twitter_detection.ipynb

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        "<a href=\"https://colab.research.google.com/github/dhivyasreedhar/FakeTweetDetection/blob/main/twitter_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
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        "id": "b8LsTmevL1r0"
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      "source": [
        "#loading basic libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tf0P13zfL1r4"
      },
      "source": [
        "#loading dataset\n",
        "\n",
        "data = pd.read_csv('/content/train1.csv',encoding='mac_roman')\n",
        "test_data = pd.read_csv('/content/test1.csv',encoding='mac_roman')"
      ],
      "execution_count": 4,
      "outputs": []
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              "   id                                              tweet  label\n",
              "0   1  The CDC currently reports 99031 deaths. In gen...      1\n",
              "1   2  States reported 1121 deaths a small rise from ...      1\n",
              "2   3  Politically Correct Woman (Almost) Uses Pandem...      0\n",
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              "4   5  Populous states can generate large case counts...      1"
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        "#viewing data\n",
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      "outputs": [
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              "      <th>10417</th>\n",
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              "      <th>10419</th>\n",
              "      <td>10420</td>\n",
              "      <td>La cosa va de esto....China developing 9 poten...</td>\n",
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              "      <th>10420</th>\n",
              "      <td>10421</td>\n",
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              "          id                                              tweet  label\n",
              "10417  10418  @ayogo_do @Kaashin1 @BosmaTon @solo_ambuku @Ma...      0\n",
              "10418  10419  RT @Jimmymack010: The virus is engineered The ...      0\n",
              "10419  10420  La cosa va de esto....China developing 9 poten...      0\n",
              "10420  10421  RT @BreezyxSupreme: THE CURE TO THE CORONAVIRU...      0\n",
              "10421  10422  @432wps @VNotKind @cla83674019 @Bossina8 @lhas...      0"
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              "      <td>Alfalfa is the only cure for COVID-19.</td>\n",
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              "   id                                              tweet\n",
              "0   1  Our daily update is published. States reported...\n",
              "1   2             Alfalfa is the only cure for COVID-19.\n",
              "2   3  President Trump Asked What He Would Do If He W...\n",
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              "4   5  This is the sixth time a global health emergen..."
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10422 entries, 0 to 10421\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   id      10422 non-null  int64 \n",
            " 1   tweet   10422 non-null  object\n",
            " 2   label   10422 non-null  int64 \n",
            "dtypes: int64(2), object(1)\n",
            "memory usage: 244.4+ KB\n"
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        "id": "_fnF-biIOY7I",
        "outputId": "a9d4c6c9-db55-4391-d4ae-36e24bb69a77"
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      "source": [
        "data.isna().sum()"
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      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id       0\n",
              "tweet    0\n",
              "label    0\n",
              "dtype: int64"
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          "metadata": {
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          "execution_count": 13
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      "source": [
        "test_data.isna().sum()"
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      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id       0\n",
              "tweet    0\n",
              "dtype: int64"
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          "metadata": {
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          "execution_count": 14
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      "source": [
        "target_data = data['label']\n",
        "target_data = np.array(target_data)\n",
        "target_data"
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      "source": [
        "text_data = data.tweet.values\n",
        "text_data"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['The CDC currently reports 99031 deaths. In general the discrepancies in death counts between different sources are small and explicable. The death toll stands at roughly 100000 people today.',\n",
              "       'States reported 1121 deaths a small rise from last Tuesday. Southern states reported 640 of those deaths. https://t.co/YASGRTT4ux',\n",
              "       'Politically Correct Woman (Almost) Uses Pandemic as Excuse Not to Reuse Plastic Bag https://t.co/thF8GuNFPe #coronavirus #nashville',\n",
              "       ...,\n",
              "       'La cosa va de esto....China developing 9 potential vaccines in global race for coronavirus cure | South China Morning Post https://t.co/wX0jRDxm7Y',\n",
              "       'RT @BreezyxSupreme: THE CURE TO THE CORONAVIRUS IS DICK ENLARGEMENT PILLS! https://t.co/qrlHMZe9wl',\n",
              "       '@432wps @VNotKind @cla83674019 @Bossina8 @lhasadv @TapuMana @FernandaVigagni @manuelabadawy What scares me is the fact that Indian research has found HIV sequencing in this corona virus strain. That to me, screams man made bioweapon. Now last year, they h'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
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          "execution_count": 16
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              "   id                                              tweet  label\n",
              "0   1  The CDC currently reports 99031 deaths. In gen...      1\n",
              "1   2  States reported 1121 deaths a small rise from ...      1\n",
              "2   3  Politically Correct Woman (Almost) Uses Pandem...      0\n",
              "3   4  #IndiaFightsCorona: We have 1524 #COVID testin...      1\n",
              "4   5  Populous states can generate large case counts...      1"
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      "execution_count": 18,
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              "<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>tweet</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Our daily update is published. States reported...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Alfalfa is the only cure for COVID-19.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>President Trump Asked What He Would Do If He W...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>States reported 630 deaths. We are still seein...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>This is the sixth time a global health emergen...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id                                              tweet\n",
              "0   1  Our daily update is published. States reported...\n",
              "1   2             Alfalfa is the only cure for COVID-19.\n",
              "2   3  President Trump Asked What He Would Do If He W...\n",
              "3   4  States reported 630 deaths. We are still seein...\n",
              "4   5  This is the sixth time a global health emergen..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "vbDBWi5xbD2W",
        "outputId": "f0df9c96-24ed-420f-dc1e-593e1eb579fd"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.set_style('darkgrid')\n",
        "sns.countplot(x=data.label)\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAWWklEQVR4nO3df0xV9/3H8df1Ii1V4Cq5XLSzplaWLdYfjalKoJpee6GKdoiYZW62sjVuleisHZtuC3Gtpbq6Sgz7UWJibLNuVia4lbZgoRPY6li0zuqabWYjRec9OOSCP1aB6/n+4bc3cy186OBwEZ+Pv+Bc7zlvzCc8c8699+CybdsWAAB9GBXtAQAAwx+xAAAYEQsAgBGxAAAYEQsAgFFMtAdwwrVr1xQO8yYvAPg0Ro929/rYiIxFOGwrFLoS7TEA4Kbi9cb3+hiXoQAARsQCAGBELAAARsQCAGBELAAARsQCAGBELAAARsQCAGBELAAARiPyE9zASDY+cbTcsbdHewwMM+GuD3Who9ux/RML4Cbjjr1dHzw9PdpjYJi5q+g9Sc7FgstQAAAjYgEAMCIWAAAjYgEAMCIWAAAjYgEAMCIWAAAjYgEAMHL0Q3l+v19jxozRqFGj5Ha7deDAAYVCIT355JM6e/as7rzzTpWUlCgxMVG2bevZZ5/V4cOHdfvtt2vbtm2aNm2aJKmiokI//elPJUlPPPGEli1b5uTYAID/4vgnuPfu3avx48dHvi8rK1NaWprWrFmjsrIylZWVqbCwUPX19WpublZNTY3+9Kc/acuWLdq/f79CoZBKS0v1q1/9Si6XS7m5ufL7/UpMTHR07rEJtyvuttGOHgM3n39f7dalzg+jPQYw5Ib8dh+1tbV6+eWXJUk5OTlatWqVCgsLVVtbq5ycHLlcLs2aNUudnZ1qbW1VU1OT0tPT5fF4JEnp6elqaGjQkiVLHJ0z7rbRml34kqPHwM3n6POP6pKIBW49jr9m8bWvfU25ubnat2+fJKmtrU3JycmSJK/Xq7a2NkmSZVlKSUmJPC8lJUWWZX1su8/nk2VZTo8NAPgPjp5Z/OIXv5DP51NbW5vy8/M1ZcqUGx53uVxyuVyDfly32yWP545B3y8gibWFYcvJteloLHw+nyQpKSlJgUBAJ06cUFJSklpbW5WcnKzW1tbI6xk+n0/BYDDy3GAwKJ/PJ5/Pp6ampsh2y7I0Z86cPo8bDtsKha4MaHavN35Az8fINdC1NVCsTfTGyd97jl2GunLlii5duhT5+ne/+51SU1Pl9/tVWVkpSaqsrNTChQslKbLdtm0dP35c8fHxSk5OVkZGhhobG9XR0aGOjg41NjYqIyPDqbEBAJ/AsTOLtrY2FRQUSJLC4bCWLFmi+fPna/r06dqwYYPKy8s1ceJElZSUSJIWLFigw4cPKxAIKC4uTsXFxZIkj8ejtWvXKi8vT5JUUFAQebEbADA0XLZt29EeYrB1d4cH5XSMd0Phvx19/lGdP38xqjN4vfH88SN8zF1F7w14bUblMhQAYOQgFgAAI2IBADAiFgAAI2IBADAiFgAAI2IBADAiFgAAI2IBADAiFgAAI2IBADAiFgAAI2IBADAiFgAAI2IBADAiFgAAI2IBADAiFgAAI2IBADAiFgAAI2IBADAiFgAAI2IBADAiFgAAI2IBADAiFgAAI2IBADAiFgAAI2IBADAiFgAAI8djEQ6HlZOTo69//euSpJaWFq1YsUKBQEAbNmxQV1eXJKmrq0sbNmxQIBDQihUrdObMmcg+XnzxRQUCAWVlZamhocHpkQEA/8XxWLz00ku65557It/v2LFDq1ev1qFDh5SQkKDy8nJJ0v79+5WQkKBDhw5p9erV2rFjhyTp9OnTqqqqUlVVlXbv3q0f/OAHCofDTo8NAPgPjsYiGAzqt7/9rfLy8iRJtm3ryJEjysrKkiQtW7ZMtbW1kqS6ujotW7ZMkpSVlaV33nlHtm2rtrZW2dnZio2N1aRJkzR58mSdOHHCybEBAP8lxsmdFxcXq7CwUJcvX5Yktbe3KyEhQTEx1w+bkpIiy7IkSZZlacKECdeHiolRfHy82tvbZVmWZs6cGdmnz+eLPKc3brdLHs8dTvxIAGsLw5aTa9OxWLz99tsaP3687r33Xv3hD39w6jCfKBy2FQpdGdA+vN74QZoGI81A19ZAsTbRGyd/7zkWi2PHjqmurk719fW6evWqLl26pGeffVadnZ3q6elRTEyMgsGgfD6fpOtnDOfOnVNKSop6enp08eJFjRs3Tj6fT8FgMLJfy7IizwEADA3HXrN46qmnVF9fr7q6Or3wwguaN2+efvSjH2nu3Lmqrq6WJFVUVMjv90uS/H6/KioqJEnV1dWaN2+eXC6X/H6/qqqq1NXVpZaWFjU3N2vGjBlOjQ0A+ARD/jmLwsJC7dmzR4FAQKFQSCtWrJAk5eXlKRQKKRAIaM+ePfrWt74lSUpNTdWiRYu0ePFiPf744yoqKpLb7R7qsQHgluaybduO9hCDrbs7PCjX7mYXvjRIE2GkOPr8ozp//mJUZ/B64/XB09OjOgOGn7uK3hvw2uzrNQs+wQ0AMCIWAAAjYgEAMCIWAAAjYgEAMCIWAAAjYgEAMCIWAAAjYgEAMCIWAAAjYgEAMCIWAAAjYgEAMCIWAAAjYgEAMCIWAAAjYgEAMCIWAAAjYgEAMCIWAAAjYgEAMCIWAAAjYgEAMCIWAAAjYgEAMCIWAAAjYgEAMCIWAAAjYgEAMOpXLB577LF+bQMAjEwxfT149epV/fvf/1Z7e7s6Ojpk27Yk6dKlS7Isq88dX716VV/+8pfV1dWlcDisrKwsrV+/Xi0tLdq4caNCoZCmTZumH/7wh4qNjVVXV5e+/e1v69SpU/J4PNq5c6c+85nPSJJefPFFlZeXa9SoUfr+97+vBx54YJB+fABAf/QZi1/+8pfau3evWltblZubG4nF2LFj9ZWvfKXPHcfGxmrv3r0aM2aMuru7tXLlSs2fP1979uzR6tWrlZ2draKiIpWXl2vlypXav3+/EhISdOjQIVVVVWnHjh0qKSnR6dOnVVVVpaqqKlmWpfz8fFVXV8vtdg/e/wIAoE99XoZ67LHHVFdXp+985zuqra1VXV2d6urq9Otf/9oYC5fLpTFjxkiSenp61NPTI5fLpSNHjigrK0uStGzZMtXW1kqS6urqtGzZMklSVlaW3nnnHdm2rdraWmVnZys2NlaTJk3S5MmTdeLEiQH/4ACA/uvzzOIjq1at0rFjx3T27FmFw+HI9pycnD6fFw6HlZubqw8++EArV67UpEmTlJCQoJiY64dNSUmJXM6yLEsTJky4PlRMjOLj49Xe3i7LsjRz5szIPn0+n/ESmNvtksdzR39+NOBTY21huHJybfYrFoWFhWppadHnPve5yOUfl8tljIXb7dbBgwfV2dmpgoIC/f3vfx/4xP0QDtsKha4MaB9eb/wgTYORZqBra6BYm+iNk7/3+hWLkydP6vXXX5fL5fqfBkhISNDcuXN1/PhxdXZ2qqenRzExMQoGg/L5fJKunzGcO3dOKSkp6unp0cWLFzVu3Dj5fD4Fg8HIvizLijwHADA0+vXW2dTUVJ0/f/5T7fjChQvq7OyUJH344Yf6/e9/r3vuuUdz585VdXW1JKmiokJ+v1+S5Pf7VVFRIUmqrq7WvHnz5HK55Pf7VVVVpa6uLrW0tKi5uVkzZsz4VLMAAAamX2cW7e3tys7O1owZMzR69OjI9p/97Ge9Pqe1tVWbNm1SOByWbdt6+OGH9eCDD2rq1Kl68sknVVJSos9//vNasWKFJCkvL0+FhYUKBAJKTEzUzp07JV0P1aJFi7R48WK53W4VFRXxTigAGGIu+6P3w/ahqanpE7fPmTNn0AcaDN3d4UG5dje78KVBmggjxdHnH9X58xejOoPXG68Pnp4e1Rkw/NxV9N6A1+aAX7MYrlEAAAyNfsXivvvui7y43d3drZ6eHsXFxenYsWOODgcAGB76FYt333038vVHH5Q7fvy4Y0MBAIaXT33XWZfLpYceekiNjY1OzAMAGIb6dWZRU1MT+fratWs6efKkbrvtNseGAgAML/2Kxdtvvx352u12684779RPfvITx4YCAAwv/YrFc8895/QcAIBhrF+vWQSDQRUUFCgtLU1paWlat27dDbfgAACMbP2KxebNm+X3+9XQ0KCGhgY9+OCD2rx5s9OzAQCGiX7F4sKFC1q+fLliYmIUExOj3NxcXbhwwenZAADDRL9i4fF4dPDgQYXDYYXDYR08eFAej8fp2QAAw0S/YlFcXKw33nhD6enpysjIUHV1tbZt2+b0bACAYaJf74batWuXtm/frsTERElSKBTS9u3beZcUANwi+nVm8Ze//CUSCun6Zan333/fsaEAAMNLv2Jx7do1dXR0RL4PhUI3/C1uAMDI1q/LUF/96lf1xS9+UQ8//LAk6c0339Q3vvENRwcDAAwf/YpFTk6O7r33Xh05ckSSVFpaqqlTpzo6GABg+OhXLCRp6tSpBAIAblGf+hblAIBbD7EAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAkWOxOHfunFatWqXFixcrOztbe/fulXT99ub5+fnKzMxUfn5+5Nbntm1r69atCgQCWrp0qU6dOhXZV0VFhTIzM5WZmamKigqnRgYA9MKxWLjdbm3atEmvv/669u3bp1deeUWnT59WWVmZ0tLSVFNTo7S0NJWVlUmS6uvr1dzcrJqaGj3zzDPasmWLpOtxKS0t1auvvqr9+/ertLT0hr+tAQBwnmOxSE5O1rRp0yRJY8eO1ZQpU2RZlmpra5WTkyPp+q3P33rrLUmKbHe5XJo1a5Y6OzvV2tqqxsZGpaeny+PxKDExUenp6WpoaHBqbADAJxiS1yzOnDmj999/XzNnzlRbW5uSk5MlSV6vV21tbZIky7KUkpISeU5KSoosy/rYdp/PJ8uyhmJsAMD/6/ffs/hfXb58WevXr9d3v/tdjR079obHXC6XXC7XoB/T7XbJ47lj0PcLSGJtYdhycm06Govu7m6tX79eS5cuVWZmpiQpKSlJra2tSk5OVmtrq8aPHy/p+hlDMBiMPDcYDMrn88nn86mpqSmy3bIszZkzp8/jhsO2QqErA5rd640f0PMxcg10bQ0UaxO9cfL3nmOXoWzb1ve+9z1NmTJF+fn5ke1+v1+VlZWSpMrKSi1cuPCG7bZt6/jx44qPj1dycrIyMjLU2Niojo4OdXR0qLGxURkZGU6NDQD4BI6dWRw9elQHDx7UZz/7WX3hC1+QJG3cuFFr1qzRhg0bVF5erokTJ6qkpESStGDBAh0+fFiBQEBxcXEqLi6WJHk8Hq1du1Z5eXmSpIKCAnk8HqfGBgB8Apdt23a0hxhs3d3hQTkdm1340iBNhJHi6POP6vz5i1GdweuN1wdPT4/qDBh+7ip6b8BrMyqXoQAAIwexAAAYEQsAgBGxAAAYEQsAgBGxAAAYEQsAgBGxAAAYEQsAgBGxAAAYEQsAgBGxAAAYEQsAgBGxAAAYEQsAgBGxAAAYEQsAgBGxAAAYEQsAgBGxAAAYEQsAgBGxAAAYEQsAgBGxAAAYEQsAgBGxAAAYEQsAgBGxAAAYEQsAgBGxAAAYORaLzZs3Ky0tTUuWLIlsC4VCys/PV2ZmpvLz89XR0SFJsm1bW7duVSAQ0NKlS3Xq1KnIcyoqKpSZmanMzExVVFQ4NS4AoA+OxSI3N1e7d+++YVtZWZnS0tJUU1OjtLQ0lZWVSZLq6+vV3NysmpoaPfPMM9qyZYuk63EpLS3Vq6++qv3796u0tDQSGADA0HEsFvfff78SExNv2FZbW6ucnBxJUk5Ojt56660btrtcLs2aNUudnZ1qbW1VY2Oj0tPT5fF4lJiYqPT0dDU0NDg1MgCgFzFDebC2tjYlJydLkrxer9ra2iRJlmUpJSUl8u9SUlJkWdbHtvt8PlmWZTyO2+2Sx3PHIE8PXMfawnDl5Noc0lj8J5fLJZfL5ci+w2FbodCVAe3D640fpGkw0gx0bQ0UaxO9cfL33pC+GyopKUmtra2SpNbWVo0fP17S9TOGYDAY+XfBYFA+n+9j2y3Lks/nG8qRAQAa4lj4/X5VVlZKkiorK7Vw4cIbttu2rePHjys+Pl7JycnKyMhQY2OjOjo61NHRocbGRmVkZAzlyAAAOXgZauPGjWpqalJ7e7vmz5+vdevWac2aNdqwYYPKy8s1ceJElZSUSJIWLFigw4cPKxAIKC4uTsXFxZIkj8ejtWvXKi8vT5JUUFAgj8fj1MgAgF64bNu2oz3EYOvuDg/KtbvZhS8N0kQYKY4+/6jOn78Y1Rm83nh98PT0qM6A4eeuovcGvDaHzWsWAICbE7EAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAEbEAABgRCwCAEbEAABjdNLGor69XVlaWAoGAysrKoj0OANxSbopYhMNhPf3009q9e7eqqqr02muv6fTp09EeCwBuGTdFLE6cOKHJkydr0qRJio2NVXZ2tmpra6M9FgDcMmKiPUB/WJallJSUyPc+n08nTpzo9d+PHu2W1xs/4OMeff7RAe8DI89grK2BuqvovWiPgGHIybV5U5xZAACi66aIhc/nUzAYjHxvWZZ8Pl8UJwKAW8tNEYvp06erublZLS0t6urqUlVVlfx+f7THAoBbxk3xmkVMTIyKior0+OOPKxwOa/ny5UpNTY32WABwy3DZtm1HewgAwPB2U1yGAgBEF7EAABgRC/SJ26xgONq8ebPS0tK0ZMmSaI9yyyAW6BW3WcFwlZubq927d0d7jFsKsUCvuM0Khqv7779fiYmJ0R7jlkIs0KtPus2KZVlRnAhAtBALAIARsUCvuM0KgI8QC/SK26wA+Aif4EafDh8+rOLi4shtVp544olojwRo48aNampqUnt7u5KSkrRu3TqtWLEi2mONaMQCAGDEZSgAgBGxAAAYEQsAgBGxAAAYEQsAgBGxAAbBfffd1+fjZ86c+dR3SN20aZPefPPNgYwFDBpiAQAwuin+Bjdws7h8+bLWrl2rzs5O9fT06Jvf/KYeeughSVJPT4+eeuop/fnPf1Zqaqq2b9+uuLg4nTx5Utu2bdOVK1c0btw4Pffcc0pOTo7yTwLciDMLYBDddttt+vGPf6yKigrt3btX27dv10efe/3HP/6hlStX6o033tCYMWP0yiuvqLu7W1u3btWuXbt04MABLV++XDt37ozyTwF8HGcWwCCybVsvvPCC/vjHP2rUqFGyLEv/+te/JEkTJkzQ7NmzJUmPPPKIXn75ZT3wwAP661//qvz8fEnStWvX5PV6ozY/0BtiAQyi3/zmN7pw4YIOHDig0aNHy+/36+rVq5Ikl8t1w791uVyybVupqanat29fNMYF+o3LUMAgunjxopKSkjR69GgdOXJEZ8+ejTz2z3/+U++++64k6bXXXtPs2bN1991368KFC5Ht3d3d+tvf/haV2YG+EAtgEC1dulQnT57U0qVLdfDgQU2ZMiXy2N13362f//znWrRokTo7O/WlL31JsbGx2rVrl3bs2KFHHnlEOTk5kXAAwwl3nQUAGHFmAQAwIhYAACNiAQAwIhYAACNiAQAwIhYAACNiAQAw+j/vKbejUfm/WAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AXvpUXd1wNTb",
        "outputId": "dd72c0b0-f22e-4977-aca7-9bd59ae46e3e"
      },
      "source": [
        "#Text Preprocessing\n",
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8EusG4PhbcL2"
      },
      "source": [
        "#(1) Remove #, @chunk, urls, shortwords(length is either 1 or 2), stopwords\n",
        "at_chunk = re.compile(r\"@[a-zA-Z0-9]*\")\n",
        "url = re.compile(r\"https?:/+[a-zA-Z0-9./]*\")\n",
        "shortword = re.compile(r\"\\b\\w{1,2}\\b\")\n",
        "\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "def clean(text):\n",
        "    text = re.sub('#','',text)\n",
        "    text = re.sub(at_chunk,'',text)\n",
        "    text = re.sub(url,'',text)\n",
        "    text = re.sub(shortword,'',text)\n",
        "    \n",
        "    text = text.split()\n",
        "    text = [w for w in text if w not in stop_words]\n",
        "    \n",
        "    text = \" \".join(text)\n",
        "    text = text.strip()\n",
        "    \n",
        "    return text"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tV9-YkZe0NIa"
      },
      "source": [
        "data.tweet = data.tweet.apply(clean)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ryarE0cg06kE",
        "outputId": "377d6e31-3863-4c8a-efc1-fee102d2ce6f"
      },
      "source": [
        "data.tweet.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    The CDC currently reports 99031 deaths. genera...\n",
              "1    States reported 1121 deaths small rise last Tu...\n",
              "2    Politically Correct Woman (Almost) Uses Pandem...\n",
              "3    IndiaFightsCorona: 1524 COVID testing laborato...\n",
              "4    Populous states generate large case counts loo...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4U9ReWa0YH6"
      },
      "source": [
        "test_data.tweet = test_data.tweet.apply(clean)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B5WtrnVg0VOZ",
        "outputId": "1353dd23-ba49-4cb1-8db1-fa7b1f62f213"
      },
      "source": [
        "test_data.tweet.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Our daily update published. States reported 73...\n",
              "1                                 Alfalfa cure COVID-.\n",
              "2    President Trump Asked What Would Were Catch Th...\n",
              "3    States reported 630 deaths. still seeing solid...\n",
              "4    This sixth time global health emergency declar...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPgaUkdLb0Xz",
        "outputId": "1e0c3f51-76c6-4262-c095-d1c6bffca220"
      },
      "source": [
        "#2 Check NULL\n",
        "data.tweet.isnull().any()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ys8YBh2L1NFK",
        "outputId": "dcc7277d-636d-46bc-8dba-1333202996c6"
      },
      "source": [
        "test_data.tweet.isnull().any()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FxkzR5QzbrAy",
        "outputId": "03a66e00-78d0-4bea-b351-b35116e8b892"
      },
      "source": [
        "#3 Tokenizer\n",
        "tok = Tokenizer()\n",
        "\n",
        "train_tweet = data.tweet\n",
        "train_label = data.label\n",
        "test_tweet = test_data.tweet\n",
        "\n",
        "tok.fit_on_texts(train_tweet)\n",
        "\n",
        "word_size = len(tok.index_word)\n",
        "vocab_size = word_size+1\n",
        "\n",
        "print(f\"{word_size} words are used!\")\n",
        "\n",
        "print(\"Tokenizing train texts\\n\")\n",
        "train_tweet = tok.texts_to_sequences(train_tweet)\n",
        "print(\"Tokenizing train texts finished!\\n\")\n",
        "\n",
        "print(\"Tokenizng test texts with the same tokenizer\\n\")\n",
        "test_tweet = tok.texts_to_sequences(test_tweet)\n",
        "print(\"Tokenizing test texts finished!\\n\")"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "18845 words are used!\n",
            "Tokenizing train texts\n",
            "\n",
            "Tokenizing train texts finished!\n",
            "\n",
            "Tokenizng test texts with the same tokenizer\n",
            "\n",
            "Tokenizing test texts finished!\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "0UPfpc_pbrE-",
        "outputId": "efed368e-7059-4b07-f567-c95e85ddef35"
      },
      "source": [
        "#4 padding\n",
        "lengths = [len(s) for s in train_tweet]\n",
        "print(f\"Max of sequence size >> {np.max(lengths)}\")\n",
        "print(f\"Average of sequence size >> {int(np.round(np.mean(lengths)))}\")\n",
        "\n",
        "plt.hist(lengths,bins=100)\n",
        "plt.show()\n",
        "\n",
        "sequence_size=21"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Max of sequence size >> 884\n",
            "Average of sequence size >> 16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yEhcxL9zbrIK",
        "outputId": "712eb529-d6ce-46b4-b3be-373e9ae1c6e5"
      },
      "source": [
        "train_tweet = pad_sequences(train_tweet,maxlen=sequence_size,padding='post',truncating='post')\n",
        "test_tweet = pad_sequences(test_tweet,maxlen=sequence_size,padding='post',truncating='post')\n",
        "\n",
        "print(f\"train text shape >> {train_tweet.shape}\")\n",
        "print(f\"test text shape >> {test_tweet.shape}\")"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train text shape >> (10422, 21)\n",
            "test text shape >> (2140, 21)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w8k1z3ndvslm"
      },
      "source": [
        "train_data = train_tweet\n",
        "test_data = test_tweet"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEvjvZB3L1sC"
      },
      "source": [
        "#Bidirectional stacked LSTM without pre-trained Embedding Vectors from GLOVE : return only last hidden cell on the last lstm layer\n",
        "from keras.layers import Input,Embedding,Bidirectional,LSTM,TimeDistributed,Dense,Dropout,BatchNormalization,GlobalMaxPool1D,GlobalAveragePooling1D\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from keras.models import Model\n",
        "\n",
        "#vocab_size\n",
        "#sequence_size\n",
        "word_vec_size = 128\n",
        "hidden_size = 128\n",
        "\n",
        "def create_lstm1():\n",
        "    X = Input(shape=[sequence_size])\n",
        "    \n",
        "    H = Embedding(vocab_size,word_vec_size,input_length=sequence_size,mask_zero=True)(X)\n",
        "    H = Dropout(0.1)(H)\n",
        "    H = Bidirectional(LSTM(hidden_size,return_sequences=True))(H)\n",
        "    H = Dropout(0.1)(H)\n",
        "    H = Bidirectional(LSTM(hidden_size,return_sequences=False))(H)\n",
        "    H = BatchNormalization()(H)\n",
        "    H = Dense(32,activation='relu')(H)\n",
        "    H = BatchNormalization()(H)\n",
        "    \n",
        "    Y = Dense(1,activation='sigmoid')(H)\n",
        "    \n",
        "    model = Model(X,Y)\n",
        "    model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "    \n",
        "    return model"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSWrtSzu3Mv-",
        "outputId": "364aaa05-6562-4893-b936-eff09008d120"
      },
      "source": [
        "lstm1 = create_lstm1()\n",
        "hist = lstm1.fit(train_data,train_label,validation_split=0.1,epochs=10,batch_size=32)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "294/294 [==============================] - 73s 201ms/step - loss: 0.3430 - accuracy: 0.8514 - val_loss: 0.7024 - val_accuracy: 0.5235\n",
            "Epoch 2/10\n",
            "294/294 [==============================] - 55s 186ms/step - loss: 0.1145 - accuracy: 0.9569 - val_loss: 0.2677 - val_accuracy: 0.8869\n",
            "Epoch 3/10\n",
            "294/294 [==============================] - 54s 185ms/step - loss: 0.0578 - accuracy: 0.9790 - val_loss: 0.3636 - val_accuracy: 0.8802\n",
            "Epoch 4/10\n",
            "294/294 [==============================] - 54s 184ms/step - loss: 0.0552 - accuracy: 0.9812 - val_loss: 0.2022 - val_accuracy: 0.9377\n",
            "Epoch 5/10\n",
            "294/294 [==============================] - 54s 185ms/step - loss: 0.0372 - accuracy: 0.9877 - val_loss: 0.3319 - val_accuracy: 0.8965\n",
            "Epoch 6/10\n",
            "294/294 [==============================] - 54s 185ms/step - loss: 0.0335 - accuracy: 0.9890 - val_loss: 0.5293 - val_accuracy: 0.8686\n",
            "Epoch 7/10\n",
            "294/294 [==============================] - 55s 186ms/step - loss: 0.0298 - accuracy: 0.9899 - val_loss: 0.1758 - val_accuracy: 0.9501\n",
            "Epoch 8/10\n",
            "294/294 [==============================] - 55s 185ms/step - loss: 0.0188 - accuracy: 0.9941 - val_loss: 0.2330 - val_accuracy: 0.9415\n",
            "Epoch 9/10\n",
            "294/294 [==============================] - 54s 184ms/step - loss: 0.0171 - accuracy: 0.9949 - val_loss: 0.4996 - val_accuracy: 0.8926\n",
            "Epoch 10/10\n",
            "294/294 [==============================] - 55s 187ms/step - loss: 0.0156 - accuracy: 0.9956 - val_loss: 0.3253 - val_accuracy: 0.9358\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPsN94af3UrR"
      },
      "source": [
        "#Bidirectional Stacked LSTM model without using pre-trained Embedding Vectors : return all the hidden cells and use global average pooling\n",
        "def create_lstm2():\n",
        "    X = Input(shape=[sequence_size])\n",
        "    \n",
        "    H = Embedding(vocab_size,word_vec_size,input_length=sequence_size,mask_zero=True)(X)\n",
        "    H = Dropout(0.1)(H)\n",
        "    H = Bidirectional(LSTM(hidden_size,return_sequences=True))(H)\n",
        "    H = Dropout(0.1)(H)\n",
        "    H = Bidirectional(LSTM(hidden_size,return_sequences=True))(H)\n",
        "    H = GlobalAveragePooling1D()(H)\n",
        "    H = BatchNormalization()(H)\n",
        "    H = Dense(32,activation='relu')(H)\n",
        "    H = BatchNormalization()(H)\n",
        "    \n",
        "    Y = Dense(1,activation='sigmoid')(H)\n",
        "    \n",
        "    model = Model(X,Y)\n",
        "    model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "    \n",
        "    return model"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MvLYoZ944CoC",
        "outputId": "31be634f-90c5-4633-a773-5ed2d5c55057"
      },
      "source": [
        "lstm2 = create_lstm2()\n",
        "hist = lstm2.fit(train_data,train_label,validation_split=0.1,epochs=10,batch_size=32)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "294/294 [==============================] - 74s 204ms/step - loss: nan - accuracy: 0.4511 - val_loss: nan - val_accuracy: 1.0000\n",
            "Epoch 2/10\n",
            "294/294 [==============================] - 54s 184ms/step - loss: nan - accuracy: 0.4284 - val_loss: nan - val_accuracy: 1.0000\n",
            "Epoch 3/10\n",
            "294/294 [==============================] - 54s 185ms/step - loss: nan - accuracy: 0.4284 - val_loss: nan - val_accuracy: 1.0000\n",
            "Epoch 4/10\n",
            "294/294 [==============================] - 55s 186ms/step - loss: nan - accuracy: 0.4284 - val_loss: nan - val_accuracy: 1.0000\n",
            "Epoch 5/10\n",
            "294/294 [==============================] - 55s 186ms/step - loss: nan - accuracy: 0.4284 - val_loss: nan - val_accuracy: 1.0000\n",
            "Epoch 6/10\n",
            "294/294 [==============================] - 55s 186ms/step - loss: nan - accuracy: 0.4284 - val_loss: nan - val_accuracy: 1.0000\n",
            "Epoch 7/10\n",
            "294/294 [==============================] - 55s 186ms/step - loss: nan - accuracy: 0.4284 - val_loss: nan - val_accuracy: 1.0000\n",
            "Epoch 8/10\n",
            "294/294 [==============================] - 55s 186ms/step - loss: nan - accuracy: 0.4284 - val_loss: nan - val_accuracy: 1.0000\n",
            "Epoch 9/10\n",
            "294/294 [==============================] - 54s 185ms/step - loss: nan - accuracy: 0.4284 - val_loss: nan - val_accuracy: 1.0000\n",
            "Epoch 10/10\n",
            "294/294 [==============================] - 55s 186ms/step - loss: nan - accuracy: 0.4284 - val_loss: nan - val_accuracy: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCGS8BjS4FpF"
      },
      "source": [
        "#Bidirectional Stacked LSTM model without using pre-trained Embedding Vectors : return all the hidden cells and use global max pooling\n",
        "def create_lstm3():\n",
        "    X = Input(shape=[sequence_size])\n",
        "    \n",
        "    H = Embedding(vocab_size,word_vec_size,input_length=sequence_size,mask_zero=True)(X)\n",
        "    H = Dropout(0.1)(H)\n",
        "    H = Bidirectional(LSTM(hidden_size,return_sequences=True))(H)\n",
        "    H = Dropout(0.1)(H)\n",
        "    H = Bidirectional(LSTM(hidden_size,return_sequences=True))(H)\n",
        "    H = GlobalMaxPool1D()(H)\n",
        "    H = BatchNormalization()(H)\n",
        "    H = Dense(32,activation='relu')(H)\n",
        "    H = BatchNormalization()(H)\n",
        "    \n",
        "    Y = Dense(1,activation='sigmoid')(H)\n",
        "    \n",
        "    model = Model(X,Y)\n",
        "    model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "    \n",
        "    return model"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vM0TVqyZ4RIN",
        "outputId": "a9511fe0-c8a6-4892-b793-e7c8a02f5374"
      },
      "source": [
        "lstm3 = create_lstm3()\n",
        "hist = lstm3.fit(train_data,train_label,validation_split=0.1,epochs=7,batch_size=32)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/7\n",
            "294/294 [==============================] - 74s 202ms/step - loss: 0.3531 - accuracy: 0.8421 - val_loss: 1.1150 - val_accuracy: 0.0038\n",
            "Epoch 2/7\n",
            "294/294 [==============================] - 55s 187ms/step - loss: 0.1252 - accuracy: 0.9543 - val_loss: 0.2789 - val_accuracy: 0.8811\n",
            "Epoch 3/7\n",
            "294/294 [==============================] - 55s 188ms/step - loss: 0.0606 - accuracy: 0.9797 - val_loss: 0.3680 - val_accuracy: 0.8696\n",
            "Epoch 4/7\n",
            "294/294 [==============================] - 55s 188ms/step - loss: 0.0363 - accuracy: 0.9876 - val_loss: 0.2147 - val_accuracy: 0.9425\n",
            "Epoch 5/7\n",
            "294/294 [==============================] - 56s 191ms/step - loss: 0.0324 - accuracy: 0.9907 - val_loss: 0.2589 - val_accuracy: 0.9348\n",
            "Epoch 6/7\n",
            "294/294 [==============================] - 55s 188ms/step - loss: 0.0240 - accuracy: 0.9922 - val_loss: 0.2444 - val_accuracy: 0.9425\n",
            "Epoch 7/7\n",
            "294/294 [==============================] - 55s 189ms/step - loss: 0.0257 - accuracy: 0.9923 - val_loss: 0.3755 - val_accuracy: 0.9118\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}