lab-04-2-multi_variable_matmul_linear_regression-tf2.ipynb

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      "0 Cost:  2043.50073 \n",
      "Prediction:\n",
      " [[112.164352]\n",
      " [135.927292]\n",
      " [133.575439]\n",
      " [144.348099]\n",
      " [104.5923]]\n",
      "10 Cost:  0.495432943 \n",
      "Prediction:\n",
      " [[152.146835]\n",
      " [183.987061]\n",
      " [180.927567]\n",
      " [195.913651]\n",
      " [141.250519]]\n",
      "1990 Cost:  0.304008543 \n",
      "Prediction:\n",
      " [[151.960083]\n",
      " [184.343475]\n",
      " [180.975952]\n",
      " [196.007706]\n",
      " [141.632736]]\n",
      "2000 Cost:  0.303522021 \n",
      "Prediction:\n",
      " [[151.958893]\n",
      " [184.344284]\n",
      " [180.975586]\n",
      " [196.007492]\n",
      " [141.633759]]\n"
     ]
    },
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       "'\\n0 Cost:  7105.46\\nPrediction:\\n [[ 80.82241058]\\n [ 92.26364136]\\n [ 93.70250702]\\n [ 98.09217834]\\n [ 72.51759338]]\\n10 Cost:  5.89726\\nPrediction:\\n [[ 155.35159302]\\n [ 181.85691833]\\n [ 181.97254944]\\n [ 194.21760559]\\n [ 140.85707092]]\\n\\n...\\n\\n1990 Cost:  3.18588\\nPrediction:\\n [[ 154.36352539]\\n [ 182.94833374]\\n [ 181.85189819]\\n [ 194.35585022]\\n [ 142.03240967]]\\n2000 Cost:  3.1781\\nPrediction:\\n [[ 154.35881042]\\n [ 182.95147705]\\n [ 181.85035706]\\n [ 194.35533142]\\n [ 142.036026  ]]\\n\\n'"
      ]
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     "execution_count": 3,
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   "source": [
    "# Lab 4 Multi-variable linear regression\n",
    "import tensorflow as tf\n",
    "assert(tf.__version__.find('2') == 0)\n",
    "tf.random.set_seed(777)  # for reproducibility\n",
    "\n",
    "x_data = [[73., 80., 75.],\n",
    "          [93., 88., 93.],\n",
    "          [89., 91., 90.],\n",
    "          [96., 98., 100.],\n",
    "          [73., 66., 70.]]\n",
    "y_data = [[152.],\n",
    "          [185.],\n",
    "          [180.],\n",
    "          [196.],\n",
    "          [142.]]\n",
    "\n",
    "\n",
    "# placeholders for a tensor that will be always fed.\n",
    "X = tf.Variable(x_data)\n",
    "Y = tf.Variable(y_data)\n",
    "\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: tf.matmul(X, W) + b\n",
    "\n",
    "# Simplified cost/loss function\n",
    "@tf.function\n",
    "def cost():\n",
    "    return tf.reduce_mean(tf.square(hypothesis() - 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(cost, [W, b])\n",
    "    cost_val = cost()\n",
    "    hy_val = hypothesis()\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",
    "'''\n",
    "0 Cost:  7105.46\n",
    "Prediction:\n",
    " [[ 80.82241058]\n",
    " [ 92.26364136]\n",
    " [ 93.70250702]\n",
    " [ 98.09217834]\n",
    " [ 72.51759338]]\n",
    "10 Cost:  5.89726\n",
    "Prediction:\n",
    " [[ 155.35159302]\n",
    " [ 181.85691833]\n",
    " [ 181.97254944]\n",
    " [ 194.21760559]\n",
    " [ 140.85707092]]\n",
    "\n",
    "...\n",
    "\n",
    "1990 Cost:  3.18588\n",
    "Prediction:\n",
    " [[ 154.36352539]\n",
    " [ 182.94833374]\n",
    " [ 181.85189819]\n",
    " [ 194.35585022]\n",
    " [ 142.03240967]]\n",
    "2000 Cost:  3.1781\n",
    "Prediction:\n",
    " [[ 154.35881042]\n",
    " [ 182.95147705]\n",
    " [ 181.85035706]\n",
    " [ 194.35533142]\n",
    " [ 142.036026  ]]\n",
    "\n",
    "'''\n"
   ]
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   "execution_count": null,
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