NLP_Simple_Semantic_Classifier_Lecture_2.ipynb

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      "source": [
        "# pandas\n",
        "import pandas as pd\n",
        "import numpy as np\n"
      ]
    },
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      "source": [
        "data = pd.read_csv('/content/training.csv')"
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        "data.head(3)"
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      "outputs": []
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        "# Binary Classification\n",
        "# labels: Sadness (0) and joy (1)\n",
        "# data.where(cond = (data['label'] == 0)).dropna()\n",
        "# data.where(cond = (data['label'] == 1)).dropna()"
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        "id": "iBB5qYxrGqPd"
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      "execution_count": 6,
      "outputs": []
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        "data[data.label.isin([0, 1])].isna().count()"
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              "text     10028\n",
              "label    10028\n",
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        "data_sad_joy = data[data.label.isin([0, 1])].dropna()"
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              "                                                    text  label\n",
              "0                                i didnt feel humiliated      0\n",
              "1      i can go from feeling so hopeless to so damned...      0\n",
              "5      ive been feeling a little burdened lately wasn...      0\n",
              "8      i have been with petronas for years i feel tha...      1\n",
              "10     i feel like i have to make the suffering i m s...      0\n",
              "...                                                  ...    ...\n",
              "15993  i most days feel like if braeden and calvin ar...      1\n",
              "15995  i just had a very brief time in the beanbag an...      0\n",
              "15996  i am now turning and i feel pathetic that i am...      0\n",
              "15997                     i feel strong and good overall      1\n",
              "15999  i know a lot but i feel so stupid because i ca...      0\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-56bbd21e-f861-4e8e-a276-c1053d4ca8c1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-56bbd21e-f861-4e8e-a276-c1053d4ca8c1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-56bbd21e-f861-4e8e-a276-c1053d4ca8c1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_d9410d20-0b8f-4b7f-b950-b738e39a9587\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data_sad_joy')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_d9410d20-0b8f-4b7f-b950-b738e39a9587 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data_sad_joy');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = list(data_sad_joy['text'].to_numpy())\n",
        "x_train[0]\n",
        "\n",
        "# x_train is our setences"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "ynKNDWrPIuUi",
        "outputId": "2cefa11c-56f1-4768-9a0d-b248acd7c8d9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'i didnt feel humiliated'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = data_sad_joy['label'].to_numpy()\n",
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3xBAYjWJKEh",
        "outputId": "b2d65370-3a97-4ca0-fca5-25c652608dca"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer"
      ],
      "metadata": {
        "id": "Excp3fTjJz-6"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_size = 10000\n",
        "max_length = 100\n",
        "embedding_dim = 16"
      ],
      "metadata": {
        "id": "V1KNxe9GKMYZ"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = Tokenizer(num_words = vocab_size, oov_token = '<OOV>')"
      ],
      "metadata": {
        "id": "SDpkSw_cKDui"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.fit_on_texts(x_train)\n",
        "# tokenizer.word_index\n",
        "sequences = tokenizer.texts_to_sequences(x_train)"
      ],
      "metadata": {
        "id": "dtsYZlm4KsH0"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "EBvxqoa0LmzE",
        "outputId": "38bea597-4be7-46ec-ddb0-a54cc4aa48ad"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'i didnt feel humiliated'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sequences[0]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AliAbl2YLCRx",
        "outputId": "758b5873-2b91-48a7-c702-94ea86798e2b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 144, 3, 451]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "metadata": {
        "id": "dsuw71UqLgr3"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "padded_sequences = pad_sequences(sequences = sequences, maxlen = max_length)"
      ],
      "metadata": {
        "id": "7kexhTA1L8wg"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = padded_sequences\n",
        "x_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fnzh0rrgMePR",
        "outputId": "f37e947a-d045-4f3f-e0af-be8918f5e391"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   2, 144,   3, 451], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "hRmqmWnsNYHP"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Deep Neural Network model\n",
        "# Binary Classification\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(input_length =max_length, input_dim = vocab_size, output_dim = embedding_dim),\n",
        "    tf.keras.layers.GlobalAveragePooling1D(),\n",
        "    tf.keras.layers.Dense(16, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(1, activation = 'sigmoid')\n",
        "])\n",
        "\n"
      ],
      "metadata": {
        "id": "_5IwZZR1MpsX"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = tf.keras.optimizers.Adam(0.08)"
      ],
      "metadata": {
        "id": "qeWrWKphUaK4"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss = 'binary_crossentropy', optimizer = optimizer, metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "QbfFyQXYTPLB"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x = x_train, y = y_train, epochs = 30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z_pSOQI0Ts-R",
        "outputId": "5115d593-3eb8-4d8a-e919-ee2ed6b3ed56"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "314/314 [==============================] - 2s 5ms/step - loss: 0.6928 - accuracy: 0.5264\n",
            "Epoch 2/30\n",
            "314/314 [==============================] - 1s 5ms/step - loss: 0.6941 - accuracy: 0.5307\n",
            "Epoch 3/30\n",
            "314/314 [==============================] - 2s 5ms/step - loss: 0.6916 - accuracy: 0.5295\n",
            "Epoch 4/30\n",
            "314/314 [==============================] - 1s 4ms/step - loss: 0.6927 - accuracy: 0.5229\n",
            "Epoch 5/30\n",
            "314/314 [==============================] - 2s 5ms/step - loss: 0.6934 - accuracy: 0.5213\n",
            "Epoch 6/30\n",
            "314/314 [==============================] - 2s 7ms/step - loss: 0.6927 - accuracy: 0.5313\n",
            "Epoch 7/30\n",
            "314/314 [==============================] - 2s 5ms/step - loss: 0.6922 - accuracy: 0.5253\n",
            "Epoch 8/30\n",
            "314/314 [==============================] - 1s 4ms/step - loss: 0.6934 - accuracy: 0.5229\n",
            "Epoch 9/30\n",
            "314/314 [==============================] - 1s 4ms/step - loss: 0.6924 - accuracy: 0.5309\n",
            "Epoch 10/30\n",
            "314/314 [==============================] - 1s 4ms/step - loss: 0.6923 - accuracy: 0.5279\n",
            "Epoch 11/30\n",
            "314/314 [==============================] - 2s 5ms/step - loss: 0.6926 - accuracy: 0.5219\n",
            "Epoch 12/30\n",
            "314/314 [==============================] - 1s 5ms/step - loss: 0.6924 - accuracy: 0.5225\n",
            "Epoch 13/30\n",
            "314/314 [==============================] - 1s 4ms/step - loss: 0.6929 - accuracy: 0.5241\n",
            "Epoch 14/30\n",
            "314/314 [==============================] - 2s 7ms/step - loss: 0.6925 - accuracy: 0.5251\n",
            "Epoch 15/30\n",
            "314/314 [==============================] - 2s 5ms/step - loss: 0.6917 - accuracy: 0.5331\n",
            "Epoch 16/30\n",
            "314/314 [==============================] - 1s 5ms/step - loss: 0.6921 - accuracy: 0.5287\n",
            "Epoch 17/30\n",
            "314/314 [==============================] - 2s 5ms/step - loss: 0.6932 - accuracy: 0.5207\n",
            "Epoch 18/30\n",
            "314/314 [==============================] - 2s 5ms/step - loss: 0.6925 - accuracy: 0.5243\n",
            "Epoch 19/30\n",
            "314/314 [==============================] - 2s 5ms/step - loss: 0.6917 - accuracy: 0.5283\n",
            "Epoch 20/30\n",
            "314/314 [==============================] - 1s 5ms/step - loss: 0.6927 - accuracy: 0.5311\n",
            "Epoch 21/30\n",
            "314/314 [==============================] - 2s 7ms/step - loss: 0.6929 - accuracy: 0.5289\n",
            "Epoch 22/30\n",
            "314/314 [==============================] - 2s 7ms/step - loss: 0.6931 - accuracy: 0.5289\n",
            "Epoch 23/30\n",
            "314/314 [==============================] - 2s 5ms/step - loss: 0.6928 - accuracy: 0.5231\n",
            "Epoch 24/30\n",
            "314/314 [==============================] - 2s 5ms/step - loss: 0.6921 - accuracy: 0.5301\n",
            "Epoch 25/30\n",
            "314/314 [==============================] - 1s 5ms/step - loss: 0.6920 - accuracy: 0.5293\n",
            "Epoch 26/30\n",
            "314/314 [==============================] - 2s 5ms/step - loss: 0.6922 - accuracy: 0.5315\n",
            "Epoch 27/30\n",
            "314/314 [==============================] - 2s 5ms/step - loss: 0.6927 - accuracy: 0.5291\n",
            "Epoch 28/30\n",
            "314/314 [==============================] - 2s 6ms/step - loss: 0.6927 - accuracy: 0.5231\n",
            "Epoch 29/30\n",
            "314/314 [==============================] - 2s 7ms/step - loss: 0.6928 - accuracy: 0.5261\n",
            "Epoch 30/30\n",
            "314/314 [==============================] - 2s 6ms/step - loss: 0.6919 - accuracy: 0.5301\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x799004b27640>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = ['i feel a little mellow today']\n",
        "seq_test = tokenizer.texts_to_sequences(test_data)"
      ],
      "metadata": {
        "id": "ClTdn7T2UHVY"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_seq = pad_sequences(sequences = seq_test, maxlen = max_length)"
      ],
      "metadata": {
        "id": "ylo7D7k6VcAf"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_seq"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lYi4s2yCVi9v",
        "outputId": "7c5272e2-99a4-4b65-90a7-6de13adb8406"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   2,   3,   7,  66, 455, 115]], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(test_seq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n2uQ1yDUVkP1",
        "outputId": "a30913c8-fe68-441f-f56e-69023329c53e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 92ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.5573381]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-pmsX_kyVuMH"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}