第六讲.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From D:\\ProgremFiles\\Anaconda3\\envs\\style\\lib\\site-packages\\tensorflow_core\\python\\compat\\v2_compat.py:65: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.utils import shuffle\n",
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()\n",
    "df = pd.read_csv(\"F:\\\\学习相关\\\\大四上\\\\毕设\\\\tensorflow课件\\\\第六讲\\\\data\\\\boston.csv\", header=0)\n",
    "df = df.values  #获取df的值\n",
    "df = np.array(df)  #将df转换为np的数组格式\n",
    "#特征数据归一化，转换为0-1之间的数\n",
    "for i in range(12):\n",
    "    df[:, i] = df[: , i] / (df[:, i].max() - df[:, i].min())\n",
    "\n",
    "x_data = df[:,:12]  #二维数组操作：选择所有行，选择前12列（共13列）\n",
    "y_data = df[:,12]  #二维数组操作：选择所有行，选择第13列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, [None, 12], name=\"X\")\n",
    "y = tf.placeholder(tf.float32, [None, 1], name=\"Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.name_scope(\"Model\"):\n",
    "    w = tf.Variable(tf.random_normal([12,1], stddev=0.01), name=\"W\")\n",
    "    b = tf.Variable(1.0, name=\"b\")\n",
    "    def model(x, w, b):\n",
    "        return tf.matmul(x, w) + b\n",
    "    pred = model(x, w, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#设置训练超参数\n",
    "train_epochs = 300  #迭代轮次\n",
    "learning_rate = 0.001  #学习率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义损失函数\n",
    "with tf.name_scope(\"LossFunction\"):\n",
    "    loss_function = tf.reduce_mean(tf.pow(y-pred, 2))  #求均方误差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss_function)  #选择优化器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch= 1 loss= 98.19559650823867 b= 3.18293 w= [[-0.23411296]\n",
      " [ 0.7226162 ]\n",
      " [-0.0208406 ]\n",
      " [ 0.18479176]\n",
      " [ 1.6991756 ]\n",
      " [ 3.3135371 ]\n",
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      " [ 1.1389989 ]\n",
      " [-0.36326176]\n",
      " [ 0.48158127]\n",
      " [ 3.469916  ]\n",
      " [-0.3194251 ]]\n",
      "epoch= 2 loss= 88.11446854022236 b= 4.3052278 w= [[-0.5372623 ]\n",
      " [ 1.6016644 ]\n",
      " [-0.70495266]\n",
      " [ 0.66042733]\n",
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      " [ 5.487857  ]\n",
      " [ 0.55612785]\n",
      " [ 1.9544486 ]\n",
      " [-1.3144764 ]\n",
      " [-0.10705823]\n",
      " [ 4.4684796 ]\n",
      " [-1.2437458 ]]\n",
      "epoch= 3 loss= 72.38329664422135 b= 4.832226 w= [[-0.792214  ]\n",
      " [ 2.245702  ]\n",
      " [-1.3625848 ]\n",
      " [ 1.0747699 ]\n",
      " [ 1.918029  ]\n",
      " [ 6.8473234 ]\n",
      " [ 0.14219989]\n",
      " [ 2.3891923 ]\n",
      " [-2.1075325 ]\n",
      " [-0.7957763 ]\n",
      " [ 4.4382653 ]\n",
      " [-2.1891644 ]]\n",
      "epoch= 4 loss= 63.67540812254145 b= 5.1478863 w= [[-0.9992034 ]\n",
      " [ 2.7304978 ]\n",
      " [-1.899756  ]\n",
      " [ 1.4415213 ]\n",
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      " [ 7.8811307 ]\n",
      " [-0.20455354]\n",
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      " [-2.6887403 ]\n",
      " [-1.3777541 ]\n",
      " [ 4.10654   ]\n",
      " [-3.0523343 ]]\n",
      "epoch= 5 loss= 57.76276039736686 b= 5.60037 w= [[-1.1574513 ]\n",
      " [ 3.1113791 ]\n",
      " [-2.2003257 ]\n",
      " [ 1.7828776 ]\n",
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      " [ 9.014612  ]\n",
      " [-0.29844987]\n",
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      " [-2.9645555 ]\n",
      " [-1.6229104 ]\n",
      " [ 4.1583405 ]\n",
      " [-3.741803  ]]\n",
      "epoch= 6 loss= 53.660301123938886 b= 5.7540507 w= [[-1.2923671 ]\n",
      " [ 3.3770564 ]\n",
      " [-2.5182943 ]\n",
      " [ 2.0975335 ]\n",
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      " [ 9.738001  ]\n",
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      " [-3.2094543 ]\n",
      " [-1.9648545 ]\n",
      " [ 3.6978192 ]\n",
      " [-4.429606  ]]\n",
      "epoch= 7 loss= 50.756676493505296 b= 5.873105 w= [[-1.4108702]\n",
      " [ 3.593714 ]\n",
      " [-2.7839751]\n",
      " [ 2.3842793]\n",
      " [ 1.6977918]\n",
      " [10.384751 ]\n",
      " [-0.6305723]\n",
      " [ 2.713884 ]\n",
      " [-3.371793 ]\n",
      " [-2.2448242]\n",
      " [ 3.2199476]\n",
      " [-5.0787   ]]\n",
      "epoch= 8 loss= 48.65931878198671 b= 6.03258 w= [[-1.5089494 ]\n",
      " [ 3.7657332 ]\n",
      " [-2.958271  ]\n",
      " [ 2.6381426 ]\n",
      " [ 1.7144346 ]\n",
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      " [ 2.6273594 ]\n",
      " [-3.4063268 ]\n",
      " [-2.3889823 ]\n",
      " [ 2.8832793 ]\n",
      " [-5.652347  ]]\n",
      "epoch= 9 loss= 46.62445965012423 b= 6.3275137 w= [[-1.5869285 ]\n",
      " [ 3.9130023 ]\n",
      " [-3.0194027 ]\n",
      " [ 2.8809063 ]\n",
      " [ 1.9222783 ]\n",
      " [11.836836  ]\n",
      " [-0.59109306]\n",
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      " [-3.310426  ]\n",
      " [-2.3573043 ]\n",
      " [ 2.8638713 ]\n",
      " [-6.1167765 ]]\n",
      "epoch= 10 loss= 44.84990175619976 b= 6.48145 w= [[-1.6585553]\n",
      " [ 4.013246 ]\n",
      " [-3.0910017]\n",
      " [ 3.0894463]\n",
      " [ 2.00998  ]\n",
      " [12.427254 ]\n",
      " [-0.5598533]\n",
      " [ 2.4233627]\n",
      " [-3.2136009]\n",
      " [-2.379074 ]\n",
      " [ 2.6093671]\n",
      " [-6.58392  ]]\n",
      "epoch= 11 loss= 43.31675861629217 b= 6.4435997 w= [[-1.7313184 ]\n",
      " [ 4.075794  ]\n",
      " [-3.2294416 ]\n",
      " [ 3.2701685 ]\n",
      " [ 1.8767211 ]\n",
      " [12.770394  ]\n",
      " [-0.65631694]\n",
      " [ 2.2138152 ]\n",
      " [-3.1969872 ]\n",
      " [-2.5418413 ]\n",
      " [ 2.019253  ]\n",
      " [-7.1039557 ]]\n",
      "epoch= 12 loss= 41.40978038463616 b= 6.610201 w= [[-1.7961975]\n",
      " [ 4.1433473]\n",
      " [-3.256449 ]\n",
      " [ 3.4288988]\n",
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      "epoch= 13 loss= 40.89188077858051 b= 6.818534 w= [[-1.8514266]\n",
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      " [ 3.5811195]\n",
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      " [-2.9095879]\n",
      " [-2.444934 ]\n",
      " [ 1.7813365]\n",
      " [-7.889324 ]]\n",
      "epoch= 14 loss= 40.11625398776461 b= 6.789804 w= [[-1.9142606]\n",
      " [ 4.2379775]\n",
      " [-3.35097  ]\n",
      " [ 3.7168162]\n",
      " [ 2.090775 ]\n",
      " [14.238231 ]\n",
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      " [-2.8542569]\n",
      " [-2.5584831]\n",
      " [ 1.2608019]\n",
      " [-8.337687 ]]\n",
      "epoch= 15 loss= 39.04406339722375 b= 6.863981 w= [[-1.970938  ]\n",
      " [ 4.2700176 ]\n",
      " [-3.384194  ]\n",
      " [ 3.8342586 ]\n",
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      " [-2.7410834 ]\n",
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      " [ 0.9653763 ]\n",
      " [-8.724084  ]]\n",
      "epoch= 16 loss= 38.23109614357029 b= 6.9960804 w= [[-2.0163465 ]\n",
      " [ 4.2896404 ]\n",
      " [-3.3775635 ]\n",
      " [ 3.945668  ]\n",
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      " [15.11158   ]\n",
      " [-0.40196952]\n",
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      " [-2.5724578 ]\n",
      " [-2.5217917 ]\n",
      " [ 0.8167667 ]\n",
      " [-9.065753  ]]\n",
      "epoch= 17 loss= 37.194263164847726 b= 7.0110793 w= [[-2.0700064 ]\n",
      " [ 4.290476  ]\n",
      " [-3.4186149 ]\n",
      " [ 4.0436764 ]\n",
      " [ 2.193782  ]\n",
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      " [-2.4651797 ]\n",
      " [-2.5645614 ]\n",
      " [ 0.45163953]\n",
      " [-9.430573  ]]\n",
      "epoch= 18 loss= 36.37113582234794 b= 7.1674337 w= [[-2.1161866 ]\n",
      " [ 4.3006554 ]\n",
      " [-3.3957152 ]\n",
      " [ 4.1363497 ]\n",
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      " [15.878924  ]\n",
      " [-0.30740815]\n",
      " [ 0.99138516]\n",
      " [-2.3045068 ]\n",
      " [-2.4999547 ]\n",
      " [ 0.3766081 ]\n",
      " [-9.736092  ]]\n",
      "epoch= 19 loss= 35.86165774878395 b= 7.193818 w= [[ -2.1638854 ]\n",
      " [  4.301989  ]\n",
      " [ -3.4212532 ]\n",
      " [  4.216236  ]\n",
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      " [ -2.2039328 ]\n",
      " [ -2.5294788 ]\n",
      " [  0.06282347]\n",
      " [-10.080323  ]]\n",
      "epoch= 20 loss= 35.030042649490625 b= 7.2579165 w= [[ -2.209892  ]\n",
      " [  4.2962723 ]\n",
      " [ -3.4320743 ]\n",
      " [  4.2872167 ]\n",
      " [  2.3021104 ]\n",
      " [ 16.507572  ]\n",
      " [ -0.27589542]\n",
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      " [ -2.0929067 ]\n",
      " [ -2.5319526 ]\n",
      " [ -0.16268492]\n",
      " [-10.397392  ]]\n",
      "epoch= 21 loss= 34.428011145807844 b= 7.2814417 w= [[ -2.2589257 ]\n",
      " [  4.302205  ]\n",
      " [ -3.4672513 ]\n",
      " [  4.3382497 ]\n",
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      " [ 16.782633  ]\n",
      " [ -0.28519705]\n",
      " [  0.44498107]\n",
      " [ -2.0144079 ]\n",
      " [ -2.5822067 ]\n",
      " [ -0.45980346]\n",
      " [-10.721886  ]]\n",
      "epoch= 22 loss= 33.78020402211158 b= 7.416382 w= [[ -2.3014534 ]\n",
      " [  4.2994523 ]\n",
      " [ -3.4398952 ]\n",
      " [  4.398668  ]\n",
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      " [ 17.175684  ]\n",
      " [ -0.20004568]\n",
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      " [ -1.8773588 ]\n",
      " [ -2.5342033 ]\n",
      " [ -0.51508623]\n",
      " [-10.9869    ]]\n",
      "epoch= 23 loss= 33.43897152839445 b= 7.394007 w= [[ -2.3473456 ]\n",
      " [  4.2794995 ]\n",
      " [ -3.4735312 ]\n",
      " [  4.4413333 ]\n",
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      " [ 17.372744  ]\n",
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      " [  0.09357842]\n",
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      " [ -2.5894063 ]\n",
      " [ -0.8689611 ]\n",
      " [-11.293667  ]]\n",
      "epoch= 24 loss= 32.82645625187321 b= 7.461815 w= [[ -2.3924243 ]\n",
      " [  4.2712436 ]\n",
      " [ -3.4709663 ]\n",
      " [  4.4825425 ]\n",
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      " [ -1.6829551 ]\n",
      " [ -2.585227  ]\n",
      " [ -1.0360025 ]\n",
      " [-11.568444  ]]\n",
      "epoch= 25 loss= 32.424509605725724 b= 7.608638 w= [[ -2.4300547 ]\n",
      " [  4.277207  ]\n",
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      " [ -1.5498152 ]\n",
      " [ -2.5253208 ]\n",
      " [ -1.0483683 ]\n",
      " [-11.803155  ]]\n",
      "epoch= 26 loss= 32.106440044082404 b= 7.6587467 w= [[ -2.4723694 ]\n",
      " [  4.261248  ]\n",
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      " [  4.5492954 ]\n",
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      " [ 18.302961  ]\n",
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      " [ -2.5246766 ]\n",
      " [ -1.230787  ]\n",
      " [-12.056749  ]]\n",
      "epoch= 27 loss= 31.762627894656344 b= 7.7432823 w= [[ -2.5152102 ]\n",
      " [  4.245419  ]\n",
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      " [ -1.3379145 ]\n",
      " [-12.29039   ]]\n",
      "epoch= 28 loss= 31.204026318340723 b= 7.7106524 w= [[ -2.5654972 ]\n",
      " [  4.2286644 ]\n",
      " [ -3.4640644 ]\n",
      " [  4.577607  ]\n",
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      " [ -1.6696124 ]\n",
      " [-12.563886  ]]\n",
      "epoch= 29 loss= 30.97751871944661 b= 7.759139 w= [[ -2.604004  ]\n",
      " [  4.2065887 ]\n",
      " [ -3.461154  ]\n",
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      " [ -2.615298  ]\n",
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      " [-12.791743  ]]\n",
      "epoch= 30 loss= 30.74415470792897 b= 7.8575954 w= [[ -2.64505   ]\n",
      " [  4.1932282 ]\n",
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      " [-12.996438  ]]\n",
      "epoch= 31 loss= 30.104536338554745 b= 7.9477134 w= [[ -2.6854987 ]\n",
      " [  4.169021  ]\n",
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      " [ -1.9417868 ]\n",
      " [-13.191864  ]]\n",
      "epoch= 32 loss= 30.064936532961294 b= 8.003154 w= [[ -2.726927 ]\n",
      " [  4.1489367]\n",
      " [ -3.4066265]\n",
      " [  4.6074677]\n",
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      " [ -2.5872874]\n",
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      "epoch= 33 loss= 29.589767418219363 b= 8.098356 w= [[ -2.7656686 ]\n",
      " [  4.1374617 ]\n",
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      " [-13.582626  ]]\n",
      "epoch= 34 loss= 29.3995822330256 b= 8.255408 w= [[ -2.7984383 ]\n",
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      " [-13.728105  ]]\n",
      "epoch= 35 loss= 29.205350597954297 b= 8.154761 w= [[ -2.8460138 ]\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch= 36 loss= 29.060795800256564 b= 8.169504 w= [[ -2.888744  ]\n",
      " [  4.053636  ]\n",
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      " [-14.158494  ]]\n",
      "epoch= 37 loss= 29.01872070533574 b= 8.3232565 w= [[ -2.9204812 ]\n",
      " [  4.040425  ]\n",
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      " [ -0.52699983]\n",
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      " [-14.283729  ]]\n",
      "epoch= 38 loss= 28.373833196565762 b= 8.21945 w= [[ -2.9617167 ]\n",
      " [  3.996129  ]\n",
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      " [-14.497142  ]]\n",
      "epoch= 39 loss= 28.21448784439391 b= 8.329501 w= [[ -2.9895437 ]\n",
      " [  3.982179  ]\n",
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      " [-14.63791   ]]\n",
      "epoch= 40 loss= 28.428005232717222 b= 8.52445 w= [[ -3.0176075 ]\n",
      " [  3.9692936 ]\n",
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      " [-14.741482  ]]\n",
      "epoch= 41 loss= 27.974275349910926 b= 8.59419 w= [[ -3.050702  ]\n",
      " [  3.9494474 ]\n",
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      " [-14.880657  ]]\n",
      "epoch= 42 loss= 27.72683884260871 b= 8.655186 w= [[ -3.0851371 ]\n",
      " [  3.9440193 ]\n",
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      " [-15.029878  ]]\n",
      "epoch= 43 loss= 27.78274363617776 b= 8.6827965 w= [[ -3.1243632 ]\n",
      " [  3.9140544 ]\n",
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      " [ -0.13875733]\n",
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      " [-15.177942  ]]\n",
      "epoch= 44 loss= 27.794138268455118 b= 8.656396 w= [[ -3.1581063 ]\n",
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      " [ -3.23869   ]\n",
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      " [-15.33821   ]]\n",
      "epoch= 45 loss= 27.375407737695223 b= 8.822297 w= [[ -3.1788092 ]\n",
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      " [ -3.15094   ]\n",
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      " [-15.414172  ]]\n",
      "epoch= 46 loss= 27.63446383870198 b= 8.843267 w= [[ -3.218272  ]\n",
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      " [-15.546897  ]]\n",
      "epoch= 47 loss= 27.533061358485178 b= 8.858502 w= [[ -3.2556934 ]\n",
      " [  3.8276668 ]\n",
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      " [-15.686411  ]]\n",
      "epoch= 48 loss= 27.33385602610852 b= 8.919615 w= [[ -3.2935529 ]\n",
      " [  3.811038  ]\n",
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      " [ -3.27038   ]\n",
      " [-15.80255   ]]\n",
      "epoch= 49 loss= 27.231317104743972 b= 8.903265 w= [[ -3.3325841 ]\n",
      " [  3.7858143 ]\n",
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      " [-15.9405    ]]\n",
      "epoch= 50 loss= 26.960791658523945 b= 9.014458 w= [[ -3.3624232 ]\n",
      " [  3.767605  ]\n",
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      " [  4.311991  ]\n",
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      " [-16.026756  ]]\n",
      "epoch= 51 loss= 26.950890179949752 b= 9.03468 w= [[ -3.3983479]\n",
      " [  3.751169 ]\n",
      " [ -3.0760527]\n",
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      " [ -2.718097 ]\n",
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      " [-16.14521  ]]\n",
      "epoch= 52 loss= 26.8407305172117 b= 9.030882 w= [[ -3.4355083 ]\n",
      " [  3.730317  ]\n",
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      " [-16.266598  ]]\n",
      "epoch= 53 loss= 26.86234879764983 b= 9.189483 w= [[ -3.4610755 ]\n",
      " [  3.7191808 ]\n",
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      " [  4.261662  ]\n",
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      " [-16.317299  ]]\n",
      "epoch= 54 loss= 26.808262434113022 b= 9.2008 w= [[ -3.4959157 ]\n",
      " [  3.694476  ]\n",
      " [ -3.012605  ]\n",
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      " [-16.426569  ]]\n",
      "epoch= 55 loss= 26.693527888158677 b= 9.234412 w= [[ -3.5289857 ]\n",
      " [  3.6771927 ]\n",
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      " [  4.199064  ]\n",
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      " [-16.52588   ]]\n",
      "epoch= 56 loss= 26.487497967605254 b= 9.366438 w= [[ -3.558748  ]\n",
      " [  3.667031  ]\n",
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      " [-16.585745  ]]\n",
      "epoch= 57 loss= 26.500389873199122 b= 9.28593 w= [[ -3.594874 ]\n",
      " [  3.635937 ]\n",
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      " [-16.716454 ]]\n",
      "epoch= 58 loss= 26.409624364701173 b= 9.32424 w= [[ -3.63082  ]\n",
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      " [-16.80584  ]]\n",
      "epoch= 59 loss= 26.405154743923653 b= 9.443546 w= [[ -3.6578622]\n",
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      " [-16.860327 ]]\n",
      "epoch= 60 loss= 26.200126758879303 b= 9.522809 w= [[ -3.6862469 ]\n",
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      " [-16.921282  ]]\n",
      "epoch= 61 loss= 26.238953712677468 b= 9.590375 w= [[ -3.713312  ]\n",
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      "epoch= 62 loss= 26.328634546518536 b= 9.555715 w= [[ -3.7520688 ]\n",
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      " [-17.097097  ]]\n",
      "epoch= 63 loss= 26.190719177778067 b= 9.617563 w= [[ -3.7826123 ]\n",
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      " [-17.157808  ]]\n",
      "epoch= 64 loss= 26.10205000523548 b= 9.669223 w= [[ -3.8063238 ]\n",
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      " [-17.211422  ]]\n",
      "epoch= 65 loss= 25.968851920779997 b= 9.754511 w= [[ -3.8324554 ]\n",
      " [  3.5344875 ]\n",
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      " [-17.254145  ]]\n",
      "epoch= 66 loss= 25.925220090039456 b= 9.689586 w= [[ -3.869997  ]\n",
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      " [-17.368412  ]]\n",
      "epoch= 67 loss= 25.974820355899254 b= 9.830707 w= [[ -3.8936193 ]\n",
      " [  3.515489  ]\n",
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      "epoch= 68 loss= 25.89489689206684 b= 9.817832 w= [[ -3.9264777 ]\n",
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      " [-17.484108  ]]\n",
      "epoch= 69 loss= 25.672916717682636 b= 9.838985 w= [[ -3.9605112 ]\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "epoch= 70 loss= 25.970761745281248 b= 9.984873 w= [[ -3.9834304 ]\n",
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      "epoch= 71 loss= 25.854727567790853 b= 10.0388775 w= [[ -4.0110173 ]\n",
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      " [-17.62132   ]]\n",
      "epoch= 72 loss= 25.882537132152827 b= 10.015211 w= [[ -4.0431924]\n",
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      "epoch= 73 loss= 25.571009116608447 b= 10.138669 w= [[ -4.0660367]\n",
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      "epoch= 74 loss= 25.803603084832467 b= 10.177205 w= [[ -4.0910554]\n",
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      "epoch= 75 loss= 25.591084017899867 b= 10.13429 w= [[ -4.121761 ]\n",
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      "epoch= 76 loss= 25.677992189743286 b= 10.181613 w= [[ -4.1477547 ]\n",
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      " [-17.870062  ]]\n",
      "epoch= 77 loss= 25.355223972652556 b= 10.285769 w= [[ -4.175248 ]\n",
      " [  3.4249923]\n",
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      " [-17.886557 ]]\n",
      "epoch= 78 loss= 25.499697013624626 b= 10.382822 w= [[ -4.198336  ]\n",
      " [  3.4280303 ]\n",
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      " [-17.906685  ]]\n",
      "epoch= 79 loss= 25.607726315290414 b= 10.304406 w= [[ -4.2307563 ]\n",
      " [  3.4066248 ]\n",
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      " [-17.987139  ]]\n",
      "epoch= 80 loss= 25.564020649285688 b= 10.397012 w= [[ -4.2565646]\n",
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      " [-18.008226 ]]\n",
      "epoch= 81 loss= 25.598311895182036 b= 10.477428 w= [[ -4.28243  ]\n",
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      " [-18.027815 ]]\n",
      "epoch= 82 loss= 25.5242572912096 b= 10.458446 w= [[ -4.311325  ]\n",
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      " [-18.09216   ]]\n",
      "epoch= 83 loss= 25.398039362410287 b= 10.54628 w= [[ -4.3368316 ]\n",
      " [  3.3883111 ]\n",
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      " [-18.1026    ]]\n",
      "epoch= 84 loss= 25.516496323136494 b= 10.5833235 w= [[ -4.3656645 ]\n",
      " [  3.3845136 ]\n",
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      "epoch= 85 loss= 25.49550840943304 b= 10.593581 w= [[ -4.3946023]\n",
      " [  3.3785841]\n",
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      " [-18.177591 ]]\n",
      "epoch= 86 loss= 25.424137747829526 b= 10.749213 w= [[ -4.413461  ]\n",
      " [  3.3887706 ]\n",
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      " [-18.166874  ]]\n",
      "epoch= 87 loss= 25.417780604694034 b= 10.802003 w= [[ -4.4377275 ]\n",
      " [  3.3874161 ]\n",
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      " [-18.186235  ]]\n",
      "epoch= 88 loss= 25.300922057799138 b= 10.820013 w= [[ -4.4628787 ]\n",
      " [  3.378028  ]\n",
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      " [-18.222906  ]]\n",
      "epoch= 89 loss= 25.04763828757037 b= 10.728344 w= [[ -4.4979944 ]\n",
      " [  3.3748906 ]\n",
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      " [  3.618183  ]\n",
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      " [-18.302567  ]]\n",
      "epoch= 90 loss= 25.394181227048787 b= 10.800849 w= [[ -4.5209184 ]\n",
      " [  3.384185  ]\n",
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      " [-18.322575  ]]\n",
      "epoch= 91 loss= 25.188706884060355 b= 10.986553 w= [[ -4.536826  ]\n",
      " [  3.3846033 ]\n",
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      " [-18.287766  ]]\n",
      "epoch= 92 loss= 25.195048343759748 b= 10.936582 w= [[ -4.564703  ]\n",
      " [  3.3746183 ]\n",
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      " [ -4.4503    ]\n",
      " [-18.345566  ]]\n",
      "epoch= 93 loss= 25.26681190465533 b= 11.084777 w= [[ -4.5862613 ]\n",
      " [  3.3846054 ]\n",
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      " [-18.329556  ]]\n",
      "epoch= 94 loss= 25.253223688073213 b= 11.024242 w= [[ -4.6174164 ]\n",
      " [  3.3814275 ]\n",
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      " [-18.396069  ]]\n",
      "epoch= 95 loss= 25.16581764792373 b= 11.012081 w= [[ -4.6455665 ]\n",
      " [  3.372745  ]\n",
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      " [-18.43322   ]]\n",
      "epoch= 96 loss= 25.290385187641263 b= 11.1438465 w= [[ -4.6682553 ]\n",
      " [  3.3790002 ]\n",
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      " [-18.42097   ]]\n",
      "epoch= 97 loss= 25.109828092835194 b= 11.197692 w= [[ -4.6911774 ]\n",
      " [  3.3787704 ]\n",
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      "epoch= 98 loss= 25.157909311158715 b= 11.230479 w= [[ -4.715812  ]\n",
      " [  3.3757095 ]\n",
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      "epoch= 99 loss= 25.1395070851223 b= 11.33355 w= [[ -4.7375097 ]\n",
      " [  3.3837404 ]\n",
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      " [-18.456547  ]]\n",
      "epoch= 100 loss= 25.208001744371163 b= 11.360425 w= [[ -4.761183  ]\n",
      " [  3.3802035 ]\n",
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      " [-18.474318  ]]\n",
      "epoch= 101 loss= 24.74321636939932 b= 11.357523 w= [[ -4.782827  ]\n",
      " [  3.37957   ]\n",
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      "epoch= 102 loss= 25.03884389441632 b= 11.467406 w= [[-4.8048692e+00]\n",
      " [ 3.3886454e+00]\n",
      " [-2.0607424e+00]\n",
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      " [-1.8496370e+01]]\n",
      "epoch= 103 loss= 25.14720125703932 b= 11.486608 w= [[ -4.827485  ]\n",
      " [  3.3846824 ]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "epoch= 104 loss= 25.00648046518426 b= 11.432485 w= [[ -4.86324   ]\n",
      " [  3.3790886 ]\n",
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      "epoch= 105 loss= 25.128018407481374 b= 11.528886 w= [[ -4.884452  ]\n",
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      "epoch= 106 loss= 25.042992971747132 b= 11.586003 w= [[ -4.907721  ]\n",
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      "epoch= 107 loss= 24.994953295542427 b= 11.548015 w= [[ -4.9367905 ]\n",
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      "epoch= 108 loss= 25.05687202298934 b= 11.689317 w= [[ -4.954139  ]\n",
      " [  3.4092002 ]\n",
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      "epoch= 109 loss= 25.019751032668978 b= 11.723431 w= [[ -4.979591  ]\n",
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      "epoch= 110 loss= 24.69057218496289 b= 11.667862 w= [[ -5.0106335 ]\n",
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      " [-18.652498  ]]\n",
      "epoch= 111 loss= 24.86190673659928 b= 11.898463 w= [[ -5.022975  ]\n",
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      "epoch= 112 loss= 25.026436185328752 b= 11.761912 w= [[ -5.05453   ]\n",
      " [  3.40716   ]\n",
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      "epoch= 113 loss= 25.078601000022438 b= 11.902182 w= [[ -5.0715284 ]\n",
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      "epoch= 114 loss= 24.998502223010362 b= 11.984876 w= [[ -5.094132  ]\n",
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      "epoch= 115 loss= 24.79646473176995 b= 12.035609 w= [[ -5.1131053 ]\n",
      " [  3.434141  ]\n",
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      "epoch= 116 loss= 24.972718414914983 b= 12.032961 w= [[ -5.136429  ]\n",
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      "epoch= 117 loss= 24.716356322485485 b= 12.061187 w= [[ -5.1569076 ]\n",
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      "epoch= 118 loss= 24.91199858536152 b= 12.062229 w= [[ -5.179697  ]\n",
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      "epoch= 119 loss= 24.914964198198454 b= 12.161764 w= [[ -5.19823  ]\n",
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      "epoch= 120 loss= 24.87645793808198 b= 12.194142 w= [[ -5.2205086]\n",
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      "epoch= 121 loss= 24.82136639648641 b= 12.216106 w= [[ -5.241233  ]\n",
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      "epoch= 122 loss= 24.725952742503765 b= 12.2915325 w= [[ -5.265857 ]\n",
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      "epoch= 123 loss= 24.599720778716907 b= 12.321979 w= [[ -5.2880898 ]\n",
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      "epoch= 124 loss= 24.873058673308428 b= 12.354531 w= [[ -5.3140783 ]\n",
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      "epoch= 125 loss= 24.785209232177323 b= 12.477844 w= [[ -5.332342  ]\n",
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      "epoch= 126 loss= 24.65967963910852 b= 12.35683 w= [[ -5.3609886 ]\n",
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      "epoch= 127 loss= 24.861980084213197 b= 12.469715 w= [[ -5.3738847 ]\n",
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      "epoch= 128 loss= 24.688738152454356 b= 12.603463 w= [[ -5.388849  ]\n",
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      "epoch= 129 loss= 24.69287328594691 b= 12.579035 w= [[ -5.408788  ]\n",
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      "epoch= 130 loss= 24.771869059508756 b= 12.582015 w= [[ -5.4337144 ]\n",
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      "epoch= 131 loss= 24.857929524904854 b= 12.681229 w= [[ -5.451833  ]\n",
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      "epoch= 132 loss= 24.50030618652326 b= 12.814929 w= [[ -5.4674463 ]\n",
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      "epoch= 133 loss= 24.621623616313194 b= 12.798779 w= [[ -5.493394  ]\n",
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      "epoch= 134 loss= 24.741899402811722 b= 12.7833395 w= [[ -5.5148344 ]\n",
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      "epoch= 135 loss= 24.56245522101177 b= 12.811906 w= [[ -5.5374937 ]\n",
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      "epoch= 136 loss= 24.789742062289022 b= 12.916383 w= [[ -5.5559    ]\n",
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      "epoch= 137 loss= 24.524612590230692 b= 12.886377 w= [[ -5.5810328 ]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "epoch= 138 loss= 24.728728111854256 b= 13.002827 w= [[ -5.598167 ]\n",
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      "epoch= 139 loss= 24.433157255670164 b= 12.942493 w= [[ -5.621072  ]\n",
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      "epoch= 140 loss= 24.610516620748708 b= 13.125789 w= [[ -5.633129 ]\n",
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      "epoch= 141 loss= 24.780529915292647 b= 13.10512 w= [[ -5.6568522 ]\n",
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      "epoch= 142 loss= 24.585014516486048 b= 13.136227 w= [[ -5.675085  ]\n",
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      "epoch= 143 loss= 24.4853737525875 b= 13.185014 w= [[ -5.697201  ]\n",
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      "epoch= 144 loss= 24.623752102318118 b= 13.227362 w= [[ -5.715378  ]\n",
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      "epoch= 145 loss= 24.497715195801863 b= 13.284497 w= [[ -5.7315335 ]\n",
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      "epoch= 146 loss= 24.415660754941854 b= 13.276329 w= [[ -5.756907  ]\n",
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      "epoch= 147 loss= 24.600409840304945 b= 13.37181 w= [[ -5.773768  ]\n",
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      "epoch= 148 loss= 24.649669473307434 b= 13.3915825 w= [[ -5.793838  ]\n",
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      "epoch= 149 loss= 24.624179917721587 b= 13.436751 w= [[ -5.8122473 ]\n",
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      "epoch= 150 loss= 24.51806947071682 b= 13.43727 w= [[ -5.8345313 ]\n",
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      "epoch= 151 loss= 24.505796681441495 b= 13.524444 w= [[ -5.8560653 ]\n",
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      "epoch= 152 loss= 24.541319299709507 b= 13.514178 w= [[ -5.880742  ]\n",
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      "epoch= 153 loss= 24.394067329941837 b= 13.588354 w= [[ -5.9013815 ]\n",
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      "epoch= 154 loss= 24.617612319533393 b= 13.64619 w= [[ -5.9161353 ]\n",
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      "epoch= 155 loss= 24.57556827830731 b= 13.711911 w= [[ -5.9329014]\n",
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      "epoch= 156 loss= 24.535831899121217 b= 13.725029 w= [[ -5.9522734 ]\n",
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      "epoch= 157 loss= 24.466098892080517 b= 13.695365 w= [[ -5.9717617]\n",
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      "epoch= 158 loss= 24.612097206106977 b= 13.818968 w= [[ -5.9860725 ]\n",
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      " [-18.688002  ]]\n",
      "epoch= 159 loss= 24.409802896576252 b= 13.792681 w= [[ -6.002188  ]\n",
      " [  3.6304448 ]\n",
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      "epoch= 160 loss= 24.470929638038623 b= 13.953837 w= [[ -6.0157285 ]\n",
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      "epoch= 161 loss= 24.526535875909577 b= 13.940471 w= [[ -6.0376916 ]\n",
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      "epoch= 162 loss= 24.448405379232433 b= 13.926067 w= [[ -6.0616198 ]\n",
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      "epoch= 163 loss= 24.14017318033065 b= 13.9720335 w= [[ -6.081473  ]\n",
      " [  3.6634178 ]\n",
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      "epoch= 164 loss= 24.431539817728616 b= 14.085313 w= [[ -6.0970078 ]\n",
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      "epoch= 165 loss= 24.331825332729338 b= 14.020752 w= [[ -6.1183844 ]\n",
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      "epoch= 166 loss= 24.542466602046563 b= 14.176232 w= [[ -6.1292024 ]\n",
      " [  3.6884117 ]\n",
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      "epoch= 167 loss= 24.51804689800657 b= 14.141004 w= [[ -6.1490903 ]\n",
      " [  3.6889029 ]\n",
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      " [-18.686253  ]]\n",
      "epoch= 168 loss= 24.111207478709854 b= 14.248874 w= [[ -6.1608963 ]\n",
      " [  3.7011507 ]\n",
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      "epoch= 169 loss= 24.373683674844077 b= 14.245466 w= [[ -6.18495   ]\n",
      " [  3.7028022 ]\n",
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      " [-18.6803    ]]\n",
      "epoch= 170 loss= 24.428134532180373 b= 14.291932 w= [[ -6.2020187 ]\n",
      " [  3.7118278 ]\n",
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      " [-18.67647   ]]\n",
      "epoch= 171 loss= 24.337866053695425 b= 14.398538 w= [[ -6.2140565 ]\n",
      " [  3.7234511 ]\n",
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      " [-18.644077  ]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch= 172 loss= 24.355713423234587 b= 14.294501 w= [[ -6.2382464 ]\n",
      " [  3.716055  ]\n",
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      "epoch= 173 loss= 24.37828234985356 b= 14.491771 w= [[ -6.251434  ]\n",
      " [  3.735893  ]\n",
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      " [-18.630278  ]]\n",
      "epoch= 174 loss= 24.447417477146253 b= 14.414588 w= [[ -6.275592  ]\n",
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      " [-18.676903  ]]\n",
      "epoch= 175 loss= 24.46509114969129 b= 14.479824 w= [[ -6.290247  ]\n",
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      "epoch= 176 loss= 24.309849586706864 b= 14.528229 w= [[ -6.306154  ]\n",
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      "epoch= 177 loss= 24.27045249970623 b= 14.664682 w= [[ -6.3152556 ]\n",
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      " [-18.6125    ]]\n",
      "epoch= 178 loss= 23.995592925679734 b= 14.544098 w= [[ -6.3385196 ]\n",
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      " [-18.669428  ]]\n",
      "epoch= 179 loss= 24.178067245701904 b= 14.724374 w= [[ -6.3464775 ]\n",
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      "epoch= 180 loss= 24.459168580737686 b= 14.724777 w= [[ -6.3653345 ]\n",
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      "epoch= 181 loss= 24.227502072538908 b= 14.825237 w= [[ -6.376487  ]\n",
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      "epoch= 182 loss= 24.280859129274923 b= 14.78226 w= [[ -6.398246  ]\n",
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      " [-18.633106  ]]\n",
      "epoch= 183 loss= 24.347504631960618 b= 14.867957 w= [[ -6.41217   ]\n",
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      " [-18.6096    ]]\n",
      "epoch= 184 loss= 24.37876675316525 b= 14.853905 w= [[ -6.4308615 ]\n",
      " [  3.8021355 ]\n",
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      "epoch= 185 loss= 24.071540002575837 b= 14.860146 w= [[ -6.4460754 ]\n",
      " [  3.803173  ]\n",
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      " [-18.630133  ]]\n",
      "epoch= 186 loss= 24.384221965796343 b= 14.897129 w= [[ -6.460666  ]\n",
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      " [-18.627459  ]]\n",
      "epoch= 187 loss= 24.16594545895586 b= 14.968916 w= [[ -6.479359 ]\n",
      " [  3.8229828]\n",
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      " [-18.622381 ]]\n",
      "epoch= 188 loss= 24.239251187167667 b= 14.998582 w= [[ -6.493637  ]\n",
      " [  3.8177555 ]\n",
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      " [-18.611921  ]]\n",
      "epoch= 189 loss= 24.265509227415972 b= 15.128735 w= [[ -6.4999137 ]\n",
      " [  3.828865  ]\n",
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      " [-18.572014  ]]\n",
      "epoch= 190 loss= 24.113701214591856 b= 15.047408 w= [[ -6.5158663 ]\n",
      " [  3.82252   ]\n",
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      " [-18.605473  ]]\n",
      "epoch= 191 loss= 24.29291661658921 b= 15.156564 w= [[ -6.5278597 ]\n",
      " [  3.8380587 ]\n",
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      " [-18.57671   ]]\n",
      "epoch= 192 loss= 24.30096406082252 b= 15.128288 w= [[ -6.547996  ]\n",
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      " [-18.593122  ]]\n",
      "epoch= 193 loss= 24.364616573327282 b= 15.221375 w= [[ -6.5625153 ]\n",
      " [  3.8470185 ]\n",
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      " [-18.57427   ]]\n",
      "epoch= 194 loss= 24.155190080811053 b= 15.303566 w= [[ -6.57607   ]\n",
      " [  3.853922  ]\n",
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      " [-18.552408  ]]\n",
      "epoch= 195 loss= 24.188082842435833 b= 15.324035 w= [[ -6.5965476 ]\n",
      " [  3.8570561 ]\n",
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      " [-18.554258  ]]\n",
      "epoch= 196 loss= 24.076212627060805 b= 15.425596 w= [[ -6.6084385 ]\n",
      " [  3.8703823 ]\n",
      " [ -0.88637424]\n",
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      " [-18.523708  ]]\n",
      "epoch= 197 loss= 24.378577253019216 b= 15.379995 w= [[ -6.628737  ]\n",
      " [  3.8691902 ]\n",
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      " [-18.55329   ]]\n",
      "epoch= 198 loss= 24.246970980509783 b= 15.357503 w= [[ -6.6468534 ]\n",
      " [  3.869171  ]\n",
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      " [-18.571693  ]]\n",
      "epoch= 199 loss= 24.11589781332688 b= 15.510485 w= [[ -6.6550684 ]\n",
      " [  3.8822768 ]\n",
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      " [-18.51709   ]]\n",
      "epoch= 200 loss= 24.19229108013443 b= 15.570856 w= [[ -6.673558  ]\n",
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      "epoch= 201 loss= 24.336783842556198 b= 15.591834 w= [[ -6.6909294 ]\n",
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      "epoch= 202 loss= 24.284991033623918 b= 15.582725 w= [[ -6.708576 ]\n",
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      "epoch= 203 loss= 23.888631649804307 b= 15.738565 w= [[ -6.7215967 ]\n",
      " [  3.907039  ]\n",
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      "epoch= 204 loss= 24.2725630689699 b= 15.731104 w= [[ -6.7372837 ]\n",
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      "epoch= 205 loss= 24.16376544971706 b= 15.718534 w= [[ -6.758469  ]\n",
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      " [-18.516623  ]]\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "epoch= 206 loss= 24.194464847899827 b= 15.757305 w= [[ -6.77285   ]\n",
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      "epoch= 207 loss= 23.986378746897433 b= 15.874775 w= [[ -6.7800703 ]\n",
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      "epoch= 208 loss= 24.274532497070375 b= 15.876423 w= [[ -6.798466  ]\n",
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      "epoch= 209 loss= 24.138216889824804 b= 15.879123 w= [[ -6.812142  ]\n",
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      "epoch= 210 loss= 24.039694938859224 b= 15.8598995 w= [[ -6.8311763 ]\n",
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      "epoch= 211 loss= 24.210302619011316 b= 15.946717 w= [[ -6.842068  ]\n",
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      "epoch= 212 loss= 24.215159954616293 b= 16.003782 w= [[ -6.8611584 ]\n",
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      "epoch= 213 loss= 24.149019856641136 b= 15.985451 w= [[ -6.8772125 ]\n",
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      "epoch= 214 loss= 24.236992452434098 b= 16.054005 w= [[ -6.8913083 ]\n",
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      "epoch= 215 loss= 24.2111354815261 b= 16.08679 w= [[ -6.909965  ]\n",
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      "epoch= 216 loss= 24.119125788601135 b= 16.06424 w= [[ -6.9272604 ]\n",
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      "epoch= 217 loss= 24.183920476840445 b= 16.132055 w= [[ -6.9419117 ]\n",
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      "epoch= 218 loss= 24.072622309477595 b= 16.190748 w= [[ -6.9552226 ]\n",
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      "epoch= 219 loss= 24.136373679286603 b= 16.20737 w= [[ -6.971433  ]\n",
      " [  3.9811587 ]\n",
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      "epoch= 220 loss= 24.076137839783698 b= 16.19528 w= [[-6.9911122e+00]\n",
      " [ 3.9815331e+00]\n",
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      "epoch= 221 loss= 24.275825604058344 b= 16.342731 w= [[ -6.9992633 ]\n",
      " [  3.98956   ]\n",
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      "epoch= 222 loss= 24.19380565638033 b= 16.39209 w= [[ -7.011795  ]\n",
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      "epoch= 223 loss= 23.97440000460299 b= 16.356726 w= [[ -7.0300198 ]\n",
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      "epoch= 224 loss= 24.173091935108726 b= 16.439491 w= [[ -7.0428944 ]\n",
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      "epoch= 225 loss= 24.01486167395477 b= 16.480242 w= [[ -7.052888  ]\n",
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      "epoch= 226 loss= 23.904172013816545 b= 16.523285 w= [[ -7.062872  ]\n",
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      "epoch= 227 loss= 24.104584187410662 b= 16.526646 w= [[ -7.076953  ]\n",
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      "epoch= 228 loss= 24.176316697201315 b= 16.562553 w= [[ -7.0955334 ]\n",
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      "epoch= 229 loss= 24.008443833319465 b= 16.561594 w= [[ -7.1101203 ]\n",
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      "epoch= 230 loss= 24.163388077403752 b= 16.666082 w= [[ -7.119695  ]\n",
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      "epoch= 231 loss= 23.95729408675079 b= 16.69939 w= [[ -7.1274724 ]\n",
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      "epoch= 232 loss= 23.973204583856475 b= 16.740446 w= [[ -7.145955  ]\n",
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      "epoch= 233 loss= 23.961682498612127 b= 16.810291 w= [[ -7.155613  ]\n",
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      "epoch= 234 loss= 24.018870696867634 b= 16.837576 w= [[ -7.172746  ]\n",
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      "epoch= 235 loss= 24.11464855193081 b= 16.861055 w= [[ -7.191455  ]\n",
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      "epoch= 236 loss= 24.047450379984785 b= 16.838488 w= [[ -7.20937   ]\n",
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      "epoch= 237 loss= 24.07841690538871 b= 16.993086 w= [[ -7.2180257 ]\n",
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      "epoch= 238 loss= 23.980006320569267 b= 16.910212 w= [[ -7.2369723 ]\n",
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      "epoch= 239 loss= 23.975101351350357 b= 16.945087 w= [[ -7.2476544 ]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "epoch= 240 loss= 24.047622183824355 b= 16.973953 w= [[-7.2628250e+00]\n",
      " [ 4.0517054e+00]\n",
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      " [ 2.6169304e+01]\n",
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      " [ 3.2244015e+00]\n",
      " [-4.7700472e+00]\n",
      " [-5.3435831e+00]\n",
      " [-1.8431623e+01]]\n",
      "epoch= 241 loss= 23.992193048661164 b= 17.166227 w= [[ -7.2680335 ]\n",
      " [  4.070296  ]\n",
      " [ -0.6158413 ]\n",
      " [  3.1418712 ]\n",
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      " [ 26.349377  ]\n",
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      " [-10.717061  ]\n",
      " [  3.3052835 ]\n",
      " [ -4.647866  ]\n",
      " [ -5.0449157 ]\n",
      " [-18.371191  ]]\n",
      "epoch= 242 loss= 24.06731303947894 b= 17.152645 w= [[ -7.283187  ]\n",
      " [  4.0694566 ]\n",
      " [ -0.63127875]\n",
      " [  3.1384943 ]\n",
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      " [ 26.282665  ]\n",
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      " [  3.298847  ]\n",
      " [ -4.6894464 ]\n",
      " [ -5.145991  ]\n",
      " [-18.387993  ]]\n",
      "epoch= 243 loss= 24.025761252696505 b= 17.229937 w= [[ -7.2936854 ]\n",
      " [  4.080468  ]\n",
      " [ -0.6124101 ]\n",
      " [  3.142062  ]\n",
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      " [  3.318208  ]\n",
      " [ -4.6675935 ]\n",
      " [ -5.0730605 ]\n",
      " [-18.375065  ]]\n",
      "epoch= 244 loss= 24.02504244443309 b= 17.121693 w= [[-7.3166275e+00]\n",
      " [ 4.0656772e+00]\n",
      " [-6.6410977e-01]\n",
      " [ 3.1331558e+00]\n",
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      " [-4.7967491e+00]\n",
      " [-5.3693027e+00]\n",
      " [-1.8425127e+01]]\n",
      "epoch= 245 loss= 23.809876430463472 b= 17.280323 w= [[ -7.327643  ]\n",
      " [  4.0785394 ]\n",
      " [ -0.60759455]\n",
      " [  3.1489294 ]\n",
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      " [ 26.286263  ]\n",
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      " [-10.796984  ]\n",
      " [  3.3049254 ]\n",
      " [ -4.7135596 ]\n",
      " [ -5.133201  ]\n",
      " [-18.372177  ]]\n",
      "epoch= 246 loss= 24.012858055261454 b= 17.327318 w= [[ -7.3421297 ]\n",
      " [  4.081245  ]\n",
      " [ -0.60001236]\n",
      " [  3.1516495 ]\n",
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      " [ 26.29284   ]\n",
      " [  0.11505382]\n",
      " [-10.808162  ]\n",
      " [  3.309257  ]\n",
      " [ -4.719155  ]\n",
      " [ -5.1218233 ]\n",
      " [-18.367535  ]]\n",
      "epoch= 247 loss= 24.045204090783436 b= 17.3601 w= [[ -7.354187  ]\n",
      " [  4.0833917 ]\n",
      " [ -0.5912431 ]\n",
      " [  3.1437771 ]\n",
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      " [ 26.282154  ]\n",
      " [  0.11812288]\n",
      " [-10.83004   ]\n",
      " [  3.3219519 ]\n",
      " [ -4.7207274 ]\n",
      " [ -5.130536  ]\n",
      " [-18.360685  ]]\n",
      "epoch= 248 loss= 23.743937022212258 b= 17.404823 w= [[ -7.36831   ]\n",
      " [  4.091486  ]\n",
      " [ -0.5778271 ]\n",
      " [  3.161769  ]\n",
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      " [ 26.287596  ]\n",
      " [  0.12534884]\n",
      " [-10.846345  ]\n",
      " [  3.344528  ]\n",
      " [ -4.716384  ]\n",
      " [ -5.1183753 ]\n",
      " [-18.355635  ]]\n",
      "epoch= 249 loss= 24.03455948898059 b= 17.345043 w= [[ -7.3858247 ]\n",
      " [  4.088194  ]\n",
      " [ -0.6191075 ]\n",
      " [  3.1612513 ]\n",
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      " [ 26.167044  ]\n",
      " [  0.04270177]\n",
      " [-10.890205  ]\n",
      " [  3.3121016 ]\n",
      " [ -4.8004503 ]\n",
      " [ -5.322692  ]\n",
      " [-18.391401  ]]\n",
      "epoch= 250 loss= 23.957164850457765 b= 17.364418 w= [[ -7.398757  ]\n",
      " [  4.0836964 ]\n",
      " [ -0.61703056]\n",
      " [  3.1581125 ]\n",
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      " [ 26.136139  ]\n",
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      " [-10.913469  ]\n",
      " [  3.316356  ]\n",
      " [ -4.815925  ]\n",
      " [ -5.3609333 ]\n",
      " [-18.390308  ]]\n",
      "epoch= 251 loss= 23.987984535502964 b= 17.398611 w= [[-7.4121413e+00]\n",
      " [ 4.0973115e+00]\n",
      " [-6.2189680e-01]\n",
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      " [-4.8238797e+00]\n",
      " [-5.3732171e+00]\n",
      " [-1.8394447e+01]]\n",
      "epoch= 252 loss= 23.964049160725782 b= 17.49366 w= [[ -7.428142  ]\n",
      " [  4.1147413 ]\n",
      " [ -0.6032523 ]\n",
      " [  3.1524072 ]\n",
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      " [ 26.19402   ]\n",
      " [  0.04242431]\n",
      " [-10.908757  ]\n",
      " [  3.3324714 ]\n",
      " [ -4.8042965 ]\n",
      " [ -5.2691293 ]\n",
      " [-18.375408  ]]\n",
      "epoch= 253 loss= 23.775716027039817 b= 17.434673 w= [[-7.4436264e+00]\n",
      " [ 4.0957961e+00]\n",
      " [-6.2468684e-01]\n",
      " [ 3.1459305e+00]\n",
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      " [-4.8663135e+00]\n",
      " [-5.4465342e+00]\n",
      " [-1.8395439e+01]]\n",
      "epoch= 254 loss= 23.87634048384623 b= 17.628117 w= [[ -7.447611  ]\n",
      " [  4.113652  ]\n",
      " [ -0.54603964]\n",
      " [  3.156978  ]\n",
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      " [ 26.250698  ]\n",
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      " [-10.934599  ]\n",
      " [  3.4128263 ]\n",
      " [ -4.7352543 ]\n",
      " [ -5.137044  ]\n",
      " [-18.327036  ]]\n",
      "epoch= 255 loss= 23.884798103030523 b= 17.66814 w= [[ -7.4633617 ]\n",
      " [  4.118037  ]\n",
      " [ -0.5499579 ]\n",
      " [  3.156644  ]\n",
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      " [ 26.248209  ]\n",
      " [  0.10082874]\n",
      " [-10.944703  ]\n",
      " [  3.4088912 ]\n",
      " [ -4.752467  ]\n",
      " [ -5.143427  ]\n",
      " [-18.32915   ]]\n",
      "epoch= 256 loss= 23.551281140487635 b= 17.521862 w= [[-7.4864931e+00]\n",
      " [ 4.0919204e+00]\n",
      " [-6.2315667e-01]\n",
      " [ 3.1512077e+00]\n",
      " [-3.5037727e+00]\n",
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      " [ 3.3266077e+00]\n",
      " [-4.9076333e+00]\n",
      " [-5.5060282e+00]\n",
      " [-1.8382429e+01]]\n",
      "epoch= 257 loss= 24.02103361129722 b= 17.687626 w= [[ -7.4893003 ]\n",
      " [  4.105228  ]\n",
      " [ -0.5687384 ]\n",
      " [  3.1580575 ]\n",
      " [ -3.3668478 ]\n",
      " [ 26.169825  ]\n",
      " [  0.07336526]\n",
      " [-10.997213  ]\n",
      " [  3.4009602 ]\n",
      " [ -4.807587  ]\n",
      " [ -5.25284   ]\n",
      " [-18.32945   ]]\n",
      "epoch= 258 loss= 23.9480093896689 b= 17.642578 w= [[-7.5045195e+00]\n",
      " [ 4.1055422e+00]\n",
      " [-6.0188013e-01]\n",
      " [ 3.1513851e+00]\n",
      " [-3.4714842e+00]\n",
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      " [ 3.3716226e+00]\n",
      " [-4.8814158e+00]\n",
      " [-5.4218760e+00]\n",
      " [-1.8357992e+01]]\n",
      "epoch= 259 loss= 23.938529949455813 b= 17.808514 w= [[ -7.512135  ]\n",
      " [  4.123724  ]\n",
      " [ -0.5429233 ]\n",
      " [  3.1523914 ]\n",
      " [ -3.3408062 ]\n",
      " [ 26.217302  ]\n",
      " [  0.11038976]\n",
      " [-11.010665  ]\n",
      " [  3.4383662 ]\n",
      " [ -4.7810264 ]\n",
      " [ -5.1647882 ]\n",
      " [-18.308664  ]]\n",
      "epoch= 260 loss= 24.072046331644547 b= 17.760511 w= [[ -7.5306187 ]\n",
      " [  4.118778  ]\n",
      " [ -0.57665586]\n",
      " [  3.141239  ]\n",
      " [ -3.4539614 ]\n",
      " [ 26.109735  ]\n",
      " [  0.04542309]\n",
      " [-11.053045  ]\n",
      " [  3.3991923 ]\n",
      " [ -4.8602214 ]\n",
      " [ -5.342415  ]\n",
      " [-18.339067  ]]\n",
      "epoch= 261 loss= 23.961138318516653 b= 17.817154 w= [[ -7.543919  ]\n",
      " [  4.1265593 ]\n",
      " [ -0.572359  ]\n",
      " [  3.1443222 ]\n",
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      " [ 26.130352  ]\n",
      " [  0.04983012]\n",
      " [-11.05855   ]\n",
      " [  3.4037838 ]\n",
      " [ -4.862676  ]\n",
      " [ -5.312177  ]\n",
      " [-18.33529   ]]\n",
      "epoch= 262 loss= 23.639759329790106 b= 17.893671 w= [[ -7.552349 ]\n",
      " [  4.12431  ]\n",
      " [ -0.5352505]\n",
      " [  3.1497805]\n",
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      " [-11.06995  ]\n",
      " [  3.4458857]\n",
      " [ -4.8220162]\n",
      " [ -5.229569 ]\n",
      " [-18.315845 ]]\n",
      "epoch= 263 loss= 23.854712993376403 b= 17.939823 w= [[ -7.5619435 ]\n",
      " [  4.125011  ]\n",
      " [ -0.51983494]\n",
      " [  3.1461482 ]\n",
      " [ -3.4005103 ]\n",
      " [ 26.177822  ]\n",
      " [  0.10431493]\n",
      " [-11.08134   ]\n",
      " [  3.4660044 ]\n",
      " [ -4.814036  ]\n",
      " [ -5.2121763 ]\n",
      " [-18.309847  ]]\n",
      "epoch= 264 loss= 23.88759634733846 b= 17.912218 w= [[ -7.580268  ]\n",
      " [  4.1271753 ]\n",
      " [ -0.5507061 ]\n",
      " [  3.1417193 ]\n",
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      " [ 26.095695  ]\n",
      " [  0.04579797]\n",
      " [-11.112144  ]\n",
      " [  3.429879  ]\n",
      " [ -4.8827734 ]\n",
      " [ -5.3520517 ]\n",
      " [-18.331963  ]]\n",
      "epoch= 265 loss= 23.984775555162056 b= 17.906466 w= [[-7.5917683e+00]\n",
      " [ 4.1284671e+00]\n",
      " [-5.6994742e-01]\n",
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      " [-4.9208918e+00]\n",
      " [-5.4404798e+00]\n",
      " [-1.8344557e+01]]\n",
      "epoch= 266 loss= 23.94027638011115 b= 17.998737 w= [[ -7.6005034 ]\n",
      " [  4.137393  ]\n",
      " [ -0.5412874 ]\n",
      " [  3.1228247 ]\n",
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      " [ 26.098589  ]\n",
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      " [-11.13505   ]\n",
      " [  3.4633412 ]\n",
      " [ -4.8764195 ]\n",
      " [ -5.33441   ]\n",
      " [-18.323217  ]]\n",
      "epoch= 267 loss= 23.90667037069733 b= 18.010902 w= [[ -7.615225  ]\n",
      " [  4.138237  ]\n",
      " [ -0.54249525]\n",
      " [  3.1235344 ]\n",
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      " [ 26.067215  ]\n",
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      " [-11.159487  ]\n",
      " [  3.4693832 ]\n",
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      " [ -5.381447  ]\n",
      " [-18.335058  ]]\n",
      "epoch= 268 loss= 23.780829368819052 b= 18.055931 w= [[ -7.6230073 ]\n",
      " [  4.147374  ]\n",
      " [ -0.53769654]\n",
      " [  3.1155126 ]\n",
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      " [ 26.07297   ]\n",
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      " [-11.171464  ]\n",
      " [  3.479517  ]\n",
      " [ -4.888513  ]\n",
      " [ -5.3660583 ]\n",
      " [-18.326958  ]]\n",
      "epoch= 269 loss= 23.87865942626484 b= 18.12091 w= [[ -7.6341763 ]\n",
      " [  4.151395  ]\n",
      " [ -0.5235492 ]\n",
      " [  3.114827  ]\n",
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      " [ 26.09935   ]\n",
      " [  0.07047448]\n",
      " [-11.173348  ]\n",
      " [  3.4994    ]\n",
      " [ -4.8708577 ]\n",
      " [ -5.316966  ]\n",
      " [-18.313442  ]]\n",
      "epoch= 270 loss= 23.927810013527022 b= 18.153269 w= [[ -7.647883  ]\n",
      " [  4.15416   ]\n",
      " [ -0.52151686]\n",
      " [  3.1170835 ]\n",
      " [ -3.5313184 ]\n",
      " [ 26.090815  ]\n",
      " [  0.06317595]\n",
      " [-11.190422  ]\n",
      " [  3.5161855 ]\n",
      " [ -4.874292  ]\n",
      " [ -5.333133  ]\n",
      " [-18.316223  ]]\n",
      "epoch= 271 loss= 23.880498841667784 b= 18.138172 w= [[-7.6625857e+00]\n",
      " [ 4.1555109e+00]\n",
      " [-5.3969806e-01]\n",
      " [ 3.1206646e+00]\n",
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      " [-4.9271908e+00]\n",
      " [-5.4436688e+00]\n",
      " [-1.8339638e+01]]\n",
      "epoch= 272 loss= 23.70743145027094 b= 18.158703 w= [[-7.6780057e+00]\n",
      " [ 4.1601114e+00]\n",
      " [-5.5293602e-01]\n",
      " [ 3.1196835e+00]\n",
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      " [-1.8345634e+01]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch= 273 loss= 23.876907099430717 b= 18.203772 w= [[-7.6927509e+00]\n",
      " [ 4.1621604e+00]\n",
      " [-5.4458916e-01]\n",
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      " [-4.9430523e+00]\n",
      " [-5.4673891e+00]\n",
      " [-1.8338280e+01]]\n",
      "epoch= 274 loss= 23.94340964961352 b= 18.306961 w= [[ -7.7013593]\n",
      " [  4.1713448]\n",
      " [ -0.5139424]\n",
      " [  3.117454 ]\n",
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      " [-11.237184 ]\n",
      " [  3.537603 ]\n",
      " [ -4.9032006]\n",
      " [ -5.3404737]\n",
      " [-18.312635 ]]\n",
      "epoch= 275 loss= 23.95253639584481 b= 18.354542 w= [[ -7.7096186 ]\n",
      " [  4.171034  ]\n",
      " [ -0.5010223 ]\n",
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      " [-11.256875  ]\n",
      " [  3.5649676 ]\n",
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      " [ -5.317346  ]\n",
      " [-18.301317  ]]\n",
      "epoch= 276 loss= 23.929277610935998 b= 18.34099 w= [[ -7.7244973 ]\n",
      " [  4.1741157 ]\n",
      " [ -0.5225557 ]\n",
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      " [-11.28342   ]\n",
      " [  3.5441139 ]\n",
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      " [ -5.424017  ]\n",
      " [-18.316715  ]]\n",
      "epoch= 277 loss= 23.888401684003313 b= 18.446447 w= [[ -7.7321057 ]\n",
      " [  4.184835  ]\n",
      " [ -0.48987803]\n",
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      " [ -3.574409  ]\n",
      " [ 26.099173  ]\n",
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      " [-11.27404   ]\n",
      " [  3.5801854 ]\n",
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      " [-18.288498  ]]\n",
      "epoch= 278 loss= 23.900274447322566 b= 18.394573 w= [[-7.7476468e+00]\n",
      " [ 4.1825538e+00]\n",
      " [-5.2865672e-01]\n",
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      " [-4.9681544e+00]\n",
      " [-5.4707842e+00]\n",
      " [-1.8316475e+01]]\n",
      "epoch= 279 loss= 23.830353522355214 b= 18.475613 w= [[ -7.756882  ]\n",
      " [  4.193242  ]\n",
      " [ -0.5154754 ]\n",
      " [  3.098991  ]\n",
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      " [ 26.03764   ]\n",
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      " [-11.3026285 ]\n",
      " [  3.5606303 ]\n",
      " [ -4.9484663 ]\n",
      " [ -5.384053  ]\n",
      " [-18.300621  ]]\n",
      "epoch= 280 loss= 23.758742109197556 b= 18.450775 w= [[-7.7713537e+00]\n",
      " [ 4.1847463e+00]\n",
      " [-5.4093182e-01]\n",
      " [ 3.0948884e+00]\n",
      " [-3.7492194e+00]\n",
      " [ 2.5962471e+01]\n",
      " [-7.4333157e-03]\n",
      " [-1.1336409e+01]\n",
      " [ 3.5286260e+00]\n",
      " [-5.0155954e+00]\n",
      " [-5.5129814e+00]\n",
      " [-1.8327955e+01]]\n",
      "epoch= 281 loss= 23.86185161959563 b= 18.614574 w= [[ -7.77338   ]\n",
      " [  4.198735  ]\n",
      " [ -0.47672853]\n",
      " [  3.10008   ]\n",
      " [ -3.6070118 ]\n",
      " [ 26.103584  ]\n",
      " [  0.09604887]\n",
      " [-11.309159  ]\n",
      " [  3.5972266 ]\n",
      " [ -4.9112544 ]\n",
      " [ -5.261586  ]\n",
      " [-18.270842  ]]\n",
      "epoch= 282 loss= 23.913483720325264 b= 18.597382 w= [[ -7.7913694 ]\n",
      " [  4.201145  ]\n",
      " [ -0.5052153 ]\n",
      " [  3.0914817 ]\n",
      " [ -3.6855893 ]\n",
      " [ 26.03865   ]\n",
      " [  0.04826956]\n",
      " [-11.333063  ]\n",
      " [  3.5649462 ]\n",
      " [ -4.9700255 ]\n",
      " [ -5.37923   ]\n",
      " [-18.292856  ]]\n",
      "epoch= 283 loss= 23.544260849846985 b= 18.66838 w= [[ -7.7983203]\n",
      " [  4.197525 ]\n",
      " [ -0.4826171]\n",
      " [  3.0948026]\n",
      " [ -3.6490593]\n",
      " [ 26.07847  ]\n",
      " [  0.0743923]\n",
      " [-11.34103  ]\n",
      " [  3.5755506]\n",
      " [ -4.95265  ]\n",
      " [ -5.3182855]\n",
      " [-18.281998 ]]\n",
      "epoch= 284 loss= 23.62197231974025 b= 18.543692 w= [[ -7.816467 ]\n",
      " [  4.1837783]\n",
      " [ -0.543477 ]\n",
      " [  3.0846753]\n",
      " [ -3.8421187]\n",
      " [ 25.879597 ]\n",
      " [ -0.0364797]\n",
      " [-11.4102545]\n",
      " [  3.5255904]\n",
      " [ -5.0768747]\n",
      " [ -5.6270742]\n",
      " [-18.330675 ]]\n",
      "epoch= 285 loss= 23.932578070425613 b= 18.670038 w= [[ -7.82073   ]\n",
      " [  4.1887245 ]\n",
      " [ -0.4917063 ]\n",
      " [  3.0906875 ]\n",
      " [ -3.7444303 ]\n",
      " [ 25.983786  ]\n",
      " [  0.03053865]\n",
      " [-11.396315  ]\n",
      " [  3.5862768 ]\n",
      " [ -4.995454  ]\n",
      " [ -5.4500756 ]\n",
      " [-18.292751  ]]\n",
      "epoch= 286 loss= 23.929978989762322 b= 18.731596 w= [[ -7.829029  ]\n",
      " [  4.191929  ]\n",
      " [ -0.4725244 ]\n",
      " [  3.0982904 ]\n",
      " [ -3.7182379 ]\n",
      " [ 26.00727   ]\n",
      " [  0.05398724]\n",
      " [-11.404529  ]\n",
      " [  3.6110368 ]\n",
      " [ -4.974909  ]\n",
      " [ -5.4011884 ]\n",
      " [-18.279123  ]]\n",
      "epoch= 287 loss= 23.826873777066492 b= 18.794197 w= [[ -7.8385205]\n",
      " [  4.1906414]\n",
      " [ -0.4513356]\n",
      " [  3.099255 ]\n",
      " [ -3.6930175]\n",
      " [ 26.030344 ]\n",
      " [  0.0797304]\n",
      " [-11.410167 ]\n",
      " [  3.634118 ]\n",
      " [ -4.951759 ]\n",
      " [ -5.3455367]\n",
      " [-18.26235  ]]\n",
      "epoch= 288 loss= 23.72764867346507 b= 18.874449 w= [[ -7.847508  ]\n",
      " [  4.1948357 ]\n",
      " [ -0.42408657]\n",
      " [  3.097477  ]\n",
      " [ -3.6488483 ]\n",
      " [ 26.077341  ]\n",
      " [  0.11463787]\n",
      " [-11.410854  ]\n",
      " [  3.6658466 ]\n",
      " [ -4.912141  ]\n",
      " [ -5.259884  ]\n",
      " [-18.240637  ]]\n",
      "epoch= 289 loss= 23.57448587747582 b= 18.705965 w= [[ -7.8681903 ]\n",
      " [  4.1871    ]\n",
      " [ -0.5227203 ]\n",
      " [  3.0786707 ]\n",
      " [ -3.910238  ]\n",
      " [ 25.826777  ]\n",
      " [ -0.05064569]\n",
      " [-11.482661  ]\n",
      " [  3.5645604 ]\n",
      " [ -5.100471  ]\n",
      " [ -5.6749697 ]\n",
      " [-18.318327  ]]\n",
      "epoch= 290 loss= 23.8022147326666 b= 18.898396 w= [[ -7.868254  ]\n",
      " [  4.2006097 ]\n",
      " [ -0.44231683]\n",
      " [  3.0772457 ]\n",
      " [ -3.7318468 ]\n",
      " [ 26.007248  ]\n",
      " [  0.07258093]\n",
      " [-11.445407  ]\n",
      " [  3.6586885 ]\n",
      " [ -4.9651694 ]\n",
      " [ -5.3618793 ]\n",
      " [-18.251259  ]]\n",
      "epoch= 291 loss= 23.85416996507995 b= 18.86786 w= [[-7.8810124e+00]\n",
      " [ 4.1955466e+00]\n",
      " [-4.6182999e-01]\n",
      " [ 3.0745182e+00]\n",
      " [-3.8141289e+00]\n",
      " [ 2.5917484e+01]\n",
      " [ 2.3964442e-02]\n",
      " [-1.1479705e+01]\n",
      " [ 3.6377463e+00]\n",
      " [-5.0169654e+00]\n",
      " [-5.4991779e+00]\n",
      " [-1.8271778e+01]]\n",
      "epoch= 292 loss= 23.82673018001571 b= 18.882189 w= [[-7.8922410e+00]\n",
      " [ 4.2019806e+00]\n",
      " [-4.6852985e-01]\n",
      " [ 3.0693147e+00]\n",
      " [-3.8495975e+00]\n",
      " [ 2.5887833e+01]\n",
      " [ 6.5570385e-03]\n",
      " [-1.1498241e+01]\n",
      " [ 3.6402943e+00]\n",
      " [-5.0356240e+00]\n",
      " [-5.5482368e+00]\n",
      " [-1.8276617e+01]]\n",
      "epoch= 293 loss= 23.757342586284707 b= 19.023682 w= [[ -7.8961062 ]\n",
      " [  4.212039  ]\n",
      " [ -0.42345405]\n",
      " [  3.0743241 ]\n",
      " [ -3.7401638 ]\n",
      " [ 26.00785   ]\n",
      " [  0.08676399]\n",
      " [-11.4749975 ]\n",
      " [  3.6939266 ]\n",
      " [ -4.9539046 ]\n",
      " [ -5.343927  ]\n",
      " [-18.23195   ]]\n",
      "epoch= 294 loss= 23.830655238257332 b= 18.94109 w= [[-7.9156756e+00]\n",
      " [ 4.2105193e+00]\n",
      " [-4.8207247e-01]\n",
      " [ 3.0586154e+00]\n",
      " [-3.8979373e+00]\n",
      " [ 2.5865927e+01]\n",
      " [-7.4925628e-03]\n",
      " [-1.1520324e+01]\n",
      " [ 3.6345499e+00]\n",
      " [-5.0716667e+00]\n",
      " [-5.5809245e+00]\n",
      " [-1.8277712e+01]]\n",
      "epoch= 295 loss= 23.81670691397685 b= 18.98078 w= [[-7.9271812e+00]\n",
      " [ 4.2153878e+00]\n",
      " [-4.7560838e-01]\n",
      " [ 3.0592282e+00]\n",
      " [-3.9033318e+00]\n",
      " [ 2.5863794e+01]\n",
      " [ 1.6398914e-04]\n",
      " [-1.1532771e+01]\n",
      " [ 3.6358068e+00]\n",
      " [-5.0771351e+00]\n",
      " [-5.5816765e+00]\n",
      " [-1.8272015e+01]]\n",
      "epoch= 296 loss= 23.653895552800915 b= 19.082195 w= [[ -7.9388094 ]\n",
      " [  4.2238    ]\n",
      " [ -0.44156826]\n",
      " [  3.068291  ]\n",
      " [ -3.8378236 ]\n",
      " [ 25.940268  ]\n",
      " [  0.04597359]\n",
      " [-11.519662  ]\n",
      " [  3.6742408 ]\n",
      " [ -5.0281315 ]\n",
      " [ -5.452155  ]\n",
      " [-18.249098  ]]\n",
      "epoch= 297 loss= 23.691591733750577 b= 19.067429 w= [[-7.9518304e+00]\n",
      " [ 4.2292681e+00]\n",
      " [-4.5601127e-01]\n",
      " [ 3.0748162e+00]\n",
      " [-3.9066641e+00]\n",
      " [ 2.5874340e+01]\n",
      " [ 5.3743031e-03]\n",
      " [-1.1549066e+01]\n",
      " [ 3.6592753e+00]\n",
      " [-5.0708418e+00]\n",
      " [-5.5623856e+00]\n",
      " [-1.8268614e+01]]\n",
      "epoch= 298 loss= 23.76522780696136 b= 19.099827 w= [[-7.9591312e+00]\n",
      " [ 4.2356896e+00]\n",
      " [-4.5117441e-01]\n",
      " [ 3.0678818e+00]\n",
      " [-3.9231703e+00]\n",
      " [ 2.5863615e+01]\n",
      " [-1.5189848e-03]\n",
      " [-1.1561023e+01]\n",
      " [ 3.6722610e+00]\n",
      " [-5.0697999e+00]\n",
      " [-5.5681152e+00]\n",
      " [-1.8269905e+01]]\n",
      "epoch= 299 loss= 23.81798988601525 b= 19.189844 w= [[ -7.967934  ]\n",
      " [  4.245988  ]\n",
      " [ -0.425761  ]\n",
      " [  3.0712552 ]\n",
      " [ -3.8672547 ]\n",
      " [ 25.924034  ]\n",
      " [  0.03538163]\n",
      " [-11.554883  ]\n",
      " [  3.7035189 ]\n",
      " [ -5.028437  ]\n",
      " [ -5.4672847 ]\n",
      " [-18.253967  ]]\n",
      "epoch= 300 loss= 23.73835486656549 b= 19.198551 w= [[-7.9802694e+00]\n",
      " [ 4.2441092e+00]\n",
      " [-4.4148275e-01]\n",
      " [ 3.0737801e+00]\n",
      " [-3.9158790e+00]\n",
      " [ 2.5891853e+01]\n",
      " [ 7.0084929e-03]\n",
      " [-1.1570299e+01]\n",
      " [ 3.6879041e+00]\n",
      " [-5.0611897e+00]\n",
      " [-5.5206037e+00]\n",
      " [-1.8271734e+01]]\n"
     ]
    }
   ],
   "source": [
    "#模型训练\n",
    "loss_list = []\n",
    "for epoch in range(train_epochs):\n",
    "    loss_sum = 0.0\n",
    "    for xs, ys in zip(x_data, y_data):\n",
    "        xs = xs.reshape(1, 12)\n",
    "        ys = ys.reshape(1, 1)\n",
    "        _, loss = sess.run([optimizer, loss_function], feed_dict={x: xs, y: ys})\n",
    "        loss_sum = loss_sum + loss\n",
    "    x_data, y_data = shuffle(x_data, y_data)\n",
    "    b0temp = b.eval(session = sess)\n",
    "    w0temp = w.eval(session = sess)\n",
    "    loss_average = loss_sum / len(y_data)\n",
    "    loss_list.append(loss_average)\n",
    "    print(\"epoch=\", epoch+1, \"loss=\", loss_average, \"b=\", b0temp, \"w=\", w0temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"639.5999695014968\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c765c5a648>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(loss_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测值： [[22.071247]]\n",
      "标签值： 22.4\n"
     ]
    }
   ],
   "source": [
    "n = 384\n",
    "x_test = x_data[n]\n",
    "x_test = x_test.reshape(1, 12)\n",
    "predict = sess.run(pred, feed_dict={x: x_test})\n",
    "print(\"预测值：\", predict)\n",
    "target = y_data[n]\n",
    "print(\"标签值：\", target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71\n",
      "预测值： [[21.513958]]\n",
      "标签值： 21.4\n"
     ]
    }
   ],
   "source": [
    "n = np.random.randint(506)\n",
    "print(n)\n",
    "x_test = x_data[n]\n",
    "x_test = x_test.reshape(1, 12)\n",
    "predict = sess.run(pred, feed_dict={x: x_test})\n",
    "print(\"预测值：\", predict)\n",
    "target = y_data[n]\n",
    "print(\"标签值：\", target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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