Project_NLP_Predicting_Emotion.ipynb

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      "name": "Dicoding NLP Submission",
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        "# Data Diri"
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      "source": [
        "Adnan Muhammad Lazuardi\n",
        "\n",
        "adnanm.lazuardi@gmail.com"
      ],
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        "Sumber dataset:\n",
        "\n",
        "https://www.kaggle.com/datasets/praveengovi/emotions-dataset-for-nlp\n",
        "\n",
        "dataset yang diperoleh digabungkan kedalam satu dokumen dengan nama emotion.**txt**"
      ],
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        "id": "1s33At8UxrG7"
      }
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      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('emotion.txt', names=['sentence', 'emotion'], sep=';')"
      ],
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        "id": "r0wPQwlNxfhb"
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        "df.head()"
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              "                                            sentence  emotion\n",
              "0  im feeling rather rotten so im not very ambiti...  sadness\n",
              "1          im updating my blog because i feel shitty  sadness\n",
              "2  i never make her separate from me because i do...  sadness\n",
              "3  i left with my bouquet of red and yellow tulip...      joy\n",
              "4    i was feeling a little vain when i did this one  sadness"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20000 entries, 0 to 19999\n",
            "Data columns (total 2 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   sentence  20000 non-null  object\n",
            " 1   emotion   20000 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 312.6+ KB\n"
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        "category = pd.get_dummies(df.emotion)\n",
        "dfn = pd.concat([df, category], axis=1)\n",
        "dfn = dfn.drop(columns='emotion')\n",
        "dfn"
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              "                                                sentence  anger  fear  joy  \\\n",
              "0      im feeling rather rotten so im not very ambiti...      0     0    0   \n",
              "1              im updating my blog because i feel shitty      0     0    0   \n",
              "2      i never make her separate from me because i do...      0     0    0   \n",
              "3      i left with my bouquet of red and yellow tulip...      0     0    1   \n",
              "4        i was feeling a little vain when i did this one      0     0    0   \n",
              "...                                                  ...    ...   ...  ...   \n",
              "19995  im having ssa examination tomorrow in the morn...      0     0    0   \n",
              "19996  i constantly worry about their fight against n...      0     0    1   \n",
              "19997  i feel its important to share this info for th...      0     0    1   \n",
              "19998  i truly feel that if you are passionate enough...      0     0    1   \n",
              "19999  i feel like i just wanna buy any cute make up ...      0     0    1   \n",
              "\n",
              "       love  sadness  surprise  \n",
              "0         0        1         0  \n",
              "1         0        1         0  \n",
              "2         0        1         0  \n",
              "3         0        0         0  \n",
              "4         0        1         0  \n",
              "...     ...      ...       ...  \n",
              "19995     0        1         0  \n",
              "19996     0        0         0  \n",
              "19997     0        0         0  \n",
              "19998     0        0         0  \n",
              "19999     0        0         0  \n",
              "\n",
              "[20000 rows x 7 columns]"
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      "source": [
        "text = dfn['sentence'].values\n",
        "emotion = dfn[['anger', 'fear', 'joy', 'love', 'sadness', 'surprise']].values"
      ],
      "metadata": {
        "id": "EUPjqHEEx54f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "txt_train, txt_test, e_train, e_test = train_test_split(text, emotion, test_size=0.2)"
      ],
      "metadata": {
        "id": "jIRpFcUOx7Ln"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        " \n",
        "tokenizer = Tokenizer(num_words=10000, oov_token='x')\n",
        "tokenizer.fit_on_texts(txt_train) \n",
        "tokenizer.fit_on_texts(txt_test)\n",
        " \n",
        "sq_train = tokenizer.texts_to_sequences(txt_train)\n",
        "sq_test = tokenizer.texts_to_sequences(txt_test)\n",
        " \n",
        "padded_train = pad_sequences(sq_train) \n",
        "padded_test = pad_sequences(sq_test)"
      ],
      "metadata": {
        "id": "WAfmSVkEx8Nk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(input_dim=10000, output_dim=16),\n",
        "    tf.keras.layers.SpatialDropout1D(0.4),\n",
        "    tf.keras.layers.LSTM(64),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dense(6, activation='softmax')\n",
        "])\n",
        "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "Roh6aj6Xx9P1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "early = tf.keras.callbacks.EarlyStopping( patience=10,\n",
        "                                          min_delta=0.001,\n",
        "                                          restore_best_weights=True)"
      ],
      "metadata": {
        "id": "w0jFFweCx-Xb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_epochs = 30\n",
        "history = model.fit(padded_train, e_train, epochs=num_epochs, \n",
        "                    validation_data=(padded_test, e_test), verbose=2, callbacks=[early])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EPoufWwBx_YO",
        "outputId": "897654e3-b7ce-44d3-dec5-36f52b1b76d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "500/500 - 35s - loss: 1.4632 - accuracy: 0.3557 - val_loss: 1.2382 - val_accuracy: 0.4230 - 35s/epoch - 70ms/step\n",
            "Epoch 2/30\n",
            "500/500 - 18s - loss: 1.0932 - accuracy: 0.4835 - val_loss: 0.9630 - val_accuracy: 0.6112 - 18s/epoch - 36ms/step\n",
            "Epoch 3/30\n",
            "500/500 - 17s - loss: 0.7603 - accuracy: 0.6940 - val_loss: 0.6826 - val_accuracy: 0.7297 - 17s/epoch - 34ms/step\n",
            "Epoch 4/30\n",
            "500/500 - 17s - loss: 0.5887 - accuracy: 0.7664 - val_loss: 0.5763 - val_accuracy: 0.7785 - 17s/epoch - 34ms/step\n",
            "Epoch 5/30\n",
            "500/500 - 26s - loss: 0.5002 - accuracy: 0.8026 - val_loss: 0.5481 - val_accuracy: 0.7987 - 26s/epoch - 53ms/step\n",
            "Epoch 6/30\n",
            "500/500 - 17s - loss: 0.4126 - accuracy: 0.8515 - val_loss: 0.4318 - val_accuracy: 0.8583 - 17s/epoch - 34ms/step\n",
            "Epoch 7/30\n",
            "500/500 - 19s - loss: 0.3321 - accuracy: 0.8847 - val_loss: 0.3894 - val_accuracy: 0.8723 - 19s/epoch - 39ms/step\n",
            "Epoch 8/30\n",
            "500/500 - 18s - loss: 0.2783 - accuracy: 0.9037 - val_loss: 0.3453 - val_accuracy: 0.8820 - 18s/epoch - 35ms/step\n",
            "Epoch 9/30\n",
            "500/500 - 18s - loss: 0.2453 - accuracy: 0.9142 - val_loss: 0.3338 - val_accuracy: 0.8875 - 18s/epoch - 36ms/step\n",
            "Epoch 10/30\n",
            "500/500 - 17s - loss: 0.2198 - accuracy: 0.9247 - val_loss: 0.3238 - val_accuracy: 0.8928 - 17s/epoch - 34ms/step\n",
            "Epoch 11/30\n",
            "500/500 - 17s - loss: 0.1865 - accuracy: 0.9345 - val_loss: 0.3081 - val_accuracy: 0.8995 - 17s/epoch - 34ms/step\n",
            "Epoch 12/30\n",
            "500/500 - 17s - loss: 0.1763 - accuracy: 0.9392 - val_loss: 0.3161 - val_accuracy: 0.8995 - 17s/epoch - 34ms/step\n",
            "Epoch 13/30\n",
            "500/500 - 17s - loss: 0.1539 - accuracy: 0.9459 - val_loss: 0.2869 - val_accuracy: 0.9045 - 17s/epoch - 35ms/step\n",
            "Epoch 14/30\n",
            "500/500 - 17s - loss: 0.1292 - accuracy: 0.9544 - val_loss: 0.3129 - val_accuracy: 0.9105 - 17s/epoch - 34ms/step\n",
            "Epoch 15/30\n",
            "500/500 - 18s - loss: 0.1201 - accuracy: 0.9551 - val_loss: 0.3131 - val_accuracy: 0.9062 - 18s/epoch - 36ms/step\n",
            "Epoch 16/30\n",
            "500/500 - 16s - loss: 0.1098 - accuracy: 0.9618 - val_loss: 0.2947 - val_accuracy: 0.9143 - 16s/epoch - 33ms/step\n",
            "Epoch 17/30\n",
            "500/500 - 16s - loss: 0.1112 - accuracy: 0.9613 - val_loss: 0.2769 - val_accuracy: 0.9145 - 16s/epoch - 33ms/step\n",
            "Epoch 18/30\n",
            "500/500 - 17s - loss: 0.0968 - accuracy: 0.9656 - val_loss: 0.3024 - val_accuracy: 0.9172 - 17s/epoch - 33ms/step\n",
            "Epoch 19/30\n",
            "500/500 - 17s - loss: 0.0858 - accuracy: 0.9696 - val_loss: 0.2784 - val_accuracy: 0.9170 - 17s/epoch - 34ms/step\n",
            "Epoch 20/30\n",
            "500/500 - 17s - loss: 0.0810 - accuracy: 0.9708 - val_loss: 0.3323 - val_accuracy: 0.9140 - 17s/epoch - 34ms/step\n",
            "Epoch 21/30\n",
            "500/500 - 17s - loss: 0.0822 - accuracy: 0.9711 - val_loss: 0.2934 - val_accuracy: 0.9160 - 17s/epoch - 33ms/step\n",
            "Epoch 22/30\n",
            "500/500 - 17s - loss: 0.0776 - accuracy: 0.9718 - val_loss: 0.3371 - val_accuracy: 0.9180 - 17s/epoch - 35ms/step\n",
            "Epoch 23/30\n",
            "500/500 - 17s - loss: 0.0698 - accuracy: 0.9754 - val_loss: 0.3147 - val_accuracy: 0.9168 - 17s/epoch - 34ms/step\n",
            "Epoch 24/30\n",
            "500/500 - 17s - loss: 0.0682 - accuracy: 0.9753 - val_loss: 0.3292 - val_accuracy: 0.9195 - 17s/epoch - 33ms/step\n",
            "Epoch 25/30\n",
            "500/500 - 17s - loss: 0.0654 - accuracy: 0.9765 - val_loss: 0.3382 - val_accuracy: 0.9147 - 17s/epoch - 34ms/step\n",
            "Epoch 26/30\n",
            "500/500 - 17s - loss: 0.0682 - accuracy: 0.9753 - val_loss: 0.3417 - val_accuracy: 0.9145 - 17s/epoch - 33ms/step\n",
            "Epoch 27/30\n",
            "500/500 - 17s - loss: 0.0615 - accuracy: 0.9786 - val_loss: 0.3342 - val_accuracy: 0.9178 - 17s/epoch - 33ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "yCakgPTfyA-1",
        "outputId": "80cec01c-18d2-4387-cd99-1519d60f09a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "2-J0W0bIyDmf",
        "outputId": "01f6d2d3-21b2-4659-ea42-ae92c91985b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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melkQHJKcBflT2gRBSz/BVusnMMZELwuC1kbPhj0fQ9XOlklTC6yfwBgT3cJ58frHRaRMRNZ0Ml9E5DciUiwiH4vIxHDV0mUtZxkfPrns8PkE1k9gjIlO4dwjeAKYfZT5c4BR7m0u8EgYa+manNGQUdCmeWhoVhID0qyfwBgTvcIWBKr6NnC0n9GXA39SxxKgn4gMDFc9XSLiXKPgk7ehqdadJEwbnsmSrfutn8AYE5Ui2UeQB5S0el7qTouswtkQbHSGnHBNG57FvppGtpTXRrAwY4wJjz7RWSwic0WkSESKysvLw/thQz8FCeltzjK2cYeMMdEskkGwExjc6nm+O+0IqjpPVSer6uScnJzwVuWNg1EXOGcZh0KA00+Qm5ZgQWCMiUqRDIL5wBfdo4emAVWqujuC9RxWOAdqy2HnCqD1uEPWT2CMiT7hPHz0GeADYLSIlIrIzSJym4jc5i7yKrAVKAYeBb4erlq6bdQFIN4jmofKqxvZus/6CYwx0cUXrjdW1WuPMV+B28P1+SckMQOGTHcuaj/zfqBtP8GInJRIVmeMMT2qT3QWR8To2VC2Fip3ADCspZ/ATiwzxkQXC4LOFLrnwrnXMj7UT7DExh0yxkQZC4LOZI2EzOGweWHLpKkF1k9gjIk+FgSdEYFRFzmXr3TPMp42PBOApdY8ZIyJIhYER1N4kXOW8SdvA1CQnUz/VDufwBgTXSwIjmboWe61jJ3RSK2fwBgTjSwIjsYX717LeGHLtYynDc+irLqRT6yfwBgTJSwIjqVwNlTvgj2rgcP9BHYYqTEmWlgQHMuoWc69exhpQXYyOdZPYIyJIhYEx5LSH/ImHdFPsPQT6ycwxkQHC4KuKJztDEBX4wyBPW14JnsPNrKtoi7ChRljzImzIOiKwosAheLXAbs+gTEmulgQdMWA0yB1YEvz0HDrJzDGRBELgq4QgVEXQvFbEGiy8wmMMVHFgqCrCmdDUzXseB+AqQXWT2CMiQ4WBF01/FzwJjgnl3G4n2CpNQ8ZY/o4C4Kuik+GgnNa+glG5CSTnWL9BMaYvs+CoDsKL4L9W2BfsdtPkMmSrXYdY2NM32ZB0B2jLnTu3b2CacOz2HOwge3WT2CM6cMsCLojYyj0HwebneEm7HwCY0w0CGsQiMhsEdkoIsUi8r0O5g8RkUUi8qGIfCwiF4eznh5ReBFsfx8aqqyfwBgTFcIWBCLiBR4C5gDjgGtFZFy7xe4FnlfVCcAXgIfDVU+PKZwNoQBseQsRYerwTJZ+Yv0Expi+q0tBICJ3ikiaOB4TkZUicuExXjYFKFbVraraBDwLXN5uGQXS3MfpwK7uFB8R+WdCYkbLaKTThmexu6qBHfutn8AY0zd1dY/gK6p6ELgQyABuBB44xmvygJJWz0vdaa39ELhBREqBV4FvdPRGIjJXRIpEpKi8vLyLJYeJxwsjZ8Hm1yEUZHrL9QmsecgY0zd1NQjEvb8YeFJV17aadiKuBZ5Q1fxD7y0iR9SkqvNUdbKqTs7JyemBjz1BhRdB3T7YuZIROSlkp8TbhWqMMX1WV4NghYgsxNlYLxCRVCB0jNfsBAa3ep7vTmvtZuB5AFX9APAD2V2sKXJGzgTxwqZ/uv0ENu6QMabv6moQ3Ax8DzhTVeuAOODLx3jNcmCUiBSISDxOZ/D8dsvsAGYCiMhYnCCIcNtPFyRmwJDp1k9gjIkKXQ2C6cBGVa0UkRtwjvapOtoLVDUA3AEsANbjHB20VkR+LCKXuYt9B/iqiHwEPAPcpH3lZ3XhhbB3NVTtZFqB00+w1JqHjDF9UFeD4BGgTkROx9l4bwH+dKwXqeqrqlqoqiNU9WfutPtVdb77eJ2qnqWqp6vqGaq68DjX4+QrnO3cb17AyP4p5KYl8PePe/9BT8YY015XgyDg/lK/HHhQVR8CUsNXVh+QXQgZw2DTAkSEr5xVwDub97GqpDLSlRljTLd0NQiqReQenMNGX3GP7IkLX1l9gIizV7B1MTTVcf20ofRLiuPBt4ojXZkxxnRLV4Pg80AjzvkEe3COAPp52KrqKwovgkADbHuHlAQfXzmrgDfW72XdroORrswYY7qsS0Hgbvz/DKSLyGeABlU9Zh9B1Bt6FsQltxw99KVPDSM1wcdDi22vwBjTd3R1iIlrgGXA1cA1wFIRuSqchfUJvgQYcZ4TBKqkJ8Zx4/ShvLp6N8VlNZGuzhhjuqSrTUP/gXMOwZdU9Ys44wjdF76y+pDC2XCwFPauBeDmswtI8Hl42PYKjDF9RFeDwKOqZa2eV3TjtdGt3cVqslISuH7qUF5etYsSO8HMGNMHdHVj/k8RWSAiN4nITcArOIPEmdRcGDQBNh8+BWLuOcPxivDIv7ZEsDBjjOmarnYW3wXMA05zb/NU9e5wFtanFM6GkmVQ64xAmpvm55oz83mhqJTdVfURLs4YY46uy807qvqiqn7bvb0UzqL6nMKLAG2zV3DrOSMIqjLv7a2Rq8sYY7rgqEEgItUicrCDW7WI2MHyhww4HfoNgbf/GxqcIZgGZyZxxYQ8nlm2g/LqxggXaIwxnTtqEKhqqqqmdXBLVdW0o702png8cMU8qNwBL30NQs4I3V+fMYLGQIjH3v0kwgUaY0zn7MifnjJ0Olz4U9j4Crz3SwCG56TwmdMG8eQH26isa4psfcYY0wkLgp409TYYfyW89VPYsgiA288bQW1TkCfe3xbZ2owxphMWBD1JBC79DWSPhhdvhsoSxgxI48JxufzhvW1UNzRHukJjjDmCBUFPS0iBzz8FgSZ4/osQaOSO80dSVd/MU0t2RLo6Y4w5ggVBOGSPhCsegV0r4bW7OS2/H+cU5vD7d7ZS3xSMdHXGGNOGBUG4jL0Uzv43WPEH+PApvnH+SCpqm3hmme0VGGN6FwuCcDrvXig4F/7xbc6M38HUgkzmvb2VxoDtFRhjeo+wBoGIzBaRjSJSLCLf62SZa0RknYisFZGnw1nPSef1wVWPQ3I2PH8j/3ZWDnsONvDiip2RrswYY1qELQhExAs8BMwBxgHXisi4dsuMAu4BzlLVU4BvhaueiEnOhmuehOo9TF11NxPy03h4cTHNwVCkKzPGGCC8ewRTgGJV3aqqTcCzwOXtlvkq8JCqHgBoN9R19MifBHP+Cyl+g//t/09KD9Qzf9WuSFdljDFAeIMgDyhp9bzUndZaIVAoIu+JyBIRmd3RG4nIXBEpEpGi8vLyMJUbZpO+DGdcz/B1D/LF7I08tLiYYEgjXZUxxkS8s9gHjAJmANcCj4pIv/YLqeo8VZ2sqpNzcnJOcok9RAQu+V8YcCr3Nf6S5n1b+fWbmyNdlTHGhDUIdgKDWz3Pd6e1VgrMV9VmVf0E2IQTDNEpLhGueRKf18Oz6Q8x/623bUA6Y0zEhTMIlgOjRKRAROKBLwDz2y3zN5y9AUQkG6epKLoH8M8sQK78PYOatrE44TtMXfhZPn72h3BgW6QrM8bEqLAFgaoGgDuABcB64HlVXSsiPxaRy9zFFgAVIrIOWATcpaoV4aqp1xg1C7nzIwKzforf7+e0Db+EX58O886D9/8PqkojXaExJoaIat/qsJw8ebIWFRVFuoweU9cU4Nvz/s7wvQv4WvbHpB5Y68wYPBVO+RyMuxzSBka2SGNMnyciK1R1cofzLAgir6q+mWvnLWHrvhqevzqX0yrfgrUvwd41gMDQs2DSTXDa1ZEu1RjTRx0tCCJ91JAB0hPj+NPNUxiUnsj1L5azZsRX4Wvvwe3LYcY9UFsGf70F1v890qUaY6KQBUEvkZ2SwFO3TCUtMY4vPr6M4rIayCmEGXfDbe/BwDNg/jfgoJ2IZozpWRYEvcigfok8efMUPAI3PraU0gN1zgxfPFz5ewg0wku3tVwT2RhjeoIFQS8zPCeFJ2+eSm1jgBt+v5Ty6kZnRvYomP0AfPIv+ODByBZpjIkqFgS90NiBafzhy1Moq27kxseWUlXnXuJy4hdhzGfgzR/DrlWRLdIYEzUsCHqpSUMzmHfjZLaW13LTE8uobQw4w1Rc9n+QnAMv3gJNdZEu0xgTBSwIerGzR2Xzm2sn8HFpFXOfLKKhOQhJmXDFb6GiGBZ8P9IlGmOigAVBLzd7/AD++8rTeK+4gjue/tC5jsHwc+GsbzqXwVz/j0iXaIzp4ywI+oArJ+Xzk8tP4Y31e/nuXz5yhq8+714YeLp7SOnuSJdojOnDLAj6iBunD+Pu2WN4edUu7v3batQbB1c+BoEG+JsdUmqMOX4WBH3I12aM4PbzRvDMshJ+9sp6NGskzP5P2LoYljwU6fKMMX2UL9IFmO757oWjqW0M8vt3PyHF7+NbM78Em1+HN34EBec4zUXGGNMNtkfQx4gI939mHFdNyudXb2zm9+9+4h5Smm2HlBpjjosFQR/k8QgPfO5ULj51AD99ZT3PrKlxDindtxkW/kekyzPG9DEWBH2Uz+vhV5+fwIzROXz/pdW8fHAUfOobUPQ4bHgl0uUZY/oQC4I+LN7n4bc3TGLKsEy+/fxHvDFwLgw4DV6+ww4pNcZ0mQVBH+eP8/L7L01m/KA0vv7cGlae+T/QXA9/mA0fPQehYKRLNMb0chYEUSDVH8cfvzKFgqxkrv/bATZe8BjEp8BLc+Hh6bDmr3aegTGmU2ENAhGZLSIbRaRYRL53lOWuFBEVkQ4vo2aOrV9SPE/eMoXctASu+qePNZf+A65+wpn5wpfhd592hqPoY5cmNcaEX9iCQES8wEPAHGAccK2IjOtguVTgTmBpuGqJFf1T/Tx1y1RSE3zc8PhyVqefD1//AD73qNNc9Nz1MG8GbFpogWCMaRHOPYIpQLGqblXVJuBZ4PIOlvsJ8F9AQxhriRn5GUk8O3c6yfE+rnt0CStKDsJp18Dty+Dyh6H+ADx9NTw2C7YsskAwxoQ1CPKAklbPS91pLURkIjBYVY96vKOIzBWRIhEpKi8v7/lKo8yQrCSev206mSnxfPGxpSzdWgFeH0y4Hu4ogs/8yrn28ZOfhScugW3vRrpkY0wERayzWEQ8wC+A7xxrWVWdp6qTVXVyTk5O+IuLAnn9Enn+1ukMSPfzpT8s493N+5wZvniY/GX45ocw5+dQscUJgyevgF0fRrZoY0xEhDMIdgKDWz3Pd6cdkgqMBxaLyDZgGjDfOox7Tm6an+dunc6wrGS+8sflLNpQdnimLwGmzoU7V8GsnzghMG8G/OUm2FccqZKNMREQziBYDowSkQIRiQe+AMw/NFNVq1Q1W1WHqeowYAlwmaoWhbGmmJOdksAzX51GYW4Kc58sYuHaPW0XiEt0LnJz50dwzl1OR/JDU+DvdzrNR8aYqBe2IFDVAHAHsABYDzyvqmtF5Mciclm4PtccKSM5nj/fMo1TBqXz9T+v5B8fd7CB96fD+fc6ewhn3gIf/hl+MwEW3gd1+09+0caYk0a0jx01MnnyZC0qsp2G41Hd0MxXnljOiu0H+N9rTueKCfmdL3xgGyz6T/j4OUhIc/Yapn0N4pNPWr3GmJ4jIitUtcOmdzuzOIYcOgN52vAsvv38Rzy3fEfnC2cMg8/9Dr72Hgw7C976Cfz6DFj2KASaTlrNxpjwsz2CGNTQHOTWJ1fwr03l/OTyU7hx+rBjv2jHUnjzR7D9PUgdCLnjIXM4ZBa498Oh3xCnE9oY45yjE2iE5jrn1lQHzbXufb3zWLyQ0h+Sc5z7MO5xH22PwIIgRjUGgtz+5w95Y/1e7r1kLLd8evixX6QKxW/Cqqecw073fwJN1a0WEEgfDJnDnGDIcEMiY6gzPTEDRMK1SibWBJvhw6egtAiGTIXh50G/wcd+3Ql9ZgBq9joHUhzc2e5+lzOvqfbwhl67OcZXXDKk5EBK7uFwSO5/eFr/cZA14rhKtyAwHWoOhvjWs6t4ZfVurp0yhHsuHkOaP67rb6AKdRWwf6sTCvu3OrcD7uO6irbLx6c4gdBvcLv7Ic59Si54rLUyqmx8DRZ8H/z94OxvwZjPgMd7Yu8ZCsGaF2HRz5z/awlp0HjQmZc10gmEEefDsLPBn9b992+sgfINsHetc19V0nZD337j7kuEtEHOLSUXElIgLsm5xSc5G/e4ROfXfss09xYKQG051JQ57z2aYq8AABP4SURBVH3ocW0Z1JQ7963/js76Fsz60XH9s1kQmE4FgiF+vmAjj76zlf6pfn52xXhmjs3tmTdvqHIConI7VJY4f1CVJVC1w7lvqGy7vDce0vIgZwwMOBUGnubc9xtqexJ9Te0+eO3fnQ12zlgINjo/DrJGwll3wmmf734zoipsWuD0V+1d4zRPzrwfRl3obLC3LIKti5wz5ZvrnGaX/DNhxHlOOORNcs6wPyTYDBXFULYO9q5z79c6/18PiUtyfqgc2tCnuvdpeYenhXtPN9js/HvWljmBmjH0uN7GgsAc00clldz94sds2FPNZacP4geXjiMrJczt/Y3VrQJix+H7svWwb9PhX14J6U4gtA6HnDHg7WDvJRR0flVV73F/Ze2B6r3u/R5n4zPuchh1EcT5e25dmhugdJnTJKAKuH9XLX9f2vYxOBuZQRMgKbPn6og0VVj9ghMCjdVw7r87v2I9Xlg/H979Jez+yOlnmn47TLoJElKP/b7b3oU3fwwlS50mx/PvhVM+1/EeZKARSpY5obBlkXvGvDp7DsM+7fwiP/R/LOge+CBeyB4F/cdC/1Mgd5zTDNNvaNTspVoQmC5pCoR4ZPEWHly0mVR/HD+4dByXnT4IicSv8aY65491z8fubbXza625zpnvjXfCILvQaRao3nN417qjdll/P0gd4Oxm15Y7G4Wxl8KpV8Gwc9r+Uuyq+gOw+XXY8A/Y/IbTJnw8skY6v1zzz4TBU5wN0Ik2n0RC1U74x7/B5gWQNxkuf9DZsLam6myg3/0lfPK2c/7KlLkw9TZIzj7yPXd9CG/+BLa86YTHuXfDhBs6/hHQmbr9zmdtXQRbFzvt/Ic29LmnODVmF0b9gQ4WBKZbNu2t5t9f+JhVJZXMHNOfn14xnoHpiZEuy/m1X7GlbThUFDu75im5zi11QKv7AZDqTj/0Rx4MwLZ3nF+t6+c7IZLcH8Z/Dk692mk+OFrwVe2Eja86G/9t7zptvCkDYMzFUDi71cbMfY+W9+rgeUOl09FZutz5BVvnjgcVnwJ5EyF/yuGASM7qwX9IVzAAJUucdvzdHzkhNOpCZyPenWAMhWDlE7DwftAgnH8fTL312GFWugLe+6VznQyfHybeCNPvcJo+yjfBop/CupchMRM+/W3nRMe4XvD/sI+yIDDdFgwpT7y/jf9ZsBGvR7jn4jFce+YQPJ4oaqtvbnB+va7+izO0RrDROX/i1KudW85o5xds+UbY8HfY8MrhgfmyRsHYzzidn4MmnnjzgarT8Vla5IRC6TLYs8bZsAJkjnBDYbITVrnjnQEEu6ux2jnya+NrzrrXH3D2rrJHO23kGnT2nkbOdEJhxEzniJXOVGxxhiPZ9g4UnAuX/to5pLg7yjfB+792Lq2qIRgyHXa87zSdTb/dCYfj6fQ1bVgQmOO2o6KOe176mPeKK5hakMkDV55GQXYUnl1cX+n8yl/9F6cZQUOQe6rTFLV/i7NM3mQYc4mz8c8pDH9NTbWwa5UTCiXLnT2HWnfgQG+C01+SN9kNh4lO23lHezMHdzl7MRtfc9Yt2OTsRRXOhtFznCNsElKdf4Oti5zmrs2vu58lznuPuhBGzYKBE5zQCwZgycPOkTveBLjopzDhxhPrNK3a6bzn+vkw5lJnL6Cj5iJzXCwIzAlRVf5SVMpPXllHUyDEnReM4sZpQ0ntzqGmfUn1Hlj7Eqz9m9OxOOYSGH0JpA2MbF2qUFUKO4ucPYedK509lEC9Mz8py9lbyJsEA05zms42vgq7VznzMwrcdbkYBk89evNPKAR7PnJDYaHzeSgkZTuBUL4Rdq103uuSX0T+38YckwWB6RF7DzZw39/WsHDdXhLjvFx2+iC+MGUwZwzuF5kOZeP8Mi9b54TDzhVOu3v5Bpwjk8RpTho9xwmA7MLj/8VeW+F02G5eCMVvOEfZXPzfzpE79t33CRYEpketKqnk2WU7mP/RLuqagowZkMp1U4dw+Rl5pCdG6V5CX9Jw0DnCKmuEc2ZqTwsFAYmawypjhQWBCYuaxgDzV+3imWU7WL2zCn+ch0tOHcR1UwczcUiG7SUY04tYEJiwW7OzimeW7eDlVbuoaQwwqn8K104Zwucm5tEv6TiObjHG9CgLAnPS1DYG+MfHu3h6WQkflVQS7/Mwa2wuF56Sy4zR/a3pyJgIsSAwEbFu10GeXb6DV1fvZl9NEz6PMHV4JheMzeWCsbkMzkyKdInGxAwLAhNRoZDyYUklr6/byxvr91JcVgPAmAGpXDgul1njBjA+L836FIwJIwsC06t8sq+W19ft4Y11ZRRt309IYUCanwvG9eeCsbl8akQ28T47IsWYnhSxIBCR2cCvAS/we1V9oN38bwO3AAGgHPiKqm4/4o1asSCILhU1jSzaWM7r6/bw9qZ91DcHyU5J4OrJ+Vx75hCGZFnzkTE9ISJBICJeYBMwCygFlgPXquq6VsucByxV1ToR+RowQ1U/f7T3tSCIXg3NQd7ZvI/nlu/grQ1lhBQ+PSqb66cOYebYXOK8tpdgzPE6WhAcx9i7XTYFKFbVrW4RzwKXAy1BoKqLWi2/BLghjPWYXs4f52XWuFxmjctld1U9zy0v4bnlJdz21EpyUhP4/OTBfP7MwdbJbEwPC+cewVXAbFW9xX1+IzBVVe/oZPkHgT2q+tMO5s0F5gIMGTJk0vbtR209MlEkEAyxeGM5Ty/bweKNZShwzqgcrps6hJlj+uOzvQRjuiRSewRdJiI3AJOBczuar6rzgHngNA2dxNJMhPm8Hi4Yl8sF43LZWXloL2EHtz65gty0BK6cmM/YgWnkZySSl5FITkqCHX1kTDeFMwh2AoNbPc93p7UhIhcA/wGcq6qNYazH9HF5/RL59qxCvnn+SN7aUMbTy3bwyL+20HqnNt7nIb+fEwp5/RJbAiKvXxL5GYnkpvnxRtM1FYzpAeEMguXAKBEpwAmALwDXtV5ARCYAv8NpQioLYy0mivi8Hi48ZQAXnjKAmsYApQfq2HmgntID9eysrHcf17F+90H21TS1eW1yvJdpw7M4e1Q2nx6Vw4icZNuDMDEvbEGgqgERuQNYgHP46OOqulZEfgwUqep84OdACvAX949xh6peFq6aTPRJSfAxZkAaYwZ0fAWr+qagEw6VTjis3XWQdzfv480Nzu+Ogel+zh6Zzdmjsjl7ZDZZKdF93VpjOmInlJmYtKOijneKy3l38z7eK97HwYYAAKcMSnP2FkbmMHlYBv64PngReWM6YGcWG3MUwZCyemcV72wq553ifXy44wDNQSXB52Ha8Cxmju3P+WP6k59hh62avsuCwJhuqG0MsPSTCt7etI/FG8vYVlEHwOjcVM4f25+ZY/ozYUiGdTqbPsWCwJgTsLW8hrc2lPHm+jKWbdtPMKRkJMUxY3R/Zo7tz6dH5djw2qbXsyAwpodU1TfzzuZy3lpfxqKNZRyoa8bnEc4clsl5Y3IY1T+VAel+Bqb7SU+MsyOSTK9hQWBMGARDyqqSA7y5voy3NpSxYU91m/n+OA8D0xMZkOYEQ64bEM7zRAb285OVHG9hYU4KCwJjToKy6gZ2HqhnT1UDu6sa2HPQva+qZ3dVA3sPNtAcbPv35o/zkJ/hnOzm3JLa3FtQmJ7S64eYMCYa9E/10z/V3+n8UEipqG1yg6KeXZXOSXClB+opraxjVUkllXXNbV7TOihS/XEEQyECQSUQcm/BEIGQEmz3OBhSBvZLZMyAVApzUxmdm8qo3BQ7HNZ0yILAmJPE4xFyUhPISU3g1Pz0Dpepbmh2Tn7b75wAdygoSg7Usb2iDp9H8HoEn1fweTwtz/1xHnwJvpbnIlCyv54ntlbQFAgBIALDspIpzE1hdG4qowekMXpACsOykm3wvhhnQWBML5Lqj2PMgLhOz5TurkAwxPb9dWzcU83GPdVs2uvcv75uLyG3lSre62F4TjJDs5IYkuncBru3/IxEEny2FxHtLAiMiWI+r4cROSmMyEnh4lMHtkxvaA5SXFbTEgyby2rYUl7L4o3lNLp7EODsRQxI8zM483BIDMlMIjfNT6rfR3piHKl+HykJPtur6MMsCIyJQf44L+Pz0hmf17aJKhRS9tU0smN/XZtb6f563t28jz0HGzp9z6R4L6l+H6n+uDb3aX4fHhGnHyOkhNx753mIYAin78OdFlIlOd5HihswKX4fqQnO41R/3OHn7vy0xDgyk+Lx2Al+x82CwBjTwuMR+qf56Z/mZ/KwzCPmNzQHKT1QT9nBBg42BKhuaKa6IeDe3MeNzn1VfTOlB+qobgigqng9glcEr9u/0fLc7fPwegSfRxCEvdUNbCl337cx0NLP0RmvR8hKjm/pg8lJSTj8uN3zlASfHYnVjgWBMabL/HFeRvZPYWT/lJP6uY2BILWNQWrcoKlxw6emMUBlXRP7apoor26kvKaR8upGNuyuZl9NI4FQx4fH+9zwifN4nI53r4c4j3PffnqCz4M/ztvu3kOCr/00Lz6v0BQI0RgI0dAcpDEQorE5REMgSGNziMZAsM08rwiZyfFkJseTkRxPVqv7lulJ8cT7wtvsZkFgjOn1nI2ul8zk+C6/JhRSKuubnYCobqS8poHy6kZqGoMth9o2Bw8djhuiOegcgtt86FDcoNIUDNEUCFFV30xjy4Y9SEOr+2AnYQMQ5xUSfN42wZHQKkyagyHW7znI/tqmIw4dbi01wUdmSjw3ThvKLZ8e3q1/u66wIDDGRCWP5/Cv7dEDUsP2OYFgqCUYDo1am+Bu+LszMGEgGKKyvpkDtU1U1Da1ud9f18T+2iZyUsNzvQwLAmOMOQE+r4cUr4eUhBPbnPq8HrJTEshOSWBUD9XWVXa8lzHGxDgLAmOMiXEWBMYYE+PCGgQiMltENopIsYh8r4P5CSLynDt/qYgMC2c9xhhjjhS2IBARL/AQMAcYB1wrIuPaLXYzcEBVRwK/BP4rXPUYY4zpWDj3CKYAxaq6VVWbgGeBy9stcznwR/fxC8BMsVP+jDHmpApnEOQBJa2el7rTOlxGVQNAFZAVxpqMMca00yc6i0VkrogUiUhReXl5pMsxxpioEs4TynYCg1s9z3endbRMqYj4gHSgov0bqeo8YB6AiJSLyPbjrCkb2Hecr+1rYmVdY2U9wdY1Gp3M9Rza2YxwBsFyYJSIFOBs8L8AXNdumfnAl4APgKuAt/QYF1FW1ZzjLUhEijq7Zme0iZV1jZX1BFvXaNRb1jNsQaCqARG5A1gAeIHHVXWtiPwYKFLV+cBjwJMiUgzsxwkLY4wxJ1FYxxpS1VeBV9tNu7/V4wbg6nDWYIwx5uj6RGdxD5oX6QJOolhZ11hZT7B1jUa9Yj3lGE3yxhhjolys7REYY4xpx4LAGGNiXMwEwbEGwIsmIrJNRFaLyCoRKYp0PT1FRB4XkTIRWdNqWqaIvC4im937jEjW2FM6WdcfishO93tdJSIXR7LGniAig0VkkYisE5G1InKnOz2qvtejrGev+E5joo/AHQBvEzALZ6iL5cC1qrouooWFiYhsAyaralSdkCMi5wA1wJ9Udbw77b+B/ar6gBvwGap6dyTr7AmdrOsPgRpV/Z9I1taTRGQgMFBVV4pIKrAC+CxwE1H0vR5lPa+hF3ynsbJH0JUB8Ewvp6pv45xv0lrrgQv/iPPH1ed1sq5RR1V3q+pK93E1sB5nDLKo+l6Psp69QqwEQVcGwIsmCiwUkRUiMjfSxYRZrqrudh/vAXIjWcxJcIeIfOw2HfXp5pL23OuRTACWEsXfa7v1hF7wncZKEMSas1V1Is61IG53mxminjs8STS3dT4CjADOAHYD/xvZcnqOiKQALwLfUtWDredF0/fawXr2iu80VoKgKwPgRQ1V3enelwEv4TSNRau9bvvroXbYsgjXEzaquldVg6oaAh4lSr5XEYnD2Tj+WVX/6k6Ouu+1o/XsLd9prARBywB4IhKPM6bR/AjXFBYikux2RiEiycCFwJqjv6pPOzRwIe79yxGsJawObRhdVxAF36t7IarHgPWq+otWs6Lqe+1sPXvLdxoTRw0BuIdl/YrDA+D9LMIlhYWIDMfZCwBnLKmno2VdReQZYAbO0L17gR8AfwOeB4YA24FrVLXPd7J2sq4zcJoQFNgG3NqqHb1PEpGzgXeA1UDInfx9nPbzqPlej7Ke19ILvtOYCQJjjDEdi5WmIWOMMZ2wIDDGmBhnQWCMMTHOgsAYY2KcBYExxsQ4CwJjTiIRmSEi/4h0Hca0ZkFgjDExzoLAmA6IyA0isswdI/53IuIVkRoR+aU7nvybIpLjLnuGiCxxBw576dDAYSIyUkTeEJGPRGSliIxw3z5FRF4QkQ0i8mf3rFNjIsaCwJh2RGQs8HngLFU9AwgC1wPJQJGqngL8C+dsX4A/AXer6mk4Z44emv5n4CFVPR34FM6gYuCMPPktYBwwHDgr7CtlzFH4Il2AMb3QTGASsNz9sZ6IM+hZCHjOXeYp4K8ikg70U9V/udP/CPzFHe8pT1VfAlDVBgD3/Zapaqn7fBUwDHg3/KtlTMcsCIw5kgB/VNV72kwUua/dcsc7Pktjq8dB7O/QRJg1DRlzpDeBq0SkP7RcP3cozt/LVe4y1wHvqmoVcEBEPu1OvxH4l3sVqlIR+az7HgkiknRS18KYLrJfIsa0o6rrRORenKu8eYBm4HagFpjizivD6UcAZ5jk37ob+q3Al93pNwK/E5Efu+9x9UlcDWO6zEYfNaaLRKRGVVMiXYcxPc2ahowxJsbZHoExxsQ42yMwxpgYZ0FgjDExzoLAGGNinAWBMcbEOAsCY4yJcf8fzN630XYaSFEAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score = model.evaluate(padded_test, e_test)\n",
        "print(accuracy_score)\n",
        "\n",
        "print(\"Accuracy: {:.4f}%\".format(accuracy_score[1] * 100)) \n",
        "print(\"Loss: \",accuracy_score[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CCk-yrPwyEud",
        "outputId": "99f27c4d-f060-4fdf-94ca-619990a3ded8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "125/125 [==============================] - 2s 12ms/step - loss: 0.2769 - accuracy: 0.9145\n",
            "[0.2769295275211334, 0.9144999980926514]\n",
            "Accuracy: 91.4500%\n",
            "Loss:  0.2769295275211334\n"
          ]
        }
      ]
    }
  ]
}