01_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "trX = np.linspace(-1, 1, 101)\n",
    "trY = 2 * trX + np.random.randn(*trX.shape) * 0.33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x144a1ab38>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(trX, trY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Dense(1),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer=tf.keras.optimizers.SGD(lr=0.01),\n",
    "    loss=tf.keras.losses.MSE\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 101 samples\n",
      "Epoch 1/100\n",
      "101/101 [==============================] - 0s 3ms/sample - loss: 1.5620\n",
      "Epoch 2/100\n",
      "101/101 [==============================] - 0s 68us/sample - loss: 1.5132\n",
      "Epoch 3/100\n",
      "101/101 [==============================] - 0s 67us/sample - loss: 1.4689\n",
      "Epoch 4/100\n",
      "101/101 [==============================] - 0s 98us/sample - loss: 1.4245\n",
      "Epoch 5/100\n",
      "101/101 [==============================] - 0s 91us/sample - loss: 1.3802\n",
      "Epoch 6/100\n",
      "101/101 [==============================] - 0s 121us/sample - loss: 1.3314\n",
      "Epoch 7/100\n",
      "101/101 [==============================] - 0s 105us/sample - loss: 1.2810\n",
      "Epoch 8/100\n",
      "101/101 [==============================] - 0s 98us/sample - loss: 1.2375\n",
      "Epoch 9/100\n",
      "101/101 [==============================] - 0s 107us/sample - loss: 1.1806\n",
      "Epoch 10/100\n",
      "101/101 [==============================] - 0s 95us/sample - loss: 1.1309\n",
      "Epoch 11/100\n",
      "101/101 [==============================] - 0s 136us/sample - loss: 1.0797\n",
      "Epoch 12/100\n",
      "101/101 [==============================] - 0s 115us/sample - loss: 1.0306\n",
      "Epoch 13/100\n",
      "101/101 [==============================] - 0s 117us/sample - loss: 0.9729\n",
      "Epoch 14/100\n",
      "101/101 [==============================] - 0s 125us/sample - loss: 0.9189\n",
      "Epoch 15/100\n",
      "101/101 [==============================] - 0s 279us/sample - loss: 0.8656\n",
      "Epoch 16/100\n",
      "101/101 [==============================] - 0s 155us/sample - loss: 0.8119\n",
      "Epoch 17/100\n",
      "101/101 [==============================] - 0s 143us/sample - loss: 0.7517\n",
      "Epoch 18/100\n",
      "101/101 [==============================] - 0s 180us/sample - loss: 0.6870\n",
      "Epoch 19/100\n",
      "101/101 [==============================] - 0s 118us/sample - loss: 0.6385\n",
      "Epoch 20/100\n",
      "101/101 [==============================] - 0s 280us/sample - loss: 0.5879\n",
      "Epoch 21/100\n",
      "101/101 [==============================] - 0s 202us/sample - loss: 0.5321\n",
      "Epoch 22/100\n",
      "101/101 [==============================] - 0s 168us/sample - loss: 0.4871\n",
      "Epoch 23/100\n",
      "101/101 [==============================] - 0s 192us/sample - loss: 0.4420\n",
      "Epoch 24/100\n",
      "101/101 [==============================] - 0s 185us/sample - loss: 0.4028\n",
      "Epoch 25/100\n",
      "101/101 [==============================] - 0s 241us/sample - loss: 0.3635\n",
      "Epoch 26/100\n",
      "101/101 [==============================] - 0s 238us/sample - loss: 0.3246\n",
      "Epoch 27/100\n",
      "101/101 [==============================] - 0s 164us/sample - loss: 0.2946\n",
      "Epoch 28/100\n",
      "101/101 [==============================] - 0s 157us/sample - loss: 0.2658\n",
      "Epoch 29/100\n",
      "101/101 [==============================] - ETA: 0s - loss: 0.250 - 0s 203us/sample - loss: 0.2441\n",
      "Epoch 30/100\n",
      "101/101 [==============================] - 0s 171us/sample - loss: 0.2222\n",
      "Epoch 31/100\n",
      "101/101 [==============================] - 0s 176us/sample - loss: 0.2045\n",
      "Epoch 32/100\n",
      "101/101 [==============================] - 0s 162us/sample - loss: 0.1876\n",
      "Epoch 33/100\n",
      "101/101 [==============================] - 0s 190us/sample - loss: 0.1742\n",
      "Epoch 34/100\n",
      "101/101 [==============================] - 0s 190us/sample - loss: 0.1635\n",
      "Epoch 35/100\n",
      "101/101 [==============================] - 0s 188us/sample - loss: 0.1534\n",
      "Epoch 36/100\n",
      "101/101 [==============================] - 0s 202us/sample - loss: 0.1426\n",
      "Epoch 37/100\n",
      "101/101 [==============================] - 0s 127us/sample - loss: 0.1340\n",
      "Epoch 38/100\n",
      "101/101 [==============================] - 0s 133us/sample - loss: 0.1276\n",
      "Epoch 39/100\n",
      "101/101 [==============================] - 0s 160us/sample - loss: 0.1216\n",
      "Epoch 40/100\n",
      "101/101 [==============================] - 0s 201us/sample - loss: 0.1163\n",
      "Epoch 41/100\n",
      "101/101 [==============================] - 0s 174us/sample - loss: 0.1125\n",
      "Epoch 42/100\n",
      "101/101 [==============================] - 0s 129us/sample - loss: 0.1091\n",
      "Epoch 43/100\n",
      "101/101 [==============================] - 0s 221us/sample - loss: 0.1071\n",
      "Epoch 44/100\n",
      "101/101 [==============================] - 0s 219us/sample - loss: 0.1046\n",
      "Epoch 45/100\n",
      "101/101 [==============================] - 0s 199us/sample - loss: 0.1037\n",
      "Epoch 46/100\n",
      "101/101 [==============================] - 0s 190us/sample - loss: 0.1022\n",
      "Epoch 47/100\n",
      "101/101 [==============================] - 0s 207us/sample - loss: 0.1001\n",
      "Epoch 48/100\n",
      "101/101 [==============================] - 0s 215us/sample - loss: 0.0983\n",
      "Epoch 49/100\n",
      "101/101 [==============================] - 0s 185us/sample - loss: 0.0985\n",
      "Epoch 50/100\n",
      "101/101 [==============================] - 0s 175us/sample - loss: 0.0972\n",
      "Epoch 51/100\n",
      "101/101 [==============================] - 0s 190us/sample - loss: 0.0972\n",
      "Epoch 52/100\n",
      "101/101 [==============================] - 0s 242us/sample - loss: 0.0963\n",
      "Epoch 53/100\n",
      "101/101 [==============================] - 0s 179us/sample - loss: 0.0959\n",
      "Epoch 54/100\n",
      "101/101 [==============================] - 0s 224us/sample - loss: 0.0962\n",
      "Epoch 55/100\n",
      "101/101 [==============================] - 0s 204us/sample - loss: 0.0956\n",
      "Epoch 56/100\n",
      "101/101 [==============================] - 0s 216us/sample - loss: 0.0954\n",
      "Epoch 57/100\n",
      "101/101 [==============================] - 0s 263us/sample - loss: 0.0955\n",
      "Epoch 58/100\n",
      "101/101 [==============================] - 0s 178us/sample - loss: 0.0955\n",
      "Epoch 59/100\n",
      "101/101 [==============================] - 0s 187us/sample - loss: 0.0955\n",
      "Epoch 60/100\n",
      "101/101 [==============================] - 0s 189us/sample - loss: 0.0956\n",
      "Epoch 61/100\n",
      "101/101 [==============================] - 0s 187us/sample - loss: 0.0957\n",
      "Epoch 62/100\n",
      "101/101 [==============================] - 0s 178us/sample - loss: 0.0953\n",
      "Epoch 63/100\n",
      "101/101 [==============================] - 0s 287us/sample - loss: 0.0952\n",
      "Epoch 64/100\n",
      "101/101 [==============================] - 0s 151us/sample - loss: 0.0954\n",
      "Epoch 65/100\n",
      "101/101 [==============================] - 0s 167us/sample - loss: 0.0954\n",
      "Epoch 66/100\n",
      "101/101 [==============================] - 0s 127us/sample - loss: 0.0954\n",
      "Epoch 67/100\n",
      "101/101 [==============================] - 0s 211us/sample - loss: 0.0953\n",
      "Epoch 68/100\n",
      "101/101 [==============================] - 0s 183us/sample - loss: 0.0954\n",
      "Epoch 69/100\n",
      "101/101 [==============================] - 0s 191us/sample - loss: 0.0954\n",
      "Epoch 70/100\n",
      "101/101 [==============================] - 0s 171us/sample - loss: 0.0955\n",
      "Epoch 71/100\n",
      "101/101 [==============================] - 0s 193us/sample - loss: 0.0950\n",
      "Epoch 72/100\n",
      "101/101 [==============================] - 0s 211us/sample - loss: 0.0953\n",
      "Epoch 73/100\n",
      "101/101 [==============================] - 0s 200us/sample - loss: 0.0953\n",
      "Epoch 74/100\n",
      "101/101 [==============================] - 0s 143us/sample - loss: 0.0951\n",
      "Epoch 75/100\n",
      "101/101 [==============================] - 0s 121us/sample - loss: 0.0953\n",
      "Epoch 76/100\n",
      "101/101 [==============================] - 0s 119us/sample - loss: 0.0954\n",
      "Epoch 77/100\n",
      "101/101 [==============================] - 0s 148us/sample - loss: 0.0951\n",
      "Epoch 78/100\n",
      "101/101 [==============================] - 0s 166us/sample - loss: 0.0954\n",
      "Epoch 79/100\n",
      "101/101 [==============================] - 0s 170us/sample - loss: 0.0952\n",
      "Epoch 80/100\n",
      "101/101 [==============================] - 0s 155us/sample - loss: 0.0952\n",
      "Epoch 81/100\n",
      "101/101 [==============================] - 0s 156us/sample - loss: 0.0951\n",
      "Epoch 82/100\n",
      "101/101 [==============================] - 0s 142us/sample - loss: 0.0950\n",
      "Epoch 83/100\n",
      "101/101 [==============================] - 0s 170us/sample - loss: 0.0950\n",
      "Epoch 84/100\n",
      "101/101 [==============================] - 0s 153us/sample - loss: 0.0953\n",
      "Epoch 85/100\n",
      "101/101 [==============================] - 0s 140us/sample - loss: 0.0950\n",
      "Epoch 86/100\n",
      "101/101 [==============================] - 0s 135us/sample - loss: 0.0951\n",
      "Epoch 87/100\n",
      "101/101 [==============================] - 0s 180us/sample - loss: 0.0952\n",
      "Epoch 88/100\n",
      "101/101 [==============================] - 0s 136us/sample - loss: 0.0953\n",
      "Epoch 89/100\n",
      "101/101 [==============================] - 0s 248us/sample - loss: 0.0953\n",
      "Epoch 90/100\n",
      "101/101 [==============================] - 0s 138us/sample - loss: 0.0955\n",
      "Epoch 91/100\n",
      "101/101 [==============================] - 0s 169us/sample - loss: 0.0950\n",
      "Epoch 92/100\n",
      "101/101 [==============================] - 0s 117us/sample - loss: 0.0951\n",
      "Epoch 93/100\n",
      "101/101 [==============================] - 0s 143us/sample - loss: 0.0954\n",
      "Epoch 94/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101/101 [==============================] - 0s 168us/sample - loss: 0.0956\n",
      "Epoch 95/100\n",
      "101/101 [==============================] - 0s 159us/sample - loss: 0.0951\n",
      "Epoch 96/100\n",
      "101/101 [==============================] - 0s 153us/sample - loss: 0.0951\n",
      "Epoch 97/100\n",
      "101/101 [==============================] - 0s 111us/sample - loss: 0.0953\n",
      "Epoch 98/100\n",
      "101/101 [==============================] - 0s 101us/sample - loss: 0.0952\n",
      "Epoch 99/100\n",
      "101/101 [==============================] - 0s 78us/sample - loss: 0.0951\n",
      "Epoch 100/100\n",
      "101/101 [==============================] - 0s 94us/sample - loss: 0.0951\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(trX, trY, epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14529bb38>]"
      ]
     },
     "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": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1453ad160>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = model.predict(trX)\n",
    "plt.plot(trX, preds, '-r', label='predictions')\n",
    "plt.scatter(trX, trY, label='values')\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}