News classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Importing data from keras "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/text-datasets/reuters.npz\n",
      "2113536/2110848 [==============================] - 2s 1us/step\n",
      "2121728/2110848 [==============================] - 2s 1us/step\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import reuters\n",
    "\n",
    "(train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8982, 2246)\n"
     ]
    }
   ],
   "source": [
    "print(len(train_data),len(test_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting data to tensors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequencial training data to tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def vectorize_data(sequences, dimension=10000):\n",
    "    results = np.zeros((len(sequences), dimension))\n",
    "    for i, sequence in enumerate(sequences):\n",
    "        results[i, sequence] = 1\n",
    "    return results\n",
    "\n",
    "x_train = vectorize_data(train_data)\n",
    "x_test = vectorize_data(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One hot encoding for the multiclass test data (here 46 diff classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_one_hot(labels, dimension=46):\n",
    "    results = np.zeros((len(labels), dimension))\n",
    "    for i, label in enumerate(labels):\n",
    "        results[i,label] = 1\n",
    "    return results\n",
    "\n",
    "one_hot_train_labels = to_one_hot(train_labels)\n",
    "one_hot_test_labels = to_one_hot(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decoding data back to text (only practice/not needed for model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://s3.amazonaws.com/text-datasets/reuters_word_index.json\n",
      "557056/550378 [==============================] - 1s 1us/step\n",
      "565248/550378 [==============================] - 1s 1us/step\n"
     ]
    }
   ],
   "source": [
    "word_index = reuters.get_word_index()\n",
    "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
    "decoded_news = ' '.join([reverse_word_index.get(i -3, '?') for i in train_data[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "? ? ? said as a result of its december acquisition of space co it expects earnings per share in 1987 of 1 15 to 1 30 dlrs per share up from 70 cts in 1986 the company said pretax net should rise to nine to 10 mln dlrs from six mln dlrs in 1986 and rental operation revenues to 19 to 22 mln dlrs from 12 5 mln dlrs it said cash flow per share this year should be 2 50 to three dlrs reuter 3\n"
     ]
    }
   ],
   "source": [
    "print(decoded_news)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making Keras machine learning model (with validation/ 20 epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "\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'))\n",
    "\n",
    "model.compile(optimizer='rmsprop',\n",
    "             loss='categorical_crossentropy',\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_val = x_train[:1000]\n",
    "partial_x_train = x_train[1000:]\n",
    "\n",
    "y_val = one_hot_train_labels[:1000]\n",
    "partial_y_train = one_hot_train_labels[1000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 7982 samples, validate on 1000 samples\n",
      "Epoch 1/20\n",
      "7982/7982 [==============================] - 5s 677us/step - loss: 2.5322 - acc: 0.4955 - val_loss: 1.7208 - val_acc: 0.6120\n",
      "Epoch 2/20\n",
      "7982/7982 [==============================] - 1s 65us/step - loss: 1.4452 - acc: 0.6879 - val_loss: 1.3459 - val_acc: 0.7060\n",
      "Epoch 3/20\n",
      "7982/7982 [==============================] - 1s 64us/step - loss: 1.0953 - acc: 0.7651 - val_loss: 1.1708 - val_acc: 0.7430\n",
      "Epoch 4/20\n",
      "7982/7982 [==============================] - 1s 65us/step - loss: 0.8697 - acc: 0.8165 - val_loss: 1.0793 - val_acc: 0.7590\n",
      "Epoch 5/20\n",
      "7982/7982 [==============================] - 1s 67us/step - loss: 0.7034 - acc: 0.8472 - val_loss: 0.9844 - val_acc: 0.7810\n",
      "Epoch 6/20\n",
      "7982/7982 [==============================] - 1s 65us/step - loss: 0.5667 - acc: 0.8802 - val_loss: 0.9411 - val_acc: 0.8040\n",
      "Epoch 7/20\n",
      "7982/7982 [==============================] - 1s 64us/step - loss: 0.4581 - acc: 0.9048 - val_loss: 0.9083 - val_acc: 0.8020\n",
      "Epoch 8/20\n",
      "7982/7982 [==============================] - 1s 65us/step - loss: 0.3695 - acc: 0.9231 - val_loss: 0.9363 - val_acc: 0.7890\n",
      "Epoch 9/20\n",
      "7982/7982 [==============================] - 1s 64us/step - loss: 0.3032 - acc: 0.9315 - val_loss: 0.8917 - val_acc: 0.8090\n",
      "Epoch 10/20\n",
      "7982/7982 [==============================] - 1s 66us/step - loss: 0.2537 - acc: 0.9414 - val_loss: 0.9071 - val_acc: 0.8110\n",
      "Epoch 11/20\n",
      "7982/7982 [==============================] - 1s 67us/step - loss: 0.2187 - acc: 0.9471 - val_loss: 0.9177 - val_acc: 0.8130\n",
      "Epoch 12/20\n",
      "7982/7982 [==============================] - 1s 65us/step - loss: 0.1873 - acc: 0.9508 - val_loss: 0.9027 - val_acc: 0.8130\n",
      "Epoch 13/20\n",
      "7982/7982 [==============================] - 1s 65us/step - loss: 0.1703 - acc: 0.9521 - val_loss: 0.9323 - val_acc: 0.8110\n",
      "Epoch 14/20\n",
      "7982/7982 [==============================] - 1s 64us/step - loss: 0.1536 - acc: 0.9554 - val_loss: 0.9689 - val_acc: 0.8050\n",
      "Epoch 15/20\n",
      "7982/7982 [==============================] - 1s 65us/step - loss: 0.1390 - acc: 0.9560 - val_loss: 0.9686 - val_acc: 0.8150\n",
      "Epoch 16/20\n",
      "7982/7982 [==============================] - 1s 67us/step - loss: 0.1313 - acc: 0.9560 - val_loss: 1.0220 - val_acc: 0.8060\n",
      "Epoch 17/20\n",
      "7982/7982 [==============================] - 1s 65us/step - loss: 0.1217 - acc: 0.9579 - val_loss: 1.0254 - val_acc: 0.7970\n",
      "Epoch 18/20\n",
      "7982/7982 [==============================] - 1s 67us/step - loss: 0.1198 - acc: 0.9582 - val_loss: 1.0430 - val_acc: 0.8060\n",
      "Epoch 19/20\n",
      "7982/7982 [==============================] - 1s 66us/step - loss: 0.1138 - acc: 0.9597 - val_loss: 1.0955 - val_acc: 0.7970\n",
      "Epoch 20/20\n",
      "7982/7982 [==============================] - 1s 65us/step - loss: 0.1111 - acc: 0.9593 - val_loss: 1.0674 - val_acc: 0.8020\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(partial_x_train,\n",
    "                   partial_y_train,\n",
    "                   epochs=20,\n",
    "                   batch_size=512,\n",
    "                   validation_data=(x_val,y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting of validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f717d980790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(loss) + 1)\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f717d91d350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation acc')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making model from scratch for whole data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/9\n",
      "8982/8982 [==============================] - 1s 69us/step - loss: 2.4437 - acc: 0.5332\n",
      "Epoch 2/9\n",
      "8982/8982 [==============================] - 1s 57us/step - loss: 1.3241 - acc: 0.7161\n",
      "Epoch 3/9\n",
      "8982/8982 [==============================] - 1s 58us/step - loss: 0.9744 - acc: 0.7869\n",
      "Epoch 4/9\n",
      "8982/8982 [==============================] - 1s 57us/step - loss: 0.7565 - acc: 0.8390\n",
      "Epoch 5/9\n",
      "8982/8982 [==============================] - 1s 57us/step - loss: 0.5963 - acc: 0.8764\n",
      "Epoch 6/9\n",
      "8982/8982 [==============================] - 1s 58us/step - loss: 0.4751 - acc: 0.9009\n",
      "Epoch 7/9\n",
      "8982/8982 [==============================] - 1s 56us/step - loss: 0.3831 - acc: 0.9175\n",
      "Epoch 8/9\n",
      "8982/8982 [==============================] - 1s 56us/step - loss: 0.3076 - acc: 0.9348\n",
      "Epoch 9/9\n",
      "8982/8982 [==============================] - 1s 56us/step - loss: 0.2591 - acc: 0.9414\n",
      "2246/2246 [==============================] - 0s 70us/step\n"
     ]
    }
   ],
   "source": [
    "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'))\n",
    "\n",
    "model.compile(optimizer='rmsprop',\n",
    "             loss='categorical_crossentropy',\n",
    "             metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train,\n",
    "          one_hot_train_labels,\n",
    "            epochs=9,\n",
    "        batch_size=512)\n",
    "\n",
    "results = model.evaluate(x_test, one_hot_test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9800314129407345, 0.7902938557966204]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ~80% accuracy"
   ]
  }
 ],
 "metadata": {
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   "name": "python2"
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    "name": "ipython",
    "version": 2
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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