CNN.ipynb

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    "colab": {
      "name": "Assignment.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWpfV-eG5JX1"
      },
      "source": [
        "%tensorflow_version 2.x"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aeHPD5kt5OFE"
      },
      "source": [
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import tensorflow as tf"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "l0VdiC0d9cFd",
        "outputId": "51c3edb8-6bcb-4be6-eaaf-8b5d8b5d8215"
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.4.1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "es5Yb1Pn-DWA",
        "outputId": "0bc60312-19f7-4198-e466-84c4a86dbe70"
      },
      "source": [
        "from google.colab import files\r\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-ad6cbbe9-b2a8-465d-9212-5907dbb27148\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-ad6cbbe9-b2a8-465d-9212-5907dbb27148\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving test.csv to test (2).csv\n",
            "Saving train.csv to train (2).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ALeS00Ee-ROS"
      },
      "source": [
        "import io\r\n",
        "train = pd.read_csv(io.BytesIO(uploaded['train.csv']))"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JplwD-YQ_-ub"
      },
      "source": [
        "import io\r\n",
        "test = pd.read_csv(io.BytesIO(uploaded['test.csv']))"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xYMwMGOq-NLr"
      },
      "source": [
        "train.columns = ['classid','title','description']\r\n",
        "test.columns = ['classid','title','description']"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "MbWMdTiP82g7",
        "outputId": "1ce1d9fd-a5bb-47aa-dd9b-f78decd23e2c"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>classid</th>\n",
              "      <th>title</th>\n",
              "      <th>description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>Carlyle Looks Toward Commercial Aerospace (Reu...</td>\n",
              "      <td>Reuters - Private investment firm Carlyle Grou...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>Oil and Economy Cloud Stocks' Outlook (Reuters)</td>\n",
              "      <td>Reuters - Soaring crude prices plus worries\\ab...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Iraq Halts Oil Exports from Main Southern Pipe...</td>\n",
              "      <td>Reuters - Authorities have halted oil export\\f...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Oil prices soar to all-time record, posing new...</td>\n",
              "      <td>AFP - Tearaway world oil prices, toppling reco...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>Stocks End Up, But Near Year Lows (Reuters)</td>\n",
              "      <td>Reuters - Stocks ended slightly higher on Frid...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   classid  ...                                        description\n",
              "0        3  ...  Reuters - Private investment firm Carlyle Grou...\n",
              "1        3  ...  Reuters - Soaring crude prices plus worries\\ab...\n",
              "2        3  ...  Reuters - Authorities have halted oil export\\f...\n",
              "3        3  ...  AFP - Tearaway world oil prices, toppling reco...\n",
              "4        3  ...  Reuters - Stocks ended slightly higher on Frid...\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f_LZxjTmAHOO"
      },
      "source": [
        "train['summary'] = train['title'] + ' ' + train['description']\r\n",
        "test['summary'] = test['title'] + ' ' + test['description']"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vK7bOcE_-q-B"
      },
      "source": [
        "train = train.drop(columns=['title', 'description'])\r\n",
        "test = test.drop(columns=['title', 'description'])"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsT4ZlRW-9eS"
      },
      "source": [
        "labels = {1:'World News', 2:'Sports News', 3:'Business News', 4:'Science-Technology News'}"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCyYMhbk_FJp"
      },
      "source": [
        "train['label'] = train['classid'].map(labels)\r\n",
        "test['label'] = test['classid'].map(labels)"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "SgtuiLcP_T6u",
        "outputId": "07f0c48e-d800-464d-fa83-be07880671bf"
      },
      "source": [
        "train = train.drop(columns=['classid'])\r\n",
        "test = test.drop(columns=['classid'])\r\n",
        "train.head()"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>summary</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Carlyle Looks Toward Commercial Aerospace (Reu...</td>\n",
              "      <td>Business News</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Oil and Economy Cloud Stocks' Outlook (Reuters...</td>\n",
              "      <td>Business News</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Iraq Halts Oil Exports from Main Southern Pipe...</td>\n",
              "      <td>Business News</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Oil prices soar to all-time record, posing new...</td>\n",
              "      <td>Business News</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Stocks End Up, But Near Year Lows (Reuters) Re...</td>\n",
              "      <td>Business News</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             summary          label\n",
              "0  Carlyle Looks Toward Commercial Aerospace (Reu...  Business News\n",
              "1  Oil and Economy Cloud Stocks' Outlook (Reuters...  Business News\n",
              "2  Iraq Halts Oil Exports from Main Southern Pipe...  Business News\n",
              "3  Oil prices soar to all-time record, posing new...  Business News\n",
              "4  Stocks End Up, But Near Year Lows (Reuters) Re...  Business News"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQMxQ9MjAKYB",
        "outputId": "af0353aa-5547-4c7e-c64d-5a5db33ce0ef"
      },
      "source": [
        "import re\r\n",
        "import string as s\r\n",
        "import string\r\n",
        "import nltk\r\n",
        "nltk.download('stopwords')\r\n",
        "nltk.download('wordnet')\r\n",
        "nltk.download('punkt')\r\n",
        "from nltk.corpus import stopwords\r\n",
        "from nltk.tokenize import word_tokenize\r\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJqYCQVm_FVZ"
      },
      "source": [
        "# split the data into train and test data\r\n",
        "\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(train['summary'], train['label'],\r\n",
        "                                                                test_size=0.2, random_state=1)"
      ],
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFp1PyrWARUy"
      },
      "source": [
        "def remove_punc(text):\r\n",
        "    text = re.sub('\\[.*?\\]', '', text)\r\n",
        "    text = re.sub('https?://\\S+|www\\.\\S+', '', text)\r\n",
        "    text = re.sub('<.*?>+', '', text)\r\n",
        "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\r\n",
        "    text = re.sub('\\n', '', text)\r\n",
        "    text = re.sub('\\w*\\d\\w*', '', text)\r\n",
        "    return text"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qXrobMV_Aw3q"
      },
      "source": [
        "\r\n",
        "train['summary'] = train['summary'].apply(lambda x: remove_punc(x))\r\n",
        "test['summary'] = test['summary'].apply(lambda x: remove_punc(x))"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yEEjde3aA0kS"
      },
      "source": [
        "def data_cleaner(text):        \r\n",
        "    lower_case = text.lower()\r\n",
        "    tokens=word_tokenize(lower_case)\r\n",
        "    return (\" \".join(tokens)).strip()\r\n",
        "\r\n",
        "def remove_stopwords (text):        \r\n",
        "    list1=[word for word in text.split() if word not in stopwords.words('english')]\r\n",
        "    return \" \".join(list1)\r\n",
        "\r\n",
        "train['summary'] = train['summary'].apply(lambda x: data_cleaner(x))\r\n",
        "test['summary'] = test['summary'].apply(lambda x: data_cleaner(x))\r\n",
        "\r\n",
        "train['summary'] = train['summary'].apply(lambda x: remove_stopwords(x))\r\n",
        "test['summary'] = test['summary'].apply(lambda x: remove_stopwords(x))"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LM-geDqlEB0f"
      },
      "source": [
        "\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(train['summary'], train['label'],\r\n",
        "                                                                test_size=0.2, random_state=1)"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RGBqSVHlEJHH",
        "outputId": "4dcd6b40-db42-461c-c416-24af1f302853"
      },
      "source": [
        "tokenized_data = []\r\n",
        "for i in train['summary']:\r\n",
        "    tokenized_data.append(i.split())\r\n",
        "print(tokenized_data[:2])"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['carlyle', 'looks', 'toward', 'commercial', 'aerospace', 'reuters', 'reuters', 'private', 'investment', 'firm', 'carlyle', 'groupwhich', 'reputation', 'making', 'welltimed', 'occasionallycontroversial', 'plays', 'defense', 'industry', 'quietly', 'placedits', 'bets', 'another', 'part', 'market'], ['oil', 'economy', 'cloud', 'stocks', 'outlook', 'reuters', 'reuters', 'soaring', 'crude', 'prices', 'plus', 'worriesabout', 'economy', 'outlook', 'earnings', 'expected', 'tohang', 'stock', 'market', 'next', 'week', 'depth', 'thesummer', 'doldrums']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIAXsSVSM9vT"
      },
      "source": [
        "from gensim.models import Word2Vec\r\n",
        "from tensorflow.keras.models import Sequential\r\n",
        "from keras.utils import to_categorical\r\n",
        "from keras.preprocessing.text import Tokenizer\r\n",
        "from keras.preprocessing.sequence import pad_sequences\r\n",
        "from sklearn import preprocessing\r\n",
        "from keras.preprocessing import sequence\r\n"
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z_c230JuM-Xk",
        "outputId": "4f2c3ca7-399b-4e73-e933-7be03b4e3d0e"
      },
      "source": [
        "w2v_model = Word2Vec(tokenized_data, size=50, workers=32, min_count=1, window=3)\r\n",
        "print(w2v_model)"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Word2Vec(vocab=89738, size=50, alpha=0.025)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kwLhXNdINOWy"
      },
      "source": [
        "token = Tokenizer(89738)\r\n",
        "token.fit_on_texts(train['summary'])\r\n",
        "token_text = token.texts_to_sequences(train['summary'])\r\n",
        "token_text = pad_sequences(token_text)"
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jr2dP3F4NOnZ",
        "outputId": "046caf31-2436-48d5-d18e-8644b96d6221"
      },
      "source": [
        "X = preprocessing.LabelEncoder()\r\n",
        "y = X.fit_transform(train['label'])\r\n",
        "y = to_categorical(y)\r\n",
        "print(y[:5])"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1. 0. 0. 0.]\n",
            " [1. 0. 0. 0.]\n",
            " [1. 0. 0. 0.]\n",
            " [1. 0. 0. 0.]\n",
            " [1. 0. 0. 0.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWsPd7LrN5oq"
      },
      "source": [
        "# spilt the data into training and testing data\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(np.array(token_text), y, test_size=0.2)"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TV2VSzXPPdly"
      },
      "source": [
        "from keras import preprocessing\r\n",
        "import keras\r\n",
        "from tensorflow.keras.models import Model, Sequential\r\n",
        "from tensorflow.keras.layers import Conv1D, MaxPool1D, Dropout, Dense, GlobalMaxPool1D, Embedding, Activation\r\n",
        "from tensorflow.keras.optimizers import Adam\r\n",
        "from sklearn.metrics import confusion_matrix, precision_score, recall_score"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m75StpeoO9c7",
        "outputId": "dd14db79-b021-4fdc-f37c-0a727cf73a60"
      },
      "source": [
        "# building the model\r\n",
        "\r\n",
        "model = Sequential()\r\n",
        "model.add(w2v_model.wv.get_keras_embedding(True))\r\n",
        "model.add(Dropout(0.2))\r\n",
        "model.add(Conv1D(50, 3, activation='relu', padding='same', strides=1))\r\n",
        "model.add(MaxPool1D())\r\n",
        "model.add(Dropout(0.2))\r\n",
        "model.add(Conv1D(100, 3, activation='relu', padding='same', strides=1))\r\n",
        "model.add(MaxPool1D())\r\n",
        "model.add(Dropout(0.2))\r\n",
        "model.add(Conv1D(200, 3, activation='relu', padding='same', strides=1))\r\n",
        "model.add(GlobalMaxPool1D())\r\n",
        "model.add(Dropout(0.2))\r\n",
        "model.add(Dense(200))\r\n",
        "model.add(Activation('relu'))\r\n",
        "model.add(Dropout(0.2))\r\n",
        "model.add(Dense(4))\r\n",
        "model.add(Activation('softmax'))\r\n",
        "model.compile(loss='categorical_crossentropy', metrics=['acc'], optimizer='adam')\r\n",
        "model.summary()"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_13\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_5 (Embedding)      (None, None, 50)          4486900   \n",
            "_________________________________________________________________\n",
            "dropout_19 (Dropout)         (None, None, 50)          0         \n",
            "_________________________________________________________________\n",
            "conv1d_6 (Conv1D)            (None, None, 50)          7550      \n",
            "_________________________________________________________________\n",
            "max_pooling1d_4 (MaxPooling1 (None, None, 50)          0         \n",
            "_________________________________________________________________\n",
            "dropout_20 (Dropout)         (None, None, 50)          0         \n",
            "_________________________________________________________________\n",
            "conv1d_7 (Conv1D)            (None, None, 100)         15100     \n",
            "_________________________________________________________________\n",
            "max_pooling1d_5 (MaxPooling1 (None, None, 100)         0         \n",
            "_________________________________________________________________\n",
            "dropout_21 (Dropout)         (None, None, 100)         0         \n",
            "_________________________________________________________________\n",
            "conv1d_8 (Conv1D)            (None, None, 200)         60200     \n",
            "_________________________________________________________________\n",
            "global_max_pooling1d_5 (Glob (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dropout_22 (Dropout)         (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 200)               40200     \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dropout_23 (Dropout)         (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 4)                 804       \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 4)                 0         \n",
            "=================================================================\n",
            "Total params: 4,610,754\n",
            "Trainable params: 4,610,754\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-mvPoyRfZbSk",
        "outputId": "d2f07d2f-038b-449c-c06b-d72dba0f7be4"
      },
      "source": [
        "# train the model\r\n",
        "model.fit(X_train, y_train, batch_size=256, epochs=10, validation_data=(X_test, y_test))"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "375/375 [==============================] - 89s 236ms/step - loss: 1.3065 - acc: 0.3751 - val_loss: 0.5932 - val_acc: 0.7935\n",
            "Epoch 2/10\n",
            "375/375 [==============================] - 91s 242ms/step - loss: 0.6148 - acc: 0.7742 - val_loss: 0.4075 - val_acc: 0.8681\n",
            "Epoch 3/10\n",
            "375/375 [==============================] - 89s 236ms/step - loss: 0.4262 - acc: 0.8501 - val_loss: 0.3351 - val_acc: 0.8918\n",
            "Epoch 4/10\n",
            "375/375 [==============================] - 88s 236ms/step - loss: 0.3487 - acc: 0.8835 - val_loss: 0.3005 - val_acc: 0.9041\n",
            "Epoch 5/10\n",
            "375/375 [==============================] - 88s 236ms/step - loss: 0.2969 - acc: 0.9018 - val_loss: 0.2762 - val_acc: 0.9078\n",
            "Epoch 6/10\n",
            "375/375 [==============================] - 88s 236ms/step - loss: 0.2579 - acc: 0.9151 - val_loss: 0.2594 - val_acc: 0.9136\n",
            "Epoch 7/10\n",
            "375/375 [==============================] - 88s 235ms/step - loss: 0.2246 - acc: 0.9261 - val_loss: 0.2561 - val_acc: 0.9162\n",
            "Epoch 8/10\n",
            "375/375 [==============================] - 88s 235ms/step - loss: 0.1968 - acc: 0.9363 - val_loss: 0.2571 - val_acc: 0.9170\n",
            "Epoch 9/10\n",
            "375/375 [==============================] - 91s 243ms/step - loss: 0.1692 - acc: 0.9447 - val_loss: 0.2645 - val_acc: 0.9156\n",
            "Epoch 10/10\n",
            "375/375 [==============================] - 88s 235ms/step - loss: 0.1479 - acc: 0.9507 - val_loss: 0.2797 - val_acc: 0.9128\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fe34e073cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBbgLZifZbro",
        "outputId": "1799e634-1b2b-4800-902d-a96080db316d"
      },
      "source": [
        "labels = X.classes_\r\n",
        "print(labels)"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Business News' 'Science-Technology News' 'Sports News' 'World News']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6nH88mOMUAKW"
      },
      "source": [
        "# check prediction\r\n",
        "\r\n",
        "predicted = model.predict(X_test)"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97t1-AZHaJBf",
        "outputId": "3d140255-c80b-4f38-cc05-f947ce7ef7c9"
      },
      "source": [
        "for i in range(10,50,3):\r\n",
        "    print(train['summary'].iloc[i][:50], \"...\")\r\n",
        "    print(\"Actual category: \", labels[np.argmax(y_test[i])])\r\n",
        "    print(\"predicted category: \", labels[np.argmax(predicted[i])])"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "need opec pump moreiran gov tehran reuters opec no ...\n",
            "Actual category:  Science-Technology News\n",
            "predicted category:  Science-Technology News\n",
            "dollar falls broadly record trade gap new york reu ...\n",
            "Actual category:  Sports News\n",
            "predicted category:  Sports News\n",
            "market head toward value funds little cause celebr ...\n",
            "Actual category:  Business News\n",
            "predicted category:  Business News\n",
            "google ipo faces playboy slipup bidding gets under ...\n",
            "Actual category:  Science-Technology News\n",
            "predicted category:  Science-Technology News\n",
            "rand falls shock sa rate cut interest rates trimme ...\n",
            "Actual category:  Science-Technology News\n",
            "predicted category:  Science-Technology News\n",
            "google auction begins friday auction shares google ...\n",
            "Actual category:  Business News\n",
            "predicted category:  Business News\n",
            "chad seeks refugee aid imf chad asks imf loan pay  ...\n",
            "Actual category:  Science-Technology News\n",
            "predicted category:  Science-Technology News\n",
            "saudi arabia open oil taps saudi arabia says ready ...\n",
            "Actual category:  Business News\n",
            "predicted category:  Business News\n",
            "steady go bedford scientists nitromed inc hope exp ...\n",
            "Actual category:  World News\n",
            "predicted category:  World News\n",
            "researchers seek untangle email thread email victi ...\n",
            "Actual category:  World News\n",
            "predicted category:  World News\n",
            "somewhere gleam gloom president bush saying us eco ...\n",
            "Actual category:  Sports News\n",
            "predicted category:  Sports News\n",
            "state grant aid lynn bank gives salem central squa ...\n",
            "Actual category:  Business News\n",
            "predicted category:  Business News\n",
            "switching titles gears dell kevin b rollins new ch ...\n",
            "Actual category:  World News\n",
            "predicted category:  World News\n",
            "making insurer pay hurricane charley blows house m ...\n",
            "Actual category:  World News\n",
            "predicted category:  World News\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kBWwHEV7O9lR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}