CNN_questionPairs_imageGeneration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from subprocess import check_output\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, HashingVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import seaborn as sns\n",
    "import scipy\n",
    "from scipy import sparse as sp\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "import matplotlib\n",
    "from scipy.sparse import coo_matrix\n",
    "from tqdm import tqdm\n",
    "%matplotlib inline\n",
    "# import plotly.offline as py\n",
    "# py.init_notebook_mode(connected=True)\n",
    "# import plotly.graph_objs as go\n",
    "# import plotly.tools as tls\n",
    "\n",
    "# print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Why am I mentally very lonely? How can I solve...</td>\n",
       "      <td>Find the remainder when [math]23^{24}[/math] i...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Which one dissolve in water quikly sugar, salt...</td>\n",
       "      <td>Which fish would survive in salt water?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  What is the step by step guide to invest in sh...   \n",
       "1   1     3     4  What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2   2     5     6  How can I increase the speed of my internet co...   \n",
       "3   3     7     8  Why am I mentally very lonely? How can I solve...   \n",
       "4   4     9    10  Which one dissolve in water quikly sugar, salt...   \n",
       "\n",
       "                                           question2  is_duplicate  \n",
       "0  What is the step by step guide to invest in sh...             0  \n",
       "1  What would happen if the Indian government sto...             0  \n",
       "2  How can Internet speed be increased by hacking...             0  \n",
       "3  Find the remainder when [math]23^{24}[/math] i...             0  \n",
       "4            Which fish would survive in salt water?             0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./Data/train.csv\").fillna(\"\")\n",
    "df.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfq1, dfq2 = df[['qid1', 'question1']], df[['qid2', 'question2']]\n",
    "dfq1.columns = ['id', 'question']\n",
    "dfq2.columns = ['id', 'question']\n",
    "\n",
    "# merge two two dfs, there are two nans for question\n",
    "dfqa = pd.concat((dfq1, dfq2), axis=0).fillna(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>question</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Why am I mentally very lonely? How can I solve...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>Which one dissolve in water quikly sugar, salt...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                           question\n",
       "0   1  What is the step by step guide to invest in sh...\n",
       "1   3  What is the story of Kohinoor (Koh-i-Noor) Dia...\n",
       "2   5  How can I increase the speed of my internet co...\n",
       "3   7  Why am I mentally very lonely? How can I solve...\n",
       "4   9  Which one dissolve in water quikly sugar, salt..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfq1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'question'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfqa.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(808580, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfqa.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mq1 = TfidfVectorizer(max_features = 1000).fit_transform(dfqa['question'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(808580, 1000)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mq1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1000)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1 = scipy.dot(mq1[0,:].T, mq1[1,:])\n",
    "r1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_coo_matrix(m):\n",
    "    if not isinstance(m, coo_matrix):\n",
    "        m = coo_matrix(m)\n",
    "    fig = plt.figure(figsize= (10,10))\n",
    "    ax = fig.add_subplot(111, facecolor='black')\n",
    "    ax.plot(m.col, m.row, 's', color='white', ms=1)\n",
    "    ax.set_xlim(0, m.shape[1])\n",
    "    ax.set_ylim(0, m.shape[0])\n",
    "    ax.set_aspect('equal')\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_visible(False)\n",
    "    ax.invert_yaxis()\n",
    "    ax.set_aspect('equal')\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0/1 [00:00<?, ?it/s]\u001b[A\n",
      "100%|██████████| 1/1 [00:00<00:00,  7.29it/s]\u001b[A\n",
      "\u001b[A"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi0AAAIuCAYAAABzfTjcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "even = [ele for ele in range(dfqa.shape[0]) if ele%2 == 0]\n",
    "odd = [ele for ele in range(dfqa.shape[0]) if ele%2 != 0]\n",
    "odd = [1]\n",
    "for ele in tqdm(odd):\n",
    "    r1 = scipy.dot(mq1[ele,:].T, mq1[ele+1,:])\n",
    "    m = coo_matrix(np.ceil(r1))\n",
    "    ax = plot_coo_matrix(m)\n",
    "    ax.figure.savefig('id_{}&id_{}.png'.format(ele,ele+1))\n",
    "    image = rgb2gray(cv2.imread('./tarining_image/id_{}&id_{}.png'.format(ele,ele+1))).reshape(-1)\n",
    "#     ax.figure.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "from skimage.color import rgb2gray\n",
    "import copy\n",
    "image = rgb2gray(cv2.imread('./tarining_image/id_1&id_2.png')).reshape(-1)\n",
    "image[image == 1] = 0\n",
    "x_train = image.reshape(1,720,720)\n",
    "x_test = image.reshape(1,720,720)\n",
    "y_test = np.array([1])\n",
    "y_train = np.array([1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1,)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (1, 720, 720, 1)\n",
      "1 train samples\n",
      "1 test samples\n",
      "Epoch 1/3\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "from keras import callbacks\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import tensorflow as tf\n",
    "tf.reset_default_graph() \n",
    "batch_size = 128\n",
    "num_classes = 1\n",
    "epochs = 3\n",
    "\n",
    "img_rows, img_cols = 720, 720\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "\t\tx_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
    "\t\tx_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
    "\t\tinput_shape = (1, img_rows, img_cols)\n",
    "else:\n",
    "\t\tx_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "\t\tx_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "\t\tinput_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "\t\t\n",
    "x_train = x_train.astype('float32')\n",
    "# x_train, _, y_train, _ = train_test_split(x_train, y_train, test_size = 0.99, random_state = 0)\n",
    "x_test = x_test.astype('float32')\n",
    "# _, x_test, _, y_test = train_test_split(x_test, y_test, test_size = 0.01, random_state = 0)\n",
    "# x_train /= 255\n",
    "# x_test /= 255\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "# y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "# y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
    "\t\t\t\t\t\t\t\t activation='relu',\n",
    "\t\t\t\t\t\t\t\t input_shape=input_shape))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "# model.add(Dense(128, activation='relu',name='aux_output'))\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.compile(loss=keras.losses.mean_absolute_error,\n",
    "\t\t\t\t\t\t\toptimizer=keras.optimizers.Adadelta(),\n",
    "\t\t\t\t\t\t\tmetrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train,\n",
    "\t\t\t\t\tbatch_size=batch_size,\n",
    "\t\t\t\t\tepochs=epochs,\n",
    "\t\t\t\t\tverbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 720, 720, 1)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 10, 2, 1)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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