Keras_Linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_2 (Dense)              (None, 1)                 2         \n",
      "=================================================================\n",
      "Total params: 2.0\n",
      "Trainable params: 2\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.contrib.keras import models\n",
    "from tensorflow.contrib.keras import layers\n",
    "from tensorflow.contrib import keras\n",
    "import pandas\n",
    "dataset=pandas.read_csv('dataset.csv')\n",
    "setx=dataset.values[:,0]\n",
    "sety=dataset.values[:,1]\n",
    "\n",
    "model = models.Sequential([layers.Dense(1, input_shape=(1,))])\n",
    "# Use Adam optimizier and Mean Squared Error (MSE) as loss function\n",
    "model.compile(optimizer=keras.optimizers.Adamax(0.1), loss='mse') \n",
    "print model.summary() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[31.88231716528535,\n",
       " 0.02045083850098308,\n",
       " 0.009396455676323967,\n",
       " 0.0034374483724604944,\n",
       " 0.0010256565983581822,\n",
       " 0.0002667229771759594,\n",
       " 3.7077795241202693e-05,\n",
       " 3.619184462877456e-06,\n",
       " 2.1000409162752475e-07,\n",
       " 5.915615020057885e-09]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traininglog=model.fit(setx, sety, epochs=100, batch_size=1,verbose=0)\n",
    "traininglog.history['loss'][::10] # print every 10 epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7KszDRHlmueQB5zjnugKpwAAz6wmMByY459oAvwA3Bq5MEQm6I52eWVngHGRlEXXzzbj0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\nBbqISIRQoIuIRIj/D+YHcNpT5oVzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feda086f210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trained_m = model.get_weights()[0][0][0]\n",
    "trained_c = model.get_weights()[1][0] \n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(setx, trained_m * setx + trained_c, label='Fitted line')\n",
    "plt.plot(setx, sety, 'ro', label='Data')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}