LSTM_RNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recurrent Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the training set\n",
    "dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1/3/2012</td>\n",
       "      <td>325.25</td>\n",
       "      <td>332.83</td>\n",
       "      <td>324.97</td>\n",
       "      <td>663.59</td>\n",
       "      <td>7,380,500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1/4/2012</td>\n",
       "      <td>331.27</td>\n",
       "      <td>333.87</td>\n",
       "      <td>329.08</td>\n",
       "      <td>666.45</td>\n",
       "      <td>5,749,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1/5/2012</td>\n",
       "      <td>329.83</td>\n",
       "      <td>330.75</td>\n",
       "      <td>326.89</td>\n",
       "      <td>657.21</td>\n",
       "      <td>6,590,300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1/6/2012</td>\n",
       "      <td>328.34</td>\n",
       "      <td>328.77</td>\n",
       "      <td>323.68</td>\n",
       "      <td>648.24</td>\n",
       "      <td>5,405,900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1/9/2012</td>\n",
       "      <td>322.04</td>\n",
       "      <td>322.29</td>\n",
       "      <td>309.46</td>\n",
       "      <td>620.76</td>\n",
       "      <td>11,688,800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date    Open    High     Low   Close      Volume\n",
       "0  1/3/2012  325.25  332.83  324.97  663.59   7,380,500\n",
       "1  1/4/2012  331.27  333.87  329.08  666.45   5,749,400\n",
       "2  1/5/2012  329.83  330.75  326.89  657.21   6,590,300\n",
       "3  1/6/2012  328.34  328.77  323.68  648.24   5,405,900\n",
       "4  1/9/2012  322.04  322.29  309.46  620.76  11,688,800"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training set on open stock price\n",
    "training_set = dataset_train.iloc[:, 1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[325.25],\n",
       "       [331.27],\n",
       "       [329.83],\n",
       "       ...,\n",
       "       [793.7 ],\n",
       "       [783.33],\n",
       "       [782.75]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler(feature_range = (0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a data structure with 60 timesteps and 1 output\n",
    "X_train = []\n",
    "y_train = []\n",
    "for i in range(60, 1258):\n",
    "    X_train.append(training_set_scaled[i-60:i, 0])\n",
    "    y_train.append(training_set_scaled[i, 0])\n",
    "X_train, y_train = np.array(X_train), np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshaping\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building the RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Importing the Keras libraries and packages\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialising the RNN\n",
    "regressor = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the first LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding a second LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding a third LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding a fourth LSTM layer and some Dropout regularisation\n",
    "regressor.add(LSTM(units = 50))\n",
    "regressor.add(Dropout(0.2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding the output layer\n",
    "regressor.add(Dense(units = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compiling the RNN\n",
    "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0342\n",
      "Epoch 2/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0066\n",
      "Epoch 3/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0053\n",
      "Epoch 4/100\n",
      "1198/1198 [==============================] - 3s 3ms/step - loss: 0.0049\n",
      "Epoch 5/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0049\n",
      "Epoch 6/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0046A: 4s - loss: 0.\n",
      "Epoch 7/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0045\n",
      "Epoch 8/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0042\n",
      "Epoch 9/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0047\n",
      "Epoch 10/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0043\n",
      "Epoch 11/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0045\n",
      "Epoch 12/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0038\n",
      "Epoch 13/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0048\n",
      "Epoch 14/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0036\n",
      "Epoch 15/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0047\n",
      "Epoch 16/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0036\n",
      "Epoch 17/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0039\n",
      "Epoch 18/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0036\n",
      "Epoch 19/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0034\n",
      "Epoch 20/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0043\n",
      "Epoch 21/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0041\n",
      "Epoch 22/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0032\n",
      "Epoch 23/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0034\n",
      "Epoch 24/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0029\n",
      "Epoch 25/100\n",
      "1198/1198 [==============================] - 5s 5ms/step - loss: 0.0032\n",
      "Epoch 26/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0030\n",
      "Epoch 27/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0033\n",
      "Epoch 28/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0030\n",
      "Epoch 29/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0034\n",
      "Epoch 30/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0029\n",
      "Epoch 31/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0029\n",
      "Epoch 32/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0031\n",
      "Epoch 33/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0030\n",
      "Epoch 34/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0032\n",
      "Epoch 35/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0028\n",
      "Epoch 36/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0030\n",
      "Epoch 37/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0032\n",
      "Epoch 38/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0027\n",
      "Epoch 39/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0025\n",
      "Epoch 40/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0024\n",
      "Epoch 41/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0024\n",
      "Epoch 42/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0027\n",
      "Epoch 43/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0023\n",
      "Epoch 44/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0023\n",
      "Epoch 45/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0025\n",
      "Epoch 46/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0026A: 3s - lo - ETA: 2s \n",
      "Epoch 47/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0023A: 2\n",
      "Epoch 48/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0024\n",
      "Epoch 49/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0021A: 0s - loss\n",
      "Epoch 50/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0022\n",
      "Epoch 51/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0023\n",
      "Epoch 52/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0021\n",
      "Epoch 53/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0026\n",
      "Epoch 54/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0023\n",
      "Epoch 55/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0023\n",
      "Epoch 56/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0023\n",
      "Epoch 57/100\n",
      "1198/1198 [==============================] - 6s 5ms/step - loss: 0.0020\n",
      "Epoch 58/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0022\n",
      "Epoch 59/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0022\n",
      "Epoch 60/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0022A: 1s - los\n",
      "Epoch 61/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0019\n",
      "Epoch 62/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0025\n",
      "Epoch 63/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0022\n",
      "Epoch 64/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0018\n",
      "Epoch 65/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0020\n",
      "Epoch 66/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0021\n",
      "Epoch 67/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0019A: 0s - loss: 0\n",
      "Epoch 68/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0019\n",
      "Epoch 69/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0018\n",
      "Epoch 70/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0018\n",
      "Epoch 71/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0017\n",
      "Epoch 72/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0017\n",
      "Epoch 73/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0018\n",
      "Epoch 74/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0016\n",
      "Epoch 75/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0016\n",
      "Epoch 76/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0020\n",
      "Epoch 77/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0021\n",
      "Epoch 78/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0016\n",
      "Epoch 79/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0018\n",
      "Epoch 80/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0016\n",
      "Epoch 81/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0015\n",
      "Epoch 82/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0015\n",
      "Epoch 83/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0016\n",
      "Epoch 84/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0016\n",
      "Epoch 85/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0015\n",
      "Epoch 86/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0015\n",
      "Epoch 87/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0016\n",
      "Epoch 88/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0015\n",
      "Epoch 89/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0015A: 0s - loss: 0.0\n",
      "Epoch 90/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0016\n",
      "Epoch 91/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0016\n",
      "Epoch 92/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0016\n",
      "Epoch 93/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0013\n",
      "Epoch 94/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0015\n",
      "Epoch 95/100\n",
      "1198/1198 [==============================] - 4s 3ms/step - loss: 0.0015\n",
      "Epoch 96/100\n",
      "1198/1198 [==============================] - 4s 4ms/step - loss: 0.0014\n",
      "Epoch 97/100\n",
      "1198/1198 [==============================] - 5s 5ms/step - loss: 0.0013A: 0s - loss: 0.\n",
      "Epoch 98/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0015\n",
      "Epoch 99/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0013\n",
      "Epoch 100/100\n",
      "1198/1198 [==============================] - 5s 4ms/step - loss: 0.0014\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x1c2938c6e08>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting the RNN to the Training set\n",
    "regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "#saving the model\n",
    "regressor.save(\"lstm_rnn_model.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making the predictions and visualising the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the real stock price of 2017\n",
    "dataset_test = pd.read_csv('Google_Stock_Price_Test.csv')\n",
    "real_stock_price = dataset_test.iloc[:, 1:2].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the predicted stock price of 2017\n",
    "dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)\n",
    "inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values\n",
    "inputs = inputs.reshape(-1,1)\n",
    "inputs = sc.transform(inputs)\n",
    "X_test = []\n",
    "for i in range(60, 80):\n",
    "    X_test.append(inputs[i-60:i, 0])\n",
    "X_test = np.array(X_test)\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
    "predicted_stock_price = regressor.predict(X_test)\n",
    "predicted_stock_price = sc.inverse_transform(predicted_stock_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Visualising the results\n",
    "plt.plot(real_stock_price, color = 'red', label = 'Real Google Stock Price')\n",
    "plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Google Stock Price')\n",
    "plt.title('Google Stock Price Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Google Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}