深度学习框架Tensorflow学习与应用/03-1 非线性回归.ipynb

{
 "metadata": {
  "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
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python_defaultSpec_1595734186906",
   "display_name": "Python 3.5.4 64-bit"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 补充\n",
    "* 这个程序为简单的三层结构组成：输入层、中间层、输出层\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "(200, 1)\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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nPVOvpFgpK4MBA2DDhvB9BQVWGm9FUVIOr8I7EJsgSNaEmdF6JemKIVYCxuhjj7US7NmZORN694bzz3c9PFkfGEVJd0JjjdxUR6FpcEIJFTCR+icjumKoK6+8YtkdQmnRwqoZfdxxtU0BYRAaMANNTzepKE0Nt3gGNxVTrP0TSULjGETkVBFZLyIbRaTAYf9JIvKhiFSJyJiQfdUistr/UxSP8SSEM86A228Pb9+zxzJGb94MhIfMh4phzbCqKMlJIKbBLZ4h1oR7qZQws96qJBHJBB4EhgJbgJUiUmSMWWvr9g1wEeBQYJkKY0zf+o6jUbj5ZlizBubNC27/7jsrYvq//43KBS6Qh0XVS4qSHDjZG0LxClJ1Eib1CnqrqYHycufkng1APFYMA4CNxpgvjDF7gXnAaHsHY8xXxpiPgZo4XC95EIHHH4djjgnft3o1XHgh27bvinwa0AI+ipJERJrQCTDo8BzHfXEJXjXGio96+GEYOxYOOABuuin64+tJPIzPucBm2/YW4NgYjm8pIsVAFTDTGFPo1ElEJgITAbp06VLHoTYAWVmWMfqYY2Dr1uB9L7/MLYNbMe2Y8a6Hh9ocQJPpKUqiCXUKcVMfBTDASx+UOJbtdDNiR/w+f/01LFny60/o++Ttt2O9rToTD8EgDm2xWLS7GGO2isjBwBIR+cQYsynshMbMAeaAZXyu21AbiI4drUI+J50EFcEP1MVLnuXTtrm8dNiJtW0BYZDr8QCmkj5SUVIZJy8ipwlbKF4TuKiCV6ur4b33oKjI+omUQeHTTy019QEHRBhZ/YmHKmkL0Nm23QnY6tI3DGPMVv/vL4ClQL84jCnx5OXBk0867pq18F6G7vyqNvXFPeP68tXMEZ4lAVM9CZeipApOaiOD84w3lJgncLt2WR6NF18MBx5o1ZqfPTv6tDpLl8Z2vToSjxXDSqCHiHQHSoDxwLnRHCgi7YByY8weEekADARmxWFMjcPYsbB2rRUhbSNzzx7+/uI0WL4cDj44aN+k4T3DjFy+DKF8bxXdC15XY7SiREldY4TcXu6BVb1b/RSIcgJXWQlvvgnPP2+pnXdFtjs6kpUVrl5qIOotGIwxVSJyDbAIyAQeN8asEZHpQLExpkhEjgFeAdoBI0VkmjGmN3AE8IiI1GCtXmaGeDOlHrfeankqvfhicHtpKYwYAe++C+3a1TaH6iPbZvnYtbeK7eWVQGoGxyhKookmqMxNcESTCsMtKtrVoFxTY33Xn38eFiyAH3+M/aZ8Pjj+eBg0CAYPtoJqW7SI/Tx1QAPcGoDX3t1I9zGn02ebQ9qMk0+Gf/7T9QNO5uAYRUlWIn1vIqW7iCYpXlQrkm3b4Ikn4LHH4IsvYrsJEcuJZfBg62fgQGjVKvJxMV1CU2I0GjP//Q17z7yFwqf/RO7PpcE7ly6Fyy6Dp5+2HoQQmkJwjKIkmkjfGyc7QsB4HJhwRXrpBwzKAQFx3fzVzF60nklDDiX/+0/h73+H116zjMrRkpUFQ4fC6NGWRiEBhuVoUMHQAGwtq8Dssx8Xnz2VF5/9M232lgd3ePZZy9YQYouABgqOUZQmTqTvjZvgKCmriMmWZ195tN9VxpnL5jJgxmLYWep5XBDZ2VY6ndGjYciQuK8K4oHmiG4AAg/j5znduOqMm6jMyAzvNH26Y/Efr+AYe9nBgTOXaBCcoviJFFTmNbGKJbB09qL1dCvZwKyF9/Luwxfzp/8+R8dohELLlpZzSmEhfPutpWoaNSophQKoYGgQ7A/psm59uWn41c4dL7vMCmSx4VbZDQgqO6gR0oryK5EqIjoJjlA885ZVV0NREbMfvo43nvgDYz/5Fy2qK70HJWKpiZ56Cr7/HubPt1YJCTIg1wc1PjcQoYaqxzcV0fPR+8I7tm0Ly5ZZ6bo9UKO0osSO/XvYNsuHCJSVV7oGrwnw5cwRvzbs2WPZA2fNgo0bo7tox45w6aVwySXQrVs97yC+qPG5kQmLfDSDoLzUcl+zs2MHnHYa/5zzErev/jksD3zgoXZ7kNUorSjOhHoilVVUkuXL5J5xfWvT4IdSq3L65ReYMwfuvjuq2AGTkYGcfjpMnAinnQbNUvvVqiuGRLJnj7W0/M9/wnZtyOnKWefexc6WVvZEX4aAQGW19+ejKwYlnXFyIQVcX/xgfWecAkuzfJn89ZROjHjnJbj/fvjpp4jX35m1L9+Ou4DDpt0IyZTDzQVdMSQjLVpYxqcTTggLge9R+jWPvTid88fdzh5fCyprIgvsmDM2KkoTwimobdILH0WcUG0tqwgLLD2sRTX3b32TniMfC6/M6MThh8O119Lmggto07p1XO4nmVDjc6LZbz9YuJDd+3UI23VMyVoeKJpFZo23H7STcU1R0g2n2ITKGhNxld02ywdY6t5lv8vjy9Yfsui+Cy0bYCShMHQovPGGld3gyiuhCQoFUMHQKBTuaMG5Z07l5+bhLnRDN67gzkUPWvnYHQgk4QO4bv7quLmtqiuskmrU1b62a28Vr727Ae66C7p3t9LY7NjhfoAIjBkDxcWweDGceipkNO1XZ9O+uyRl9qL1fNi+OxPPnMKezHBt3viPF/On/zwb1i5At/ZZcXdbtZcfVVdYJVWoS9Bni6q9XLC8kIHDB0BBgbcdoVkzKwvqZ5/BCy9A//71GG1qoYKhEQjMdN7rejTXj/gTNQ4Jfn//3nwuKg4ugW2Adzf95BraX1e80gUoSrLiFJvgyxB8meHfJzE1nPHpEpbMuYJbl/yd/X4pcz9x8+ZwzTWwaZNVobFn+tnx1PjcCNjD918/4kQ6lJcx7V+PhPW77a05/NyiNS8deUptW7Ruq7GkINb8TEoqYjcgl5RVkClCZY0h2xavkCHC8V98yOSlT9D7+whJ7Zo1s2IPpkyBzp29+zZxdMXQCITOdJ7qP5L/G+hc/nPWG/dx2rr/RjynfVkdq2rIq6i5oiQz+f1ya79PgXoJZRWV7K6s4bF+zXl36SyeXXCLp1Colgy+HjnW8hR85JG0FwqggqFRcArfP/D+2Va0ZAiZpob7XvsrJ29yj9sIdVuNVTUUl+LlitJIhD7vB+0s5Y7C2Zx8znAOWP6O63E1CIW9fsOQyx7m5F4X0m3OZ+p44UcD3JKJqioYPx5eeils1+5mzZlw9jRWdLHyJtnrRg86PIe315VGVcj8K3u4v426Vr9SlMame8HrGCBr726uXPEiV7z/Mi2r9noe8+ahxzL7pAv4PKdb2D6nWgxNhWgD3FQwJBt79kB+vlXMJ4Sfm2dx3vg7+figwwD3CE63QuYC3DOub5N84JX0ZeCMt+j/3j+Z/PYTHPRLhEppAwYw7tAzWdG5j2e3pppRIFrBoKqkZKNFC2vFcOKJYbv23VvB0wtupWfpV4BlHHYrZO6EAfU0UpoWxcUUzS/g/tf+6i0UDj7Yym66fDlbjjwm4mnT3fFCBUMy0qoV/OMfkBcu2LN3/8Kz86Zw6A/fRFQbOZHuD7zSRPj2W8uD6JhjaP+Rh/agfXu47z4rFmHsWBCJKgV3ujtexEUwiMipIrJeRDaKSIHD/pNE5EMRqRKRMSH7JojIBv/PhHiMp0nQpo2lTnJIx51TXsa8eTcxtvVOhwgIb9L9gVdSnD17+PTamynverBVW9mN5s3hz3+2YhH+8Adr24/d+QMI+w6p40UcBIOIZAIPAqcBvYBzRKRXSLdvgIuA50OO3Q+YChwLDACmiki7+o6pydC+Pbz5Jhx6aNiuDrvKOP+mSzj4h81Rn04feCWleeMNfjm0J33u/wut9nqsfEeP5s0FbzGw3XC6z/hvkKdRIPXLdfNXA3DvuL7cM66va4GfdKXexmcROR64zRgz3L89GcAYM8Oh75PAP4wxL/q3zwFONsZc4d9+BFhqjJnrdc0mbXx24uuv4Te/sX6HUNo6m/HjZ7Cpg7vvtUBMnkbqoaQkFV9/DX/8o5WZ2IteveDeeyns0MsxpfZZ/XN56YOSsPZ0EgSJND7nAvZp6xZ/W1yPFZGJIlIsIsWlpTEU3m4KdO0KS5c65nvP2VXGvHmTOeRH55VDbnYWX84cwbKCwVELBc2bpCQFe/fCjBlwxBGeQmFHi9bcNuQK+OgjGDrUNY5n7orNmvolSuIhGJzU3NEuQ6I+1hgzxxiTZ4zJy8nJiXpwTYZu3TyFw9y5N4UJh7qojjRvkpIUvPUWHHUU3HQTVDirjaolg6f7jeDkiXN485SxtVXT3Bwsql20I+qQEU48BMMWwK7H6AREroVX/2PTj+7d4e23HYXD/ru2s2DuZAbs3FwvXanmTVIalZISGDcOhgwJK2Zl570uR3L6xfdz67Cr2N12v6AJkJuDRaY4u2qoQ0Y48RAMK4EeItJdRJoD44GiCMcEWAQME5F2fqPzMH+b4sbBB7P4gblsbRO+amq/q4wFcyfz5RkdolYdhaJ5k5RGobLSqq98+OGwYIF7vwMPpPjO/+WGK+/h85xujhMgtxQv5xzbWVO/REm9s6saY6pE5BqsF3om8LgxZo2ITAeKjTFFInIM8ArQDhgpItOMMb2NMT+JyO1YwgVgujEmcqHVNGfamt1knDODec9PJvfnEHtLWRmccgq89hoMGhTzud1q4eqXR2kw/v1vuPpq+PRT9z6ZmfD738O0aeS1acMyj9OFlu20O1Dkdd1PHSuiQFNipCCB3DCdy75l7tzJdNrpYIxv2RJefBFGOOdG8kK9kpT6EPXz8913MGkSPPOM9wkHDoQHH4Sjj26YAacRmiupCTNw5pLaiOeDdpby7PwpHPKTg9dQs2bw/PNw9tkJHqGSrgS82jxdQqur4eGHrboHXiU1c3Jg1iy48MImX0ozUWiupCaMXYe6rU0O486dybr9u4d3DGRrfeyxBI9QSVcierUtXw7HHGOphVyEQg3CF2dfCOvXU3j0UAbOWqq1yBOMVnBLQUJ1qC1yO7JpfhGHF1wGK1YEd66pgcsugy1brKLnIZ4ZqjZSYiXwzASqplUbU5vp1817rWLrt3D55fDoo57nXn3QYUwZ9ju2dD+C274qD1p9BGJqAH1GGxhVJTUlfv6Z0sHDySl+z3n/ZZfx6sQpzHprE1vLKmib5WPX3ioqq399BpwiQVV4KAGcVEUBsnyZtPRlsL28srZNTA3jPlrM5H8/RduKn13PW9ZyH+76zUXMO3oYRrwVGYGU2Ppcxo6qktKQwo07GXrKjbx1iEta4UcfZd/xY/jp++0YrBKIdqEA4cFsGgmt2HFSFQWoqKzGGGrVnH2+3cgrz9zAzEUPeAqFeUcNY9DljzC376kRhQJYz+CUwk/0uWxAdMXQhAgYpX3VlcxaeB9nrF3q2O+jA3twyZip/Ng623F/oKBPQF3gRFMtZKJ4E/CIc0OAB07rxu6Cmzhj+WtkePXu1493rr2NCZ95p8B2u47TmfW59EZXDGlIQL9bmenj+t9ez0PHjXHsd/S3G3j52Rvo5uTJBLTN8tXOxiJdS0kvPAMdjeGSL/7DiLMHcdbyIneh0KYN3H8/rFzJbyaMol0rX8zjcBM3+lzGBxUMTQj7l9ZIBrN+cxG3DL2SGoeUVF3LvqXwmT9xwlerg9qzfJmI4KoucLqWkj64FbnpWfoVL86dzC0v3AVeSS4vuAA+/9zySsq0zjN1ZO+IhXOiRZ/L+KCCoQnh9KV98djR3DD+FnY3ax7WP3v3Lzy94Fau/GQhYgzZWb4w46ETGgmdvoQWuWmzt4KblzzK60/+gbzNHpHLvXvDO+/A00/DAQd4njN0GhNtMSp9LuOH2hiaGE6eGgDz75vPQ/Nvo91uZyPgi/1P59YhEymP4MGcq94fCoAxVg3lP/0JtnrkvWzdGm67Da69FnzRqYxCn+FBh+eE1VEIRZ/L6NDIZyWIwlUlzH/mTe56vIAuO75z7LOicx+uzJ/M9lZtw/YF3FjBOQeNkkasXWuVy3zrLe9+Y8daifE6darTZewCom2Wj7IK55WsAF/OjD31SzqixmcliPx+ucz920Vc8fuHea/LkY59jt38KUVPX8/h338Z1B7IYAmoi2A6s327NfM/6ihvodCjByxebK0o6iEU7M9aWUWlq0pJ7QrxRwVDmrGusjkXjL2dZ/qd7rimuMWBAAAaJklEQVS/847veOWZGzjrE+uLH3D/y++Xq0V80pXqanjkETjsMMubqNpFpZOVBXfcAZ98AkOH1uuSTs+aIdzeoHaFhkFTYqQZHbOzKCmr4JZhv2N9Tjdue/P/aGZqgvpkVe3h7oX3cNzWz2jx8AO17dEU8dFo1KZB4HPM/WQld7z9KIdt2+h9wKhRcN99VqXBOOD2rBmsyYo+Xw2LCoY0w15v4dl+p7Npv048VDjD0Sh99up/UnZxPmN/eyMrM9uR4c+LE0pgKR+aLkFz26QmhatKuP+Jtyh481FGrvuPd+fu3a1VxG9/G/M1nCYQgXY3y6cGsCUGVSWlGaGuge93O5rRF/6NTQc4ZGcFstd9yqP3X8HIte84CgX7Ul5VTU2A8nJ++PPNvP7wRG+h0KoV3HmnZYiug1BwslXZ01w4oWqjxKFeSYrFrl1w1VWeRVNe6j2IqUOv4pcWrWrbWjfPpHxvda2Kygn1GkkBqqvhqafgllu83U8BzjsP7roLcuu2CrTXE7GT6bIiBXVHjRfqlaTERuvW1ovhkUegeXgwHMBZa95m4RO/539KPqtt27W3unbWp14jKYgx8MYb0LcvXHqpp1BYl3sYLFsGzz5bZ6EA7vYDN6EgUOca5krdUMGg/IoITJwI775r6Y4d6LLjOxY8dyPX/edZfNXBfuXqNZJirFpleQ+dfrpnveXSVtlMGfFH1r36LzjhhHpfNtaJgk4sEk9cBIOInCoi60Vko4gUOOxvISLz/ftXiEg3f3s3EakQkdX+n/+Lx3iUetK/P3z4IVuGjnTc3czUcO278yh66jr6fBvsrRLwGhF+jX+oy0yvcFUJA2cu0cpdDcE338CECdbn7BGPUJWRyaPH5HPepKfIu/0G8vt3du0by+c1aXhP19WlTiySg3rbGEQkE/gcGApsAVYC5xhj1tr6/A44yhhzpYiMB84wxozzC4h/GGP6xHJNtTEkCGP4cPq9HP6Xm2i1d7djlyrJ4P+OG8P9J5zD3mY+MkW4e+zRtR4mtxWtqY1YbdfKx9SRvSMKiqjqBiuxs20bzJhhqQv37vXue+aZVt/DDgO83ZDr8nl1K3jd9dLqjtpwJCwlhogcD9xmjBnu354MYIyZYeuzyN/nPRFpBnwL5ABdUcGQ/GzcCOeeCytXunbZ0L4zU4b9jhVdjiTLl8lZ/XOZ//5mKmvCn69IAsLNOFlXV8W0j6344QeYNQseeAAqIqSlPv54mD0bBg6sbXKr2hb4HN3qdmRn+Vg9dZjjZeL9GSvRkUjjcy6w2ba9xd/m2McYUwXsANr793UXkVUi8o6InOh2ERGZKCLFIlJc6pXWVwmj3mqZQw+1jI633grNnENfevy4mflzJ3P363+jddkPzF3hLBQAtpdXeqbScDNOetWHcCOtK9CVlVmfWffu1sveSygccgi88IL1OduEArhXbQt8jm6fS1lFpev/2SkTsKqNkod4CAYndWHoG8GtzzagizGmH3A98LyItHG6iDFmjjEmzxiTl5OTU68BpxNxezH6fDBtGrz/PhsOOsS121mfLuGtv1/JOR++TkaNezZMr/gGN2OjQMzjrk9sRcraOUpL4aaboGtXuP12+OUX977t21sBamvXwpgxlgNCCF7Fbyoqq8l0OCaA2//ZHk9TX3uUEn/iIRi2AHarVCcg1Oetto9fldQW+MkYs8cY8yOAMeYDYBNwWBzGpPiJe9BZv36sLXyT/z3pfCoznIurtN2zizsWP8TrT17LSV984HoqtxeOm3HS4P6iifUakSp9peRKY8sW+OMfLYEwYwbs3Oned599rJiFTZusojkuLsoQ2SvIzc0UrP+bm3DN75fLsoLBfDlzhLqjJhnxEAwrgR4i0l1EmgPjgaKQPkXABP/fY4AlxhgjIjl+4zUicjDQA/giDmNS/NT1xejF6AHd6XzvTC675mFWH+Qux48o/YqnX5jK0/NvCcvYCu4vnPx+uXEr3eh2jUgvu5SK4v78c7j8cjj4YCtfkZfKKCsLJk2CL7+E6dOhbXiK9VDcqrYFyM3Oci3PKZBawlUB4iAY/DaDa4BFwGfAAmPMGhGZLiKj/N0eA9qLyEYslVHApfUk4GMR+Qh4EbjSGPNTfcek/EpdX4yRyO+Xy1P3XU7fLZ+x6uaZ7Mza17XvSV+tYuETf+Cvr99D1+3WYjKSPjk3TuOuqy67IQRqtESlwjLGcjUdORJ69oRHH4VKj8p7Ph9cc421Qpg1Czp0iHo8AbVPdlb4yz/wv3QqzymE65QrKqv54/zVqaWaS0M0JUYTJ2Gun6WlcOON8MQTnt2qJINF/zOU5rdMYejo/+faL57jrotXUmN5zUS874oKeP55uPdez6C0Wlq0gEsusT6brl1rrxHN/yPQr6SsojZdRXaWDxEoK690dFuNprCO430pCUEruCm1JNJd899PvkqbKQX0LVnn3TEz08q58+c/W/WAHWjIcUc6d2PFUrgJpGOrfmR+s7Xw2GOW+2kkWreGK6+0Sm8edFBtc7T35eai6tbfjtexoah7amJRwaA0GoUfbuGDv87h8oVzXMuIBjFsGFx3HQwf7ugVE/fxxfByTHT8Q/eC12vVLy0rd3Pa+ncZ9/FijtscxeoAIDvbMiZfe63lcRRCtCsht35u/aO5hhOaYDGxRCsYtB6DEnfy/6cT+c9Phz03w4MPWumZf/IwHS1ebP0ccYSlBz/3XOsF10B4GZbtL/78frkJV3N0bNuSnM8+4sxPl5C/dilt9uyK6rit2Qfw08UT6XPbDdAm3OPbrhZyPD6kPZItxWt/LHaYtg52C6Xx0SR6SsPRogVcfz189RX85S+w337e/T/7DK6+mj05B1DYexC/v/xuCj/Y7H1MHWhMw7IjxsDHH8NNN7H4wUsofOZPXLjq9aiEwopOvbki/yZOvHwOZ2QdR+Gm8IJLdtdbN0KN+pGM/F77Y3EQ2LW3So3QSYgKBqXh2XdfmDzZEhAzZjiqOOy0qNpL/tql/O+jN9B/yAA+v/hqKC62XqBxoKE8tWKiuhr++1/LKNyrFxx9NMyYQeuSbyIeujejGS/1HsRvJ9zLuPPuYlHPE6jOyKSy2ji607pFLgdw8tLyclGN5NXldKybgtBtzErjoqokJXHsuy8UFFjqoieftHzuN3rXEu5c9i08+ZD107WrFZ07cqSV08cjKMsLe3nTAAlJx7B1K7z9tqU2W7gwOiOyjQ3tOzPvqGG80mcwP7Vyjj9wWvV4rYTcCuAEtkO9kkL7e9lhQtuvm7/aMT6l0VZqiisqGJSEEfwS6cWkuW+RX7IK7rkH3nkn8gm+/hruvtv6ad2ab/sdx7zsnryxfy/KDzmMP53WKyqbgNuLK672BGOs9NYrVsC//w1Llliqship8LWk6PATmX/UMD7MPTyicd5p1eNWXS+SR1AkG0ukGt+hx7rZOLTeQvKhXklKXKiP+yfAy08uZNCy18hfs5R2u8P15JH4uXkWn3Y8jPaD/h+HjRoCffpYyeMy3SN240ZVlRVJvHYtfPIJvP++9fNdFB5ZTmRkwODBcM45/KPHCUxa/FXQ/82XYc3eQ3MU+jKF2WOODnshN5TrbayxHppOvfFRryQlYUSaOYK7J9BtRWvYU1VDRVZH/j3kCmaefDFDNqxg3MeLGfj1R2SamqjGsO/eCo7/6iN44iN44kGrsXlzq57A4Ydb2UM7dvz158ADLdXWPvtAy5bOM3FjrFrYO3ZYmUq3b4eSEti8+defTZtg/XrYs6eO/z0bJ5wA55wDZ58NBxwAwG+Bqn32DRO6QFS1LgICu6KyOigSuaWv/ubFWI34CVmpKXFBVwxKvYlm5mj3z4+WPpkV3MEm+r7/L0s3Xx05YKpOZGR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   "source": [
    "# 使用mp生成200个点 等差数列：linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0) \n",
    "x_data = np.linspace(-0.5, 0.5, 200)[:, np.newaxis]# 转化为200列 reshape(-1,1)也可以\n",
    "print(x_data.shape)\n",
    "noise =np.random.normal(0, 0.02, x_data.shape)# 生成随机值和x_data同样的行列数字  (200, 1)一样\n",
    "y_data=np.square(x_data)+noise\n",
    "\n",
    "\n",
    "# 定义两个placeholder  形参\n",
    "x = tf.placeholder(tf.float32, [None,1])# 行取决于，1列\n",
    "y = tf.placeholder(tf.float32, [None,1])# 行，1列\n",
    "\n",
    "# 定义神经网络中间层\n",
    "Weights_L1 = tf.Variable(tf.random.normal([1,10])) # 一行十列 （ 一个输出单元，10个中间层）\n",
    "biases_L1 = tf.Variable(tf.zeros([1,10])) # 一行\n",
    "Wx_plus_b_L1 = tf.matmul(x, Weights_L1) + biases_L1\n",
    "L1 = tf.nn.tanh(Wx_plus_b_L1) # i激活函数tf.nn.tanh\n",
    "\n",
    "# 定义神经网络输出层\n",
    "\n",
    "Weights_L2 = tf.Variable(tf.random.normal([10, 1])) # \n",
    "biases_L2 = tf.Variable(tf.zeros([1,1])) # \n",
    "Wx_plus_b_L2 = tf.matmul(L1, Weights_L2) + biases_L2\n",
    "prediction = tf.nn.tanh(Wx_plus_b_L2) # i激活函数tf.nn.tanh\n",
    "# 通过神经网络得到预测值\n",
    "\n",
    "# 二次代价函数\n",
    "loss = tf.reduce_mean(tf.square(y-prediction))\n",
    "# 使用梯度下降法训练\n",
    "train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    # 变量初始化\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    for _ in range(200):\n",
    "        sess.run(train_step, feed_dict={x:x_data, y:y_data})\n",
    "    # 获得预测值\n",
    "    prediction_value = sess.run(prediction, feed_dict = {x:x_data})\n",
    "    # 画图\n",
    "    plt.figure()\n",
    "    plt.scatter(x_data, y_data) # scatter散点图\n",
    "    plt.plot(x_data, prediction_value, 'r-',lw=5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}