linreg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Multiple Ways"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making the data\n",
    "\n",
    "We'll first construct a synthetic data set..using a function from the `scikit-learn` library. Synthetic data is nice in the sense that we can constrain how the noise behaves, and thus isolate effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from sklearn.datasets import make_regression\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#code adapted from http://tillbergmann.com/blog/python-gradient-descent.html\n",
    "X, y, coef = make_regression(n_samples = 100, \n",
    "                       n_features=1, \n",
    "                       noise=20,\n",
    "                       random_state=2017,\n",
    "                       coef=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the X is in the canonical array-of-arrays format.\n",
    "**Try and print its shape**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are fitting a model with an intercept. Lets see what it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(80.88336208)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,y, 'o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purposes of drawing the regression line, lets create a uniform grid of points, and then reshape it into the canonical format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgrid = np.linspace(-2.5,2.5,1000)\n",
    "Xgrid = xgrid.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit using sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([82.90456142]), -3.709228868670302)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LinearRegression()\n",
    "lr.fit(X,y)\n",
    "ypgrid = lr.predict(Xgrid)\n",
    "lr.coef_, lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a145a0518>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Xgrid, ypgrid)\n",
    "plt.plot(X, y, '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9342344891672815"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y, lr.predict(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The impact of sample size\n",
    "\n",
    "We'll sample 20 points from the data set. We do this by sampling 20 indices, index into X and y, and then fit on the sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([98, 29,  6, 62,  5, 22, 91, 89, 94, 70, 75, 54, 94, 27, 88, 22, 45,\n",
       "       30, 19,  7])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_indices = np.random.choice(range(100), size=20)\n",
    "sample_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xsample = X[sample_indices]\n",
    "ysample = y[sample_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9421910899822037, 0.9421910899822037)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_s = LinearRegression().fit(Xsample, ysample)\n",
    "r2_score(ysample, lr_s.predict(Xsample)), lr_s.score(Xsample, ysample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets check the sensitivity of our prediction to our sample. We'll do this 1000 times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = []\n",
    "for i in range(1000):\n",
    "    sample_indices = np.random.choice(range(100), size=20)\n",
    "    Xsample = X[sample_indices]\n",
    "    ysample = y[sample_indices]\n",
    "    scores.append(LinearRegression().fit(Xsample, ysample).score(Xsample, ysample))\n",
    "plt.hist(scores,  bins=np.linspace(0.7, 1, 30))\n",
    "plt.xlim(0.7,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Redo this with a higher amount of noise (about 400). For this you will need to create a new dataset. Plot the data. Plot the histogram of the R^2 as well as that of the coefficients.Try a smaller dataset as well. What conclusions can you draw?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code here\n",
    "Xnew, ynew, coefnew = make_regression(n_samples = 100, \n",
    "                       n_features=1, \n",
    "                       noise=400,\n",
    "                       random_state=2017,\n",
    "                       coef=True)\n",
    "plt.plot(Xnew, ynew, 'o');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7, 1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T6Nm/L41tm7nJ9BX07dQkNyb5aJLbk5w7tu3i7rh7k7xsbStfmUn7l2Q+yX+PvXa/v/bVL28F/XtWkhu6vn0wydaxbRvhvXek/s36e+/yJAcO97H6jLy96/vtSc4Y27b6166qBvvHaKL4X4BvAo4FbgNOP8L+r2c0sQxwInB/9/OEbvmEIetdy/516/+53n3o0zdG5yd/uls+HXhwbPk24DhgW/c4x6x3n6bYv3ngzvXuwxT696fAzm75pcAfdcsb4r13uP516zP73uvqexFwxuF+z4Bzgb8CApwF3NLntRt6RLDaS01cAFzVLb8MuL6qHq2qzwLXA+cMWu3q9enfrFtJ3wp4Wrf8dOBfu+XtwHur6vGqegDY2z3eLOnTv6PBSvp3OvD33fKNY9s3ynvvcP2beVV1E/DoEXbZDry7Rm4Gjk9yMhO+dkMHwaEuNbHlUDsmeRajvx6ffOFWfOw66tM/gKckWUxyc5LzhytzIivp268Cr06yj9EnxF6/imPXW5/+AWzrThn9Q5IXDlrpZFbSv9uAH+mWfxj4+iTPWOGx661P/2C233srcbj+T/TazdJk8Q7gmqr60noXMpBD9e9ZNfrW448Bb0vyzetT2sQuAK6oqq2Mhqp/lGSWfqf6Olz/HgZOrarnA28A/jjJ047wOLPql4HvTfJR4HsZfft/I73/jtS/o/29N1VDv2mXvdTEmB18+WmT1Ry7Xvr0j6ra3/28H/gg8PzplzixlfRtF3A1QFV9CHgKo2u7bJTX7pD96055/VvXfiujc9XPHrzi1VnJZV7+tap+pAu0N3dtj63k2BnQp3+z/t5bicP1f7LXbuAJj02MJiu28f8TOs89xH7fCjxI972GsUmPBxhNeJzQLZ+43pM4U+zfCcBx3fJm4D6OMNE8i31jNFn1mm752xidQw/wXL58svh+Zm+yuE//5p7sD6PJyv1H4+9m93v3Vd3ypcCvdcsb4r13hP7N9HtvrP55Dj9ZfB5fPln84T6v3Vp05lzgE4z+anpz1/ZrwCvG9vlV4LJDHPtTjCYa9wKvXe8XZpr9A74HuKP7Bb4D2LXefVlt3xhNxv1T14ePAT8wduybu+PuBV6+3n2ZZv+AHwXu6to+AvzQevdlwv69svtP8BPAO5/8z7HbdtS/9w7Xv6PkvXcVo1OQX2R0nn8X8Drgdd32MLr51790fVjo89r5zWJJatxGmtiTJE3AIJCkxhkEktQ4g0CSGmcQSFLjDAJJapxBIEmNMwgkqXH/C2DCOFHObM1/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code here\n",
    "scores = []\n",
    "models = []\n",
    "for i in range(1000):\n",
    "    sample_indices = np.random.choice(range(100), size=20)\n",
    "    Xsample = X[sample_indices]\n",
    "    ysample = y[sample_indices]\n",
    "    models.append(LinearRegression().fit(Xsample, ysample))\n",
    "    scores.append(models[i].score(Xsample, ysample))\n",
    "plt.hist(scores,  bins=np.linspace(0.7, 1, 30));\n",
    "plt.xlim(0.7,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7, 1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code here\n",
    "scores = []\n",
    "models = []\n",
    "for i in range(1000):\n",
    "    sample_indices = np.random.choice(range(100), size=10)\n",
    "    Xsample = X[sample_indices]\n",
    "    ysample = y[sample_indices]\n",
    "    models.append(LinearRegression().fit(Xsample, ysample))\n",
    "    scores.append(models[i].score(Xsample, ysample))\n",
    "plt.hist(scores, bins=np.linspace(0.7, 1, 30));\n",
    "plt.xlim(0.7,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code here\n",
    "plt.hist([models[i].coef_[0] for i in range(1000)], bins=10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAADZpJREFUeJzt3X+o3fV9x/Hna2q3UQvqvGSZNbvShQ032lgu4mgZXW23qKPRsQ1ldBkTbv9QUBBGrDA7tkFKV4XB5khRzMDpylTMGreaBkEKq93VZTGaOn8spUpMrrhOy8At+t4f55v1THJ3zr3nnHtuPvf5gMP5fj/f77nf95ubvO73fvI5J6kqJEmnvx+ZdgGSpPEw0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNOHM1L3b++efX7Ozsal5Skk57Tz311OtVNTPovFUN9NnZWRYWFlbzkpJ02kvy3WHOc8pFkhphoEtSIwx0SWqEgS5JjTDQJakRAwM9yY8l+XaSf0nybJI/7MYvSvJkkheT/E2S902+XEnSUoa5Q38b+GRVfQTYAmxNchnwReDOqvoZ4N+B6ydXpiRpkIGBXj0/6HbP6h4FfBL42258N3D1RCqUJA1lqDn0JGckOQAcB/YBLwHfr6oT3SmvABdMpkRJ0jCGeqdoVb0DbElyDvAw8HPDXiDJPDAPsGnTppXUqHVkdsfeqVz3yM6rpnJdaZyWtcqlqr4PPA78InBOkpM/ED4IvLrEa3ZV1VxVzc3MDPwoAknSCg2zymWmuzMnyY8DnwYO0wv23+hO2w48MqkiJUmDDTPlshHYneQMej8AvlpVX0vyHPBAkj8G/hm4e4J1SpIGGBjoVXUQuOQU4y8Dl06iKEnS8vlOUUlqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0YGOhJLkzyeJLnkjyb5KZu/AtJXk1yoHtcOflyJUlLOXOIc04At1TV00k+ADyVZF937M6q+tPJlSdJGtbAQK+qo8DRbvutJIeBCyZdmCRpeZY1h55kFrgEeLIbujHJwST3JDl3idfMJ1lIsrC4uDhSsZKkpQ0d6EnOBh4Ebq6qN4G7gA8BW+jdwX/5VK+rql1VNVdVczMzM2MoWZJ0KkMFepKz6IX5fVX1EEBVHauqd6rqXeArwKWTK1OSNMgwq1wC3A0crqo7+sY39p12DXBo/OVJkoY1zCqXjwGfBZ5JcqAb+zxwXZItQAFHgM9NpEJJ0lCGWeXyTSCnOPTo+MuRJK2U7xSVpEYMM+UiNW92x96pXfvIzqumdm21xTt0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhoxMNCTXJjk8STPJXk2yU3d+HlJ9iV5oXs+d/LlSpKWMswd+gnglqq6GLgMuCHJxcAOYH9VbQb2d/uSpCkZGOhVdbSqnu623wIOAxcA24Dd3Wm7gasnVaQkabBlzaEnmQUuAZ4ENlTV0e7Qa8CGsVYmSVqWoQM9ydnAg8DNVfVm/7GqKqCWeN18koUkC4uLiyMVK0la2lCBnuQsemF+X1U91A0fS7KxO74ROH6q11bVrqqaq6q5mZmZcdQsSTqFYVa5BLgbOFxVd/Qd2gNs77a3A4+MvzxJ0rDOHOKcjwGfBZ5JcqAb+zywE/hqkuuB7wK/NZkSJUnDGBjoVfVNIEscvny85UiSVsp3ikpSIwx0SWrEMHPokiZodsfeqVz3yM6rpnJdTY536JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1YmCgJ7knyfEkh/rGvpDk1SQHuseVky1TkjTIMHfo9wJbTzF+Z1Vt6R6PjrcsSdJyDQz0qnoCeGMVapEkjWCUOfQbkxzspmTOXeqkJPNJFpIsLC4ujnA5SdL/Z6WBfhfwIWALcBT48lInVtWuqpqrqrmZmZkVXk6SNMiKAr2qjlXVO1X1LvAV4NLxliVJWq4VBXqSjX271wCHljpXkrQ6zhx0QpL7gU8A5yd5Bbgd+ESSLUABR4DPTbBGSdIQBgZ6VV13iuG7J1CLJGkEvlNUkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhoxMNCT3JPkeJJDfWPnJdmX5IXu+dzJlilJGmSYO/R7ga3vGdsB7K+qzcD+bl+SNEUDA72qngDeeM/wNmB3t70buHrMdUmSlmmlc+gbqupot/0asGFM9UiSVujMUb9AVVWSWup4knlgHmDTpk2jXk6rYHbH3mmXIGkFVnqHfizJRoDu+fhSJ1bVrqqaq6q5mZmZFV5OkjTISgN9D7C9294OPDKeciRJKzXMssX7gX8EfjbJK0muB3YCn07yAvCpbl+SNEUD59Cr6rolDl0+5lokSSPwnaKS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRZ067AC1tdsfeaZeghk3zz9eRnVdN7dot8w5dkhphoEtSIwx0SWrESHPoSY4AbwHvACeqam4cRUmSlm8c/yj6y1X1+hi+jiRpBE65SFIjRg30Ah5L8lSS+VOdkGQ+yUKShcXFxREvJ0layqiB/vGq+ihwBXBDkl967wlVtauq5qpqbmZmZsTLSZKWMlKgV9Wr3fNx4GHg0nEUJUlavhUHepL3J/nAyW3gV4BD4ypMkrQ8o6xy2QA8nOTk1/nrqvqHsVQlSVq2FQd6Vb0MfGSMtUiSRuCyRUlqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhoxyn9wIUkrMrtj71Sue2TnVVO57mrxDl2SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ14rRZtjitZU6S2jHNHFmNJZPeoUtSIwx0SWrESIGeZGuS55O8mGTHuIqSJC3figM9yRnAnwNXABcD1yW5eFyFSZKWZ5Q79EuBF6vq5ar6L+ABYNt4ypIkLdcogX4B8L2+/Ve6MUnSFEx82WKSeWC+2/1BkueHeNn5wOuTq2pNWU+9wvrqdz31Cuur32X3mi+OdL2fHuakUQL9VeDCvv0PdmP/R1XtAnYt5wsnWaiquRFqO22sp15hffW7nnqF9dXvWu11lCmXfwI2J7koyfuAa4E94ylLkrRcK75Dr6oTSW4Evg6cAdxTVc+OrTJJ0rKMNIdeVY8Cj46pln7LmqI5za2nXmF99bueeoX11e+a7DVVNe0aJElj4Fv/JakRaybQk/xRkoNJDiR5LMlPdeNJ8mfdxwscTPLRadc6Dkm+lOQ7XU8PJzmn79itXb/PJ/nVadY5Dkl+M8mzSd5NMveeY031elLrH4uR5J4kx5Mc6hs7L8m+JC90z+dOs8ZxSXJhkseTPNf9Ob6pG19z/a6ZQAe+VFUfrqotwNeAP+jGrwA2d4954K4p1Tdu+4BfqKoPA/8K3ArQfXzCtcDPA1uBv+g+ZuF0dgj4deCJ/sFGe10vH4txL73vWb8dwP6q2gzs7/ZbcAK4paouBi4Dbui+n2uu3zUT6FX1Zt/u+4GTk/vbgL+qnm8B5yTZuOoFjllVPVZVJ7rdb9Fbxw+9fh+oqrer6t+AF+l9zMJpq6oOV9Wp3lDWXK+d5j8Wo6qeAN54z/A2YHe3vRu4elWLmpCqOlpVT3fbbwGH6b0rfs31u2YCHSDJnyT5HvDb/PAOfT18xMDvAX/fba+Hfk9qtddW+xpkQ1Ud7bZfAzZMs5hJSDILXAI8yRrsd1X/x6Ik3wB+8hSHbquqR6rqNuC2JLcCNwK3r2Z94zao3+6c2+j9SnffatY2bsP0qvWjqipJU0vokpwNPAjcXFVvJvnfY2ul31UN9Kr61JCn3kdvffvtDPkRA2vRoH6T/C7wa8Dl9cP1o6dlv8v43vY7LXsdQqt9DXIsycaqOtpNix6fdkHjkuQsemF+X1U91A2vuX7XzJRLks19u9uA73Tbe4Df6Va7XAb8R9+vOaetJFuB3wc+U1X/2XdoD3Btkh9NchG9fwz+9jRqXAWt9rpePxZjD7C9294ONPGbWXq34ncDh6vqjr5Da6/fqloTD3o//Q4BB4G/Ay7oxkNvxcBLwDPA3LRrHVO/L9KbZz3QPf6y79htXb/PA1dMu9Yx9HoNvXnkt4FjwNdb7bWvryvprV56id6009RrGnN/9wNHgf/uvrfXAz9Bb7XHC8A3gPOmXeeYev04vUUaB/v+vl65Fvv1naKS1Ig1M+UiSRqNgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiP+B9fSDdMqTDzvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# your code here\n",
    "plt.hist([models[i].intercept_ for i in range(100)], bins=10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing and training\n",
    "\n",
    "A grid like the one we created might contain some of the points we fit this model on. This is called **Data Contamination** and is a big no-no. If we want an independent estimate of the error, we should hold out some points in a test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.2, random_state=2017)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets fit the model on the training set and evaluate it both on the training set and the test set. We print the R^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr2 = LinearRegression().fit(Xtrain, ytrain)\n",
    "r2_test = r2_score(ytest, lr.predict(Xtest))\n",
    "r2_train = r2_score(ytrain, lr.predict(Xtrain))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Train R2 is 0.9302930567263316, while test R^2 is 0.9446796445954511'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"Train R2 is {}, while test R^2 is {}\".format(r2_train, r2_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Keras to fit the model\n",
    "\n",
    "We'll use plain and simple gradient descent (why?) and Keras's Sequential API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_16 (Dense)             (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "lr3 = Sequential()\n",
    "lr3.add(Dense(1, input_shape=(1,)))\n",
    "lr3.compile(optimizer='sgd', loss='mean_squared_error',  metrics=['mae','accuracy'])\n",
    "lr3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/400\n",
      "80/80 [==============================] - 1s 8ms/step - loss: 5361.4824 - mean_absolute_error: 59.8523 - acc: 0.0000e+00\n",
      "Epoch 2/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 5216.2017 - mean_absolute_error: 59.0328 - acc: 0.0000e+00\n",
      "Epoch 3/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 5075.1665 - mean_absolute_error: 58.2255 - acc: 0.0000e+00\n",
      "Epoch 4/400\n",
      "80/80 [==============================] - 0s 23us/step - loss: 4938.2520 - mean_absolute_error: 57.4300 - acc: 0.0000e+00\n",
      "Epoch 5/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 4805.3369 - mean_absolute_error: 56.6463 - acc: 0.0000e+00\n",
      "Epoch 6/400\n",
      "80/80 [==============================] - 0s 22us/step - loss: 4676.3037 - mean_absolute_error: 55.8877 - acc: 0.0000e+00\n",
      "Epoch 7/400\n",
      "80/80 [==============================] - 0s 20us/step - loss: 4551.0400 - mean_absolute_error: 55.1432 - acc: 0.0000e+00\n",
      "Epoch 8/400\n",
      "80/80 [==============================] - 0s 21us/step - loss: 4429.4346 - mean_absolute_error: 54.4204 - acc: 0.0000e+00\n",
      "Epoch 9/400\n",
      "80/80 [==============================] - 0s 19us/step - loss: 4311.3779 - mean_absolute_error: 53.7129 - acc: 0.0000e+00\n",
      "Epoch 10/400\n",
      "80/80 [==============================] - 0s 20us/step - loss: 4196.7686 - mean_absolute_error: 53.0277 - acc: 0.0000e+00\n",
      "Epoch 11/400\n",
      "80/80 [==============================] - 0s 22us/step - loss: 4085.5054 - mean_absolute_error: 52.3527 - acc: 0.0000e+00\n",
      "Epoch 12/400\n",
      "80/80 [==============================] - 0s 21us/step - loss: 3977.4888 - mean_absolute_error: 51.6875 - acc: 0.0000e+00\n",
      "Epoch 13/400\n",
      "80/80 [==============================] - 0s 21us/step - loss: 3872.6238 - mean_absolute_error: 51.0322 - acc: 0.0000e+00\n",
      "Epoch 14/400\n",
      "80/80 [==============================] - 0s 30us/step - loss: 3770.8196 - mean_absolute_error: 50.3865 - acc: 0.0000e+00\n",
      "Epoch 15/400\n",
      "80/80 [==============================] - 0s 23us/step - loss: 3671.9844 - mean_absolute_error: 49.7503 - acc: 0.0000e+00\n",
      "Epoch 16/400\n",
      "80/80 [==============================] - 0s 21us/step - loss: 3576.0332 - mean_absolute_error: 49.1235 - acc: 0.0000e+00\n",
      "Epoch 17/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 3482.8796 - mean_absolute_error: 48.5060 - acc: 0.0000e+00\n",
      "Epoch 18/400\n",
      "80/80 [==============================] - 0s 29us/step - loss: 3392.4438 - mean_absolute_error: 47.8975 - acc: 0.0000e+00\n",
      "Epoch 19/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 3304.6440 - mean_absolute_error: 47.2980 - acc: 0.0000e+00\n",
      "Epoch 20/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 3219.4048 - mean_absolute_error: 46.7073 - acc: 0.0000e+00\n",
      "Epoch 21/400\n",
      "80/80 [==============================] - 0s 28us/step - loss: 3136.6506 - mean_absolute_error: 46.1254 - acc: 0.0000e+00\n",
      "Epoch 22/400\n",
      "80/80 [==============================] - 0s 32us/step - loss: 3056.3083 - mean_absolute_error: 45.5519 - acc: 0.0000e+00\n",
      "Epoch 23/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 2978.3079 - mean_absolute_error: 44.9870 - acc: 0.0000e+00\n",
      "Epoch 24/400\n",
      "80/80 [==============================] - 0s 28us/step - loss: 2902.5806 - mean_absolute_error: 44.4303 - acc: 0.0000e+00\n",
      "Epoch 25/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 2829.0598 - mean_absolute_error: 43.8819 - acc: 0.0000e+00\n",
      "Epoch 26/400\n",
      "80/80 [==============================] - 0s 44us/step - loss: 2757.6809 - mean_absolute_error: 43.3415 - acc: 0.0000e+00\n",
      "Epoch 27/400\n",
      "80/80 [==============================] - 0s 23us/step - loss: 2688.3816 - mean_absolute_error: 42.8091 - acc: 0.0000e+00\n",
      "Epoch 28/400\n",
      "80/80 [==============================] - 0s 23us/step - loss: 2621.1013 - mean_absolute_error: 42.2845 - acc: 0.0000e+00\n",
      "Epoch 29/400\n",
      "80/80 [==============================] - 0s 27us/step - loss: 2555.7800 - mean_absolute_error: 41.7676 - acc: 0.0000e+00\n",
      "Epoch 30/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 2492.3613 - mean_absolute_error: 41.2583 - acc: 0.0000e+00\n",
      "Epoch 31/400\n",
      "80/80 [==============================] - 0s 96us/step - loss: 2430.7896 - mean_absolute_error: 40.7566 - acc: 0.0000e+00\n",
      "Epoch 32/400\n",
      "80/80 [==============================] - 0s 41us/step - loss: 2371.0103 - mean_absolute_error: 40.2622 - acc: 0.0000e+00\n",
      "Epoch 33/400\n",
      "80/80 [==============================] - 0s 62us/step - loss: 2312.9714 - mean_absolute_error: 39.7751 - acc: 0.0000e+00\n",
      "Epoch 34/400\n",
      "80/80 [==============================] - 0s 32us/step - loss: 2256.6223 - mean_absolute_error: 39.2951 - acc: 0.0000e+00\n",
      "Epoch 35/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 2201.9131 - mean_absolute_error: 38.8222 - acc: 0.0000e+00\n",
      "Epoch 36/400\n",
      "80/80 [==============================] - 0s 22us/step - loss: 2148.7961 - mean_absolute_error: 38.3563 - acc: 0.0000e+00\n",
      "Epoch 37/400\n",
      "80/80 [==============================] - 0s 38us/step - loss: 2097.2253 - mean_absolute_error: 37.8972 - acc: 0.0000e+00\n",
      "Epoch 38/400\n",
      "80/80 [==============================] - 0s 42us/step - loss: 2047.1547 - mean_absolute_error: 37.4449 - acc: 0.0000e+00\n",
      "Epoch 39/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 1998.5413 - mean_absolute_error: 36.9992 - acc: 0.0000e+00\n",
      "Epoch 40/400\n",
      "80/80 [==============================] - 0s 39us/step - loss: 1951.3420 - mean_absolute_error: 36.5601 - acc: 0.0000e+00\n",
      "Epoch 41/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 1905.5159 - mean_absolute_error: 36.1274 - acc: 0.0000e+00\n",
      "Epoch 42/400\n",
      "80/80 [==============================] - 0s 30us/step - loss: 1861.0227 - mean_absolute_error: 35.7011 - acc: 0.0000e+00\n",
      "Epoch 43/400\n",
      "80/80 [==============================] - 0s 32us/step - loss: 1817.8236 - mean_absolute_error: 35.2811 - acc: 0.0000e+00\n",
      "Epoch 44/400\n",
      "80/80 [==============================] - 0s 22us/step - loss: 1775.8805 - mean_absolute_error: 34.8672 - acc: 0.0000e+00\n",
      "Epoch 45/400\n",
      "80/80 [==============================] - 0s 22us/step - loss: 1735.1572 - mean_absolute_error: 34.4595 - acc: 0.0000e+00\n",
      "Epoch 46/400\n",
      "80/80 [==============================] - 0s 21us/step - loss: 1695.6179 - mean_absolute_error: 34.0577 - acc: 0.0000e+00\n",
      "Epoch 47/400\n",
      "80/80 [==============================] - 0s 70us/step - loss: 1657.2285 - mean_absolute_error: 33.6618 - acc: 0.0000e+00\n",
      "Epoch 48/400\n",
      "80/80 [==============================] - 0s 48us/step - loss: 1619.9551 - mean_absolute_error: 33.2723 - acc: 0.0000e+00\n",
      "Epoch 49/400\n",
      "80/80 [==============================] - 0s 26us/step - loss: 1583.7650 - mean_absolute_error: 32.8999 - acc: 0.0000e+00\n",
      "Epoch 50/400\n",
      "80/80 [==============================] - 0s 41us/step - loss: 1548.6268 - mean_absolute_error: 32.5330 - acc: 0.0000e+00\n",
      "Epoch 51/400\n",
      "80/80 [==============================] - 0s 35us/step - loss: 1514.5098 - mean_absolute_error: 32.1760 - acc: 0.0000e+00\n",
      "Epoch 52/400\n",
      "80/80 [==============================] - 0s 29us/step - loss: 1481.3845 - mean_absolute_error: 31.8261 - acc: 0.0000e+00\n",
      "Epoch 53/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 1449.2217 - mean_absolute_error: 31.4813 - acc: 0.0000e+00\n",
      "Epoch 54/400\n",
      "80/80 [==============================] - 0s 53us/step - loss: 1417.9934 - mean_absolute_error: 31.1416 - acc: 0.0000e+00\n",
      "Epoch 55/400\n",
      "80/80 [==============================] - 0s 49us/step - loss: 1387.6721 - mean_absolute_error: 30.8069 - acc: 0.0000e+00\n",
      "Epoch 56/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 1358.2319 - mean_absolute_error: 30.4770 - acc: 0.0000e+00\n",
      "Epoch 57/400\n",
      "80/80 [==============================] - 0s 41us/step - loss: 1329.6469 - mean_absolute_error: 30.1520 - acc: 0.0000e+00\n",
      "Epoch 58/400\n",
      "80/80 [==============================] - 0s 37us/step - loss: 1301.8922 - mean_absolute_error: 29.8330 - acc: 0.0000e+00\n",
      "Epoch 59/400\n",
      "80/80 [==============================] - 0s 50us/step - loss: 1274.9436 - mean_absolute_error: 29.5247 - acc: 0.0000e+00\n",
      "Epoch 60/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 1248.7777 - mean_absolute_error: 29.2210 - acc: 0.0000e+00\n",
      "Epoch 61/400\n",
      "80/80 [==============================] - 0s 37us/step - loss: 1223.3716 - mean_absolute_error: 28.9216 - acc: 0.0000e+00\n",
      "Epoch 62/400\n",
      "80/80 [==============================] - 0s 43us/step - loss: 1198.7031 - mean_absolute_error: 28.6266 - acc: 0.0000e+00\n",
      "Epoch 63/400\n",
      "80/80 [==============================] - 0s 37us/step - loss: 1174.7511 - mean_absolute_error: 28.3360 - acc: 0.0000e+00\n",
      "Epoch 64/400\n",
      "80/80 [==============================] - 0s 27us/step - loss: 1151.4945 - mean_absolute_error: 28.0495 - acc: 0.0000e+00\n",
      "Epoch 65/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 1128.9127 - mean_absolute_error: 27.7673 - acc: 0.0000e+00\n",
      "Epoch 66/400\n",
      "80/80 [==============================] - 0s 33us/step - loss: 1106.9867 - mean_absolute_error: 27.4955 - acc: 0.0000e+00\n",
      "Epoch 67/400\n",
      "80/80 [==============================] - 0s 39us/step - loss: 1085.6970 - mean_absolute_error: 27.2355 - acc: 0.0000e+00\n",
      "Epoch 68/400\n",
      "80/80 [==============================] - 0s 41us/step - loss: 1065.0254 - mean_absolute_error: 26.9793 - acc: 0.0000e+00\n",
      "Epoch 69/400\n",
      "80/80 [==============================] - 0s 30us/step - loss: 1044.9535 - mean_absolute_error: 26.7268 - acc: 0.0000e+00\n",
      "Epoch 70/400\n",
      "80/80 [==============================] - 0s 29us/step - loss: 1025.4642 - mean_absolute_error: 26.4781 - acc: 0.0000e+00\n",
      "Epoch 71/400\n",
      "80/80 [==============================] - 0s 35us/step - loss: 1006.5404 - mean_absolute_error: 26.2329 - acc: 0.0000e+00\n",
      "Epoch 72/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 988.1658 - mean_absolute_error: 25.9913 - acc: 0.0000e+00\n",
      "Epoch 73/400\n",
      "80/80 [==============================] - 0s 32us/step - loss: 970.3243 - mean_absolute_error: 25.7533 - acc: 0.0000e+00\n",
      "Epoch 74/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 953.0004 - mean_absolute_error: 25.5187 - acc: 0.0000e+00\n",
      "Epoch 75/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 936.1791 - mean_absolute_error: 25.2876 - acc: 0.0000e+00\n",
      "Epoch 76/400\n",
      "80/80 [==============================] - 0s 22us/step - loss: 919.8456 - mean_absolute_error: 25.0615 - acc: 0.0000e+00\n",
      "Epoch 77/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 903.9861 - mean_absolute_error: 24.8401 - acc: 0.0000e+00\n",
      "Epoch 78/400\n",
      "80/80 [==============================] - 0s 35us/step - loss: 888.5863 - mean_absolute_error: 24.6219 - acc: 0.0000e+00\n",
      "Epoch 79/400\n",
      "80/80 [==============================] - 0s 22us/step - loss: 873.6335 - mean_absolute_error: 24.4069 - acc: 0.0000e+00\n",
      "Epoch 80/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 859.1141 - mean_absolute_error: 24.1950 - acc: 0.0000e+00\n",
      "Epoch 81/400\n",
      "80/80 [==============================] - 0s 30us/step - loss: 845.0158 - mean_absolute_error: 23.9862 - acc: 0.0000e+00\n",
      "Epoch 82/400\n",
      "80/80 [==============================] - 0s 23us/step - loss: 831.3263 - mean_absolute_error: 23.7804 - acc: 0.0000e+00\n",
      "Epoch 83/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 818.0336 - mean_absolute_error: 23.5835 - acc: 0.0000e+00\n",
      "Epoch 84/400\n",
      "80/80 [==============================] - 0s 20us/step - loss: 805.1266 - mean_absolute_error: 23.4010 - acc: 0.0000e+00\n",
      "Epoch 85/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 792.5936 - mean_absolute_error: 23.2212 - acc: 0.0000e+00\n",
      "Epoch 86/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 780.4240 - mean_absolute_error: 23.0440 - acc: 0.0000e+00\n",
      "Epoch 87/400\n",
      "80/80 [==============================] - 0s 47us/step - loss: 768.6071 - mean_absolute_error: 22.8694 - acc: 0.0000e+00\n",
      "Epoch 88/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 757.1329 - mean_absolute_error: 22.6973 - acc: 0.0000e+00\n",
      "Epoch 89/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 745.9910 - mean_absolute_error: 22.5277 - acc: 0.0000e+00\n",
      "Epoch 90/400\n",
      "80/80 [==============================] - 0s 75us/step - loss: 735.1722 - mean_absolute_error: 22.3606 - acc: 0.0000e+00\n",
      "Epoch 91/400\n",
      "80/80 [==============================] - 0s 33us/step - loss: 724.6669 - mean_absolute_error: 22.1959 - acc: 0.0000e+00\n",
      "Epoch 92/400\n",
      "80/80 [==============================] - 0s 28us/step - loss: 714.4661 - mean_absolute_error: 22.0337 - acc: 0.0000e+00\n",
      "Epoch 93/400\n",
      "80/80 [==============================] - 0s 61us/step - loss: 704.5609 - mean_absolute_error: 21.8738 - acc: 0.0000e+00\n",
      "Epoch 94/400\n",
      "80/80 [==============================] - 0s 55us/step - loss: 694.9426 - mean_absolute_error: 21.7162 - acc: 0.0000e+00\n",
      "Epoch 95/400\n",
      "80/80 [==============================] - 0s 37us/step - loss: 685.6031 - mean_absolute_error: 21.5627 - acc: 0.0000e+00\n",
      "Epoch 96/400\n",
      "80/80 [==============================] - 0s 29us/step - loss: 676.5342 - mean_absolute_error: 21.4176 - acc: 0.0000e+00\n",
      "Epoch 97/400\n",
      "80/80 [==============================] - 0s 37us/step - loss: 667.7280 - mean_absolute_error: 21.2788 - acc: 0.0000e+00\n",
      "Epoch 98/400\n",
      "80/80 [==============================] - 0s 33us/step - loss: 659.1769 - mean_absolute_error: 21.1420 - acc: 0.0000e+00\n",
      "Epoch 99/400\n",
      "80/80 [==============================] - 0s 29us/step - loss: 650.8737 - mean_absolute_error: 21.0072 - acc: 0.0000e+00\n",
      "Epoch 100/400\n",
      "80/80 [==============================] - 0s 26us/step - loss: 642.8108 - mean_absolute_error: 20.8743 - acc: 0.0000e+00\n",
      "Epoch 101/400\n",
      "80/80 [==============================] - 0s 35us/step - loss: 634.9816 - mean_absolute_error: 20.7434 - acc: 0.0000e+00\n",
      "Epoch 102/400\n",
      "80/80 [==============================] - 0s 38us/step - loss: 627.3790 - mean_absolute_error: 20.6144 - acc: 0.0000e+00\n",
      "Epoch 103/400\n",
      "80/80 [==============================] - 0s 26us/step - loss: 619.9968 - mean_absolute_error: 20.4872 - acc: 0.0000e+00\n",
      "Epoch 104/400\n",
      "80/80 [==============================] - 0s 27us/step - loss: 612.8282 - mean_absolute_error: 20.3667 - acc: 0.0000e+00\n",
      "Epoch 105/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 605.8673 - mean_absolute_error: 20.2493 - acc: 0.0000e+00\n",
      "Epoch 106/400\n",
      "80/80 [==============================] - 0s 32us/step - loss: 599.1079 - mean_absolute_error: 20.1344 - acc: 0.0000e+00\n",
      "Epoch 107/400\n",
      "80/80 [==============================] - 0s 36us/step - loss: 592.5443 - mean_absolute_error: 20.0236 - acc: 0.0000e+00\n",
      "Epoch 108/400\n",
      "80/80 [==============================] - 0s 50us/step - loss: 586.1708 - mean_absolute_error: 19.9143 - acc: 0.0000e+00\n",
      "Epoch 109/400\n",
      "80/80 [==============================] - 0s 53us/step - loss: 579.9819 - mean_absolute_error: 19.8067 - acc: 0.0000e+00\n",
      "Epoch 110/400\n",
      "80/80 [==============================] - 0s 32us/step - loss: 573.9722 - mean_absolute_error: 19.7006 - acc: 0.0000e+00\n",
      "Epoch 111/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 568.1364 - mean_absolute_error: 19.5960 - acc: 0.0000e+00\n",
      "Epoch 112/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 562.4694 - mean_absolute_error: 19.4929 - acc: 0.0000e+00\n",
      "Epoch 113/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 556.9667 - mean_absolute_error: 19.3913 - acc: 0.0000e+00\n",
      "Epoch 114/400\n",
      "80/80 [==============================] - 0s 27us/step - loss: 551.6233 - mean_absolute_error: 19.2912 - acc: 0.0000e+00\n",
      "Epoch 115/400\n",
      "80/80 [==============================] - 0s 21us/step - loss: 546.4344 - mean_absolute_error: 19.1925 - acc: 0.0000e+00\n",
      "Epoch 116/400\n",
      "80/80 [==============================] - 0s 23us/step - loss: 541.3959 - mean_absolute_error: 19.0953 - acc: 0.0000e+00\n",
      "Epoch 117/400\n",
      "80/80 [==============================] - 0s 37us/step - loss: 536.5031 - mean_absolute_error: 19.0072 - acc: 0.0000e+00\n",
      "Epoch 118/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 531.7518 - mean_absolute_error: 18.9220 - acc: 0.0000e+00\n",
      "Epoch 119/400\n",
      "80/80 [==============================] - 0s 21us/step - loss: 527.1381 - mean_absolute_error: 18.8381 - acc: 0.0000e+00\n",
      "Epoch 120/400\n",
      "80/80 [==============================] - 0s 49us/step - loss: 522.6578 - mean_absolute_error: 18.7590 - acc: 0.0000e+00\n",
      "Epoch 121/400\n",
      "80/80 [==============================] - 0s 37us/step - loss: 518.3074 - mean_absolute_error: 18.6850 - acc: 0.0000e+00\n",
      "Epoch 122/400\n",
      "80/80 [==============================] - 0s 40us/step - loss: 514.0828 - mean_absolute_error: 18.6121 - acc: 0.0000e+00\n",
      "Epoch 123/400\n",
      "80/80 [==============================] - 0s 29us/step - loss: 509.9805 - mean_absolute_error: 18.5402 - acc: 0.0000e+00\n",
      "Epoch 124/400\n",
      "80/80 [==============================] - 0s 33us/step - loss: 505.9968 - mean_absolute_error: 18.4694 - acc: 0.0000e+00\n",
      "Epoch 125/400\n",
      "80/80 [==============================] - 0s 33us/step - loss: 502.1284 - mean_absolute_error: 18.4050 - acc: 0.0000e+00\n",
      "Epoch 126/400\n",
      "80/80 [==============================] - 0s 23us/step - loss: 498.3719 - mean_absolute_error: 18.3420 - acc: 0.0000e+00\n",
      "Epoch 127/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 494.7242 - mean_absolute_error: 18.2800 - acc: 0.0000e+00\n",
      "Epoch 128/400\n",
      "80/80 [==============================] - 0s 37us/step - loss: 491.1820 - mean_absolute_error: 18.2189 - acc: 0.0000e+00\n",
      "Epoch 129/400\n",
      "80/80 [==============================] - 0s 30us/step - loss: 487.7423 - mean_absolute_error: 18.1586 - acc: 0.0000e+00\n",
      "Epoch 130/400\n",
      "80/80 [==============================] - 0s 28us/step - loss: 484.4020 - mean_absolute_error: 18.0993 - acc: 0.0000e+00\n",
      "Epoch 131/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 481.1585 - mean_absolute_error: 18.0408 - acc: 0.0000e+00\n",
      "Epoch 132/400\n",
      "80/80 [==============================] - 0s 48us/step - loss: 478.0087 - mean_absolute_error: 17.9831 - acc: 0.0000e+00\n",
      "Epoch 133/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 474.9500 - mean_absolute_error: 17.9263 - acc: 0.0000e+00\n",
      "Epoch 134/400\n",
      "80/80 [==============================] - 0s 43us/step - loss: 471.9799 - mean_absolute_error: 17.8703 - acc: 0.0000e+00\n",
      "Epoch 135/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 469.0956 - mean_absolute_error: 17.8152 - acc: 0.0000e+00\n",
      "Epoch 136/400\n",
      "80/80 [==============================] - 0s 46us/step - loss: 466.2947 - mean_absolute_error: 17.7608 - acc: 0.0000e+00\n",
      "Epoch 137/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 463.5749 - mean_absolute_error: 17.7072 - acc: 0.0000e+00\n",
      "Epoch 138/400\n",
      "80/80 [==============================] - 0s 26us/step - loss: 460.9336 - mean_absolute_error: 17.6544 - acc: 0.0000e+00\n",
      "Epoch 139/400\n",
      "80/80 [==============================] - 0s 78us/step - loss: 458.3689 - mean_absolute_error: 17.6070 - acc: 0.0000e+00\n",
      "Epoch 140/400\n",
      "80/80 [==============================] - 0s 54us/step - loss: 455.8783 - mean_absolute_error: 17.5605 - acc: 0.0000e+00\n",
      "Epoch 141/400\n",
      "80/80 [==============================] - 0s 27us/step - loss: 453.4597 - mean_absolute_error: 17.5147 - acc: 0.0000e+00\n",
      "Epoch 142/400\n",
      "80/80 [==============================] - 0s 93us/step - loss: 451.1110 - mean_absolute_error: 17.4696 - acc: 0.0000e+00\n",
      "Epoch 143/400\n",
      "80/80 [==============================] - 0s 30us/step - loss: 448.8303 - mean_absolute_error: 17.4251 - acc: 0.0000e+00\n",
      "Epoch 144/400\n",
      "80/80 [==============================] - 0s 367us/step - loss: 446.6155 - mean_absolute_error: 17.3813 - acc: 0.0000e+00\n",
      "Epoch 145/400\n",
      "80/80 [==============================] - 0s 212us/step - loss: 444.4648 - mean_absolute_error: 17.3426 - acc: 0.0000e+00\n",
      "Epoch 146/400\n",
      "80/80 [==============================] - 0s 26us/step - loss: 442.3763 - mean_absolute_error: 17.3054 - acc: 0.0000e+00\n",
      "Epoch 147/400\n",
      "80/80 [==============================] - 0s 41us/step - loss: 440.3481 - mean_absolute_error: 17.2688 - acc: 0.0000e+00\n",
      "Epoch 148/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 438.3787 - mean_absolute_error: 17.2327 - acc: 0.0000e+00\n",
      "Epoch 149/400\n",
      "80/80 [==============================] - 0s 185us/step - loss: 436.4661 - mean_absolute_error: 17.2007 - acc: 0.0000e+00\n",
      "Epoch 150/400\n",
      "80/80 [==============================] - 0s 45us/step - loss: 434.6089 - mean_absolute_error: 17.1698 - acc: 0.0000e+00\n",
      "Epoch 151/400\n",
      "80/80 [==============================] - 0s 57us/step - loss: 432.8053 - mean_absolute_error: 17.1393 - acc: 0.0000e+00\n",
      "Epoch 152/400\n",
      "80/80 [==============================] - 0s 33us/step - loss: 431.0538 - mean_absolute_error: 17.1093 - acc: 0.0000e+00\n",
      "Epoch 153/400\n",
      "80/80 [==============================] - 0s 68us/step - loss: 429.3530 - mean_absolute_error: 17.0798 - acc: 0.0000e+00\n",
      "Epoch 154/400\n",
      "80/80 [==============================] - 0s 48us/step - loss: 427.7014 - mean_absolute_error: 17.0506 - acc: 0.0000e+00\n",
      "Epoch 155/400\n",
      "80/80 [==============================] - 0s 36us/step - loss: 426.0975 - mean_absolute_error: 17.0219 - acc: 0.0000e+00\n",
      "Epoch 156/400\n",
      "80/80 [==============================] - 0s 60us/step - loss: 424.5399 - mean_absolute_error: 16.9936 - acc: 0.0000e+00\n",
      "Epoch 157/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 423.0274 - mean_absolute_error: 16.9657 - acc: 0.0000e+00\n",
      "Epoch 158/400\n",
      "80/80 [==============================] - 0s 32us/step - loss: 421.5586 - mean_absolute_error: 16.9382 - acc: 0.0000e+00\n",
      "Epoch 159/400\n",
      "80/80 [==============================] - 0s 50us/step - loss: 420.1322 - mean_absolute_error: 16.9111 - acc: 0.0000e+00\n",
      "Epoch 160/400\n",
      "80/80 [==============================] - 0s 50us/step - loss: 418.7470 - mean_absolute_error: 16.8844 - acc: 0.0000e+00\n",
      "Epoch 161/400\n",
      "80/80 [==============================] - 0s 48us/step - loss: 417.4020 - mean_absolute_error: 16.8581 - acc: 0.0000e+00\n",
      "Epoch 162/400\n",
      "80/80 [==============================] - 0s 44us/step - loss: 416.0957 - mean_absolute_error: 16.8322 - acc: 0.0000e+00\n",
      "Epoch 163/400\n",
      "80/80 [==============================] - 0s 52us/step - loss: 414.8272 - mean_absolute_error: 16.8074 - acc: 0.0000e+00\n",
      "Epoch 164/400\n",
      "80/80 [==============================] - 0s 47us/step - loss: 413.5954 - mean_absolute_error: 16.7844 - acc: 0.0000e+00\n",
      "Epoch 165/400\n",
      "80/80 [==============================] - 0s 48us/step - loss: 412.3992 - mean_absolute_error: 16.7617 - acc: 0.0000e+00\n",
      "Epoch 166/400\n",
      "80/80 [==============================] - 0s 55us/step - loss: 411.2375 - mean_absolute_error: 16.7393 - acc: 0.0000e+00\n",
      "Epoch 167/400\n",
      "80/80 [==============================] - 0s 37us/step - loss: 410.1095 - mean_absolute_error: 16.7185 - acc: 0.0000e+00\n",
      "Epoch 168/400\n",
      "80/80 [==============================] - 0s 65us/step - loss: 409.0140 - mean_absolute_error: 16.7000 - acc: 0.0000e+00\n",
      "Epoch 169/400\n",
      "80/80 [==============================] - 0s 26us/step - loss: 407.9501 - mean_absolute_error: 16.6837 - acc: 0.0000e+00\n",
      "Epoch 170/400\n",
      "80/80 [==============================] - 0s 23us/step - loss: 406.9170 - mean_absolute_error: 16.6678 - acc: 0.0000e+00\n",
      "Epoch 171/400\n",
      "80/80 [==============================] - 0s 39us/step - loss: 405.9138 - mean_absolute_error: 16.6520 - acc: 0.0000e+00\n",
      "Epoch 172/400\n",
      "80/80 [==============================] - 0s 40us/step - loss: 404.9395 - mean_absolute_error: 16.6372 - acc: 0.0000e+00\n",
      "Epoch 173/400\n",
      "80/80 [==============================] - 0s 26us/step - loss: 403.9934 - mean_absolute_error: 16.6239 - acc: 0.0000e+00\n",
      "Epoch 174/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 403.0746 - mean_absolute_error: 16.6108 - acc: 0.0000e+00\n",
      "Epoch 175/400\n",
      "80/80 [==============================] - 0s 39us/step - loss: 402.1824 - mean_absolute_error: 16.5978 - acc: 0.0000e+00\n",
      "Epoch 176/400\n",
      "80/80 [==============================] - 0s 35us/step - loss: 401.3160 - mean_absolute_error: 16.5851 - acc: 0.0000e+00\n",
      "Epoch 177/400\n",
      "80/80 [==============================] - 0s 35us/step - loss: 400.4745 - mean_absolute_error: 16.5726 - acc: 0.0000e+00\n",
      "Epoch 178/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 399.6574 - mean_absolute_error: 16.5602 - acc: 0.0000e+00\n",
      "Epoch 179/400\n",
      "80/80 [==============================] - 0s 30us/step - loss: 398.8640 - mean_absolute_error: 16.5480 - acc: 0.0000e+00\n",
      "Epoch 180/400\n",
      "80/80 [==============================] - 0s 36us/step - loss: 398.0934 - mean_absolute_error: 16.5360 - acc: 0.0000e+00\n",
      "Epoch 181/400\n",
      "80/80 [==============================] - 0s 30us/step - loss: 397.3450 - mean_absolute_error: 16.5242 - acc: 0.0000e+00\n",
      "Epoch 182/400\n",
      "80/80 [==============================] - 0s 30us/step - loss: 396.6183 - mean_absolute_error: 16.5126 - acc: 0.0000e+00\n",
      "Epoch 183/400\n",
      "80/80 [==============================] - 0s 33us/step - loss: 395.9126 - mean_absolute_error: 16.5011 - acc: 0.0000e+00\n",
      "Epoch 184/400\n",
      "80/80 [==============================] - 0s 33us/step - loss: 395.2273 - mean_absolute_error: 16.4898 - acc: 0.0000e+00\n",
      "Epoch 185/400\n",
      "80/80 [==============================] - 0s 46us/step - loss: 394.5618 - mean_absolute_error: 16.4786 - acc: 0.0000e+00\n",
      "Epoch 186/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 393.9155 - mean_absolute_error: 16.4677 - acc: 0.0000e+00\n",
      "Epoch 187/400\n",
      "80/80 [==============================] - 0s 43us/step - loss: 393.2879 - mean_absolute_error: 16.4568 - acc: 0.0000e+00\n",
      "Epoch 188/400\n",
      "80/80 [==============================] - 0s 28us/step - loss: 392.6784 - mean_absolute_error: 16.4462 - acc: 0.0000e+00\n",
      "Epoch 189/400\n",
      "80/80 [==============================] - 0s 38us/step - loss: 392.0865 - mean_absolute_error: 16.4357 - acc: 0.0000e+00\n",
      "Epoch 190/400\n",
      "80/80 [==============================] - 0s 75us/step - loss: 391.5117 - mean_absolute_error: 16.4253 - acc: 0.0000e+00\n",
      "Epoch 191/400\n",
      "80/80 [==============================] - 0s 49us/step - loss: 390.9534 - mean_absolute_error: 16.4151 - acc: 0.0000e+00\n",
      "Epoch 192/400\n",
      "80/80 [==============================] - 0s 50us/step - loss: 390.4113 - mean_absolute_error: 16.4051 - acc: 0.0000e+00\n",
      "Epoch 193/400\n",
      "80/80 [==============================] - 0s 27us/step - loss: 389.8849 - mean_absolute_error: 16.3952 - acc: 0.0000e+00\n",
      "Epoch 194/400\n",
      "80/80 [==============================] - 0s 29us/step - loss: 389.3737 - mean_absolute_error: 16.3854 - acc: 0.0000e+00\n",
      "Epoch 195/400\n",
      "80/80 [==============================] - 0s 35us/step - loss: 388.8773 - mean_absolute_error: 16.3758 - acc: 0.0000e+00\n",
      "Epoch 196/400\n",
      "80/80 [==============================] - 0s 28us/step - loss: 388.3951 - mean_absolute_error: 16.3664 - acc: 0.0000e+00\n",
      "Epoch 197/400\n",
      "80/80 [==============================] - 0s 38us/step - loss: 387.9269 - mean_absolute_error: 16.3570 - acc: 0.0000e+00\n",
      "Epoch 198/400\n",
      "80/80 [==============================] - 0s 29us/step - loss: 387.4723 - mean_absolute_error: 16.3478 - acc: 0.0000e+00\n",
      "Epoch 199/400\n",
      "80/80 [==============================] - 0s 49us/step - loss: 387.0308 - mean_absolute_error: 16.3388 - acc: 0.0000e+00\n",
      "Epoch 200/400\n",
      "80/80 [==============================] - 0s 39us/step - loss: 386.6020 - mean_absolute_error: 16.3299 - acc: 0.0000e+00\n",
      "Epoch 201/400\n",
      "80/80 [==============================] - 0s 35us/step - loss: 386.1856 - mean_absolute_error: 16.3218 - acc: 0.0000e+00\n",
      "Epoch 202/400\n",
      "80/80 [==============================] - 0s 106us/step - loss: 385.7812 - mean_absolute_error: 16.3151 - acc: 0.0000e+00\n",
      "Epoch 203/400\n",
      "80/80 [==============================] - 0s 57us/step - loss: 385.3885 - mean_absolute_error: 16.3084 - acc: 0.0000e+00\n",
      "Epoch 204/400\n",
      "80/80 [==============================] - 0s 38us/step - loss: 385.0071 - mean_absolute_error: 16.3019 - acc: 0.0000e+00\n",
      "Epoch 205/400\n",
      "80/80 [==============================] - 0s 27us/step - loss: 384.6367 - mean_absolute_error: 16.2966 - acc: 0.0000e+00\n",
      "Epoch 206/400\n",
      "80/80 [==============================] - 0s 46us/step - loss: 384.2770 - mean_absolute_error: 16.2916 - acc: 0.0000e+00\n",
      "Epoch 207/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 383.9278 - mean_absolute_error: 16.2868 - acc: 0.0000e+00\n",
      "Epoch 208/400\n",
      "80/80 [==============================] - 0s 49us/step - loss: 383.5886 - mean_absolute_error: 16.2820 - acc: 0.0000e+00\n",
      "Epoch 209/400\n",
      "80/80 [==============================] - 0s 59us/step - loss: 383.2592 - mean_absolute_error: 16.2772 - acc: 0.0000e+00\n",
      "Epoch 210/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 382.9393 - mean_absolute_error: 16.2726 - acc: 0.0000e+00\n",
      "Epoch 211/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 382.6286 - mean_absolute_error: 16.2680 - acc: 0.0000e+00\n",
      "Epoch 212/400\n",
      "80/80 [==============================] - 0s 40us/step - loss: 382.3270 - mean_absolute_error: 16.2635 - acc: 0.0000e+00\n",
      "Epoch 213/400\n",
      "80/80 [==============================] - 0s 148us/step - loss: 382.0340 - mean_absolute_error: 16.2590 - acc: 0.0000e+00\n",
      "Epoch 214/400\n",
      "80/80 [==============================] - 0s 39us/step - loss: 381.7495 - mean_absolute_error: 16.2546 - acc: 0.0000e+00\n",
      "Epoch 215/400\n",
      "80/80 [==============================] - 0s 46us/step - loss: 381.4732 - mean_absolute_error: 16.2503 - acc: 0.0000e+00\n",
      "Epoch 216/400\n",
      "80/80 [==============================] - 0s 41us/step - loss: 381.2049 - mean_absolute_error: 16.2460 - acc: 0.0000e+00\n",
      "Epoch 217/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 380.9442 - mean_absolute_error: 16.2418 - acc: 0.0000e+00\n",
      "Epoch 218/400\n",
      "80/80 [==============================] - 0s 21us/step - loss: 380.6913 - mean_absolute_error: 16.2376 - acc: 0.0000e+00\n",
      "Epoch 219/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 380.4455 - mean_absolute_error: 16.2335 - acc: 0.0000e+00\n",
      "Epoch 220/400\n",
      "80/80 [==============================] - 0s 29us/step - loss: 380.2069 - mean_absolute_error: 16.2295 - acc: 0.0000e+00\n",
      "Epoch 221/400\n",
      "80/80 [==============================] - 0s 29us/step - loss: 379.9751 - mean_absolute_error: 16.2255 - acc: 0.0000e+00\n",
      "Epoch 222/400\n",
      "80/80 [==============================] - 0s 45us/step - loss: 379.7500 - mean_absolute_error: 16.2216 - acc: 0.0000e+00\n",
      "Epoch 223/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 379.5314 - mean_absolute_error: 16.2178 - acc: 0.0000e+00\n",
      "Epoch 224/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 379.3192 - mean_absolute_error: 16.2140 - acc: 0.0000e+00\n",
      "Epoch 225/400\n",
      "80/80 [==============================] - 0s 53us/step - loss: 379.1130 - mean_absolute_error: 16.2102 - acc: 0.0000e+00\n",
      "Epoch 226/400\n",
      "80/80 [==============================] - 0s 40us/step - loss: 378.9128 - mean_absolute_error: 16.2065 - acc: 0.0000e+00\n",
      "Epoch 227/400\n",
      "80/80 [==============================] - 0s 46us/step - loss: 378.7184 - mean_absolute_error: 16.2029 - acc: 0.0000e+00\n",
      "Epoch 228/400\n",
      "80/80 [==============================] - 0s 42us/step - loss: 378.5296 - mean_absolute_error: 16.1993 - acc: 0.0000e+00\n",
      "Epoch 229/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 378.3463 - mean_absolute_error: 16.1958 - acc: 0.0000e+00\n",
      "Epoch 230/400\n",
      "80/80 [==============================] - 0s 28us/step - loss: 378.1682 - mean_absolute_error: 16.1923 - acc: 0.0000e+00\n",
      "Epoch 231/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 377.9953 - mean_absolute_error: 16.1889 - acc: 0.0000e+00\n",
      "Epoch 232/400\n",
      "80/80 [==============================] - 0s 35us/step - loss: 377.8274 - mean_absolute_error: 16.1855 - acc: 0.0000e+00\n",
      "Epoch 233/400\n",
      "80/80 [==============================] - 0s 38us/step - loss: 377.6643 - mean_absolute_error: 16.1821 - acc: 0.0000e+00\n",
      "Epoch 234/400\n",
      "80/80 [==============================] - 0s 161us/step - loss: 377.5059 - mean_absolute_error: 16.1788 - acc: 0.0000e+00\n",
      "Epoch 235/400\n",
      "80/80 [==============================] - 0s 56us/step - loss: 377.3521 - mean_absolute_error: 16.1756 - acc: 0.0000e+00\n",
      "Epoch 236/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 377.2028 - mean_absolute_error: 16.1724 - acc: 0.0000e+00\n",
      "Epoch 237/400\n",
      "80/80 [==============================] - 0s 29us/step - loss: 377.0577 - mean_absolute_error: 16.1693 - acc: 0.0000e+00\n",
      "Epoch 238/400\n",
      "80/80 [==============================] - 0s 71us/step - loss: 376.9168 - mean_absolute_error: 16.1662 - acc: 0.0000e+00\n",
      "Epoch 239/400\n",
      "80/80 [==============================] - 0s 39us/step - loss: 376.7800 - mean_absolute_error: 16.1631 - acc: 0.0000e+00\n",
      "Epoch 240/400\n",
      "80/80 [==============================] - 0s 28us/step - loss: 376.6472 - mean_absolute_error: 16.1601 - acc: 0.0000e+00\n",
      "Epoch 241/400\n",
      "80/80 [==============================] - 0s 82us/step - loss: 376.5182 - mean_absolute_error: 16.1580 - acc: 0.0000e+00\n",
      "Epoch 242/400\n",
      "80/80 [==============================] - 0s 57us/step - loss: 376.3929 - mean_absolute_error: 16.1563 - acc: 0.0000e+00\n",
      "Epoch 243/400\n",
      "80/80 [==============================] - 0s 54us/step - loss: 376.2712 - mean_absolute_error: 16.1546 - acc: 0.0000e+00\n",
      "Epoch 244/400\n",
      "80/80 [==============================] - 0s 40us/step - loss: 376.1531 - mean_absolute_error: 16.1530 - acc: 0.0000e+00\n",
      "Epoch 245/400\n",
      "80/80 [==============================] - 0s 33us/step - loss: 376.0383 - mean_absolute_error: 16.1513 - acc: 0.0000e+00\n",
      "Epoch 246/400\n",
      "80/80 [==============================] - 0s 76us/step - loss: 375.9269 - mean_absolute_error: 16.1497 - acc: 0.0000e+00\n",
      "Epoch 247/400\n",
      "80/80 [==============================] - 0s 35us/step - loss: 375.8187 - mean_absolute_error: 16.1481 - acc: 0.0000e+00\n",
      "Epoch 248/400\n",
      "80/80 [==============================] - 0s 41us/step - loss: 375.7136 - mean_absolute_error: 16.1465 - acc: 0.0000e+00\n",
      "Epoch 249/400\n",
      "80/80 [==============================] - 0s 37us/step - loss: 375.6115 - mean_absolute_error: 16.1450 - acc: 0.0000e+00\n",
      "Epoch 250/400\n",
      "80/80 [==============================] - 0s 59us/step - loss: 375.5124 - mean_absolute_error: 16.1434 - acc: 0.0000e+00\n",
      "Epoch 251/400\n",
      "80/80 [==============================] - 0s 51us/step - loss: 375.4161 - mean_absolute_error: 16.1419 - acc: 0.0000e+00\n",
      "Epoch 252/400\n",
      "80/80 [==============================] - 0s 28us/step - loss: 375.3226 - mean_absolute_error: 16.1405 - acc: 0.0000e+00\n",
      "Epoch 253/400\n",
      "80/80 [==============================] - 0s 59us/step - loss: 375.2318 - mean_absolute_error: 16.1390 - acc: 0.0000e+00\n",
      "Epoch 254/400\n",
      "80/80 [==============================] - 0s 41us/step - loss: 375.1436 - mean_absolute_error: 16.1375 - acc: 0.0000e+00\n",
      "Epoch 255/400\n",
      "80/80 [==============================] - 0s 58us/step - loss: 375.0580 - mean_absolute_error: 16.1361 - acc: 0.0000e+00\n",
      "Epoch 256/400\n",
      "80/80 [==============================] - 0s 49us/step - loss: 374.9749 - mean_absolute_error: 16.1347 - acc: 0.0000e+00\n",
      "Epoch 257/400\n",
      "80/80 [==============================] - 0s 46us/step - loss: 374.8941 - mean_absolute_error: 16.1333 - acc: 0.0000e+00\n",
      "Epoch 258/400\n",
      "80/80 [==============================] - 0s 47us/step - loss: 374.8157 - mean_absolute_error: 16.1320 - acc: 0.0000e+00\n",
      "Epoch 259/400\n",
      "80/80 [==============================] - 0s 43us/step - loss: 374.7396 - mean_absolute_error: 16.1306 - acc: 0.0000e+00\n",
      "Epoch 260/400\n",
      "80/80 [==============================] - 0s 45us/step - loss: 374.6656 - mean_absolute_error: 16.1293 - acc: 0.0000e+00\n",
      "Epoch 261/400\n",
      "80/80 [==============================] - 0s 55us/step - loss: 374.5938 - mean_absolute_error: 16.1280 - acc: 0.0000e+00\n",
      "Epoch 262/400\n",
      "80/80 [==============================] - 0s 23us/step - loss: 374.5240 - mean_absolute_error: 16.1267 - acc: 0.0000e+00\n",
      "Epoch 263/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 374.4563 - mean_absolute_error: 16.1255 - acc: 0.0000e+00\n",
      "Epoch 264/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 374.3905 - mean_absolute_error: 16.1242 - acc: 0.0000e+00\n",
      "Epoch 265/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 374.3266 - mean_absolute_error: 16.1230 - acc: 0.0000e+00\n",
      "Epoch 266/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 374.2646 - mean_absolute_error: 16.1218 - acc: 0.0000e+00\n",
      "Epoch 267/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 374.2043 - mean_absolute_error: 16.1206 - acc: 0.0000e+00\n",
      "Epoch 268/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 374.1458 - mean_absolute_error: 16.1194 - acc: 0.0000e+00\n",
      "Epoch 269/400\n",
      "80/80 [==============================] - 0s 26us/step - loss: 374.0890 - mean_absolute_error: 16.1183 - acc: 0.0000e+00\n",
      "Epoch 270/400\n",
      "80/80 [==============================] - 0s 26us/step - loss: 374.0338 - mean_absolute_error: 16.1171 - acc: 0.0000e+00\n",
      "Epoch 271/400\n",
      "80/80 [==============================] - 0s 36us/step - loss: 373.9802 - mean_absolute_error: 16.1160 - acc: 0.0000e+00\n",
      "Epoch 272/400\n",
      "80/80 [==============================] - 0s 26us/step - loss: 373.9282 - mean_absolute_error: 16.1149 - acc: 0.0000e+00\n",
      "Epoch 273/400\n",
      "80/80 [==============================] - 0s 33us/step - loss: 373.8776 - mean_absolute_error: 16.1138 - acc: 0.0000e+00\n",
      "Epoch 274/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 373.8286 - mean_absolute_error: 16.1127 - acc: 0.0000e+00\n",
      "Epoch 275/400\n",
      "80/80 [==============================] - 0s 71us/step - loss: 373.7809 - mean_absolute_error: 16.1117 - acc: 0.0000e+00\n",
      "Epoch 276/400\n",
      "80/80 [==============================] - 0s 35us/step - loss: 373.7346 - mean_absolute_error: 16.1106 - acc: 0.0000e+00\n",
      "Epoch 277/400\n",
      "80/80 [==============================] - 0s 154us/step - loss: 373.6896 - mean_absolute_error: 16.1096 - acc: 0.0000e+00\n",
      "Epoch 278/400\n",
      "80/80 [==============================] - 0s 63us/step - loss: 373.6459 - mean_absolute_error: 16.1086 - acc: 0.0000e+00\n",
      "Epoch 279/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 373.6035 - mean_absolute_error: 16.1076 - acc: 0.0000e+00\n",
      "Epoch 280/400\n",
      "80/80 [==============================] - 0s 22us/step - loss: 373.5624 - mean_absolute_error: 16.1066 - acc: 0.0000e+00\n",
      "Epoch 281/400\n",
      "80/80 [==============================] - 0s 42us/step - loss: 373.5224 - mean_absolute_error: 16.1057 - acc: 0.0000e+00\n",
      "Epoch 282/400\n",
      "80/80 [==============================] - 0s 23us/step - loss: 373.4836 - mean_absolute_error: 16.1053 - acc: 0.0000e+00\n",
      "Epoch 283/400\n",
      "80/80 [==============================] - 0s 44us/step - loss: 373.4458 - mean_absolute_error: 16.1049 - acc: 0.0000e+00\n",
      "Epoch 284/400\n",
      "80/80 [==============================] - 0s 67us/step - loss: 373.4092 - mean_absolute_error: 16.1044 - acc: 0.0000e+00\n",
      "Epoch 285/400\n",
      "80/80 [==============================] - 0s 49us/step - loss: 373.3737 - mean_absolute_error: 16.1040 - acc: 0.0000e+00\n",
      "Epoch 286/400\n",
      "80/80 [==============================] - 0s 197us/step - loss: 373.3391 - mean_absolute_error: 16.1036 - acc: 0.0000e+00\n",
      "Epoch 287/400\n",
      "80/80 [==============================] - 0s 51us/step - loss: 373.3055 - mean_absolute_error: 16.1032 - acc: 0.0000e+00\n",
      "Epoch 288/400\n",
      "80/80 [==============================] - 0s 216us/step - loss: 373.2730 - mean_absolute_error: 16.1027 - acc: 0.0000e+00\n",
      "Epoch 289/400\n",
      "80/80 [==============================] - 0s 81us/step - loss: 373.2414 - mean_absolute_error: 16.1023 - acc: 0.0000e+00\n",
      "Epoch 290/400\n",
      "80/80 [==============================] - 0s 242us/step - loss: 373.2106 - mean_absolute_error: 16.1019 - acc: 0.0000e+00\n",
      "Epoch 291/400\n",
      "80/80 [==============================] - 0s 65us/step - loss: 373.1808 - mean_absolute_error: 16.1016 - acc: 0.0000e+00\n",
      "Epoch 292/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 373.1518 - mean_absolute_error: 16.1012 - acc: 0.0000e+00\n",
      "Epoch 293/400\n",
      "80/80 [==============================] - 0s 36us/step - loss: 373.1236 - mean_absolute_error: 16.1008 - acc: 0.0000e+00\n",
      "Epoch 294/400\n",
      "80/80 [==============================] - 0s 61us/step - loss: 373.0963 - mean_absolute_error: 16.1004 - acc: 0.0000e+00\n",
      "Epoch 295/400\n",
      "80/80 [==============================] - 0s 27us/step - loss: 373.0698 - mean_absolute_error: 16.1000 - acc: 0.0000e+00\n",
      "Epoch 296/400\n",
      "80/80 [==============================] - 0s 83us/step - loss: 373.0440 - mean_absolute_error: 16.0997 - acc: 0.0000e+00\n",
      "Epoch 297/400\n",
      "80/80 [==============================] - 0s 44us/step - loss: 373.0190 - mean_absolute_error: 16.0993 - acc: 0.0000e+00\n",
      "Epoch 298/400\n",
      "80/80 [==============================] - 0s 29us/step - loss: 372.9947 - mean_absolute_error: 16.0990 - acc: 0.0000e+00\n",
      "Epoch 299/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 372.9710 - mean_absolute_error: 16.0986 - acc: 0.0000e+00\n",
      "Epoch 300/400\n",
      "80/80 [==============================] - 0s 32us/step - loss: 372.9481 - mean_absolute_error: 16.0983 - acc: 0.0000e+00\n",
      "Epoch 301/400\n",
      "80/80 [==============================] - 0s 36us/step - loss: 372.9258 - mean_absolute_error: 16.0979 - acc: 0.0000e+00\n",
      "Epoch 302/400\n",
      "80/80 [==============================] - 0s 32us/step - loss: 372.9042 - mean_absolute_error: 16.0976 - acc: 0.0000e+00\n",
      "Epoch 303/400\n",
      "80/80 [==============================] - 0s 40us/step - loss: 372.8832 - mean_absolute_error: 16.0973 - acc: 0.0000e+00\n",
      "Epoch 304/400\n",
      "80/80 [==============================] - 0s 37us/step - loss: 372.8628 - mean_absolute_error: 16.0969 - acc: 0.0000e+00\n",
      "Epoch 305/400\n",
      "80/80 [==============================] - 0s 41us/step - loss: 372.8430 - mean_absolute_error: 16.0966 - acc: 0.0000e+00\n",
      "Epoch 306/400\n",
      "80/80 [==============================] - 0s 43us/step - loss: 372.8237 - mean_absolute_error: 16.0963 - acc: 0.0000e+00\n",
      "Epoch 307/400\n",
      "80/80 [==============================] - 0s 29us/step - loss: 372.8050 - mean_absolute_error: 16.0960 - acc: 0.0000e+00\n",
      "Epoch 308/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 372.7869 - mean_absolute_error: 16.0957 - acc: 0.0000e+00\n",
      "Epoch 309/400\n",
      "80/80 [==============================] - 0s 27us/step - loss: 372.7693 - mean_absolute_error: 16.0954 - acc: 0.0000e+00\n",
      "Epoch 310/400\n",
      "80/80 [==============================] - 0s 27us/step - loss: 372.7522 - mean_absolute_error: 16.0951 - acc: 0.0000e+00\n",
      "Epoch 311/400\n",
      "80/80 [==============================] - 0s 47us/step - loss: 372.7355 - mean_absolute_error: 16.0948 - acc: 0.0000e+00\n",
      "Epoch 312/400\n",
      "80/80 [==============================] - 0s 30us/step - loss: 372.7194 - mean_absolute_error: 16.0945 - acc: 0.0000e+00\n",
      "Epoch 313/400\n",
      "80/80 [==============================] - 0s 30us/step - loss: 372.7037 - mean_absolute_error: 16.0942 - acc: 0.0000e+00\n",
      "Epoch 314/400\n",
      "80/80 [==============================] - 0s 37us/step - loss: 372.6886 - mean_absolute_error: 16.0939 - acc: 0.0000e+00\n",
      "Epoch 315/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 372.6737 - mean_absolute_error: 16.0936 - acc: 0.0000e+00\n",
      "Epoch 316/400\n",
      "80/80 [==============================] - 0s 51us/step - loss: 372.6594 - mean_absolute_error: 16.0934 - acc: 0.0000e+00\n",
      "Epoch 317/400\n",
      "80/80 [==============================] - 0s 55us/step - loss: 372.6454 - mean_absolute_error: 16.0931 - acc: 0.0000e+00\n",
      "Epoch 318/400\n",
      "80/80 [==============================] - 0s 27us/step - loss: 372.6319 - mean_absolute_error: 16.0928 - acc: 0.0000e+00\n",
      "Epoch 319/400\n",
      "80/80 [==============================] - 0s 40us/step - loss: 372.6187 - mean_absolute_error: 16.0926 - acc: 0.0000e+00\n",
      "Epoch 320/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 372.6060 - mean_absolute_error: 16.0923 - acc: 0.0000e+00\n",
      "Epoch 321/400\n",
      "80/80 [==============================] - 0s 27us/step - loss: 372.5936 - mean_absolute_error: 16.0921 - acc: 0.0000e+00\n",
      "Epoch 322/400\n",
      "80/80 [==============================] - 0s 28us/step - loss: 372.5815 - mean_absolute_error: 16.0918 - acc: 0.0000e+00\n",
      "Epoch 323/400\n",
      "80/80 [==============================] - 0s 41us/step - loss: 372.5699 - mean_absolute_error: 16.0916 - acc: 0.0000e+00\n",
      "Epoch 324/400\n",
      "80/80 [==============================] - 0s 36us/step - loss: 372.5585 - mean_absolute_error: 16.0913 - acc: 0.0000e+00\n",
      "Epoch 325/400\n",
      "80/80 [==============================] - 0s 57us/step - loss: 372.5475 - mean_absolute_error: 16.0911 - acc: 0.0000e+00\n",
      "Epoch 326/400\n",
      "80/80 [==============================] - 0s 32us/step - loss: 372.5367 - mean_absolute_error: 16.0908 - acc: 0.0000e+00\n",
      "Epoch 327/400\n",
      "80/80 [==============================] - 0s 29us/step - loss: 372.5263 - mean_absolute_error: 16.0906 - acc: 0.0000e+00\n",
      "Epoch 328/400\n",
      "80/80 [==============================] - 0s 47us/step - loss: 372.5162 - mean_absolute_error: 16.0904 - acc: 0.0000e+00\n",
      "Epoch 329/400\n",
      "80/80 [==============================] - 0s 33us/step - loss: 372.5064 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 330/400\n",
      "80/80 [==============================] - 0s 38us/step - loss: 372.4969 - mean_absolute_error: 16.0899 - acc: 0.0000e+00\n",
      "Epoch 331/400\n",
      "80/80 [==============================] - 0s 32us/step - loss: 372.4877 - mean_absolute_error: 16.0897 - acc: 0.0000e+00\n",
      "Epoch 332/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 372.4786 - mean_absolute_error: 16.0895 - acc: 0.0000e+00\n",
      "Epoch 333/400\n",
      "80/80 [==============================] - 0s 42us/step - loss: 372.4699 - mean_absolute_error: 16.0895 - acc: 0.0000e+00\n",
      "Epoch 334/400\n",
      "80/80 [==============================] - 0s 37us/step - loss: 372.4615 - mean_absolute_error: 16.0895 - acc: 0.0000e+00\n",
      "Epoch 335/400\n",
      "80/80 [==============================] - 0s 53us/step - loss: 372.4532 - mean_absolute_error: 16.0896 - acc: 0.0000e+00\n",
      "Epoch 336/400\n",
      "80/80 [==============================] - 0s 42us/step - loss: 372.4452 - mean_absolute_error: 16.0896 - acc: 0.0000e+00\n",
      "Epoch 337/400\n",
      "80/80 [==============================] - 0s 24us/step - loss: 372.4374 - mean_absolute_error: 16.0896 - acc: 0.0000e+00\n",
      "Epoch 338/400\n",
      "80/80 [==============================] - 0s 36us/step - loss: 372.4299 - mean_absolute_error: 16.0896 - acc: 0.0000e+00\n",
      "Epoch 339/400\n",
      "80/80 [==============================] - 0s 59us/step - loss: 372.4226 - mean_absolute_error: 16.0896 - acc: 0.0000e+00\n",
      "Epoch 340/400\n",
      "80/80 [==============================] - 0s 32us/step - loss: 372.4155 - mean_absolute_error: 16.0896 - acc: 0.0000e+00\n",
      "Epoch 341/400\n",
      "80/80 [==============================] - 0s 64us/step - loss: 372.4086 - mean_absolute_error: 16.0896 - acc: 0.0000e+00\n",
      "Epoch 342/400\n",
      "80/80 [==============================] - 0s 49us/step - loss: 372.4019 - mean_absolute_error: 16.0897 - acc: 0.0000e+00\n",
      "Epoch 343/400\n",
      "80/80 [==============================] - 0s 60us/step - loss: 372.3954 - mean_absolute_error: 16.0897 - acc: 0.0000e+00\n",
      "Epoch 344/400\n",
      "80/80 [==============================] - 0s 41us/step - loss: 372.3890 - mean_absolute_error: 16.0897 - acc: 0.0000e+00\n",
      "Epoch 345/400\n",
      "80/80 [==============================] - 0s 23us/step - loss: 372.3829 - mean_absolute_error: 16.0897 - acc: 0.0000e+00\n",
      "Epoch 346/400\n",
      "80/80 [==============================] - 0s 46us/step - loss: 372.3769 - mean_absolute_error: 16.0897 - acc: 0.0000e+00\n",
      "Epoch 347/400\n",
      "80/80 [==============================] - 0s 46us/step - loss: 372.3712 - mean_absolute_error: 16.0897 - acc: 0.0000e+00\n",
      "Epoch 348/400\n",
      "80/80 [==============================] - 0s 43us/step - loss: 372.3655 - mean_absolute_error: 16.0897 - acc: 0.0000e+00\n",
      "Epoch 349/400\n",
      "80/80 [==============================] - 0s 41us/step - loss: 372.3600 - mean_absolute_error: 16.0897 - acc: 0.0000e+00\n",
      "Epoch 350/400\n",
      "80/80 [==============================] - 0s 21us/step - loss: 372.3547 - mean_absolute_error: 16.0898 - acc: 0.0000e+00\n",
      "Epoch 351/400\n",
      "80/80 [==============================] - 0s 51us/step - loss: 372.3495 - mean_absolute_error: 16.0898 - acc: 0.0000e+00\n",
      "Epoch 352/400\n",
      "80/80 [==============================] - 0s 43us/step - loss: 372.3446 - mean_absolute_error: 16.0898 - acc: 0.0000e+00\n",
      "Epoch 353/400\n",
      "80/80 [==============================] - 0s 26us/step - loss: 372.3397 - mean_absolute_error: 16.0898 - acc: 0.0000e+00\n",
      "Epoch 354/400\n",
      "80/80 [==============================] - 0s 39us/step - loss: 372.3350 - mean_absolute_error: 16.0898 - acc: 0.0000e+00\n",
      "Epoch 355/400\n",
      "80/80 [==============================] - 0s 47us/step - loss: 372.3304 - mean_absolute_error: 16.0898 - acc: 0.0000e+00\n",
      "Epoch 356/400\n",
      "80/80 [==============================] - 0s 39us/step - loss: 372.3260 - mean_absolute_error: 16.0898 - acc: 0.0000e+00\n",
      "Epoch 357/400\n",
      "80/80 [==============================] - 0s 61us/step - loss: 372.3216 - mean_absolute_error: 16.0898 - acc: 0.0000e+00\n",
      "Epoch 358/400\n",
      "80/80 [==============================] - 0s 26us/step - loss: 372.3174 - mean_absolute_error: 16.0898 - acc: 0.0000e+00\n",
      "Epoch 359/400\n",
      "80/80 [==============================] - 0s 73us/step - loss: 372.3133 - mean_absolute_error: 16.0899 - acc: 0.0000e+00\n",
      "Epoch 360/400\n",
      "80/80 [==============================] - 0s 49us/step - loss: 372.3094 - mean_absolute_error: 16.0899 - acc: 0.0000e+00\n",
      "Epoch 361/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 372.3056 - mean_absolute_error: 16.0899 - acc: 0.0000e+00\n",
      "Epoch 362/400\n",
      "80/80 [==============================] - 0s 41us/step - loss: 372.3018 - mean_absolute_error: 16.0899 - acc: 0.0000e+00\n",
      "Epoch 363/400\n",
      "80/80 [==============================] - 0s 46us/step - loss: 372.2982 - mean_absolute_error: 16.0899 - acc: 0.0000e+00\n",
      "Epoch 364/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 372.2946 - mean_absolute_error: 16.0899 - acc: 0.0000e+00\n",
      "Epoch 365/400\n",
      "80/80 [==============================] - 0s 67us/step - loss: 372.2912 - mean_absolute_error: 16.0899 - acc: 0.0000e+00\n",
      "Epoch 366/400\n",
      "80/80 [==============================] - 0s 42us/step - loss: 372.2879 - mean_absolute_error: 16.0899 - acc: 0.0000e+00\n",
      "Epoch 367/400\n",
      "80/80 [==============================] - 0s 44us/step - loss: 372.2847 - mean_absolute_error: 16.0899 - acc: 0.0000e+00\n",
      "Epoch 368/400\n",
      "80/80 [==============================] - 0s 108us/step - loss: 372.2816 - mean_absolute_error: 16.0899 - acc: 0.0000e+00\n",
      "Epoch 369/400\n",
      "80/80 [==============================] - 0s 49us/step - loss: 372.2785 - mean_absolute_error: 16.0900 - acc: 0.0000e+00\n",
      "Epoch 370/400\n",
      "80/80 [==============================] - 0s 70us/step - loss: 372.2756 - mean_absolute_error: 16.0900 - acc: 0.0000e+00\n",
      "Epoch 371/400\n",
      "80/80 [==============================] - 0s 52us/step - loss: 372.2727 - mean_absolute_error: 16.0900 - acc: 0.0000e+00\n",
      "Epoch 372/400\n",
      "80/80 [==============================] - 0s 44us/step - loss: 372.2699 - mean_absolute_error: 16.0900 - acc: 0.0000e+00\n",
      "Epoch 373/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 372.2672 - mean_absolute_error: 16.0900 - acc: 0.0000e+00\n",
      "Epoch 374/400\n",
      "80/80 [==============================] - 0s 42us/step - loss: 372.2646 - mean_absolute_error: 16.0900 - acc: 0.0000e+00\n",
      "Epoch 375/400\n",
      "80/80 [==============================] - 0s 38us/step - loss: 372.2620 - mean_absolute_error: 16.0900 - acc: 0.0000e+00\n",
      "Epoch 376/400\n",
      "80/80 [==============================] - 0s 62us/step - loss: 372.2595 - mean_absolute_error: 16.0900 - acc: 0.0000e+00\n",
      "Epoch 377/400\n",
      "80/80 [==============================] - 0s 129us/step - loss: 372.2571 - mean_absolute_error: 16.0900 - acc: 0.0000e+00\n",
      "Epoch 378/400\n",
      "80/80 [==============================] - 0s 54us/step - loss: 372.2548 - mean_absolute_error: 16.0900 - acc: 0.0000e+00\n",
      "Epoch 379/400\n",
      "80/80 [==============================] - 0s 49us/step - loss: 372.2525 - mean_absolute_error: 16.0900 - acc: 0.0000e+00\n",
      "Epoch 380/400\n",
      "80/80 [==============================] - 0s 49us/step - loss: 372.2503 - mean_absolute_error: 16.0900 - acc: 0.0000e+00\n",
      "Epoch 381/400\n",
      "80/80 [==============================] - 0s 26us/step - loss: 372.2481 - mean_absolute_error: 16.0900 - acc: 0.0000e+00\n",
      "Epoch 382/400\n",
      "80/80 [==============================] - 0s 44us/step - loss: 372.2461 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 383/400\n",
      "80/80 [==============================] - 0s 27us/step - loss: 372.2441 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 384/400\n",
      "80/80 [==============================] - 0s 42us/step - loss: 372.2421 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 385/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 372.2402 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 386/400\n",
      "80/80 [==============================] - 0s 71us/step - loss: 372.2383 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 387/400\n",
      "80/80 [==============================] - 0s 32us/step - loss: 372.2366 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 388/400\n",
      "80/80 [==============================] - 0s 52us/step - loss: 372.2348 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 389/400\n",
      "80/80 [==============================] - 0s 41us/step - loss: 372.2332 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 390/400\n",
      "80/80 [==============================] - 0s 26us/step - loss: 372.2314 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 391/400\n",
      "80/80 [==============================] - 0s 34us/step - loss: 372.2299 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 392/400\n",
      "80/80 [==============================] - 0s 50us/step - loss: 372.2283 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 393/400\n",
      "80/80 [==============================] - 0s 25us/step - loss: 372.2267 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 394/400\n",
      "80/80 [==============================] - 0s 57us/step - loss: 372.2253 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 395/400\n",
      "80/80 [==============================] - 0s 44us/step - loss: 372.2239 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 396/400\n",
      "80/80 [==============================] - 0s 42us/step - loss: 372.2225 - mean_absolute_error: 16.0901 - acc: 0.0000e+00\n",
      "Epoch 397/400\n",
      "80/80 [==============================] - 0s 31us/step - loss: 372.2212 - mean_absolute_error: 16.0902 - acc: 0.0000e+00\n",
      "Epoch 398/400\n",
      "80/80 [==============================] - 0s 20us/step - loss: 372.2199 - mean_absolute_error: 16.0902 - acc: 0.0000e+00\n",
      "Epoch 399/400\n",
      "80/80 [==============================] - 0s 38us/step - loss: 372.2186 - mean_absolute_error: 16.0902 - acc: 0.0000e+00\n",
      "Epoch 400/400\n",
      "80/80 [==============================] - 0s 78us/step - loss: 372.2174 - mean_absolute_error: 16.0902 - acc: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "history = lr3.fit(Xtrain, ytrain, epochs=400, batch_size=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a411d7a20>]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[82.38442]], dtype=float32), array([-3.3930035], dtype=float32)]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr3.get_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the Keras Functional API\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 2\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "\n",
    "inputs_placeholder = Input(shape=(1,))\n",
    "outputs_placeholder = Dense(1, activation='linear')(inputs_placeholder)\n",
    "\n",
    "m = Model(inputs=inputs_placeholder, outputs=outputs_placeholder)\n",
    "m.compile(optimizer='sgd', loss='mean_squared_error',  metrics=['mae','accuracy'])\n",
    "m.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "80/80 [==============================] - 0s 6ms/step - loss: 5279.3291 - mean_absolute_error: 59.3928 - acc: 0.0000e+00\n",
      "Epoch 2/250\n",
      "80/80 [==============================] - 0s 22us/step - loss: 5136.4331 - mean_absolute_error: 58.5800 - acc: 0.0000e+00\n",
      "Epoch 3/250\n",
      "80/80 [==============================] - 0s 21us/step - loss: 4997.7144 - mean_absolute_error: 57.7793 - acc: 0.0000e+00\n",
      "Epoch 4/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 4863.0483 - mean_absolute_error: 56.9904 - acc: 0.0000e+00\n",
      "Epoch 5/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 4732.3164 - mean_absolute_error: 56.2221 - acc: 0.0000e+00\n",
      "Epoch 6/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 4605.4038 - mean_absolute_error: 55.4700 - acc: 0.0000e+00\n",
      "Epoch 7/250\n",
      "80/80 [==============================] - 0s 25us/step - loss: 4482.1968 - mean_absolute_error: 54.7378 - acc: 0.0000e+00\n",
      "Epoch 8/250\n",
      "80/80 [==============================] - 0s 22us/step - loss: 4362.5889 - mean_absolute_error: 54.0210 - acc: 0.0000e+00\n",
      "Epoch 9/250\n",
      "80/80 [==============================] - 0s 28us/step - loss: 4246.4736 - mean_absolute_error: 53.3273 - acc: 0.0000e+00\n",
      "Epoch 10/250\n",
      "80/80 [==============================] - 0s 37us/step - loss: 4133.7476 - mean_absolute_error: 52.6478 - acc: 0.0000e+00\n",
      "Epoch 11/250\n",
      "80/80 [==============================] - 0s 25us/step - loss: 4024.3125 - mean_absolute_error: 51.9782 - acc: 0.0000e+00\n",
      "Epoch 12/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 3918.0710 - mean_absolute_error: 51.3186 - acc: 0.0000e+00\n",
      "Epoch 13/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 3814.9304 - mean_absolute_error: 50.6686 - acc: 0.0000e+00\n",
      "Epoch 14/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 3714.7996 - mean_absolute_error: 50.0283 - acc: 0.0000e+00\n",
      "Epoch 15/250\n",
      "80/80 [==============================] - 0s 24us/step - loss: 3617.5903 - mean_absolute_error: 49.3974 - acc: 0.0000e+00\n",
      "Epoch 16/250\n",
      "80/80 [==============================] - 0s 32us/step - loss: 3523.2163 - mean_absolute_error: 48.7757 - acc: 0.0000e+00\n",
      "Epoch 17/250\n",
      "80/80 [==============================] - 0s 37us/step - loss: 3431.5950 - mean_absolute_error: 48.1633 - acc: 0.0000e+00\n",
      "Epoch 18/250\n",
      "80/80 [==============================] - 0s 24us/step - loss: 3342.6460 - mean_absolute_error: 47.5598 - acc: 0.0000e+00\n",
      "Epoch 19/250\n",
      "80/80 [==============================] - 0s 25us/step - loss: 3256.2915 - mean_absolute_error: 46.9652 - acc: 0.0000e+00\n",
      "Epoch 20/250\n",
      "80/80 [==============================] - 0s 38us/step - loss: 3172.4543 - mean_absolute_error: 46.3794 - acc: 0.0000e+00\n",
      "Epoch 21/250\n",
      "80/80 [==============================] - 0s 40us/step - loss: 3091.0610 - mean_absolute_error: 45.8022 - acc: 0.0000e+00\n",
      "Epoch 22/250\n",
      "80/80 [==============================] - 0s 28us/step - loss: 3012.0410 - mean_absolute_error: 45.2336 - acc: 0.0000e+00\n",
      "Epoch 23/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 2935.3240 - mean_absolute_error: 44.6732 - acc: 0.0000e+00\n",
      "Epoch 24/250\n",
      "80/80 [==============================] - 0s 23us/step - loss: 2860.8425 - mean_absolute_error: 44.1212 - acc: 0.0000e+00\n",
      "Epoch 25/250\n",
      "80/80 [==============================] - 0s 48us/step - loss: 2788.5320 - mean_absolute_error: 43.5773 - acc: 0.0000e+00\n",
      "Epoch 26/250\n",
      "80/80 [==============================] - 0s 41us/step - loss: 2718.3281 - mean_absolute_error: 43.0413 - acc: 0.0000e+00\n",
      "Epoch 27/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 2650.1694 - mean_absolute_error: 42.5133 - acc: 0.0000e+00\n",
      "Epoch 28/250\n",
      "80/80 [==============================] - 0s 21us/step - loss: 2583.9966 - mean_absolute_error: 41.9930 - acc: 0.0000e+00\n",
      "Epoch 29/250\n",
      "80/80 [==============================] - 0s 36us/step - loss: 2519.7510 - mean_absolute_error: 41.4804 - acc: 0.0000e+00\n",
      "Epoch 30/250\n",
      "80/80 [==============================] - 0s 32us/step - loss: 2457.3765 - mean_absolute_error: 40.9753 - acc: 0.0000e+00\n",
      "Epoch 31/250\n",
      "80/80 [==============================] - 0s 28us/step - loss: 2396.8184 - mean_absolute_error: 40.4777 - acc: 0.0000e+00\n",
      "Epoch 32/250\n",
      "80/80 [==============================] - 0s 24us/step - loss: 2338.0237 - mean_absolute_error: 39.9874 - acc: 0.0000e+00\n",
      "Epoch 33/250\n",
      "80/80 [==============================] - 0s 25us/step - loss: 2280.9407 - mean_absolute_error: 39.5043 - acc: 0.0000e+00\n",
      "Epoch 34/250\n",
      "80/80 [==============================] - 0s 36us/step - loss: 2225.5195 - mean_absolute_error: 39.0283 - acc: 0.0000e+00\n",
      "Epoch 35/250\n",
      "80/80 [==============================] - 0s 34us/step - loss: 2171.7117 - mean_absolute_error: 38.5593 - acc: 0.0000e+00\n",
      "Epoch 36/250\n",
      "80/80 [==============================] - 0s 25us/step - loss: 2119.4700 - mean_absolute_error: 38.0972 - acc: 0.0000e+00\n",
      "Epoch 37/250\n",
      "80/80 [==============================] - 0s 34us/step - loss: 2068.7485 - mean_absolute_error: 37.6419 - acc: 0.0000e+00\n",
      "Epoch 38/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 2019.5032 - mean_absolute_error: 37.1934 - acc: 0.0000e+00\n",
      "Epoch 39/250\n",
      "80/80 [==============================] - 0s 28us/step - loss: 1971.6908 - mean_absolute_error: 36.7514 - acc: 0.0000e+00\n",
      "Epoch 40/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 1925.2692 - mean_absolute_error: 36.3159 - acc: 0.0000e+00\n",
      "Epoch 41/250\n",
      "80/80 [==============================] - 0s 23us/step - loss: 1880.1980 - mean_absolute_error: 35.8868 - acc: 0.0000e+00\n",
      "Epoch 42/250\n",
      "80/80 [==============================] - 0s 76us/step - loss: 1836.4381 - mean_absolute_error: 35.4640 - acc: 0.0000e+00\n",
      "Epoch 43/250\n",
      "80/80 [==============================] - 0s 30us/step - loss: 1793.9512 - mean_absolute_error: 35.0474 - acc: 0.0000e+00\n",
      "Epoch 44/250\n",
      "80/80 [==============================] - 0s 29us/step - loss: 1752.6996 - mean_absolute_error: 34.6370 - acc: 0.0000e+00\n",
      "Epoch 45/250\n",
      "80/80 [==============================] - 0s 31us/step - loss: 1712.6477 - mean_absolute_error: 34.2326 - acc: 0.0000e+00\n",
      "Epoch 46/250\n",
      "80/80 [==============================] - 0s 36us/step - loss: 1673.7605 - mean_absolute_error: 33.8341 - acc: 0.0000e+00\n",
      "Epoch 47/250\n",
      "80/80 [==============================] - 0s 24us/step - loss: 1636.0037 - mean_absolute_error: 33.4415 - acc: 0.0000e+00\n",
      "Epoch 48/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 1599.3445 - mean_absolute_error: 33.0614 - acc: 0.0000e+00\n",
      "Epoch 49/250\n",
      "80/80 [==============================] - 0s 34us/step - loss: 1563.7513 - mean_absolute_error: 32.6921 - acc: 0.0000e+00\n",
      "Epoch 50/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 1529.1927 - mean_absolute_error: 32.3294 - acc: 0.0000e+00\n",
      "Epoch 51/250\n",
      "80/80 [==============================] - 0s 40us/step - loss: 1495.6383 - mean_absolute_error: 31.9772 - acc: 0.0000e+00\n",
      "Epoch 52/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 1463.0592 - mean_absolute_error: 31.6302 - acc: 0.0000e+00\n",
      "Epoch 53/250\n",
      "80/80 [==============================] - 0s 42us/step - loss: 1431.4266 - mean_absolute_error: 31.2883 - acc: 0.0000e+00\n",
      "Epoch 54/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 1400.7135 - mean_absolute_error: 30.9514 - acc: 0.0000e+00\n",
      "Epoch 55/250\n",
      "80/80 [==============================] - 0s 25us/step - loss: 1370.8926 - mean_absolute_error: 30.6194 - acc: 0.0000e+00\n",
      "Epoch 56/250\n",
      "80/80 [==============================] - 0s 28us/step - loss: 1341.9381 - mean_absolute_error: 30.2923 - acc: 0.0000e+00\n",
      "Epoch 57/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 1313.8248 - mean_absolute_error: 29.9700 - acc: 0.0000e+00\n",
      "Epoch 58/250\n",
      "80/80 [==============================] - 0s 28us/step - loss: 1286.5281 - mean_absolute_error: 29.6573 - acc: 0.0000e+00\n",
      "Epoch 59/250\n",
      "80/80 [==============================] - 0s 41us/step - loss: 1260.0240 - mean_absolute_error: 29.3516 - acc: 0.0000e+00\n",
      "Epoch 60/250\n",
      "80/80 [==============================] - 0s 31us/step - loss: 1234.2898 - mean_absolute_error: 29.0504 - acc: 0.0000e+00\n",
      "Epoch 61/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 1209.3029 - mean_absolute_error: 28.7535 - acc: 0.0000e+00\n",
      "Epoch 62/250\n",
      "80/80 [==============================] - 0s 25us/step - loss: 1185.0417 - mean_absolute_error: 28.4610 - acc: 0.0000e+00\n",
      "Epoch 63/250\n",
      "80/80 [==============================] - 0s 48us/step - loss: 1161.4849 - mean_absolute_error: 28.1727 - acc: 0.0000e+00\n",
      "Epoch 64/250\n",
      "80/80 [==============================] - 0s 25us/step - loss: 1138.6119 - mean_absolute_error: 27.8886 - acc: 0.0000e+00\n",
      "Epoch 65/250\n",
      "80/80 [==============================] - 0s 43us/step - loss: 1116.4031 - mean_absolute_error: 27.6096 - acc: 0.0000e+00\n",
      "Epoch 66/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 1094.8390 - mean_absolute_error: 27.3473 - acc: 0.0000e+00\n",
      "Epoch 67/250\n",
      "80/80 [==============================] - 0s 41us/step - loss: 1073.9008 - mean_absolute_error: 27.0894 - acc: 0.0000e+00\n",
      "Epoch 68/250\n",
      "80/80 [==============================] - 0s 39us/step - loss: 1053.5703 - mean_absolute_error: 26.8354 - acc: 0.0000e+00\n",
      "Epoch 69/250\n",
      "80/80 [==============================] - 0s 25us/step - loss: 1033.8300 - mean_absolute_error: 26.5850 - acc: 0.0000e+00\n",
      "Epoch 70/250\n",
      "80/80 [==============================] - 0s 34us/step - loss: 1014.6624 - mean_absolute_error: 26.3383 - acc: 0.0000e+00\n",
      "Epoch 71/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 996.0510 - mean_absolute_error: 26.0952 - acc: 0.0000e+00\n",
      "Epoch 72/250\n",
      "80/80 [==============================] - 0s 19us/step - loss: 977.9798 - mean_absolute_error: 25.8556 - acc: 0.0000e+00\n",
      "Epoch 73/250\n",
      "80/80 [==============================] - 0s 21us/step - loss: 960.4329 - mean_absolute_error: 25.6195 - acc: 0.0000e+00\n",
      "Epoch 74/250\n",
      "80/80 [==============================] - 0s 38us/step - loss: 943.3951 - mean_absolute_error: 25.3869 - acc: 0.0000e+00\n",
      "Epoch 75/250\n",
      "80/80 [==============================] - 0s 36us/step - loss: 926.8517 - mean_absolute_error: 25.1577 - acc: 0.0000e+00\n",
      "Epoch 76/250\n",
      "80/80 [==============================] - 0s 45us/step - loss: 910.7880 - mean_absolute_error: 24.9349 - acc: 0.0000e+00\n",
      "Epoch 77/250\n",
      "80/80 [==============================] - 0s 31us/step - loss: 895.1902 - mean_absolute_error: 24.7153 - acc: 0.0000e+00\n",
      "Epoch 78/250\n",
      "80/80 [==============================] - 0s 34us/step - loss: 880.0451 - mean_absolute_error: 24.4989 - acc: 0.0000e+00\n",
      "Epoch 79/250\n",
      "80/80 [==============================] - 0s 22us/step - loss: 865.3391 - mean_absolute_error: 24.2857 - acc: 0.0000e+00\n",
      "Epoch 80/250\n",
      "80/80 [==============================] - 0s 48us/step - loss: 851.0598 - mean_absolute_error: 24.0756 - acc: 0.0000e+00\n",
      "Epoch 81/250\n",
      "80/80 [==============================] - 0s 32us/step - loss: 837.1943 - mean_absolute_error: 23.8685 - acc: 0.0000e+00\n",
      "Epoch 82/250\n",
      "80/80 [==============================] - 0s 37us/step - loss: 823.7310 - mean_absolute_error: 23.6647 - acc: 0.0000e+00\n",
      "Epoch 83/250\n",
      "80/80 [==============================] - 0s 45us/step - loss: 810.6580 - mean_absolute_error: 23.4791 - acc: 0.0000e+00\n",
      "Epoch 84/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 797.9642 - mean_absolute_error: 23.2981 - acc: 0.0000e+00\n",
      "Epoch 85/250\n",
      "80/80 [==============================] - 0s 34us/step - loss: 785.6383 - mean_absolute_error: 23.1198 - acc: 0.0000e+00\n",
      "Epoch 86/250\n",
      "80/80 [==============================] - 0s 36us/step - loss: 773.6697 - mean_absolute_error: 22.9441 - acc: 0.0000e+00\n",
      "Epoch 87/250\n",
      "80/80 [==============================] - 0s 45us/step - loss: 762.0481 - mean_absolute_error: 22.7709 - acc: 0.0000e+00\n",
      "Epoch 88/250\n",
      "80/80 [==============================] - 0s 24us/step - loss: 750.7635 - mean_absolute_error: 22.6003 - acc: 0.0000e+00\n",
      "Epoch 89/250\n",
      "80/80 [==============================] - 0s 40us/step - loss: 739.8058 - mean_absolute_error: 22.4321 - acc: 0.0000e+00\n",
      "Epoch 90/250\n",
      "80/80 [==============================] - 0s 44us/step - loss: 729.1658 - mean_absolute_error: 22.2664 - acc: 0.0000e+00\n",
      "Epoch 91/250\n",
      "80/80 [==============================] - 0s 39us/step - loss: 718.8342 - mean_absolute_error: 22.1031 - acc: 0.0000e+00\n",
      "Epoch 92/250\n",
      "80/80 [==============================] - 0s 170us/step - loss: 708.8019 - mean_absolute_error: 21.9422 - acc: 0.0000e+00\n",
      "Epoch 93/250\n",
      "80/80 [==============================] - 0s 37us/step - loss: 699.0604 - mean_absolute_error: 21.7836 - acc: 0.0000e+00\n",
      "Epoch 94/250\n",
      "80/80 [==============================] - 0s 43us/step - loss: 689.6012 - mean_absolute_error: 21.6273 - acc: 0.0000e+00\n",
      "Epoch 95/250\n",
      "80/80 [==============================] - 0s 42us/step - loss: 680.4161 - mean_absolute_error: 21.4781 - acc: 0.0000e+00\n",
      "Epoch 96/250\n",
      "80/80 [==============================] - 0s 38us/step - loss: 671.4971 - mean_absolute_error: 21.3377 - acc: 0.0000e+00\n",
      "Epoch 97/250\n",
      "80/80 [==============================] - 0s 70us/step - loss: 662.8365 - mean_absolute_error: 21.2001 - acc: 0.0000e+00\n",
      "Epoch 98/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 654.4269 - mean_absolute_error: 21.0644 - acc: 0.0000e+00\n",
      "Epoch 99/250\n",
      "80/80 [==============================] - 0s 36us/step - loss: 646.2609 - mean_absolute_error: 20.9307 - acc: 0.0000e+00\n",
      "Epoch 100/250\n",
      "80/80 [==============================] - 0s 54us/step - loss: 638.3312 - mean_absolute_error: 20.7990 - acc: 0.0000e+00\n",
      "Epoch 101/250\n",
      "80/80 [==============================] - 0s 30us/step - loss: 630.6315 - mean_absolute_error: 20.6691 - acc: 0.0000e+00\n",
      "Epoch 102/250\n",
      "80/80 [==============================] - 0s 32us/step - loss: 623.1547 - mean_absolute_error: 20.5412 - acc: 0.0000e+00\n",
      "Epoch 103/250\n",
      "80/80 [==============================] - 0s 35us/step - loss: 615.8947 - mean_absolute_error: 20.4170 - acc: 0.0000e+00\n",
      "Epoch 104/250\n",
      "80/80 [==============================] - 0s 34us/step - loss: 608.8446 - mean_absolute_error: 20.2989 - acc: 0.0000e+00\n",
      "Epoch 105/250\n",
      "80/80 [==============================] - 0s 38us/step - loss: 601.9988 - mean_absolute_error: 20.1825 - acc: 0.0000e+00\n",
      "Epoch 106/250\n",
      "80/80 [==============================] - 0s 41us/step - loss: 595.3514 - mean_absolute_error: 20.0702 - acc: 0.0000e+00\n",
      "Epoch 107/250\n",
      "80/80 [==============================] - 0s 37us/step - loss: 588.8964 - mean_absolute_error: 19.9603 - acc: 0.0000e+00\n",
      "Epoch 108/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 582.6282 - mean_absolute_error: 19.8519 - acc: 0.0000e+00\n",
      "Epoch 109/250\n",
      "80/80 [==============================] - 0s 35us/step - loss: 576.5417 - mean_absolute_error: 19.7452 - acc: 0.0000e+00\n",
      "Epoch 110/250\n",
      "80/80 [==============================] - 0s 48us/step - loss: 570.6313 - mean_absolute_error: 19.6400 - acc: 0.0000e+00\n",
      "Epoch 111/250\n",
      "80/80 [==============================] - 0s 29us/step - loss: 564.8921 - mean_absolute_error: 19.5363 - acc: 0.0000e+00\n",
      "Epoch 112/250\n",
      "80/80 [==============================] - 0s 84us/step - loss: 559.3188 - mean_absolute_error: 19.4341 - acc: 0.0000e+00\n",
      "Epoch 113/250\n",
      "80/80 [==============================] - 0s 49us/step - loss: 553.9071 - mean_absolute_error: 19.3333 - acc: 0.0000e+00\n",
      "Epoch 114/250\n",
      "80/80 [==============================] - 0s 101us/step - loss: 548.6520 - mean_absolute_error: 19.2340 - acc: 0.0000e+00\n",
      "Epoch 115/250\n",
      "80/80 [==============================] - 0s 54us/step - loss: 543.5490 - mean_absolute_error: 19.1362 - acc: 0.0000e+00\n",
      "Epoch 116/250\n",
      "80/80 [==============================] - 0s 25us/step - loss: 538.5938 - mean_absolute_error: 19.0439 - acc: 0.0000e+00\n",
      "Epoch 117/250\n",
      "80/80 [==============================] - 0s 51us/step - loss: 533.7819 - mean_absolute_error: 18.9581 - acc: 0.0000e+00\n",
      "Epoch 118/250\n",
      "80/80 [==============================] - 0s 37us/step - loss: 529.1094 - mean_absolute_error: 18.8736 - acc: 0.0000e+00\n",
      "Epoch 119/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 524.5721 - mean_absolute_error: 18.7909 - acc: 0.0000e+00\n",
      "Epoch 120/250\n",
      "80/80 [==============================] - 0s 32us/step - loss: 520.1661 - mean_absolute_error: 18.7165 - acc: 0.0000e+00\n",
      "Epoch 121/250\n",
      "80/80 [==============================] - 0s 33us/step - loss: 515.8876 - mean_absolute_error: 18.6431 - acc: 0.0000e+00\n",
      "Epoch 122/250\n",
      "80/80 [==============================] - 0s 30us/step - loss: 511.7330 - mean_absolute_error: 18.5708 - acc: 0.0000e+00\n",
      "Epoch 123/250\n",
      "80/80 [==============================] - 0s 48us/step - loss: 507.6984 - mean_absolute_error: 18.4995 - acc: 0.0000e+00\n",
      "Epoch 124/250\n",
      "80/80 [==============================] - 0s 44us/step - loss: 503.7808 - mean_absolute_error: 18.4322 - acc: 0.0000e+00\n",
      "Epoch 125/250\n",
      "80/80 [==============================] - 0s 43us/step - loss: 499.9764 - mean_absolute_error: 18.3689 - acc: 0.0000e+00\n",
      "Epoch 126/250\n",
      "80/80 [==============================] - 0s 36us/step - loss: 496.2822 - mean_absolute_error: 18.3065 - acc: 0.0000e+00\n",
      "Epoch 127/250\n",
      "80/80 [==============================] - 0s 42us/step - loss: 492.6948 - mean_absolute_error: 18.2450 - acc: 0.0000e+00\n",
      "Epoch 128/250\n",
      "80/80 [==============================] - 0s 36us/step - loss: 489.2113 - mean_absolute_error: 18.1844 - acc: 0.0000e+00\n",
      "Epoch 129/250\n",
      "80/80 [==============================] - 0s 30us/step - loss: 485.8285 - mean_absolute_error: 18.1246 - acc: 0.0000e+00\n",
      "Epoch 130/250\n",
      "80/80 [==============================] - 0s 67us/step - loss: 482.5435 - mean_absolute_error: 18.0658 - acc: 0.0000e+00\n",
      "Epoch 131/250\n",
      "80/80 [==============================] - 0s 50us/step - loss: 479.3537 - mean_absolute_error: 18.0077 - acc: 0.0000e+00\n",
      "Epoch 132/250\n",
      "80/80 [==============================] - 0s 42us/step - loss: 476.2561 - mean_absolute_error: 17.9506 - acc: 0.0000e+00\n",
      "Epoch 133/250\n",
      "80/80 [==============================] - 0s 56us/step - loss: 473.2480 - mean_absolute_error: 17.8942 - acc: 0.0000e+00\n",
      "Epoch 134/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 470.3271 - mean_absolute_error: 17.8387 - acc: 0.0000e+00\n",
      "Epoch 135/250\n",
      "80/80 [==============================] - 0s 43us/step - loss: 467.4905 - mean_absolute_error: 17.7840 - acc: 0.0000e+00\n",
      "Epoch 136/250\n",
      "80/80 [==============================] - 0s 59us/step - loss: 464.7360 - mean_absolute_error: 17.7301 - acc: 0.0000e+00\n",
      "Epoch 137/250\n",
      "80/80 [==============================] - 0s 33us/step - loss: 462.0611 - mean_absolute_error: 17.6770 - acc: 0.0000e+00\n",
      "Epoch 138/250\n",
      "80/80 [==============================] - 0s 46us/step - loss: 459.4637 - mean_absolute_error: 17.6272 - acc: 0.0000e+00\n",
      "Epoch 139/250\n",
      "80/80 [==============================] - 0s 69us/step - loss: 456.9413 - mean_absolute_error: 17.5804 - acc: 0.0000e+00\n",
      "Epoch 140/250\n",
      "80/80 [==============================] - 0s 31us/step - loss: 454.4919 - mean_absolute_error: 17.5343 - acc: 0.0000e+00\n",
      "Epoch 141/250\n",
      "80/80 [==============================] - 0s 45us/step - loss: 452.1133 - mean_absolute_error: 17.4889 - acc: 0.0000e+00\n",
      "Epoch 142/250\n",
      "80/80 [==============================] - 0s 29us/step - loss: 449.8036 - mean_absolute_error: 17.4442 - acc: 0.0000e+00\n",
      "Epoch 143/250\n",
      "80/80 [==============================] - 0s 37us/step - loss: 447.5606 - mean_absolute_error: 17.4001 - acc: 0.0000e+00\n",
      "Epoch 144/250\n",
      "80/80 [==============================] - 0s 74us/step - loss: 445.3824 - mean_absolute_error: 17.3586 - acc: 0.0000e+00\n",
      "Epoch 145/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 443.2673 - mean_absolute_error: 17.3212 - acc: 0.0000e+00\n",
      "Epoch 146/250\n",
      "80/80 [==============================] - 0s 31us/step - loss: 441.2134 - mean_absolute_error: 17.2844 - acc: 0.0000e+00\n",
      "Epoch 147/250\n",
      "80/80 [==============================] - 0s 34us/step - loss: 439.2188 - mean_absolute_error: 17.2481 - acc: 0.0000e+00\n",
      "Epoch 148/250\n",
      "80/80 [==============================] - 0s 48us/step - loss: 437.2818 - mean_absolute_error: 17.2142 - acc: 0.0000e+00\n",
      "Epoch 149/250\n",
      "80/80 [==============================] - 0s 24us/step - loss: 435.4009 - mean_absolute_error: 17.1831 - acc: 0.0000e+00\n",
      "Epoch 150/250\n",
      "80/80 [==============================] - 0s 33us/step - loss: 433.5744 - mean_absolute_error: 17.1524 - acc: 0.0000e+00\n",
      "Epoch 151/250\n",
      "80/80 [==============================] - 0s 29us/step - loss: 431.8007 - mean_absolute_error: 17.1222 - acc: 0.0000e+00\n",
      "Epoch 152/250\n",
      "80/80 [==============================] - 0s 30us/step - loss: 430.0783 - mean_absolute_error: 17.0924 - acc: 0.0000e+00\n",
      "Epoch 153/250\n",
      "80/80 [==============================] - 0s 59us/step - loss: 428.4056 - mean_absolute_error: 17.0631 - acc: 0.0000e+00\n",
      "Epoch 154/250\n",
      "80/80 [==============================] - 0s 42us/step - loss: 426.7813 - mean_absolute_error: 17.0342 - acc: 0.0000e+00\n",
      "Epoch 155/250\n",
      "80/80 [==============================] - 0s 49us/step - loss: 425.2039 - mean_absolute_error: 17.0057 - acc: 0.0000e+00\n",
      "Epoch 156/250\n",
      "80/80 [==============================] - 0s 39us/step - loss: 423.6722 - mean_absolute_error: 16.9776 - acc: 0.0000e+00\n",
      "Epoch 157/250\n",
      "80/80 [==============================] - 0s 39us/step - loss: 422.1847 - mean_absolute_error: 16.9500 - acc: 0.0000e+00\n",
      "Epoch 158/250\n",
      "80/80 [==============================] - 0s 24us/step - loss: 420.7402 - mean_absolute_error: 16.9227 - acc: 0.0000e+00\n",
      "Epoch 159/250\n",
      "80/80 [==============================] - 0s 46us/step - loss: 419.3375 - mean_absolute_error: 16.8959 - acc: 0.0000e+00\n",
      "Epoch 160/250\n",
      "80/80 [==============================] - 0s 37us/step - loss: 417.9753 - mean_absolute_error: 16.8694 - acc: 0.0000e+00\n",
      "Epoch 161/250\n",
      "80/80 [==============================] - 0s 31us/step - loss: 416.6524 - mean_absolute_error: 16.8433 - acc: 0.0000e+00\n",
      "Epoch 162/250\n",
      "80/80 [==============================] - 0s 34us/step - loss: 415.3679 - mean_absolute_error: 16.8176 - acc: 0.0000e+00\n",
      "Epoch 163/250\n",
      "80/80 [==============================] - 0s 36us/step - loss: 414.1204 - mean_absolute_error: 16.7943 - acc: 0.0000e+00\n",
      "Epoch 164/250\n",
      "80/80 [==============================] - 0s 25us/step - loss: 412.9090 - mean_absolute_error: 16.7714 - acc: 0.0000e+00\n",
      "Epoch 165/250\n",
      "80/80 [==============================] - 0s 19us/step - loss: 411.7326 - mean_absolute_error: 16.7489 - acc: 0.0000e+00\n",
      "Epoch 166/250\n",
      "80/80 [==============================] - 0s 28us/step - loss: 410.5902 - mean_absolute_error: 16.7268 - acc: 0.0000e+00\n",
      "Epoch 167/250\n",
      "80/80 [==============================] - 0s 28us/step - loss: 409.4808 - mean_absolute_error: 16.7080 - acc: 0.0000e+00\n",
      "Epoch 168/250\n",
      "80/80 [==============================] - 0s 53us/step - loss: 408.4034 - mean_absolute_error: 16.6907 - acc: 0.0000e+00\n",
      "Epoch 169/250\n",
      "80/80 [==============================] - 0s 30us/step - loss: 407.3572 - mean_absolute_error: 16.6747 - acc: 0.0000e+00\n",
      "Epoch 170/250\n",
      "80/80 [==============================] - 0s 44us/step - loss: 406.3413 - mean_absolute_error: 16.6588 - acc: 0.0000e+00\n",
      "Epoch 171/250\n",
      "80/80 [==============================] - 0s 29us/step - loss: 405.3547 - mean_absolute_error: 16.6432 - acc: 0.0000e+00\n",
      "Epoch 172/250\n",
      "80/80 [==============================] - 0s 35us/step - loss: 404.3966 - mean_absolute_error: 16.6297 - acc: 0.0000e+00\n",
      "Epoch 173/250\n",
      "80/80 [==============================] - 0s 28us/step - loss: 403.4662 - mean_absolute_error: 16.6165 - acc: 0.0000e+00\n",
      "Epoch 174/250\n",
      "80/80 [==============================] - 0s 29us/step - loss: 402.5626 - mean_absolute_error: 16.6035 - acc: 0.0000e+00\n",
      "Epoch 175/250\n",
      "80/80 [==============================] - 0s 32us/step - loss: 401.6851 - mean_absolute_error: 16.5907 - acc: 0.0000e+00\n",
      "Epoch 176/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 400.8331 - mean_absolute_error: 16.5780 - acc: 0.0000e+00\n",
      "Epoch 177/250\n",
      "80/80 [==============================] - 0s 32us/step - loss: 400.0056 - mean_absolute_error: 16.5656 - acc: 0.0000e+00\n",
      "Epoch 178/250\n",
      "80/80 [==============================] - 0s 33us/step - loss: 399.2020 - mean_absolute_error: 16.5533 - acc: 0.0000e+00\n",
      "Epoch 179/250\n",
      "80/80 [==============================] - 0s 32us/step - loss: 398.4216 - mean_absolute_error: 16.5413 - acc: 0.0000e+00\n",
      "Epoch 180/250\n",
      "80/80 [==============================] - 0s 29us/step - loss: 397.6638 - mean_absolute_error: 16.5294 - acc: 0.0000e+00\n",
      "Epoch 181/250\n",
      "80/80 [==============================] - 0s 28us/step - loss: 396.9278 - mean_absolute_error: 16.5176 - acc: 0.0000e+00\n",
      "Epoch 182/250\n",
      "80/80 [==============================] - 0s 39us/step - loss: 396.2132 - mean_absolute_error: 16.5061 - acc: 0.0000e+00\n",
      "Epoch 183/250\n",
      "80/80 [==============================] - 0s 38us/step - loss: 395.5192 - mean_absolute_error: 16.4947 - acc: 0.0000e+00\n",
      "Epoch 184/250\n",
      "80/80 [==============================] - 0s 30us/step - loss: 394.8452 - mean_absolute_error: 16.4835 - acc: 0.0000e+00\n",
      "Epoch 185/250\n",
      "80/80 [==============================] - 0s 38us/step - loss: 394.1907 - mean_absolute_error: 16.4724 - acc: 0.0000e+00\n",
      "Epoch 186/250\n",
      "80/80 [==============================] - 0s 29us/step - loss: 393.5551 - mean_absolute_error: 16.4615 - acc: 0.0000e+00\n",
      "Epoch 187/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 392.9379 - mean_absolute_error: 16.4508 - acc: 0.0000e+00\n",
      "Epoch 188/250\n",
      "80/80 [==============================] - 0s 34us/step - loss: 392.3386 - mean_absolute_error: 16.4403 - acc: 0.0000e+00\n",
      "Epoch 189/250\n",
      "80/80 [==============================] - 0s 36us/step - loss: 391.7564 - mean_absolute_error: 16.4298 - acc: 0.0000e+00\n",
      "Epoch 190/250\n",
      "80/80 [==============================] - 0s 36us/step - loss: 391.1912 - mean_absolute_error: 16.4196 - acc: 0.0000e+00\n",
      "Epoch 191/250\n",
      "80/80 [==============================] - 0s 39us/step - loss: 390.6422 - mean_absolute_error: 16.4095 - acc: 0.0000e+00\n",
      "Epoch 192/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 390.1091 - mean_absolute_error: 16.3995 - acc: 0.0000e+00\n",
      "Epoch 193/250\n",
      "80/80 [==============================] - 0s 28us/step - loss: 389.5914 - mean_absolute_error: 16.3897 - acc: 0.0000e+00\n",
      "Epoch 194/250\n",
      "80/80 [==============================] - 0s 56us/step - loss: 389.0887 - mean_absolute_error: 16.3800 - acc: 0.0000e+00\n",
      "Epoch 195/250\n",
      "80/80 [==============================] - 0s 37us/step - loss: 388.6004 - mean_absolute_error: 16.3705 - acc: 0.0000e+00\n",
      "Epoch 196/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 388.1263 - mean_absolute_error: 16.3611 - acc: 0.0000e+00\n",
      "Epoch 197/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 387.6659 - mean_absolute_error: 16.3518 - acc: 0.0000e+00\n",
      "Epoch 198/250\n",
      "80/80 [==============================] - 0s 23us/step - loss: 387.2187 - mean_absolute_error: 16.3427 - acc: 0.0000e+00\n",
      "Epoch 199/250\n",
      "80/80 [==============================] - 0s 58us/step - loss: 386.7844 - mean_absolute_error: 16.3337 - acc: 0.0000e+00\n",
      "Epoch 200/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 386.3627 - mean_absolute_error: 16.3250 - acc: 0.0000e+00\n",
      "Epoch 201/250\n",
      "80/80 [==============================] - 0s 35us/step - loss: 385.9532 - mean_absolute_error: 16.3179 - acc: 0.0000e+00\n",
      "Epoch 202/250\n",
      "80/80 [==============================] - 0s 41us/step - loss: 385.5555 - mean_absolute_error: 16.3113 - acc: 0.0000e+00\n",
      "Epoch 203/250\n",
      "80/80 [==============================] - 0s 35us/step - loss: 385.1693 - mean_absolute_error: 16.3047 - acc: 0.0000e+00\n",
      "Epoch 204/250\n",
      "80/80 [==============================] - 0s 34us/step - loss: 384.7943 - mean_absolute_error: 16.2987 - acc: 0.0000e+00\n",
      "Epoch 205/250\n",
      "80/80 [==============================] - 0s 26us/step - loss: 384.4301 - mean_absolute_error: 16.2937 - acc: 0.0000e+00\n",
      "Epoch 206/250\n",
      "80/80 [==============================] - 0s 25us/step - loss: 384.0764 - mean_absolute_error: 16.2888 - acc: 0.0000e+00\n",
      "Epoch 207/250\n",
      "80/80 [==============================] - 0s 32us/step - loss: 383.7329 - mean_absolute_error: 16.2840 - acc: 0.0000e+00\n",
      "Epoch 208/250\n",
      "80/80 [==============================] - 0s 40us/step - loss: 383.3993 - mean_absolute_error: 16.2792 - acc: 0.0000e+00\n",
      "Epoch 209/250\n",
      "80/80 [==============================] - 0s 40us/step - loss: 383.0754 - mean_absolute_error: 16.2745 - acc: 0.0000e+00\n",
      "Epoch 210/250\n",
      "80/80 [==============================] - 0s 49us/step - loss: 382.7608 - mean_absolute_error: 16.2699 - acc: 0.0000e+00\n",
      "Epoch 211/250\n",
      "80/80 [==============================] - 0s 34us/step - loss: 382.4553 - mean_absolute_error: 16.2654 - acc: 0.0000e+00\n",
      "Epoch 212/250\n",
      "80/80 [==============================] - 0s 47us/step - loss: 382.1586 - mean_absolute_error: 16.2609 - acc: 0.0000e+00\n",
      "Epoch 213/250\n",
      "80/80 [==============================] - 0s 27us/step - loss: 381.8705 - mean_absolute_error: 16.2565 - acc: 0.0000e+00\n",
      "Epoch 214/250\n",
      "80/80 [==============================] - 0s 40us/step - loss: 381.5907 - mean_absolute_error: 16.2521 - acc: 0.0000e+00\n",
      "Epoch 215/250\n",
      "80/80 [==============================] - 0s 34us/step - loss: 381.3190 - mean_absolute_error: 16.2478 - acc: 0.0000e+00\n",
      "Epoch 216/250\n",
      "80/80 [==============================] - 0s 100us/step - loss: 381.0552 - mean_absolute_error: 16.2436 - acc: 0.0000e+00\n",
      "Epoch 217/250\n",
      "80/80 [==============================] - 0s 38us/step - loss: 380.7988 - mean_absolute_error: 16.2394 - acc: 0.0000e+00\n",
      "Epoch 218/250\n",
      "80/80 [==============================] - 0s 29us/step - loss: 380.5500 - mean_absolute_error: 16.2353 - acc: 0.0000e+00\n",
      "Epoch 219/250\n",
      "80/80 [==============================] - 0s 52us/step - loss: 380.3083 - mean_absolute_error: 16.2312 - acc: 0.0000e+00\n",
      "Epoch 220/250\n",
      "80/80 [==============================] - 0s 42us/step - loss: 380.0736 - mean_absolute_error: 16.2272 - acc: 0.0000e+00\n",
      "Epoch 221/250\n",
      "80/80 [==============================] - 0s 45us/step - loss: 379.8457 - mean_absolute_error: 16.2233 - acc: 0.0000e+00\n",
      "Epoch 222/250\n",
      "80/80 [==============================] - 0s 38us/step - loss: 379.6244 - mean_absolute_error: 16.2194 - acc: 0.0000e+00\n",
      "Epoch 223/250\n",
      "80/80 [==============================] - 0s 35us/step - loss: 379.4094 - mean_absolute_error: 16.2156 - acc: 0.0000e+00\n",
      "Epoch 224/250\n",
      "80/80 [==============================] - 0s 48us/step - loss: 379.2007 - mean_absolute_error: 16.2118 - acc: 0.0000e+00\n",
      "Epoch 225/250\n",
      "80/80 [==============================] - 0s 40us/step - loss: 378.9979 - mean_absolute_error: 16.2081 - acc: 0.0000e+00\n",
      "Epoch 226/250\n",
      "80/80 [==============================] - 0s 28us/step - loss: 378.8011 - mean_absolute_error: 16.2044 - acc: 0.0000e+00\n",
      "Epoch 227/250\n",
      "80/80 [==============================] - 0s 40us/step - loss: 378.6098 - mean_absolute_error: 16.2008 - acc: 0.0000e+00\n",
      "Epoch 228/250\n",
      "80/80 [==============================] - 0s 45us/step - loss: 378.4242 - mean_absolute_error: 16.1973 - acc: 0.0000e+00\n",
      "Epoch 229/250\n",
      "80/80 [==============================] - 0s 89us/step - loss: 378.2439 - mean_absolute_error: 16.1938 - acc: 0.0000e+00\n",
      "Epoch 230/250\n",
      "80/80 [==============================] - 0s 31us/step - loss: 378.0688 - mean_absolute_error: 16.1903 - acc: 0.0000e+00\n",
      "Epoch 231/250\n",
      "80/80 [==============================] - 0s 36us/step - loss: 377.8988 - mean_absolute_error: 16.1869 - acc: 0.0000e+00\n",
      "Epoch 232/250\n",
      "80/80 [==============================] - 0s 45us/step - loss: 377.7336 - mean_absolute_error: 16.1835 - acc: 0.0000e+00\n",
      "Epoch 233/250\n",
      "80/80 [==============================] - 0s 44us/step - loss: 377.5732 - mean_absolute_error: 16.1802 - acc: 0.0000e+00\n",
      "Epoch 234/250\n",
      "80/80 [==============================] - 0s 55us/step - loss: 377.4175 - mean_absolute_error: 16.1770 - acc: 0.0000e+00\n",
      "Epoch 235/250\n",
      "80/80 [==============================] - 0s 48us/step - loss: 377.2663 - mean_absolute_error: 16.1738 - acc: 0.0000e+00\n",
      "Epoch 236/250\n",
      "80/80 [==============================] - 0s 38us/step - loss: 377.1194 - mean_absolute_error: 16.1706 - acc: 0.0000e+00\n",
      "Epoch 237/250\n",
      "80/80 [==============================] - 0s 36us/step - loss: 376.9767 - mean_absolute_error: 16.1675 - acc: 0.0000e+00\n",
      "Epoch 238/250\n",
      "80/80 [==============================] - 0s 40us/step - loss: 376.8382 - mean_absolute_error: 16.1644 - acc: 0.0000e+00\n",
      "Epoch 239/250\n",
      "80/80 [==============================] - 0s 23us/step - loss: 376.7037 - mean_absolute_error: 16.1614 - acc: 0.0000e+00\n",
      "Epoch 240/250\n",
      "80/80 [==============================] - 0s 31us/step - loss: 376.5731 - mean_absolute_error: 16.1588 - acc: 0.0000e+00\n",
      "Epoch 241/250\n",
      "80/80 [==============================] - 0s 29us/step - loss: 376.4462 - mean_absolute_error: 16.1571 - acc: 0.0000e+00\n",
      "Epoch 242/250\n",
      "80/80 [==============================] - 0s 34us/step - loss: 376.3230 - mean_absolute_error: 16.1554 - acc: 0.0000e+00\n",
      "Epoch 243/250\n",
      "80/80 [==============================] - 0s 29us/step - loss: 376.2034 - mean_absolute_error: 16.1537 - acc: 0.0000e+00\n",
      "Epoch 244/250\n",
      "80/80 [==============================] - 0s 52us/step - loss: 376.0871 - mean_absolute_error: 16.1520 - acc: 0.0000e+00\n",
      "Epoch 245/250\n",
      "80/80 [==============================] - 0s 50us/step - loss: 375.9743 - mean_absolute_error: 16.1504 - acc: 0.0000e+00\n",
      "Epoch 246/250\n",
      "80/80 [==============================] - 0s 36us/step - loss: 375.8647 - mean_absolute_error: 16.1488 - acc: 0.0000e+00\n",
      "Epoch 247/250\n",
      "80/80 [==============================] - 0s 64us/step - loss: 375.7582 - mean_absolute_error: 16.1472 - acc: 0.0000e+00\n",
      "Epoch 248/250\n",
      "80/80 [==============================] - 0s 38us/step - loss: 375.6549 - mean_absolute_error: 16.1456 - acc: 0.0000e+00\n",
      "Epoch 249/250\n",
      "80/80 [==============================] - 0s 48us/step - loss: 375.5545 - mean_absolute_error: 16.1441 - acc: 0.0000e+00\n",
      "Epoch 250/250\n",
      "80/80 [==============================] - 0s 51us/step - loss: 375.4571 - mean_absolute_error: 16.1426 - acc: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "history2 = m.fit(Xtrain, ytrain, epochs=250, batch_size=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[80.528725]], dtype=float32), array([-3.5062547], dtype=float32)]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.get_weights()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
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  "nteract": {
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