787dN2Omw+0sIi4S+NzqXI+4YryasBAQ3j/AvAO4ANhpj1vm+92tr7UIXxxSptkNZefzpo2QWbDhEh6b1mDE+jpG9W1ftzjlHYM1rzuWIsw+UacLeCQ1buVu4SDW5eZbOckCHNRKwCotLmbF8N/9esp2SUstDo7pzzyVdz3z9mwqbsJfC1U9Bj6vUhJWApWemhKRl29L53Qeb2ZVxglG9W/P49X3o2OwMV5qsqAk7dJJzOeIW3f1TuMg5UOBLSDlwPI8/fLiFRZsPE9u8Pi9PGMplvc4w9ZKx3Xc54jm+Jmw/uO5fMOBWNWElqCjwJSTkF5Uw/atdPLd0BwAPX9mTSRd1rvytBkuKYdvHzgXMTjVh+4x2TqvsmKAmrAQlBb7UatZaPt50mD8vTGZ/Zh7X9G/Do9f2oX2TSi6JkJvmWwn7CmTvh0btYcRvfCth1YSV4KbAl1pr04EsnliwhZW7j9GrTTSzJyVwQbcW39/QWkhNdI7mt7xfpgn7F+hxtZqwUmvomSy1TlpOPn//JIW3kvbTrH4d/nxTf340tOP3F08VnoANp5qwG6FuI6cBG3c3tOzhTfEiLlLgS62RX1TCjOW7eWHpDgpLSpl8URfuH9GNRuWvfZOx3Qn5dXOgIOu7Jmz/W6BuQ2+KF/EDBb4EvfLz9Ff0ac2vr+lNbNk3JCkphm2LnHPnd33ha8Le4KyEjRmmJqyEBAW+BLWkvZk8uTCZ1Xsz6dUmmjmTEhhedp6+oibsZb9xVsJGV3E1rUgtocCXoLQrPZenFqWwaPNhWkbX5ckf9OfWON88vbWQutI5mt/8ntOE7XyJmrAS8vTMl6CSnlPAM4u3MXdlKlERYfzs8h5Muqgz9etEOE3YjW85i6QOqwkrUp4CX4LCiYJipn+1m6nLdlJQXMq4hBh+OrI7LRrWhYwdsHoGrJ3tNGFb9YXr/gn9b1UTVqQMBb4EtOKSUt5cvZ9/fr6N9JwCru7Xhoev7EmXZlGw/RPn3PldSyEswlkJqyasSKUU+BKQSkstH208xD8/28aujBPEdWrKi7cPYUjzYljzIiS9AlmpEN0OLnvUWQmrJqzIaSnwJaBYa1myNY2/f7qN5EPZ9GjdkJduH8wVjfZiVv0StrwHJYXQ+WK48s/Q8xo1YUWqSK8UCRjf7Mzgb5+ksHbfcTo1r8+/f9iTa83XhC1/9Lsm7JC7nEZsy55elysSdBT44rm1+zL5+6cpfL3jKG0aRfHvKxpxTcFHhC+eA/lZ0KoPXPsPGPAjNWFFzoECXzyTfCibpz/dxufJR2hZP5xpCWmMyHmf8GW+JmzvG5zLEcecryasSA1Q4IvfbT6YxbOLt/PJ5iPERJ1kbq+1JBx7n7D1+8s0Ye+E6DZelypSqyjwxW82HcjimcXb+WzLYS6su5uFHb+m97HFmD2FEHsRXHWqCRt55h8mItWmwBfXbdyfxTOLt7E8OZVboxJJbL6U1idSICsahkxw3hdWTVgR1ynwxTXrU4/zzOLt7EpZz8S6S3iuwTKiSnKgfm+49GlfEzba6zJFQoZrgW+MmQlcB6RZa/u5NY4EnlV7jvHCkhTCd3zGpDqfM7zuemxYBKbX9c5K2E7D1YQV8YCbR/ivAM8Br7k4hgQIay1LU9J4fXESvQ6+x58jF9O2TgalDdtA3K8xQ8arCSviMdcC31q7zBgT69bPl8BQXFLKRxsOsnTxQi7Oep8Xw1dQJ7KYkk4XQfwkwnpdqyasSIDQHL6clfyiEt5ZuZ19X77GdfkfMTpsD0V1GxA2aALETya8VS+vSxSRcjwPfGPMFGAKQExMjMfVyJlk5RWxYOly7KoZXFe6hCbmBDlNulN64dNEDlQTViSQeR741tqpwFSAuLg463E5Uol96Tks/3gOHXfOZpxZTzHhHI+9EnvpfUTHXqgmrEgQ8DzwJXBZa9mwbQe7Pn2RoRnvMdZkkBXZnLSB/49Wl/yYFo3ael2iiFSDm6dlzgUuBVoYY/YDj1trZ7g1ntSc4uISViz/lKL/TmV4/jIGmmL2NR5M5kVP0nTwTWrCigQpN8/SGePWzxZ3ZOdksW7hDFptncWFdhcniWJPzA+JueqnxLTXUgqRYKcpHWFXygYOfv4c/dMWcLE5QWpEDMn9f0uPKybTs14jr8sTkRqiwA9RRUVFrF/6FhFJMxhUsJoYG8bmxpcQfeE9dBl6pZqwIrWQAj/EZBw5SMrHz9N5z5vEkUYGTVkdO4VuVz/AwNY6LVakNlPghwBbWkpy0lJyv3qJgVlLuMAUkVx3ABmDH6XviLG0iKzjdYki4gcK/Fos83gWGz6ZSZuU1+lTuoMTNor1La+nzaj76d1riNfliYifKfBrGWsta9av4fiXLzLk2EdcYk6wLzyGNX0fpeeVk4hv1MzrEkXEIwr8WiI96ySrPn+TZlteJb54LaXGkNL0ErIvupeYwVcQoyasSMhT4AexopJSvt6QwrGvZhJ39D2uMWkcC2tGSs976XzlffRt3tHrEkUkgCjwg4y1ls0Hs/nvV5/SNmUWl5d+Q11TxJ7o8zg8/Pe0SbiFZloJKyIVUOAHibTsfD5M2knmynlcfuJDJoftIt/UI63bLbQZ9RNi22olrIicngI/gOUVlvB58hG+SlxFt33zuCX8S5qaXI5Hd+Hk+X+hftw4OkZpJayIVI0CP8AUFpeybFs6H65LpWDrp9xqP+Ev4eshIoyTXa6Ei+6lSexFWgkrItWmwA8AJaWWFbuO8sG6g3yzaRtXF33Ow5GL6RCWRmG9ljD0YcLi7qJho3ZelyoiQUyB75HSUsva1Ew+XH+IBRsO0e7EFibW+Zw/hv2XyMhCSmOGQ/xfqNPreojQSlgROXcKfD8qLill5e5jLNp8mE82H+Z4dg43Rq5gfv2lxNZNwUY2wAy8HYZOIqx1X6/LFZFaRoHvsvyiEr7ekcGiTYf5PPkImSeL6BaZzh+af8OldhF1irIgugeM+Btm4I8gqrHXJYtILaXAd0FOfhHLtmWwaPNhlm5NI7egmEZRYdxgBIgOAAAKeUlEQVTfcS83Fi2k5eFlmKww6HUtDJ0EnS9WE1ZEXKfAryG7M06wZGsaS7YeYeXuYxSVWJo3qMOP+tZnbJ1ldNkzD5O6Fxq0gosfhiEToHF7r8sWkRCiwD9LRSWlrNpzjCXJaSzZmsaujBMAdG/VkIkXdub65ofoc+Atwja/A8X5EDMcRj0OasKKiEcU+NVw8Hgey7dn8OW2dJZtSyenoJg64WEM69qc8cNjGdGtER0PLoKVv4PENRDZAAaOcaZt2mglrIh4S4F/GrkFxazYeZTlOzL4ans6O9Odo/hW0XW5dkBbRvRqxQXdWtDg5H5YNQNefh3yjkGLHnD1UzDwNjVhRSRgKPDLKC4pZeOBLL7ansHy7Rms2ZdJcaklKjKMhM7NGRMfw0XdW9KjdUOMtbBzMcyfBts/BRMGva7xNWEvURNWRAJOSAd+kS/gE3cdI3H3UVbvySS3oBhjoG+7Rky+uAsXdWvBkNim1I0Id+508hh8829YPQMy9/iasL+AIXepCSsiAc3VwDfGXAU8A4QD0621f3FzvDMpKC5hw/4sEncdJXH3MZL2ZnKysASAbq0acsOgdpzfpTkXdGtBswblGqsH1jjTNpvm+5qw58OIx6D3DWrCikhQcC3wjTHhwPPA5cB+YJUx5gNr7Ra3xiwvI7eANXszWZt6nDV7M1mXepyC4lIAerWJ5pYhHUjo0pz4zs1o0bDu939AUT5sfhdWTYMDSRBZ39eEvRva9PfXf0NEpEa4eYQfD+yw1u4CMMa8AYwGXAn8opJSkg9lfxfw+zJJPZYHQESYoW+7RoxL6ERCl2bExzajafkj+LIy98DqmbBmltOEbd4drvorDBqjJqyIBC03A789kFrm6/1AQk0PUlBcwh3TV7J+/3dH760b1WVwTFPuGNaJwTFN6de+MVGR4af/QaWlThN21XTY9onTdO15DcRPVhNWRGoFNwO/ooS039vImCnAFICYmJhqD1I3IpwW0XUYl9CJwZ2aMDimKW0bR2GqGtAnj8G62c78fObuMk3YCdC4Q7XrEREJVG4G/n6g7LtodwAOlt/IWjsVmAoQFxf3vV8IVfHCuCHVv9PBtbByerkm7G/UhBWRWsvNwF8FdDfGdAYOALcBY10c78yK8mHLe7ByGhxY7WvC3uZbCasmrIjUbq4FvrW22BhzP/AJzmmZM621m90a77Qy9zpN2LWz4OTR75qwA2+Dek08KUlExN9cPQ/fWrsQWOjmGJUqLYWdS3xN2EXfNWGHToIul6oJKyIhp/attD15DNbNcVbCHtsFDVrCRT+HuLvUhBWRkFZ7Av/gOmeB1Ma3oTgPOg6DS38NfW6AiAoWVYmIhJjgD/yCHJh1E+xf5TRhB9zqTNu0HeB1ZSIiASX4A79uNDTtDP1+6Fz2QE1YEZEKBX/gA/xwmtcViIgEvDCvCxAREf9Q4IuIhAgFvohIiFDgi4iECAW+iEiIUOCLiIQIBb6ISIhQ4IuIhAhj7Vm954grjDHpwN6zvHsLIKMGy6kpqqv6ArU21VU9qqv6zqa2TtballXZMKAC/1wYY1Zba+O8rqM81VV9gVqb6qoe1VV9btemKR0RkRChwBcRCRG1KfCnel1AJVRX9QVqbaqrelRX9blaW62ZwxcRkdOrTUf4IiJyGkEX+MaYq4wxKcaYHcaYRyq4va4xZp7v9kRjTKwfaupojFlqjEk2xmw2xjxYwTaXGmOyjDHrfP9+63ZdvnH3GGM2+sZcXcHtxhjzrG9/bTDGDPZDTT3L7Id1xphsY8xD5bbx2/4yxsw0xqQZYzaV+V4zY8xnxpjtvo9NK7nveN82240x4/1Q19+MMVt9j9W7xpgK3/HnTI+7C3X9zhhzoMzjdU0l9z3t69eFuuaVqWmPMWZdJfd1c39VmA+ePMestUHzDwgHdgJdgDrAeqBPuW3uA170fX4bMM8PdbUFBvs+jwa2VVDXpcACD/bZHqDFaW6/BvgYMMAwINGDx/QwzrnEnuwv4GJgMLCpzPeeAh7xff4I8NcK7tcM2OX72NT3eVOX67oCiPB9/teK6qrK4+5CXb8DflGFx/q0r9+arqvc7U8Dv/Vgf1WYD148x4LtCD8e2GGt3WWtLQTeAEaX22Y08Krv8/nASGOMcbMoa+0ha+0a3+c5QDLQ3s0xa9Bo4DXrWAE0Mca09eP4I4Gd1tqzXXB3zqy1y4Bj5b5d9nn0KnBjBXe9EvjMWnvMWpsJfAZc5WZd1tpPrbXFvi9XAB1qarxzqauKqvL6daUuXwbcCsytqfGq6jT54PfnWLAFfnsgtczX+/l+sH67je+FkQU090t1gG8K6TwgsYKbzzfGrDfGfGyM6eunkizwqTEmyRgzpYLbq7JP3XQblb8Ivdhfp7S21h4C5wULtKpgG6/33UScv84qcqbH3Q33+6aaZlYyPeHl/roIOGKt3V7J7X7ZX+Xywe/PsWAL/IqO1MufZlSVbVxhjGkIvA08ZK3NLnfzGpxpi4HAv4H3/FETcIG1djBwNfATY8zF5W73cn/VAW4A3qrgZq/2V3V4ue8eBYqB2ZVscqbHvab9B+gKDAIO4UyflOfZ/gLGcPqje9f31xnyodK7VfC9s95nwRb4+4GOZb7uABysbBtjTATQmLP787NajDGROA/mbGvtO+Vvt9ZmW2tzfZ8vBCKNMS3crstae9D3MQ14F+fP6rKqsk/dcjWwxlp7pPwNXu2vMo6cmtryfUyrYBtP9p2vcXcdMM76JnrLq8LjXqOstUestSXW2lJgWiXjebW/IoAfAPMq28bt/VVJPvj9ORZsgb8K6G6M6ew7OrwN+KDcNh8ApzrZNwNLKntR1BTf/OAMINla+49KtmlzqpdgjInH2fdHXa6rgTEm+tTnOA2/TeU2+wC40ziGAVmn/sz0g0qPurzYX+WUfR6NB96vYJtPgCuMMU19UxhX+L7nGmPMVcD/ATdYa09Wsk1VHvearqts3+emSsaryuvXDaOArdba/RXd6Pb+Ok0++P855kZX2s1/OGeVbMPp9j/q+94TOC8AgCicKYIdwEqgix9quhDnz6wNwDrfv2uAe4B7fNvcD2zGOTNhBTDcD3V18Y233jf2qf1Vti4DPO/bnxuBOD89jvVxArxxme95sr9wfukcAopwjqjuxun7LAa2+z42820bB0wvc9+JvufaDuAuP9S1A2dO99Tz7NQZae2Ahad73F2ua5bv+bMBJ8jalq/L9/X3Xr9u1uX7/iunnldltvXn/qosH/z+HNNKWxGREBFsUzoiInKWFPgiIiFCgS8iEiIU+CIiIUKBLyISIhT4IiIhQoEvIhIiFPgilTDGDPVdDCzKtxpzszGmn9d1iZwtLbwSOQ1jzB9xVm/XA/Zba5/0uCSRs6bAFzkN3zVfVgH5OJd3KPG4JJGzpikdkdNrBjTEeaeiKI9rETknOsIXOQ1jzAc478zUGeeCYPd7XJLIWYvwugCRQGWMuRMottbOMcaEA98YY0ZYa5d4XZvI2dARvohIiNAcvohIiFDgi4iECAW+iEiIUOCLiIQIBb6ISIhQ4IuIhAgFvohIiFDgi4iEiP8Pf0+Lrishb18AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "\n",
    "def numerical_diff(f, x):\n",
    "    h = 1e-4 # 0.0001\n",
    "    return (f(x+h) - f(x-h)) / (2*h)\n",
    "\n",
    "\n",
    "def function_1(x):\n",
    "    return 0.01*x**2 + 0.1*x \n",
    "\n",
    "\n",
    "def tangent_line(f, x):\n",
    "    d = numerical_diff(f, x)\n",
    "    print(d)\n",
    "    y = f(x) - d*x\n",
    "    return lambda t: d*t + y\n",
    "     \n",
    "x = np.arange(0.0, 20.0, 0.1)\n",
    "y = function_1(x)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"f(x)\")\n",
    "\n",
    "tf = tangent_line(function_1, 5)\n",
    "y2 = tf(x)\n",
    "\n",
    "plt.plot(x, y)\n",
    "plt.plot(x, y2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 梯度：2维\n",
    "\n",
    "https://github.com/oreilly-japan/deep-learning-from-scratch/blob/master/ch04/gradient_2d.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "# cf.http://d.hatena.ne.jp/white_wheels/20100327/p3\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "\n",
    "def _numerical_gradient_no_batch(f, x):\n",
    "    h = 1e-4  # 0.0001\n",
    "    grad = np.zeros_like(x)\n",
    "    \n",
    "    for idx in range(x.size):\n",
    "        tmp_val = x[idx]\n",
    "        x[idx] = float(tmp_val) + h\n",
    "        fxh1 = f(x)  # f(x+h)\n",
    "        \n",
    "        x[idx] = tmp_val - h \n",
    "        fxh2 = f(x)  # f(x-h)\n",
    "        grad[idx] = (fxh1 - fxh2) / (2*h)\n",
    "        \n",
    "        x[idx] = tmp_val  # 恢复为之前的值\n",
    "        \n",
    "    return grad\n",
    "\n",
    "\n",
    "def numerical_gradient(f, X):\n",
    "    if X.ndim == 1:\n",
    "        return _numerical_gradient_no_batch(f, X)\n",
    "    else:\n",
    "        grad = np.zeros_like(X)\n",
    "        \n",
    "        for idx, x in enumerate(X):\n",
    "            grad[idx] = _numerical_gradient_no_batch(f, x)\n",
    "        \n",
    "        return grad\n",
    "\n",
    "\n",
    "def function_2(x):\n",
    "    if x.ndim == 1:\n",
    "        return np.sum(x**2)\n",
    "    else:\n",
    "        return np.sum(x**2, axis=1)\n",
    "\n",
    "\n",
    "def tangent_line(f, x):\n",
    "    d = numerical_gradient(f, x)\n",
    "    print(d)\n",
    "    y = f(x) - d*x\n",
    "    return lambda t: d*t + y\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    x0 = np.arange(-2, 2.5, 0.25)\n",
    "    x1 = np.arange(-2, 2.5, 0.25)\n",
    "    X, Y = np.meshgrid(x0, x1)\n",
    "    \n",
    "    X = X.flatten()\n",
    "    Y = Y.flatten()\n",
    "\n",
    "    grad = numerical_gradient(function_2, np.array([X, Y]).T).T\n",
    "\n",
    "    plt.figure()\n",
    "    plt.quiver(X, Y, -grad[0], -grad[1],  angles=\"xy\",color=\"#666666\")\n",
    "    plt.xlim([-2, 2])\n",
    "    plt.ylim([-2, 2])\n",
    "    plt.xlabel('x0')\n",
    "    plt.ylabel('x1')\n",
    "    plt.grid()\n",
    "    plt.draw()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 梯度法\n",
    "\n",
    "https://github.com/oreilly-japan/deep-learning-from-scratch/blob/master/ch04/gradient_method.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "#from gradient_2d import numerical_gradient\n",
    "\n",
    "\n",
    "def gradient_descent(f, init_x, lr=0.01, step_num=100):\n",
    "    x = init_x\n",
    "    x_history = []\n",
    "\n",
    "    for i in range(step_num):\n",
    "        x_history.append( x.copy() )\n",
    "\n",
    "        grad = numerical_gradient(f, x)\n",
    "        x -= lr * grad\n",
    "\n",
    "    return x, np.array(x_history)\n",
    "\n",
    "\n",
    "def function_2(x):\n",
    "    return x[0]**2 + x[1]**2\n",
    "\n",
    "init_x = np.array([-3.0, 4.0])    \n",
    "\n",
    "lr = 0.1\n",
    "step_num = 20\n",
    "x, x_history = gradient_descent(function_2, init_x, lr=lr, step_num=step_num)\n",
    "\n",
    "plt.plot( [-5, 5], [0,0], '--b')\n",
    "plt.plot( [0,0], [-5, 5], '--b')\n",
    "plt.plot(x_history[:,0], x_history[:,1], 'o')\n",
    "\n",
    "plt.xlim(-3.5, 3.5)\n",
    "plt.ylim(-4.5, 4.5)\n",
    "plt.xlabel(\"X0\")\n",
    "plt.ylabel(\"X1\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用梯度训练简单的网络\n",
    "\n",
    "https://github.com/oreilly-japan/deep-learning-from-scratch/blob/master/ch04/gradient_simplenet.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.26503902  0.24133142 -0.50637044]\n",
      " [ 0.39755854  0.36199713 -0.75955567]]\n"
     ]
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "import sys, os\n",
    "sys.path.append(os.pardir)  # 为导入父目录中的文件而进行的设置\n",
    "import numpy as np\n",
    "from common.functions import softmax, cross_entropy_error\n",
    "from common.gradient import numerical_gradient\n",
    "\n",
    "\n",
    "class simpleNet:\n",
    "    def __init__(self):\n",
    "        self.W = np.random.randn(2,3)\n",
    "\n",
    "    def predict(self, x):\n",
    "        return np.dot(x, self.W)\n",
    "\n",
    "    def loss(self, x, t):\n",
    "        z = self.predict(x)\n",
    "        y = softmax(z)\n",
    "        loss = cross_entropy_error(y, t)\n",
    "\n",
    "        return loss\n",
    "\n",
    "x = np.array([0.6, 0.9])\n",
    "t = np.array([0, 0, 1])\n",
    "\n",
    "net = simpleNet()\n",
    "\n",
    "f = lambda w: net.loss(x, t)\n",
    "dW = numerical_gradient(f, net.W)\n",
    "\n",
    "print(dW)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 层神经网络\n",
    "\n",
    "https://github.com/oreilly-japan/deep-learning-from-scratch/blob/master/ch04/two_layer_net.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coding: utf-8\n",
    "import sys, os\n",
    "sys.path.append(os.pardir)  # 为导入父目录中的文件而进行的设置\n",
    "from common.functions import *\n",
    "from common.gradient import numerical_gradient\n",
    "\n",
    "\n",
    "class TwoLayerNet:\n",
    "\n",
    "    def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):\n",
    "        # 初始化权重\n",
    "        self.params = {}\n",
    "        self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)\n",
    "        self.params['b1'] = np.zeros(hidden_size)\n",
    "        self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)\n",
    "        self.params['b2'] = np.zeros(output_size)\n",
    "\n",
    "    def predict(self, x):\n",
    "        W1, W2 = self.params['W1'], self.params['W2']\n",
    "        b1, b2 = self.params['b1'], self.params['b2']\n",
    "    \n",
    "        a1 = np.dot(x, W1) + b1\n",
    "        z1 = sigmoid(a1)\n",
    "        a2 = np.dot(z1, W2) + b2\n",
    "        y = softmax(a2)\n",
    "        \n",
    "        return y\n",
    "        \n",
    "    # x:输入数据, t:监督数据\n",
    "    def loss(self, x, t):\n",
    "        y = self.predict(x)\n",
    "        \n",
    "        return cross_entropy_error(y, t)\n",
    "    \n",
    "    def accuracy(self, x, t):\n",
    "        y = self.predict(x)\n",
    "        y = np.argmax(y, axis=1)\n",
    "        t = np.argmax(t, axis=1)\n",
    "        \n",
    "        accuracy = np.sum(y == t) / float(x.shape[0])\n",
    "        return accuracy\n",
    "        \n",
    "    # x:输入数据, t:监督数据\n",
    "    def numerical_gradient(self, x, t):\n",
    "        loss_W = lambda W: self.loss(x, t)\n",
    "        \n",
    "        grads = {}\n",
    "        grads['W1'] = numerical_gradient(loss_W, self.params['W1'])\n",
    "        grads['b1'] = numerical_gradient(loss_W, self.params['b1'])\n",
    "        grads['W2'] = numerical_gradient(loss_W, self.params['W2'])\n",
    "        grads['b2'] = numerical_gradient(loss_W, self.params['b2'])\n",
    "        \n",
    "        return grads\n",
    "        \n",
    "    def gradient(self, x, t):\n",
    "        W1, W2 = self.params['W1'], self.params['W2']\n",
    "        b1, b2 = self.params['b1'], self.params['b2']\n",
    "        grads = {}\n",
    "        \n",
    "        batch_num = x.shape[0]\n",
    "        \n",
    "        # forward\n",
    "        a1 = np.dot(x, W1) + b1\n",
    "        z1 = sigmoid(a1)\n",
    "        a2 = np.dot(z1, W2) + b2\n",
    "        y = softmax(a2)\n",
    "        \n",
    "        # backward\n",
    "        dy = (y - t) / batch_num\n",
    "        grads['W2'] = np.dot(z1.T, dy)\n",
    "        grads['b2'] = np.sum(dy, axis=0)\n",
    "        \n",
    "        dz1 = np.dot(dy, W2.T)\n",
    "        da1 = sigmoid_grad(a1) * dz1\n",
    "        grads['W1'] = np.dot(x.T, da1)\n",
    "        grads['b1'] = np.sum(da1, axis=0)\n",
    "\n",
    "        return grads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练2层神经网络\n",
    "\n",
    "https://github.com/oreilly-japan/deep-learning-from-scratch/blob/master/ch04/train_neuralnet.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train acc, test acc | 0.11236666666666667, 0.1135\n",
      "train acc, test acc | 0.7994666666666667, 0.8031\n",
      "train acc, test acc | 0.87805, 0.8832\n",
      "train acc, test acc | 0.8974666666666666, 0.9022\n",
      "train acc, test acc | 0.9068666666666667, 0.9098\n",
      "train acc, test acc | 0.9144333333333333, 0.9175\n",
      "train acc, test acc | 0.9197666666666666, 0.9214\n",
      "train acc, test acc | 0.92355, 0.925\n",
      "train acc, test acc | 0.9278166666666666, 0.93\n",
      "train acc, test acc | 0.9310666666666667, 0.9317\n",
      "train acc, test acc | 0.9340833333333334, 0.9344\n",
      "train acc, test acc | 0.9365666666666667, 0.9354\n",
      "train acc, test acc | 0.93985, 0.9375\n",
      "train acc, test acc | 0.94175, 0.9386\n",
      "train acc, test acc | 0.9435166666666667, 0.9417\n",
      "train acc, test acc | 0.94625, 0.9431\n",
      "train acc, test acc | 0.9469666666666666, 0.9441\n"
     ]
    },
    {
     "data": {
      "image/png": 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A4+u1+Rg4HMDMdiFTFBoCzLTl0imqUw20lNSGnUREpNcFVhTcPQlcCjwFzCJzldFbZnajmZ2QbfZN4CIz+zfwIPB5d1//EFO/0rKsnigpvHLbsKOIiPS6QDuvufsUMpeZdp53bafnbwMHBpmhty2rf48RQGH1qLCjiIj0Oo3W1kMrP/kAgLLhO4ScRESk96ko9NDMsk8zqfW/GVKnoiAiA4+KQg99tNKYUzCKIZVlYUcREel1Kgo9tO2HD3F66RtECjQstogMPBoltYcOX3I/dYV7hh1DRCQQ2lPoiVQ7VemltJaqj4KIDEwqCj2wZslcIqQx9VEQkQFKRaEHltTPBiBRMzrkJCIiwVBR6IFVn3wIQMUIXY4qIgOTikIPvFw5ib1a7lIfBREZsFQUemBe4xpaY+XUlOmmOiIyMKko9MCe79/JF0v+iZn6KIjIwKR+Cj1wwPLHGVQ8PuwYIiKB0Z5CrpKtVPsy2srqum8rIrKVUlHI0apFmdFRbZD6KIjIwKWikKOl8+cAUDRku5CTiIgER0UhRyuWNdDsCQbpPgoiMoCpKOTo5ZJD2LX1HobWak9BRAYuFYUc1TeuoTQRo7IkHnYUEZHA6JLUHB0y+yZGFtVgdnTYUUREAqM9hRzt1fQPdowtDjuGiEigVBRy4G2rGeTLSZbpPgoiMrCpKORgxcJMH4WCweqjICIDm4pCDpbNz9xHoVh9FERkgNOJ5hwsXbUGTw9nkO6jICIDnPYUcvBK4X4c3vZDhteOCjuKiEigVBRyUN/YTGVxjLLCWNhRREQCpcNHOTj53avYKz4cOCrsKCIigdKeQg52WPM6Q+OtYccQEQmcikI3vGUFFayivXybsKOIiARORaEby+a/D0C0Sn0URGTgU1HoRuOCzH0USoZsH3ISEZHg6URzNxa1FLAwtTsjRo4JO4qISOC0p9CN1yJ7cW77txgxQuMeicjAF2hRMLOJZvaumc0xs6s30uYMM3vbzN4ys98GmWdz1C9rpro0TlE8EnYUEZHABXb4yMwiwO3AkUA98LKZPe7ub3dqMwa4BjjQ3RvNbEhQeTbXF9+9iMOio8n8GiIiA1uQewoTgDnu/oG7twEPASeu1+Yi4HZ3bwRw9/51wwJ3hrXPI1FYFHYSEZE+EWRRGAnM6zRdn53X2Y7Ajmb2TzN70cwmdvVGZjbZzGaY2YyGhoaA4m4o1bycUppJVaiPgojkhyCLgnUxz9ebjgJjgEOAs4C7zaxygxe53+Xu4919fE1NTa8H3Zhl89/LhBw8qs/WKSISppyKgpn93syONbOeFJF6oK7TdC2woIs2f3L3dnf/EHiXTJHoFxrnZ/soDNV9FEQkP+S6kb8D+Bww28xuNrOdc3jNy8AYMxttZnHgTODx9do8BhwKYGbVZA4nfZBjpsDVpyp5OPkZqut2DDuKiEifyKkouPsz7n42sDfwEfC0mf3LzC4wsy7Hk3b3JHAp8BQwC3jY3d8ysxvN7IRss6eApWb2NjANuNLdl27Zr9R7XmcMV6UuZtjQoWFHERHpEzlfkmpmVcA5wLnAa8ADwEHA+WTOCWzA3acAU9abd22n5w58I/vodxoaGhhamiARVR8FEckPORUFM/sDsDNwH3C8uy/MLvqdmc0IKlzYLnn/SxwfqQWOCDuKiEifyHVP4TZ3/7+uFrj7+F7M03+4U5VcxAflE8JOIiLSZ3I90bxL50tFzWyQmX05oEz9QvuqBoppIV1e131jEZEBIteicJG7L187ke2BfFEwkfqHpfNmAxCrHh1yEhGRvpNrUSgws47OaNlxjeLBROofln+S6aNQOkx9FEQkf+RaFJ4CHjazw83sMOBB4MngYoXvI0byv8lTqK7dKewoIiJ9JtcTzVcBFwNfIjN8xVTg7qBC9QdvJuu4I306X6mpCjuKiEifyakouHuaTK/mO4KN03+0LHqPHcvTRCO6D5GI5I9c+ymMAf4b2BUoXDvf3QfsAfcL5l7NkbFRbDjat4jIwJXr1+BfkdlLSJIZq+g3ZDqyDUzuVKUWs6ZEt+AUkfySa1EocvdnAXP3ue5+PXBYcLHC1dK4gELaoFL3URCR/JLrieaW7LDZs83sUmA+0O9undlblsyfTS3qoyAi+SfXPYWvAcXAV4F9yAyMd35QocK2ckGmj0L58O1DTiIi0re6LQrZjmpnuHuTu9e7+wXufqq7v9gH+ULxXmwXrmq/iOq6fnO/HxGRPtFtUXD3FLBP5x7NA92s1sH8kcMZOmhQ2FFERPpUrucUXgP+ZGaPAKvXznT3PwSSKmSJBS+xX0WEgoK8qYMiIkDuRWEwsJR1rzhyYEAWhTPn38wBiTFkTp2IiOSPXHs0XxB0kH4jnaImvZj3Sg8NO4mISJ/LtUfzr8jsGazD3b/Q64lC1ry0nmKSULlt2FFERPpcroeP/tzpeSFwMrCg9+OEb0n9bLYBEjXqoyAi+SfXw0e/7zxtZg8CzwSSKGSrPnkfgAr1URCRPLS5Q4COAQbkGBCvF47nvLarqKnbMewoIiJ9LtdzCqtY95zCJ2TusTDgzFldxEuRcVRXlIYdRUSkz+V6+Kgs6CD9Rc28J5lYVkQe9dUTEemQ0+EjMzvZzCo6TVea2UnBxQrPSQ0/57NMDTuGiEgocj2ncJ27r1g74e7LgeuCiRSiVJLqdANtZXVhJxERCUWuRaGrdrlezrrVWNkwlyhpGDQgz6GLiHQr16Iww8x+ZGbbm9l2ZvZj4JUgg4Vh6bzZABTVDNi7jIqIbFKuReEyoA34HfAwsAb4SlChwtK0SH0URCS/5Xr10Wrg6oCzhG5G6aF8vfUWHt1G91EQkfyU69VHT5tZZafpQWb2VHCxwjF3pbMoPoqKkqKwo4iIhCLXw0fV2SuOAHD3RgbgPZp3+OhBTiuZqT4KIpK3ci0KaTPruCTHzEbRxaipW7ujGx/kyIIZYccQEQlNrpeVfhv4h5k9l53+NDA5mEjh8GQrVemlvFVWG3YUEZHQ5LSn4O5PAuOBd8lcgfRNMlcgDRjLF35AgTkFg0aFHUVEJDS5nmj+IvAsmWLwTeA+4PocXjfRzN41szlmttGrl8zsNDNzMxufW+zet2x+5nLU4iHqoyAi+SvXcwqXA/sCc939UGAc0LCpF5hZBLgdmATsCpxlZrt20a4M+CowvQe5e92qhrkAVIxQHwURyV+5FoUWd28BMLOEu78D7NTNayYAc9z9A3dvAx4CTuyi3feAW4CWHLME4sXyiezecjfD6lQURCR/5VoU6rP9FB4DnjazP9H97ThHAvM6v0d2XgczGwfUuXvn231uwMwmm9kMM5vR0LDJHZTNNm9ZM9HiCsqKEoG8v4jI1iDXHs0nZ59eb2bTgArgyW5e1tXF/h2XsZpZAfBj4PM5rP8u4C6A8ePHB3Ip7IQP72BYURVwVBBvLyKyVejxSKfu/lz3rYDMnkHnMahrWXfvogzYHfhbtrPYMOBxMzvB3fu8s8BBq6bwTtn+fb1aEZF+ZXPv0ZyLl4ExZjbazOLAmcDjaxe6+wp3r3b3Ue4+CngRCKUgpFubqfJGkuqjICJ5LrCi4O5J4FLgKWAW8LC7v2VmN5rZCUGtd3M0LsxcjhoZvG3ISUREwhXojXLcfQowZb15126k7SFBZtmUZfPnUAUUD9GVRyKS34I8fLTVaGxcykovYtBIFQURyW8qCsBLxZ9hz9ZfMmykejOLSH5TUQDmLVtDdWmCosSAu+20iEiPaCsITPzg/7FbYjhwRNhRRERCpT0FYFzzv9guuiTsGCIiocv7opBqWUUlK0mV13XfWERkgMv7orCkfjYA0cGjwg0iItIP5H1RaJw/B4CSYbrySEQk74vC0qZW3knXMXjkjmFHEREJXd4XhZfin2JS+/8wdITGPRIRyfuiMK+xmWHlhSSikbCjiIiELu/7KZzz/hV8JrotcHjYUUREQpf3ewo7tL5NVTwZdgwRkX4hr4tCW1Mj5awmVbFN2FFERPqFvC4KS+rfAyCmPgoiIkCeF4XlCzI31ykdpiGzRUQgz080f9ISY1FqL3asHRN2FBGRfiGv9xReiezBF1NXM3To8LCjiIj0C3ldFOqXNTOispBoJK8/BhGRDnl9+Oir71/E3PgOwGFhRxER6Rfy9yuyO0OT84kXlYSdRESk38jbotCycgmlrCGtPgoiIh3ytig0zMvcRyFePTrkJCIi/UfeFoWVCzNFoWyo+iiIiKyVt0Xh49Rgfps8jOq6ncKOIiLSb+RtUZiZ3oHrfTI11dVhRxER6Tfytig0LvmEuso4BQUWdhQRkX4jb/spXDr3Mj6J6z4KIiKd5eeegjs1qUW0lYwMO4mISL+Sl0VhdeNCimjDK9VHQUSks7wsCmv7KCTUR0FEZB15WRRWLZwDQNlw9VEQEeksL4vCHNuGH7SfTk3djmFHERHpV/KyKLzZPpJfFpxG1aBBYUcREelXAi0KZjbRzN41szlmdnUXy79hZm+b2etm9qyZbRtknrXaP5nF7hWtmKmPgohIZ4EVBTOLALcDk4BdgbPMbNf1mr0GjHf3PYFHgVuCytPZ5AXf5b/Sd/fFqkREtipB7ilMAOa4+wfu3gY8BJzYuYG7T3P35uzki0BtgHky0mmGpBfRWhr8qkREtjZBFoWRwLxO0/XZeRtzIfDXrhaY2WQzm2FmMxoaGrYo1MqGeuIkobJPjlSJiGxVgiwKXR2w9y4bmp0DjAdu7Wq5u9/l7uPdfXxNTc0WhVpS/x4AiRr1URARWV+QYx/VA3WdpmuBBes3MrMjgG8Dn3H31gDzALBq0QcAVIzYIehViYhsdYLcU3gZGGNmo80sDpwJPN65gZmNA+4ETnD3xQFm6TAruivfbLuEoeqjICKygcCKgrsngUuBp4BZwMPu/paZ3WhmJ2Sb3QqUAo+Y2Uwze3wjb9drZq2pZGrsMMrLSoNelYjIVifQobPdfQowZb1513Z6fkSQ6+9K8YIXOKCiWH0URES6kHf3Uzi34QfMK9oVODvsKCKyEe3t7dTX19PS0hJ2lK1OYWEhtbW1xGKxzXp9XhUFTyWpSTXwflld941FJDT19fWUlZUxatQo7dX3gLuzdOlS6uvrGT16866wzKuxj5YvmkvMUhQM0n0URPqzlpYWqqqqVBB6yMyoqqraoj2svCoKS+ozQ2YX1mwXchIR6Y4KwubZ0s8tr4pC0yfvA1A5UvdREBHpSl4VhZmFEzir7dvqoyAim7R8+XJ+9rOfbdZrjznmGJYvX97LifpOXhWF2asTvFM4ltLiorCjiEg/tqmikEqlNvnaKVOmUFlZGUSsPpFXVx+NrJ/CxLLysGOISA/c8MRbvL1gZa++564jyrnu+N02uvzqq6/m/fffZ+zYsRx55JEce+yx3HDDDQwfPpyZM2fy9ttvc9JJJzFv3jxaWlq4/PLLmTx5MgCjRo1ixowZNDU1MWnSJA466CD+9a9/MXLkSP70pz9RVLTul9InnniC73//+7S1tVFVVcUDDzzA0KFDaWpq4rLLLmPGjBmYGddddx2nnnoqTz75JN/61rdIpVJUV1fz7LPP9upnk1dF4bRlv+Cj0rHAl8KOIiL92M0338ybb77JzJkzAfjb3/7GSy+9xJtvvtlxqec999zD4MGDWbNmDfvuuy+nnnoqVVVV67zP7NmzefDBB/nFL37BGWecwe9//3vOOeecddocdNBBvPjii5gZd999N7fccgs//OEP+d73vkdFRQVvvPEGAI2NjTQ0NHDRRRfx/PPPM3r0aJYtW9brv3veFIV0exvV6SXMVh8Fka3Kpr7R96UJEyasc+3/T3/6U/74xz8CMG/ePGbPnr1BURg9ejRjx44FYJ999uGjjz7a4H3r6+v57Gc/y8KFC2lra+tYxzPPPMNDDz3U0W7QoEE88cQTfPrTn+5oM3jw4F79HSGPziksW/gBEXMKBus+CiLScyUlJR3P//a3v/HMM8/wwgsv8O9//5tx48Z12TcgkUh0PI9EIiSTyQ3aXHbZZVx66aW88cYb3HnnnR3v4+4bXF7a1bzeljdFYen8TB+FoiHqoyAim1ZWVsaqVas2unzFihUMGjSI4uJi3nnnHV588cXNXteKFSsYOTJz/7E5lR2pAAALXElEQVR77723Y/5RRx3Fbbfd1jHd2NjI/vvvz3PPPceHH34IEMjho7wpCs3Z+ygM0n0URKQbVVVVHHjggey+++5ceeWVGyyfOHEiyWSSPffck+9+97vst99+m72u66+/ntNPP52DDz6Y6urqjvnf+c53aGxsZPfdd2evvfZi2rRp1NTUcNddd3HKKaew11578dnPfnaz17sx5t7lzdD6rfHjx/uMGTN6/Lrbn36LR/7vBZ68/jwKE/EAkolIb5k1axa77LJL2DG2Wl19fmb2iruP7+61eXOi+cJDdubYcaNUEERENiFvDh8VxiKMqi7pvqGISB7Lm6IgIiLdU1EQEZEOKgoiItJBRUFERDqoKIiIrGdLhs4G+MlPfkJzc3MvJuo7KgoiIuvJ56KQN/0URGQr9qtjN5y320kw4SJoa4YHTt9w+djPwbizYfVSePi8dZdd8JdNrm79obNvvfVWbr31Vh5++GFaW1s5+eSTueGGG1i9ejVnnHEG9fX1pFIpvvvd77Jo0SIWLFjAoYceSnV1NdOmTVvnvW+88UaeeOIJ1qxZwwEHHMCdd96JmTFnzhwuueQSGhoaiEQiPPLII2y//fbccsst3HfffRQUFDBp0iRuvvnmnn56PaKiICKynvWHzp46dSqzZ8/mpZdewt054YQTeP7552loaGDEiBH85S+ZIrNixQoqKir40Y9+xLRp09YZtmKtSy+9lGuvvRaAc889lz//+c8cf/zxnH322Vx99dWcfPLJtLS0kE6n+etf/8pjjz3G9OnTKS4uDmSso/WpKIhI/7epb/bx4k0vL6nqds+gO1OnTmXq1KmMGzcOgKamJmbPns3BBx/MFVdcwVVXXcVxxx3HwQcf3O17TZs2jVtuuYXm5maWLVvGbrvtxiGHHML8+fM5+eSTASgsLAQyw2dfcMEFFBcXA8EMlb0+FQURkW64O9dccw0XX3zxBsteeeUVpkyZwjXXXMNRRx3VsRfQlZaWFr785S8zY8YM6urquP7662lpaWFjY9D1xVDZ69OJZhGR9aw/dPbRRx/NPffcQ1NTEwDz589n8eLFLFiwgOLiYs455xyuuOIKXn311S5fv9baeyVUV1fT1NTEo48+CkB5eTm1tbU89thjALS2ttLc3MxRRx3FPffc03HSWoePRERC0Hno7EmTJnHrrbcya9Ys9t9/fwBKS0u5//77mTNnDldeeSUFBQXEYjHuuOMOACZPnsykSZMYPnz4OieaKysrueiii9hjjz0YNWoU++67b8ey++67j4svvphrr72WWCzGI488wsSJE5k5cybjx48nHo9zzDHHcNNNNwX6u+fN0NkisvXQ0NlbZkuGztbhIxER6aCiICIiHVQURKRf2toObfcXW/q5qSiISL9TWFjI0qVLVRh6yN1ZunRpRz+HzaGrj0Sk36mtraW+vp6Ghoawo2x1CgsLqa2t3ezXqyiISL8Ti8UYPXp02DHyUqCHj8xsopm9a2ZzzOzqLpYnzOx32eXTzWxUkHlERGTTAisKZhYBbgcmAbsCZ5nZrus1uxBodPcdgB8D/xNUHhER6V6QewoTgDnu/oG7twEPASeu1+ZE4N7s80eBw62vB/oQEZEOQZ5TGAnM6zRdD3xqY23cPWlmK4AqYEnnRmY2GZicnWwys3c3M1P1+u/dTyhXzyhXz/XXbMrVM1uSa9tcGgVZFLr6xr/+9WW5tMHd7wLu2uJAZjNy6ebd15SrZ5Sr5/prNuXqmb7IFeTho3qgrtN0LbBgY23MLApUAMEPAygiIl0Ksii8DIwxs9FmFgfOBB5fr83jwPnZ56cB/+fqrSIiEprADh9lzxFcCjwFRIB73P0tM7sRmOHujwO/BO4zszlk9hDODCpP1hYfggqIcvWMcvVcf82mXD0TeK6tbuhsEREJjsY+EhGRDioKIiLSIW+KQndDboTBzOrMbJqZzTKzt8zs8rAzdWZmETN7zcz+HHaWtcys0sweNbN3sp/b/mFnAjCzr2f/hm+a2YNmtvnDVG5ZjnvMbLGZvdlp3mAze9rMZmd/DuonuW7N/h1fN7M/mlllf8jVadkVZuZmVt1fcpnZZdnt2FtmdksQ686LopDjkBthSALfdPddgP2Ar/STXGtdDswKO8R6/hd40t13BvaiH+Qzs5HAV4Hx7r47mQsrgr5oYmN+DUxcb97VwLPuPgZ4Njvd137NhrmeBnZ39z2B94Br+joUXefCzOqAI4GP+zpQ1q9ZL5eZHUpmFIg93X034AdBrDgvigK5DbnR59x9obu/mn2+iswGbmS4qTLMrBY4Frg77CxrmVk58GkyV63h7m3uvjzcVB2iQFG2v00xG/bJ6RPu/jwb9vXpPJzMvcBJfRqKrnO5+1R3T2YnXyTTlyn0XFk/Bv6LLjrT9oWN5PoScLO7t2bbLA5i3flSFLoacqNfbHzXyo4QOw6YHm6SDj8h858iHXaQTrYDGoBfZQ9r3W1mJWGHcvf5ZL61fQwsBFa4+9RwU61jqLsvhMwXEWBIyHm68gXgr2GHADCzE4D57v7vsLOsZ0fg4OyI0s+Z2b5BrCRfikJOw2mExcxKgd8DX3P3lf0gz3HAYnd/Jews64kCewN3uPs4YDXhHApZR/YY/YnAaGAEUGJm54SbauthZt8mcyj1gX6QpRj4NnBt2Fm6EAUGkTnUfCXwcBADiOZLUchlyI1QmFmMTEF4wN3/EHaerAOBE8zsIzKH2g4zs/vDjQRk/o717r52b+pRMkUibEcAH7p7g7u3A38ADgg5U2eLzGw4QPZnIIcdNoeZnQ8cB5zdT0Yz2J5Mcf939t9/LfCqmQ0LNVVGPfAHz3iJzF58r58Ez5eikMuQG30uW+V/Ccxy9x+FnWctd7/G3WvdfRSZz+r/3D30b77u/gkwz8x2ys46HHg7xEhrfQzsZ2bF2b/p4fSDE+CddB5O5nzgTyFm6WBmE4GrgBPcvTnsPADu/oa7D3H3Udl///XA3tl/e2F7DDgMwMx2BOIEMJJrXhSF7MmstUNuzAIedve3wk0FZL6Rn0vmm/jM7OOYsEP1c5cBD5jZ68BY4KaQ85Ddc3kUeBV4g8z/q1CGSTCzB4EXgJ3MrN7MLgRuBo40s9lkrqi5uZ/kug0oA57O/tv/eT/JFbqN5LoH2C57mepDwPlB7F1pmAsREemQF3sKIiKSGxUFERHpoKIgIiIdVBRERKSDioKIiHRQURAJmJkd0p9GmhXZFBUFERHpoKIgkmVm55jZS9mOVHdm7yfRZGY/NLNXzexZM6vJth1rZi92uhfAoOz8HczsGTP7d/Y122ffvrTTfSAeWDtmjZndbGZvZ98nkKGQRXpCRUEEMLNdgM8CB7r7WCAFnA2UAK+6+97Ac8B12Zf8Brgqey+ANzrNfwC43d33IjP+0cLs/HHA18jcz2M74EAzGwycDOyWfZ/vB/tbinRPRUEk43BgH+BlM5uZnd6OzKBjv8u2uR84yMwqgEp3fy47/17g02ZWBox09z8CuHtLpzF9XnL3endPAzOBUcBKoAW428xOAfrF+D+S31QURDIMuNfdx2YfO7n79V2029S4MJsaxri10/MUEM2OyTWBzCi5JwFP9jCzSK9TURDJeBY4zcyGQMd9jbcl83/ktGybzwH/cPcVQKOZHZydfy7wXPZeGPVmdlL2PRLZ8fm7lL2PRoW7TyFzaGlsEL+YSE9Eww4g0h+4+9tm9h1gqpkVAO3AV8jcyGc3M3sFWEHmvANkhqD+eXaj/wFwQXb+ucCdZnZj9j1O38Rqy4A/mVkhmb2Mr/fyryXSYxolVWQTzKzJ3UvDziHSV3T4SEREOmhPQUREOmhPQUREOqgoiIhIBxUFERHpoKIgIiIdVBRERKTD/weY62fwTgxLLwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# coding: utf-8\n",
    "import sys, os\n",
    "sys.path.append(os.pardir)  # 为导入父目录中的文件而进行的设置\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from dataset.mnist import load_mnist\n",
    "#from two_layer_net import TwoLayerNet\n",
    "\n",
    "# 读入数据\n",
    "(x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, one_hot_label=True)\n",
    "\n",
    "network = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)\n",
    "\n",
    "iters_num = 10000  # 适当地设置重复次数\n",
    "train_size = x_train.shape[0]\n",
    "batch_size = 100\n",
    "learning_rate = 0.1\n",
    "\n",
    "train_loss_list = []\n",
    "train_acc_list = []\n",
    "test_acc_list = []\n",
    "\n",
    "iter_per_epoch = max(train_size / batch_size, 1)\n",
    "\n",
    "for i in range(iters_num):\n",
    "    batch_mask = np.random.choice(train_size, batch_size)\n",
    "    x_batch = x_train[batch_mask]\n",
    "    t_batch = t_train[batch_mask]\n",
    "    \n",
    "    # 计算梯度\n",
    "    #grad = network.numerical_gradient(x_batch, t_batch)\n",
    "    grad = network.gradient(x_batch, t_batch)\n",
    "    \n",
    "    # 更新参数\n",
    "    for key in ('W1', 'b1', 'W2', 'b2'):\n",
    "        network.params[key] -= learning_rate * grad[key]\n",
    "    \n",
    "    loss = network.loss(x_batch, t_batch)\n",
    "    train_loss_list.append(loss)\n",
    "    \n",
    "    if i % iter_per_epoch == 0:\n",
    "        train_acc = network.accuracy(x_train, t_train)\n",
    "        test_acc = network.accuracy(x_test, t_test)\n",
    "        train_acc_list.append(train_acc)\n",
    "        test_acc_list.append(test_acc)\n",
    "        print(\"train acc, test acc | \" + str(train_acc) + \", \" + str(test_acc))\n",
    "\n",
    "# 绘制图形\n",
    "markers = {'train': 'o', 'test': 's'}\n",
    "x = np.arange(len(train_acc_list))\n",
    "plt.plot(x, train_acc_list, label='train acc')\n",
    "plt.plot(x, test_acc_list, label='test acc', linestyle='--')\n",
    "plt.xlabel(\"epochs\")\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.ylim(0, 1.0)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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