lab-04-3-file_input_linear_regression-tf2.ipynb

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      "[[ 73.  80.  75.]\n",
      " [ 93.  88.  93.]\n",
      " [ 89.  91.  90.]\n",
      " [ 96.  98. 100.]\n",
      " [ 73.  66.  70.]\n",
      " [ 53.  46.  55.]\n",
      " [ 69.  74.  77.]\n",
      " [ 47.  56.  60.]\n",
      " [ 87.  79.  90.]\n",
      " [ 79.  70.  88.]\n",
      " [ 69.  70.  73.]\n",
      " [ 70.  65.  74.]\n",
      " [ 93.  95.  91.]\n",
      " [ 79.  80.  73.]\n",
      " [ 70.  73.  78.]\n",
      " [ 93.  89.  96.]\n",
      " [ 78.  75.  68.]\n",
      " [ 81.  90.  93.]\n",
      " [ 88.  92.  86.]\n",
      " [ 78.  83.  77.]\n",
      " [ 82.  86.  90.]\n",
      " [ 86.  82.  89.]\n",
      " [ 78.  83.  85.]\n",
      " [ 76.  83.  71.]\n",
      " [ 96.  93.  95.]] \n",
      "x_data shape: (25, 3)\n",
      "[[152.]\n",
      " [185.]\n",
      " [180.]\n",
      " [196.]\n",
      " [142.]\n",
      " [101.]\n",
      " [149.]\n",
      " [115.]\n",
      " [175.]\n",
      " [164.]\n",
      " [141.]\n",
      " [141.]\n",
      " [184.]\n",
      " [152.]\n",
      " [148.]\n",
      " [192.]\n",
      " [147.]\n",
      " [183.]\n",
      " [177.]\n",
      " [159.]\n",
      " [177.]\n",
      " [175.]\n",
      " [175.]\n",
      " [149.]\n",
      " [192.]] \n",
      "y_data shape: (25, 1)\n",
      "0 Cost:  2278.57495 \n",
      "Prediction:\n",
      " [[109.386681]\n",
      " [132.589]\n",
      " [130.285645]\n",
      " ...\n",
      " [116.086456]\n",
      " [113.276413]\n",
      " [137.912918]]\n",
      "10 Cost:  27.966507 \n",
      "Prediction:\n",
      " [[153.673981]\n",
      " [185.829239]\n",
      " [182.737671]\n",
      " ...\n",
      " [163.908463]\n",
      " [157.905914]\n",
      " [193.083969]]\n",
      "1990 Cost:  11.5017128 \n",
      "Prediction:\n",
      " [[152.846146]\n",
      " [185.910904]\n",
      " [182.153931]\n",
      " ...\n",
      " [165.594]\n",
      " [153.937195]\n",
      " [192.370209]]\n",
      "2000 Cost:  11.4653025 \n",
      "Prediction:\n",
      " [[152.843277]\n",
      " [185.909134]\n",
      " [182.150955]\n",
      " ...\n",
      " [165.599182]\n",
      " [153.923782]\n",
      " [192.366013]]\n",
      "Your score will be  [[187.752243]]\n",
      "Other scores will be  [[162.469955]\n",
      " [180.480148]]\n"
     ]
    },
    {
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      "text/plain": [
       "'\\nYour score will be  [[ 181.73277283]]\\nOther scores will be  [[ 145.86265564]\\n [ 187.23129272]]\\n\\n'"
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   "source": [
    "# Lab 4 Multi-variable linear regression\n",
    "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.loadtxt('data-01-test-score.csv', delimiter=',', dtype=np.float32)\n",
    "x_data = xy[:, 0:-1]\n",
    "y_data = xy[:, [-1]]\n",
    "\n",
    "# Make sure the shape and data are OK\n",
    "print(x_data, \"\\nx_data shape:\", x_data.shape)\n",
    "print(y_data, \"\\ny_data shape:\", y_data.shape)\n",
    "\n",
    "# data output\n",
    "'''\n",
    "[[ 73.  80.  75.]\n",
    " [ 93.  88.  93.]\n",
    " ...\n",
    " [ 76.  83.  71.]\n",
    " [ 96.  93.  95.]] \n",
    "x_data shape: (25, 3)\n",
    "[[152.]\n",
    " [185.]\n",
    " ...\n",
    " [149.]\n",
    " [192.]] \n",
    "y_data shape: (25, 1)\n",
    "'''\n",
    "\n",
    "# placeholders for a tensor that will be always fed.\n",
    "W = tf.Variable(tf.random.normal([3, 1]), name='weight')\n",
    "b = tf.Variable(tf.random.normal([1]), name='bias')\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.optimizers.SGD(learning_rate=1e-5)\n",
    "\n",
    "# Launch the graph in a session.\n",
    "\n",
    "for step in range(2001):\n",
    "    optimizer.minimize(bind(cost, x_data, y_data), [W, b])\n",
    "    cost_val = cost(x_data, y_data)\n",
    "    hy_val = hypothesis(x_data)\n",
    "    if step == 0 or step == 10 or step == 1990 or step == 2000:\n",
    "        tf.print(step, \"Cost: \", cost_val, \"\\nPrediction:\\n\", hy_val)\n",
    "\n",
    "# train output\n",
    "'''\n",
    "0 Cost: 21027.0 \n",
    "Prediction:\n",
    " [[22.048063 ]\n",
    " [21.619772 ]\n",
    " ...\n",
    " [31.36112  ]\n",
    " [24.986364 ]]\n",
    "10 Cost: 95.976326 \n",
    "Prediction:\n",
    " [[157.11063 ]\n",
    " [183.99283 ]\n",
    " ...\n",
    " [167.48862 ]\n",
    " [193.25117 ]]\n",
    " 1990 Cost: 24.863274 \n",
    "Prediction:\n",
    " [[154.4393  ]\n",
    " [185.5584  ]\n",
    " ...\n",
    " [158.27443 ]\n",
    " [192.79778 ]]\n",
    "2000 Cost: 24.722485 \n",
    "Prediction:\n",
    " [[154.42894 ]\n",
    " [185.5586  ]\n",
    " ...\n",
    " [158.24257 ]\n",
    " [192.79166 ]]\n",
    "'''\n",
    "\n",
    "# Ask my score\n",
    "prediction = hypothesis(tf.Variable([[100, 70, 101]], dtype=tf.float32))\n",
    "tf.print(\"Your score will be \", prediction)\n",
    "\n",
    "prediction = hypothesis(tf.Variable([[60, 70, 110], [90, 100, 80]], dtype=tf.float32))\n",
    "tf.print(\"Other scores will be \", prediction)\n",
    "\n",
    "'''\n",
    "Your score will be  [[ 181.73277283]]\n",
    "Other scores will be  [[ 145.86265564]\n",
    " [ 187.23129272]]\n",
    "\n",
    "'''\n"
   ]
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