Newswires - Multi-class classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/text-datasets/reuters.npz\n",
      "2113536/2110848 [==============================] - 3s 1us/step\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import reuters\n",
    "(train_data, train_labels), (test_data,test_labels) = reuters.load_data(num_words = 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def vectorize(seq, dim=10000):\n",
    "    res = np.zeros((len(seq),dim))\n",
    "    for i, s in enumerate(seq):\n",
    "        res[i,s] = 1.\n",
    "    return res\n",
    "x = vectorize(train_data)\n",
    "xTest = vectorize(test_data)\n",
    "\n",
    "from keras.utils.np_utils import to_categorical\n",
    "y = to_categorical(train_labels)\n",
    "yTest = to_categorical(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(64, activation = 'relu',input_shape = (10000,)))\n",
    "model.add(layers.Dense(64, activation = 'relu'))\n",
    "model.add(layers.Dense(46, activation = 'softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer = 'rmsprop', loss = 'categorical_crossentropy',metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xv = x[:1000]\n",
    "yv = y[:1000]\n",
    "xt = x[1000:]\n",
    "yt = y[1000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7982 samples, validate on 1000 samples\n",
      "Epoch 1/20\n",
      "7982/7982 [==============================] - 1s 178us/step - loss: 2.5247 - acc: 0.4954 - val_loss: 1.7099 - val_acc: 0.6130\n",
      "Epoch 2/20\n",
      "7982/7982 [==============================] - 1s 138us/step - loss: 1.4390 - acc: 0.6903 - val_loss: 1.3425 - val_acc: 0.7090\n",
      "Epoch 3/20\n",
      "7982/7982 [==============================] - 1s 138us/step - loss: 1.0907 - acc: 0.7647 - val_loss: 1.1638 - val_acc: 0.7420\n",
      "Epoch 4/20\n",
      "7982/7982 [==============================] - 1s 142us/step - loss: 0.8658 - acc: 0.8168 - val_loss: 1.0633 - val_acc: 0.7650\n",
      "Epoch 5/20\n",
      "7982/7982 [==============================] - 1s 138us/step - loss: 0.7001 - acc: 0.8490 - val_loss: 0.9831 - val_acc: 0.7820\n",
      "Epoch 6/20\n",
      "7982/7982 [==============================] - 1s 141us/step - loss: 0.5640 - acc: 0.8801 - val_loss: 0.9463 - val_acc: 0.8010\n",
      "Epoch 7/20\n",
      "7982/7982 [==============================] - 1s 139us/step - loss: 0.4577 - acc: 0.9053 - val_loss: 0.9128 - val_acc: 0.8010\n",
      "Epoch 8/20\n",
      "7982/7982 [==============================] - 1s 141us/step - loss: 0.3711 - acc: 0.9225 - val_loss: 0.9380 - val_acc: 0.7900\n",
      "Epoch 9/20\n",
      "7982/7982 [==============================] - 1s 140us/step - loss: 0.3048 - acc: 0.9307 - val_loss: 0.8900 - val_acc: 0.8100\n",
      "Epoch 10/20\n",
      "7982/7982 [==============================] - 1s 139us/step - loss: 0.2536 - acc: 0.9412 - val_loss: 0.9115 - val_acc: 0.8060\n",
      "Epoch 11/20\n",
      "7982/7982 [==============================] - 1s 140us/step - loss: 0.2198 - acc: 0.9475 - val_loss: 0.9075 - val_acc: 0.8150\n",
      "Epoch 12/20\n",
      "7982/7982 [==============================] - 1s 140us/step - loss: 0.1867 - acc: 0.9504 - val_loss: 0.9038 - val_acc: 0.8160\n",
      "Epoch 13/20\n",
      "7982/7982 [==============================] - 1s 140us/step - loss: 0.1714 - acc: 0.9526 - val_loss: 0.9325 - val_acc: 0.8120\n",
      "Epoch 14/20\n",
      "7982/7982 [==============================] - 1s 142us/step - loss: 0.1539 - acc: 0.9548 - val_loss: 0.9719 - val_acc: 0.8040\n",
      "Epoch 15/20\n",
      "7982/7982 [==============================] - 1s 140us/step - loss: 0.1390 - acc: 0.9557 - val_loss: 0.9625 - val_acc: 0.8160\n",
      "Epoch 16/20\n",
      "7982/7982 [==============================] - 1s 138us/step - loss: 0.1303 - acc: 0.9563 - val_loss: 1.0265 - val_acc: 0.8030\n",
      "Epoch 17/20\n",
      "7982/7982 [==============================] - 1s 140us/step - loss: 0.1222 - acc: 0.9578 - val_loss: 1.0234 - val_acc: 0.7980\n",
      "Epoch 18/20\n",
      "7982/7982 [==============================] - 1s 140us/step - loss: 0.1196 - acc: 0.9577 - val_loss: 1.0387 - val_acc: 0.8040\n",
      "Epoch 19/20\n",
      "7982/7982 [==============================] - 1s 140us/step - loss: 0.1132 - acc: 0.9592 - val_loss: 1.0931 - val_acc: 0.7970\n",
      "Epoch 20/20\n",
      "7982/7982 [==============================] - 1s 143us/step - loss: 0.1109 - acc: 0.9590 - val_loss: 1.0680 - val_acc: 0.8040\n"
     ]
    }
   ],
   "source": [
    "hist = model.fit(xt,yt, epochs = 20, batch_size = 510, validation_data=(xv,yv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0kyaFPaPLLw9j5pctC32Yy5fDAw+EfkiAffeFo47aM/RnzFCffbNxD6O6li4Nx3Iyl6+8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\nFO4iIgmkcBcRSaD/A/OvXKpTDfqiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xee8bf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "loss = hist.history['loss']\n",
    "vloss = hist.history['val_loss']\n",
    "epochs = range(1,len(loss)+1)\n",
    "plt.plot(epochs, loss,'bo',label = 'Train loss')\n",
    "plt.plot(epochs, vloss, 'b',label = 'Val loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}