Irfan_Dino_NLP(tweet).ipynb

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        "# Submission I (NLP Project)\n",
        "\n",
        "* Nama : Irfan Dino\n",
        "* Alamat : Pekanbaru, Riau."
      ],
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              "   id keyword  ...                                               text target\n",
              "0   0  ablaze  ...  Communal violence in Bhainsa, Telangana. \"Ston...      1\n",
              "1   1  ablaze  ...  Telangana: Section 144 has been imposed in Bha...      1\n",
              "2   2  ablaze  ...  Arsonist sets cars ablaze at dealership https:...      1\n",
              "3   3  ablaze  ...  Arsonist sets cars ablaze at dealership https:...      1\n",
              "4   4  ablaze  ...  \"Lord Jesus, your love brings freedom and pard...      0\n",
              "\n",
              "[5 rows x 5 columns]"
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        "import pandas as pd\n",
        "data = pd.read_csv('/content/sample_data/tweets.csv')\n",
        "data.head()"
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              "                                                    text  target\n",
              "0      Communal violence in Bhainsa, Telangana. \"Ston...       1\n",
              "1      Telangana: Section 144 has been imposed in Bha...       1\n",
              "2      Arsonist sets cars ablaze at dealership https:...       1\n",
              "3      Arsonist sets cars ablaze at dealership https:...       1\n",
              "4      \"Lord Jesus, your love brings freedom and pard...       0\n",
              "...                                                  ...     ...\n",
              "11365  Media should have warned us well in advance. T...       0\n",
              "11366  i feel directly attacked 💀 i consider moonbin ...       0\n",
              "11367  i feel directly attacked 💀 i consider moonbin ...       0\n",
              "11368  ok who remember \"outcast\" nd the \"dora\" au?? T...       0\n",
              "11369     Jake Corway wrecked while running 14th at IRP.       1\n",
              "\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 11370 entries, 0 to 11369\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   text    11370 non-null  object\n",
            " 1   target  11370 non-null  int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 177.8+ KB\n"
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        "category = pd.get_dummies(data1['target']).rename(columns=lambda x:'star_'+str(x))\n",
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        "new_data = new_data.drop(columns='target')\n",
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              "      <th>11366</th>\n",
              "      <td>i feel directly attacked 💀 i consider moonbin ...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11367</th>\n",
              "      <td>i feel directly attacked 💀 i consider moonbin ...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11368</th>\n",
              "      <td>ok who remember \"outcast\" nd the \"dora\" au?? T...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11369</th>\n",
              "      <td>Jake Corway wrecked while running 14th at IRP.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11370 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3c9d5bb-c542-42b7-9e2b-8b5b2da92bdb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-b3c9d5bb-c542-42b7-9e2b-8b5b2da92bdb button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b3c9d5bb-c542-42b7-9e2b-8b5b2da92bdb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                    text  star_0  star_1\n",
              "0      Communal violence in Bhainsa, Telangana. \"Ston...       0       1\n",
              "1      Telangana: Section 144 has been imposed in Bha...       0       1\n",
              "2      Arsonist sets cars ablaze at dealership https:...       0       1\n",
              "3      Arsonist sets cars ablaze at dealership https:...       0       1\n",
              "4      \"Lord Jesus, your love brings freedom and pard...       1       0\n",
              "...                                                  ...     ...     ...\n",
              "11365  Media should have warned us well in advance. T...       1       0\n",
              "11366  i feel directly attacked 💀 i consider moonbin ...       1       0\n",
              "11367  i feel directly attacked 💀 i consider moonbin ...       1       0\n",
              "11368  ok who remember \"outcast\" nd the \"dora\" au?? T...       1       0\n",
              "11369     Jake Corway wrecked while running 14th at IRP.       0       1\n",
              "\n",
              "[11370 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_dat = new_data.rename({'text':'tweet','star_0': 'no_disaster',\t'star_1':'disaster'}, axis=1)\n",
        "new_dat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "iK1w8BbxfVpu",
        "outputId": "7962f41e-e6b9-4af6-cf2a-31972421c593"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e6c7c669-cea0-4735-a50f-04b4b07e6911\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tweet</th>\n",
              "      <th>no_disaster</th>\n",
              "      <th>disaster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>\"Lord Jesus, your love brings freedom and pard...</td>\n",
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              "      <td>i feel directly attacked 💀 i consider moonbin ...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>11367</th>\n",
              "      <td>i feel directly attacked 💀 i consider moonbin ...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11368</th>\n",
              "      <td>ok who remember \"outcast\" nd the \"dora\" au?? T...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11369</th>\n",
              "      <td>Jake Corway wrecked while running 14th at IRP.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11370 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6c7c669-cea0-4735-a50f-04b4b07e6911')\"\n",
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              "      </button>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e6c7c669-cea0-4735-a50f-04b4b07e6911');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                   tweet  no_disaster  disaster\n",
              "0      Communal violence in Bhainsa, Telangana. \"Ston...            0         1\n",
              "1      Telangana: Section 144 has been imposed in Bha...            0         1\n",
              "2      Arsonist sets cars ablaze at dealership https:...            0         1\n",
              "3      Arsonist sets cars ablaze at dealership https:...            0         1\n",
              "4      \"Lord Jesus, your love brings freedom and pard...            1         0\n",
              "...                                                  ...          ...       ...\n",
              "11365  Media should have warned us well in advance. T...            1         0\n",
              "11366  i feel directly attacked 💀 i consider moonbin ...            1         0\n",
              "11367  i feel directly attacked 💀 i consider moonbin ...            1         0\n",
              "11368  ok who remember \"outcast\" nd the \"dora\" au?? T...            1         0\n",
              "11369     Jake Corway wrecked while running 14th at IRP.            0         1\n",
              "\n",
              "[11370 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.stem.api import StemmerI\n",
        "import re\n",
        "import string\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "nltk.download('wordnet')\n",
        "\n",
        "def remove_URL(tweet):\n",
        "    url = re.compile(r'https?://\\S+|www\\.\\S+')\n",
        "    return url.sub(r'', tweet)\n",
        "\n",
        "def remove_punct(tweet):\n",
        "    translator = str.maketrans('', '', string.punctuation)\n",
        "    return tweet.translate(translator)\n",
        "\n",
        "stop = set(stopwords.words('english'))\n",
        "def remove_stopwords(tweet):\n",
        "    filtered_words = [word.lower() for word in tweet.split() if word.lower() not in stop]\n",
        "    return ' '.join(filtered_words)\n",
        "\n",
        "stemmer =  PorterStemmer()\n",
        "def remove_stem(tweet):\n",
        "    stemmered_word = [stemmer.stem(word) for word in tweet.split()]\n",
        "    return ' '.join(stemmered_word)\n",
        "\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "def remove_lemmatizer(tweet):\n",
        "    lemmatizered_word = [lemmatizer.lemmatize(word) for word in tweet.split()]\n",
        "    return ' '.join(lemmatizered_word)"
      ],
      "metadata": {
        "id": "I6zUk28QCV8J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1032b8bb-5ffb-4c23-c9d4-517844c14e38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_dat['tweet'] = new_dat.tweet.map(remove_URL)\n",
        "new_dat['tweet'] = new_dat.tweet.map(remove_punct)\n",
        "new_dat['tweet'] = new_dat.tweet.map(remove_stopwords)\n",
        "new_dat['tweet'] = new_dat.tweet.map(remove_stem)\n",
        "new_dat['tweet'] = new_dat.tweet.map(remove_lemmatizer)"
      ],
      "metadata": {
        "id": "wOJN1BkNvBHU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_dat.tweet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hQ9I72HF8cnx",
        "outputId": "63c677ca-fade-4a47-f0cf-b02ad2230ad7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        commun violenc bhainsa telangana stone pelt mu...\n",
              "1        telangana section 144 impos bhainsa januari 13...\n",
              "2                        arsonist set car ablaz dealership\n",
              "3                        arsonist set car ablaz dealership\n",
              "4        lord jesu love bring freedom pardon fill holi ...\n",
              "                               ...                        \n",
              "11365    medium warn u well advanc wreck whole night re...\n",
              "11366    feel directli attack 💀 consid moonbin amp jinj...\n",
              "11367    feel directli attack 💀 consid moonbin amp jinj...\n",
              "11368    ok rememb outcast nd dora au au wreck nerv nd ...\n",
              "11369                       jake corway wreck run 14th irp\n",
              "Name: tweet, Length: 11370, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "def counter_word(tweet_col):\n",
        "    count = Counter()\n",
        "    for tweet in tweet_col.values:\n",
        "        for word in tweet.split():\n",
        "            count[word] += 1\n",
        "    return count\n",
        "\n",
        "counter = counter_word(new_dat.tweet)"
      ],
      "metadata": {
        "id": "XAQHk4bhhNK2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_unique = len(counter)"
      ],
      "metadata": {
        "id": "guU9qYsrk36I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text = new_dat['tweet']\n",
        "target = new_dat[['no_disaster', 'disaster']]"
      ],
      "metadata": {
        "id": "2O-TyCfZ_RJ4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_text, val_text, train_target, val_target = train_test_split(text, target, test_size=0.2)"
      ],
      "metadata": {
        "id": "xv4pHHfptot_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "token = Tokenizer(num_words=num_unique, oov_token='x')\n",
        "token.fit_on_texts(train_text)\n",
        "token.fit_on_texts(val_text)\n",
        "\n",
        "train_seq = token.texts_to_sequences(train_text)\n",
        "val_seq = token.texts_to_sequences(val_text)\n",
        "\n",
        "train_pad = pad_sequences(train_seq)\n",
        "val_pad = pad_sequences(val_seq)"
      ],
      "metadata": {
        "id": "3kR5xtRnu2dc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "        tf.keras.layers.Embedding(input_dim=num_unique, output_dim=16),\n",
        "        tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, dropout=0.2)),\n",
        "        tf.keras.layers.Flatten(),\n",
        "        tf.keras.layers.Dense(128, activation='relu'),\n",
        "        tf.keras.layers.Dropout(0.2),\n",
        "        tf.keras.layers.Dense(64, activation='relu'),\n",
        "        tf.keras.layers.Dropout(0.2),\n",
        "        tf.keras.layers.Dense(32, activation='relu'),\n",
        "        tf.keras.layers.Dropout(0.2),\n",
        "        tf.keras.layers.Dense(2, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "MLDdrU7Iy7mh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('accuracy')>0.9):\n",
        "      print(\"\\nAkurasi telah mencapai >90%!\")\n",
        "      self.model.stop_training = True\n",
        "callbacks = myCallback()\n",
        "\n",
        "num_epochs=20\n",
        "history = model.fit(train_pad, train_target, epochs=num_epochs,\n",
        "                    validation_data=(val_pad, val_target), verbose=2)"
      ],
      "metadata": {
        "id": "bGZrdLC74gcg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6cbd2f3-5f2a-41f7-defc-94d6a5eb7e9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "285/285 - 8s - loss: 0.1040 - accuracy: 0.9670 - val_loss: 0.3538 - val_accuracy: 0.8712 - 8s/epoch - 29ms/step\n",
            "Epoch 2/20\n",
            "285/285 - 8s - loss: 0.0492 - accuracy: 0.9843 - val_loss: 0.5004 - val_accuracy: 0.8707 - 8s/epoch - 29ms/step\n",
            "Epoch 3/20\n",
            "285/285 - 8s - loss: 0.0313 - accuracy: 0.9916 - val_loss: 0.5093 - val_accuracy: 0.8698 - 8s/epoch - 29ms/step\n",
            "Epoch 4/20\n",
            "285/285 - 8s - loss: 0.0182 - accuracy: 0.9943 - val_loss: 0.6952 - val_accuracy: 0.8747 - 8s/epoch - 30ms/step\n",
            "Epoch 5/20\n",
            "285/285 - 8s - loss: 0.0144 - accuracy: 0.9965 - val_loss: 0.8123 - val_accuracy: 0.8628 - 8s/epoch - 30ms/step\n",
            "Epoch 6/20\n",
            "285/285 - 8s - loss: 0.0146 - accuracy: 0.9956 - val_loss: 0.7133 - val_accuracy: 0.8769 - 8s/epoch - 29ms/step\n",
            "Epoch 7/20\n",
            "285/285 - 8s - loss: 0.0103 - accuracy: 0.9973 - val_loss: 1.1636 - val_accuracy: 0.8500 - 8s/epoch - 30ms/step\n",
            "Epoch 8/20\n",
            "285/285 - 8s - loss: 0.0111 - accuracy: 0.9971 - val_loss: 0.6675 - val_accuracy: 0.8672 - 8s/epoch - 30ms/step\n",
            "Epoch 9/20\n",
            "285/285 - 8s - loss: 0.0100 - accuracy: 0.9979 - val_loss: 0.6181 - val_accuracy: 0.8540 - 8s/epoch - 29ms/step\n",
            "Epoch 10/20\n",
            "285/285 - 8s - loss: 0.0074 - accuracy: 0.9977 - val_loss: 1.1952 - val_accuracy: 0.8672 - 8s/epoch - 29ms/step\n",
            "Epoch 11/20\n",
            "285/285 - 8s - loss: 0.0114 - accuracy: 0.9977 - val_loss: 0.7744 - val_accuracy: 0.8580 - 8s/epoch - 30ms/step\n",
            "Epoch 12/20\n",
            "285/285 - 8s - loss: 0.0071 - accuracy: 0.9981 - val_loss: 0.8791 - val_accuracy: 0.8690 - 8s/epoch - 29ms/step\n",
            "Epoch 13/20\n",
            "285/285 - 8s - loss: 0.0043 - accuracy: 0.9985 - val_loss: 1.1226 - val_accuracy: 0.8725 - 8s/epoch - 29ms/step\n",
            "Epoch 14/20\n",
            "285/285 - 8s - loss: 0.0053 - accuracy: 0.9982 - val_loss: 0.9315 - val_accuracy: 0.8646 - 8s/epoch - 30ms/step\n",
            "Epoch 15/20\n",
            "285/285 - 8s - loss: 0.0023 - accuracy: 0.9986 - val_loss: 1.2537 - val_accuracy: 0.8641 - 8s/epoch - 29ms/step\n",
            "Epoch 16/20\n",
            "285/285 - 8s - loss: 0.0023 - accuracy: 0.9988 - val_loss: 1.3069 - val_accuracy: 0.8668 - 8s/epoch - 29ms/step\n",
            "Epoch 17/20\n",
            "285/285 - 8s - loss: 0.0196 - accuracy: 0.9956 - val_loss: 0.9575 - val_accuracy: 0.8386 - 8s/epoch - 29ms/step\n",
            "Epoch 18/20\n",
            "285/285 - 8s - loss: 0.0118 - accuracy: 0.9966 - val_loss: 0.9243 - val_accuracy: 0.8659 - 8s/epoch - 30ms/step\n",
            "Epoch 19/20\n",
            "285/285 - 8s - loss: 0.0048 - accuracy: 0.9977 - val_loss: 1.1775 - val_accuracy: 0.8712 - 8s/epoch - 30ms/step\n",
            "Epoch 20/20\n",
            "285/285 - 8s - loss: 0.0044 - accuracy: 0.9985 - val_loss: 0.9508 - val_accuracy: 0.8536 - 8s/epoch - 29ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "MhV0YEzAiNXm"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}