lab-07-2-linear_regression_without_min_max-tf2.ipynb

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 "cells": [
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     "text": [
      "0 Cost:  5.13683456e+09 \n",
      "Prediction:\n",
      " [[-49687.1563]\n",
      " [-100995.703]\n",
      " [-79251.8047]\n",
      " ...\n",
      " [-65869.8828]\n",
      " [-60302.1]\n",
      " [-77032.25]]\n",
      "10 Cost:  -nan(ind) \n",
      "Prediction:\n",
      " [[-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " ...\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]]\n",
      "20 Cost:  -nan(ind) \n",
      "Prediction:\n",
      " [[-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " ...\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]]\n",
      "30 Cost:  -nan(ind) \n",
      "Prediction:\n",
      " [[-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " ...\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]]\n",
      "40 Cost:  -nan(ind) \n",
      "Prediction:\n",
      " [[-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " ...\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]]\n",
      "50 Cost:  -nan(ind) \n",
      "Prediction:\n",
      " [[-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " ...\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]]\n",
      "60 Cost:  -nan(ind) \n",
      "Prediction:\n",
      " [[-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " ...\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]]\n",
      "70 Cost:  -nan(ind) \n",
      "Prediction:\n",
      " [[-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " ...\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]]\n",
      "80 Cost:  -nan(ind) \n",
      "Prediction:\n",
      " [[-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " ...\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]]\n",
      "90 Cost:  -nan(ind) \n",
      "Prediction:\n",
      " [[-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " ...\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]]\n",
      "100 Cost:  -nan(ind) \n",
      "Prediction:\n",
      " [[-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " ...\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]\n",
      " [-nan(ind)]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\n0 Cost:  2.45533e+12\\nPrediction:\\n [[-1104436.375]\\n [-2224342.75 ]\\n [-1749606.75 ]\\n [-1226179.375]\\n [-1445287.125]\\n [-1457459.5  ]\\n [-1335740.5  ]\\n [-1700924.625]]\\n1 Cost:  2.69762e+27\\nPrediction:\\n [[  3.66371490e+13]\\n [  7.37543360e+13]\\n [  5.80198785e+13]\\n [  4.06716290e+13]\\n [  4.79336847e+13]\\n [  4.83371348e+13]\\n [  4.43026590e+13]\\n [  5.64060907e+13]]\\n2 Cost:  inf\\nPrediction:\\n [[ -1.21438790e+21]\\n [ -2.44468702e+21]\\n [ -1.92314724e+21]\\n [ -1.34811610e+21]\\n [ -1.58882674e+21]\\n [ -1.60219962e+21]\\n [ -1.46847142e+21]\\n [ -1.86965602e+21]]\\n3 Cost:  inf\\nPrediction:\\n [[  4.02525216e+28]\\n [  8.10324465e+28]\\n [  6.37453079e+28]\\n [  4.46851237e+28]\\n [  5.26638074e+28]\\n [  5.31070676e+28]\\n [  4.86744608e+28]\\n [  6.19722623e+28]]\\n4 Cost:  inf\\nPrediction:\\n [[ -1.33422428e+36]\\n [ -2.68593010e+36]\\n [ -2.11292430e+36]\\n [ -1.48114879e+36]\\n [ -1.74561303e+36]\\n [ -1.76030542e+36]\\n [ -1.61338091e+36]\\n [ -2.05415459e+36]]\\n5 Cost:  inf\\nPrediction:\\n [[ inf]\\n [ inf]\\n [ inf]\\n [ inf]\\n [ inf]\\n [ inf]\\n [ inf]\\n [ inf]]\\n6 Cost:  nan\\nPrediction:\\n [[ nan]\\n [ nan]\\n [ nan]\\n [ nan]\\n [ nan]\\n [ nan]\\n [ nan]\\n [ nan]]\\n'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from functools import partial as bind\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "assert(tf.__version__.find('2') == 0)\n",
    "tf.random.set_seed(777)  # for reproducibility\n",
    "\n",
    "xy = np.array([[828.659973, 833.450012, 908100, 828.349976, 831.659973],\n",
    "               [823.02002, 828.070007, 1828100, 821.655029, 828.070007],\n",
    "               [819.929993, 824.400024, 1438100, 818.97998, 824.159973],\n",
    "               [816, 820.958984, 1008100, 815.48999, 819.23999],\n",
    "               [819.359985, 823, 1188100, 818.469971, 818.97998],\n",
    "               [819, 823, 1198100, 816, 820.450012],\n",
    "               [811.700012, 815.25, 1098100, 809.780029, 813.669983],\n",
    "               [809.51001, 816.659973, 1398100, 804.539978, 809.559998]])\n",
    "\n",
    "x_data = tf.Variable(xy[:, 0:-1], dtype=tf.float32)\n",
    "y_data = tf.Variable(xy[:, [-1]], dtype=tf.float32)\n",
    "\n",
    "W = tf.Variable(tf.random.normal([4, 1]), name='weight', dtype=tf.float32)\n",
    "b = tf.Variable(tf.random.normal([1]), name='bias', dtype=tf.float32)\n",
    "\n",
    "# Hypothesis\n",
    "hypothesis = lambda X: tf.matmul(X, W) + b\n",
    "\n",
    "# Simplified cost/loss function\n",
    "@tf.function\n",
    "def cost(X, Y):\n",
    "    return tf.reduce_mean(tf.square(hypothesis(X) - Y))\n",
    "\n",
    "# Minimize\n",
    "optimizer = tf.keras.optimizers.SGD(learning_rate=1e-5)\n",
    "\n",
    "for step in range(101):\n",
    "    cost_val = cost(x_data, y_data)\n",
    "    hy_val = hypothesis(x_data)\n",
    "\n",
    "    optimizer.minimize(bind(cost, x_data, y_data), var_list=[W, b])\n",
    "\n",
    "    if step % 10 == 0:\n",
    "        tf.print(step, \"Cost: \", cost_val, \"\\nPrediction:\\n\", hy_val)\n",
    "\n",
    "\n",
    "'''\n",
    "0 Cost:  2.45533e+12\n",
    "Prediction:\n",
    " [[-1104436.375]\n",
    " [-2224342.75 ]\n",
    " [-1749606.75 ]\n",
    " [-1226179.375]\n",
    " [-1445287.125]\n",
    " [-1457459.5  ]\n",
    " [-1335740.5  ]\n",
    " [-1700924.625]]\n",
    "1 Cost:  2.69762e+27\n",
    "Prediction:\n",
    " [[  3.66371490e+13]\n",
    " [  7.37543360e+13]\n",
    " [  5.80198785e+13]\n",
    " [  4.06716290e+13]\n",
    " [  4.79336847e+13]\n",
    " [  4.83371348e+13]\n",
    " [  4.43026590e+13]\n",
    " [  5.64060907e+13]]\n",
    "2 Cost:  inf\n",
    "Prediction:\n",
    " [[ -1.21438790e+21]\n",
    " [ -2.44468702e+21]\n",
    " [ -1.92314724e+21]\n",
    " [ -1.34811610e+21]\n",
    " [ -1.58882674e+21]\n",
    " [ -1.60219962e+21]\n",
    " [ -1.46847142e+21]\n",
    " [ -1.86965602e+21]]\n",
    "3 Cost:  inf\n",
    "Prediction:\n",
    " [[  4.02525216e+28]\n",
    " [  8.10324465e+28]\n",
    " [  6.37453079e+28]\n",
    " [  4.46851237e+28]\n",
    " [  5.26638074e+28]\n",
    " [  5.31070676e+28]\n",
    " [  4.86744608e+28]\n",
    " [  6.19722623e+28]]\n",
    "4 Cost:  inf\n",
    "Prediction:\n",
    " [[ -1.33422428e+36]\n",
    " [ -2.68593010e+36]\n",
    " [ -2.11292430e+36]\n",
    " [ -1.48114879e+36]\n",
    " [ -1.74561303e+36]\n",
    " [ -1.76030542e+36]\n",
    " [ -1.61338091e+36]\n",
    " [ -2.05415459e+36]]\n",
    "5 Cost:  inf\n",
    "Prediction:\n",
    " [[ inf]\n",
    " [ inf]\n",
    " [ inf]\n",
    " [ inf]\n",
    " [ inf]\n",
    " [ inf]\n",
    " [ inf]\n",
    " [ inf]]\n",
    "6 Cost:  nan\n",
    "Prediction:\n",
    " [[ nan]\n",
    " [ nan]\n",
    " [ nan]\n",
    " [ nan]\n",
    " [ nan]\n",
    " [ nan]\n",
    " [ nan]\n",
    " [ nan]]\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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