3-1非线性回归 test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5.69709635e-03]\n",
      " [ 2.81354553e-02]\n",
      " [-4.26672111e-02]\n",
      " [-2.98776283e-02]\n",
      " [-1.88206194e-02]\n",
      " [ 1.84324567e-02]\n",
      " [ 1.89892980e-02]\n",
      " [-3.54314932e-02]\n",
      " [-2.25571630e-02]\n",
      " [-5.38637535e-04]\n",
      " [-5.99816536e-03]\n",
      " [ 1.46747572e-02]\n",
      " [ 3.12447294e-03]\n",
      " [ 1.19801477e-02]\n",
      " [-5.50696754e-03]\n",
      " [-1.99080687e-02]\n",
      " [-6.12516257e-03]\n",
      " [ 3.85850778e-02]\n",
      " [-4.15283461e-03]\n",
      " [-2.82011679e-02]\n",
      " [ 3.68917887e-02]\n",
      " [ 2.15248528e-02]\n",
      " [-2.16798908e-02]\n",
      " [ 1.99281377e-02]\n",
      " [-8.38485752e-03]\n",
      " [ 1.48307259e-03]\n",
      " [ 9.77469626e-03]\n",
      " [ 7.72260276e-03]\n",
      " [ 4.87572444e-03]\n",
      " [-8.19607436e-03]\n",
      " [ 1.50994684e-03]\n",
      " [-3.98688144e-02]\n",
      " [ 2.09744643e-02]\n",
      " [ 1.78606742e-02]\n",
      " [ 1.36859111e-02]\n",
      " [ 3.97897887e-02]\n",
      " [ 2.93836670e-03]\n",
      " [ 5.53097316e-02]\n",
      " [ 5.65477616e-03]\n",
      " [ 2.31134910e-02]\n",
      " [ 2.24931318e-02]\n",
      " [ 1.06586980e-02]\n",
      " [ 1.18472459e-02]\n",
      " [-2.69795839e-02]\n",
      " [ 1.86955461e-03]\n",
      " [-1.95751206e-02]\n",
      " [-1.26792453e-02]\n",
      " [-1.15551394e-02]\n",
      " [ 5.72432340e-02]\n",
      " [ 1.32382779e-02]\n",
      " [-9.29982029e-03]\n",
      " [ 2.01291170e-02]\n",
      " [ 1.41401217e-02]\n",
      " [ 3.52473132e-02]\n",
      " [-8.35296636e-03]\n",
      " [-2.69050738e-02]\n",
      " [ 1.24827184e-02]\n",
      " [-1.64795492e-03]\n",
      " [-2.49545151e-02]\n",
      " [-8.40694094e-03]\n",
      " [-1.56043579e-03]\n",
      " [-2.57257601e-02]\n",
      " [ 3.54366644e-03]\n",
      " [ 1.16088900e-02]\n",
      " [-1.45096191e-02]\n",
      " [-7.13636055e-03]\n",
      " [ 1.52726480e-02]\n",
      " [-7.51705615e-03]\n",
      " [-1.90310967e-02]\n",
      " [-1.55239768e-02]\n",
      " [ 1.51410365e-02]\n",
      " [ 2.12412938e-02]\n",
      " [-2.66741357e-02]\n",
      " [-5.10031363e-03]\n",
      " [ 1.65581145e-02]\n",
      " [ 1.94568125e-02]\n",
      " [ 1.66440480e-02]\n",
      " [ 2.58769386e-02]\n",
      " [-9.57885164e-04]\n",
      " [-2.77419209e-02]\n",
      " [ 1.18846934e-03]\n",
      " [ 2.28015318e-03]\n",
      " [ 8.78158104e-03]\n",
      " [ 1.06925260e-02]\n",
      " [ 1.03307310e-03]\n",
      " [ 7.10552023e-03]\n",
      " [-2.56972833e-03]\n",
      " [-1.16487691e-02]\n",
      " [-1.43382604e-02]\n",
      " [-1.07610691e-03]\n",
      " [-3.80657612e-03]\n",
      " [-1.70028345e-02]\n",
      " [ 1.49404401e-02]\n",
      " [-1.28792610e-03]\n",
      " [ 7.02942949e-03]\n",
      " [-9.78851936e-03]\n",
      " [-7.27148497e-03]\n",
      " [-2.54764449e-02]\n",
      " [-1.09641332e-02]\n",
      " [ 3.94607408e-02]\n",
      " [ 8.40492774e-03]\n",
      " [-1.53903046e-02]\n",
      " [-8.76267422e-03]\n",
      " [-8.68179001e-04]\n",
      " [ 1.20515701e-04]\n",
      " [ 2.90194472e-02]\n",
      " [-1.95198720e-02]\n",
      " [-7.41049462e-03]\n",
      " [-1.39117213e-02]\n",
      " [-5.22447433e-03]\n",
      " [-1.82204816e-02]\n",
      " [-5.29501910e-03]\n",
      " [ 1.26885798e-02]\n",
      " [ 3.45712195e-04]\n",
      " [-2.41301422e-02]\n",
      " [-6.10411036e-03]\n",
      " [ 3.06157996e-03]\n",
      " [-1.26522956e-02]\n",
      " [-3.87995232e-03]\n",
      " [ 1.50907534e-02]\n",
      " [-2.47152593e-02]\n",
      " [ 1.68214417e-02]\n",
      " [-5.92575349e-03]\n",
      " [ 2.65067804e-02]\n",
      " [-1.08404351e-02]\n",
      " [ 1.71989867e-02]\n",
      " [-3.70898842e-02]\n",
      " [ 5.24267298e-03]\n",
      " [ 2.10992761e-02]\n",
      " [ 2.69186070e-02]\n",
      " [ 9.35778218e-03]\n",
      " [-2.92759418e-02]\n",
      " [-7.67458708e-04]\n",
      " [-1.43817742e-02]\n",
      " [ 3.15965700e-03]\n",
      " [-3.51390281e-02]\n",
      " [ 8.18560816e-03]\n",
      " [-2.43020773e-02]\n",
      " [ 6.79656652e-03]\n",
      " [ 4.68093791e-02]\n",
      " [-1.54477820e-03]\n",
      " [ 1.35028619e-02]\n",
      " [-5.34446131e-03]\n",
      " [-7.30061658e-04]\n",
      " [-2.51626980e-02]\n",
      " [ 2.39431905e-02]\n",
      " [ 2.60530544e-02]\n",
      " [-8.46231856e-03]\n",
      " [ 5.59745807e-03]\n",
      " [ 6.24802977e-03]\n",
      " [ 1.43609212e-02]\n",
      " [ 3.19072915e-02]\n",
      " [ 5.29021695e-02]\n",
      " [-2.93952429e-03]\n",
      " [-1.67251767e-02]\n",
      " [-1.33959932e-02]\n",
      " [-3.07307169e-02]\n",
      " [ 4.44628905e-03]\n",
      " [-3.45742068e-02]\n",
      " [ 1.67207708e-03]\n",
      " [-4.29393509e-03]\n",
      " [-1.45411489e-02]\n",
      " [ 1.24430500e-02]\n",
      " [ 9.34659389e-03]\n",
      " [ 5.85216508e-04]\n",
      " [ 1.32496620e-02]\n",
      " [ 6.83230606e-03]\n",
      " [-2.64893170e-02]\n",
      " [ 4.83356436e-02]\n",
      " [-1.37400433e-02]\n",
      " [ 1.22209354e-02]\n",
      " [ 1.60540408e-02]\n",
      " [-1.94521553e-02]\n",
      " [-3.76769325e-02]\n",
      " [-7.06948340e-03]\n",
      " [-1.22387771e-03]\n",
      " [ 2.27849865e-02]\n",
      " [-6.33872873e-03]\n",
      " [-7.43491594e-05]\n",
      " [ 1.20595692e-02]\n",
      " [ 1.38466234e-03]\n",
      " [ 1.40809278e-02]\n",
      " [-1.38998237e-02]\n",
      " [ 1.41111878e-02]\n",
      " [ 2.02451205e-02]\n",
      " [-3.22207609e-03]\n",
      " [-1.93499423e-02]\n",
      " [-2.62067952e-03]\n",
      " [ 2.88559566e-02]\n",
      " [-6.59857950e-03]\n",
      " [-1.12305415e-02]\n",
      " [ 4.88523725e-04]\n",
      " [ 1.39074154e-02]\n",
      " [-1.98405698e-02]\n",
      " [ 3.92762508e-02]\n",
      " [ 3.58499839e-02]\n",
      " [-1.27279421e-02]\n",
      " [-1.76513476e-02]\n",
      " [-1.85021196e-02]\n",
      " [ 1.91829064e-02]]\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# linspace 是均匀分布 200个名在-0,5和 0.5之间\n",
    "# [:, np.newaxis] 把数据 竖过来\n",
    "x_data = np.linspace(-0.5, 0.5, 200)[:, np.newaxis]\n",
    "# 一个随机数据， 和x_data的 结构一样\n",
    "noise = np.random.normal(0, 0.02, x_data.shape)\n",
    "y_data = np.square(x_data) + noise\n",
    "\n",
    "x = tf.placeholder(tf.float32,[None, 1])\n",
    "y = tf.placeholder(tf.float32,[None, 1])\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "#     print(x_data)   \n",
    "    print(noise) \n",
    "#     print(y_data) \n",
    "    plt.figure()\n",
    "    plt.scatter(x_data, x_data, c='g')\n",
    "    plt.scatter(x_data, noise, c='r')\n",
    "    plt.scatter(x_data, y_data, c='b')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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
}