IT350_FineTune_BERT_Final.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "LCC6FjmQpUdc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08d66e0f-670f-40fd-d125-26b834c0282c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.models import model_from_json\n",
        "import numpy as np\n",
        "import os"
      ],
      "metadata": {
        "id": "McgObgz5pa8z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "!pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZQoOoNENsSas",
        "outputId": "ed247e52-d779-4af1-e57b-36d708b06d3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.18.0-py3-none-any.whl (4.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.0 MB 12.3 MB/s \n",
            "\u001b[?25hCollecting sacremoses\n",
            "  Downloading sacremoses-0.0.49-py3-none-any.whl (895 kB)\n",
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            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.5.1-py3-none-any.whl (77 kB)\n",
            "\u001b[K     |████████████████████████████████| 77 kB 3.5 MB/s \n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
            "  Downloading tokenizers-0.11.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.5 MB 38.4 MB/s \n",
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            "Collecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 5.3 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.11.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.7)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.7.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.5.1 pyyaml-6.0 sacremoses-0.0.49 tokenizers-0.11.6 transformers-4.18.0\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.0.0-py3-none-any.whl (325 kB)\n",
            "\u001b[K     |████████████████████████████████| 325 kB 11.9 MB/s \n",
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            "Collecting responses<0.19\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from datasets) (21.3)\n",
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            "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from datasets) (0.3.4)\n",
            "Collecting fsspec[http]>=2021.05.0\n",
            "  Downloading fsspec-2022.3.0-py3-none-any.whl (136 kB)\n",
            "\u001b[K     |████████████████████████████████| 136 kB 23.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (0.5.1)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.7/dist-packages (from datasets) (2.23.0)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.7/dist-packages (from datasets) (0.70.12.2)\n",
            "Collecting xxhash\n",
            "  Downloading xxhash-3.0.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
            "\u001b[K     |████████████████████████████████| 212 kB 45.5 MB/s \n",
            "\u001b[?25hCollecting aiohttp\n",
            "  Downloading aiohttp-3.8.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 40.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.7/dist-packages (from datasets) (4.63.0)\n",
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            "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
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            "Installing collected packages: multidict, frozenlist, yarl, urllib3, asynctest, async-timeout, aiosignal, fsspec, aiohttp, xxhash, responses, datasets\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
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            "      Successfully uninstalled urllib3-1.24.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-2.0.0 frozenlist-1.3.0 fsspec-2022.3.0 multidict-6.0.2 responses-0.18.0 urllib3-1.25.11 xxhash-3.0.0 yarl-1.7.2\n"
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    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/drive/MyDrive/IT350/datasets/mqp/mqp.csv',names=['id','question1','question2','label'],header=None)\n",
        "# df = df.astype({'label':int})\n",
        "df['label']=pd.to_numeric(df['label'])\n",
        "del df[\"id\"]\n",
        "# print(df[\"label\"].dtype)"
      ],
      "metadata": {
        "id": "BPDUZq2_peYD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "D9mViL-5Mu0c",
        "outputId": "f77ae2c3-0b33-4b25-d309-bc08d7ec929b"
      },
      "execution_count": null,
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              "                                              question1  \\\n",
              "0     After how many hour from drinking an antibioti...   \n",
              "1     After how many hour from drinking an antibioti...   \n",
              "2             Am I over weight (192.9) for my age (39)?   \n",
              "3             Am I over weight (192.9) for my age (39)?   \n",
              "4     Aspirin allergy - is it worth getting a bracelet?   \n",
              "...                                                 ...   \n",
              "3043                 15 million  sperm can be a father?   \n",
              "3044  26 yo m with history of progressive venous ins...   \n",
              "3045  26 yo m with history of progressive venous ins...   \n",
              "3046  32 weeks pregnant. Headache strange leg weakne...   \n",
              "3047  32 weeks pregnant. Headache strange leg weakne...   \n",
              "\n",
              "                                              question2  label  \n",
              "0     I have a party tonight and I took my last dose...      1  \n",
              "1     I vomited this morning and I am not sure if it...      0  \n",
              "2     I am a 39 y/o male currently weighing about 19...      1  \n",
              "3     What diet is good for losing weight? Keto or v...      0  \n",
              "4     How much Aspirin can I take for my headache wi...      0  \n",
              "...                                                 ...    ...  \n",
              "3043  My sperm count is 15 millions and is there a c...      1  \n",
              "3044  Hello doctor, can you please tell me some of t...      0  \n",
              "3045  Hello doctor, I am 26 year old male wth progre...      1  \n",
              "3046  I am 32 weeks pregnant and had severe headache...      0  \n",
              "3047  I have headache, leg weakness and 32 weeks pre...      1  \n",
              "\n",
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              "      <td>Am I over weight (192.9) for my age (39)?</td>\n",
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              "      <td>0</td>\n",
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              "      <th>4</th>\n",
              "      <td>Aspirin allergy - is it worth getting a bracelet?</td>\n",
              "      <td>How much Aspirin can I take for my headache wi...</td>\n",
              "      <td>0</td>\n",
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              "      <td>26 yo m with history of progressive venous ins...</td>\n",
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              "      <td>1</td>\n",
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              "      <td>I am 32 weeks pregnant and had severe headache...</td>\n",
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              "      <td>32 weeks pregnant. Headache strange leg weakne...</td>\n",
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          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len_of_answers=np.array([])\n",
        "len_of_questions=np.array([])\n",
        "df_p = df\n",
        "for index,row in df.iterrows():\n",
        "  al = len(row['question2'].split())\n",
        "  ql = len(row['question1'].split())\n",
        "  if al > 70 or ql > 70:\n",
        "    df_p.drop(index, inplace=True)\n",
        "  len_of_answers=np.append(len_of_answers,al)\n",
        "  len_of_questions=np.append(len_of_questions,ql)\n",
        "  \n",
        "print('questions\\nmedian:-',np.median(len_of_questions),'\\nmean:-',np.mean(len_of_questions),'\\nmin:-',np.min(len_of_questions),'\\nmax:-',np.max(len_of_questions))\n",
        "print('answers\\nmedian:-',np.median(len_of_answers),'\\nmean:-',np.mean(len_of_answers),'\\nmin:-',np.min(len_of_answers),'\\nmax:-',np.max(len_of_answers))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tbnewrySM4Ee",
        "outputId": "f1e31b8e-db8f-4202-9569-7df2ff106c05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "questions\n",
            "median:- 22.0 \n",
            "mean:- 38.66691071791743 \n",
            "min:- 3.0 \n",
            "max:- 139.0\n",
            "answers\n",
            "median:- 70.0 \n",
            "mean:- 100.98202155786048 \n",
            "min:- 1.0 \n",
            "max:- 1148.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "VnG6cgxxNcrJ",
        "outputId": "1c0502bc-d71e-4587-e7f4-e1fac1139193"
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      "execution_count": null,
      "outputs": [
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          "data": {
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              "                                               question1  \\\n",
              "1      i have a urine test that tests for nicotine so...   \n",
              "2        what are the symptoms of lupus sun sensitivity?   \n",
              "3      what's the major difference between cfc and hf...   \n",
              "5      my 9 yr old daughter has been extremely fatigu...   \n",
              "8      after taking antibiotic for pneumonia my son i...   \n",
              "...                                                  ...   \n",
              "24572  our daughter is planning on flying coast to co...   \n",
              "24573  im 44yrs old and if its safe to have another p...   \n",
              "24575  i do not have insurance right now so is there ...   \n",
              "24576  i am on medicare supplement insurance (aarp me...   \n",
              "24578  can acetaminophen or ibuprofen be taken for a ...   \n",
              "\n",
              "                                               question2  label  \n",
              "1      my son has ha several concussion at least 5 fr...      0  \n",
              "2      maybe. but some medications for bacterial vagi...      0  \n",
              "3      cost. cfc inhalers run about $5 to $25 apiece ...      1  \n",
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              "8      go to a different office. see another optometr...      0  \n",
              "...                                                  ...    ...  \n",
              "24572  if he is still having sperm he did not have a ...      0  \n",
              "24573  hi sorry but i think your body has been throug...      1  \n",
              "24575  no. shingles is a viral infection (herpes zost...      0  \n",
              "24576  i would suggest tylenol and mucinex drink lots...      0  \n",
              "24578  it really would depend on the seizure medicati...      1  \n",
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              "    <tr>\n",
              "      <th>24572</th>\n",
              "      <td>our daughter is planning on flying coast to co...</td>\n",
              "      <td>if he is still having sperm he did not have a ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24573</th>\n",
              "      <td>im 44yrs old and if its safe to have another p...</td>\n",
              "      <td>hi sorry but i think your body has been throug...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24575</th>\n",
              "      <td>i do not have insurance right now so is there ...</td>\n",
              "      <td>no. shingles is a viral infection (herpes zost...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24576</th>\n",
              "      <td>i am on medicare supplement insurance (aarp me...</td>\n",
              "      <td>i would suggest tylenol and mucinex drink lots...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24578</th>\n",
              "      <td>can acetaminophen or ibuprofen be taken for a ...</td>\n",
              "      <td>it really would depend on the seizure medicati...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9709 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-759f4e1c-5122-49d1-86ba-6e8fc7ebf15e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
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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",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-convert: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-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert: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",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-759f4e1c-5122-49d1-86ba-6e8fc7ebf15e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-759f4e1c-5122-49d1-86ba-6e8fc7ebf15e');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train, test = train_test_split(df, test_size=0.20)\n",
        "train.to_csv('/content/drive/MyDrive/IT350/datasets/mqp/train.csv',index=False)\n",
        "test.to_csv('/content/drive/MyDrive/IT350/datasets/mqp/test.csv',index=False)"
      ],
      "metadata": {
        "id": "0cskeorJrnR1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset"
      ],
      "metadata": {
        "id": "wMTzrKEvueC-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_dataset = load_dataset('csv', data_files={'train': '/content/drive/MyDrive/IT350/datasets/mqp/train.csv',\n",
        "                                              'test': '/content/drive/MyDrive/IT350/datasets/mqp/test.csv'})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185,
          "referenced_widgets": [
            "887325ff1ace4731b370698e7f34238c",
            "5d4cb7f943ae405db17817f9b5e9f4b2",
            "5b379531b0fa46448986979403f32fec",
            "3487bfaad07f43b783edc0270874d78d",
            "3aedcfe6ace546cfa887dac14ff4247c",
            "3f69943925d44eae9080e7514eb4acc2",
            "88b3440e98f64978bdefeca3a15a68cf",
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            "8b9a3bdff751458d890e7337c484447c",
            "3d1c8c754b1c4beeb6f7f11e7ad18444",
            "b814acb1464849c68a3fc0029d5738cb",
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            "ee3ab03fe3994f3f9fde37618a6db78c",
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            "cccadf0a98204730b647835dedb102bf",
            "210b4d27b6654da18856cf76dcba3a16",
            "6ae43a8283924e7dbe1070c47cc75509"
          ]
        },
        "id": "jZpB2h6sM3Nx",
        "outputId": "fc421913-76c1-4c18-ac71-cba83b45f234"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using custom data configuration default-282da162f2788c95\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-282da162f2788c95/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "887325ff1ace4731b370698e7f34238c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8b9a3bdff751458d890e7337c484447c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-282da162f2788c95/0.0.0/433e0ccc46f9880962cc2b12065189766fbb2bee57a221866138fb9203c83519. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a7d1422baf914ca4a4dee453d3574a22"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "raw_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bzelYopNHR5",
        "outputId": "18c80a82-54d9-4572-af18-9f8bb95447ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['question1', 'question2', 'label'],\n",
              "        num_rows: 2438\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['question1', 'question2', 'label'],\n",
              "        num_rows: 610\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModel\n",
        "import torch\n",
        "tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')"
      ],
      "metadata": {
        "id": "mnud-tH6r9f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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        },
        "outputId": "0ff22489-c863-459f-828d-e75de01e2e79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
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        {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"question1\"],examples[\"question2\"],max_length=128, padding=\"max_length\",truncation=True)\n",
        "tokenized_datasets = raw_dataset.map(tokenize_function, batched=True)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        {
          "output_type": "display_data",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(tokenized_datasets['train'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H6PD3Y91uO-C",
        "outputId": "c7114f1a-4926-4951-9638-1b5676f4099a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datasets.arrow_dataset.Dataset"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_datasets['test'][200]['input_ids'][111]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wgZfMTkeL2NJ",
        "outputId": "776df3a0-a8b2-481d-c84b-376876c218bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_features(tokenized_datasets,train_test):\n",
        "  Y=[]\n",
        "  X={'input_ids':[],'token_type_ids':[],'attention_mask':[]}\n",
        "  for i in range(len(tokenized_datasets[train_test])):\n",
        "    input_ids=tokenized_datasets[train_test][i]['input_ids']\n",
        "    token_type_ids=tokenized_datasets[train_test][i]['token_type_ids']\n",
        "    attention_mask=tokenized_datasets[train_test][i]['attention_mask']\n",
        "    X['input_ids'].append(input_ids)\n",
        "    X['token_type_ids'].append(token_type_ids)\n",
        "    X['attention_mask'].append(attention_mask)\n",
        "    Y.append(tokenized_datasets[train_test][i]['label'])\n",
        "  X['input_ids']=np.array(X['input_ids'],dtype=np.int32)\n",
        "  X['token_type_ids']=np.array(X['token_type_ids'],dtype=np.int32)\n",
        "  X['attention_mask']=np.array(X['attention_mask'],dtype=np.int32)\n",
        "  Y=np.array(Y,dtype=np.float32)\n",
        "  return X,Y"
      ],
      "metadata": {
        "id": "FaoNRv8Rrvyj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data,test_y=extract_features(tokenized_datasets,'test')\n",
        "train_data,train_y=extract_features(tokenized_datasets,'train')"
      ],
      "metadata": {
        "id": "XegE8RgbxoMt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "from transformers import TFBertModel\n",
        "max_length=128\n",
        "input_ids = tf.keras.layers.Input(\n",
        "    shape=(max_length,), dtype=tf.int32, name=\"input_ids\"\n",
        ")\n",
        "# Attention masks indicates to the model which tokens should be attended to.\n",
        "attention_masks = tf.keras.layers.Input(\n",
        "    shape=(max_length,), dtype=tf.int32, name=\"attention_masks\"\n",
        ")\n",
        "# Token type ids are binary masks identifying different sequences in the model.\n",
        "token_type_ids = tf.keras.layers.Input(\n",
        "    shape=(max_length,), dtype=tf.int32, name=\"token_type_ids\"\n",
        ")\n",
        "# Loading pretrained BERT model.\n",
        "bert_model = transformers.TFBertModel.from_pretrained(\"bert-base-uncased\")\n",
        "# Freeze the BERT model to reuse the pretrained features without modifying them.\n",
        "bert_model.trainable = True\n",
        "\n",
        "bert_output = bert_model(\n",
        "    input_ids, attention_mask=attention_masks, token_type_ids=token_type_ids\n",
        ")\n",
        "sequence_output = bert_output.last_hidden_state\n",
        "pooled_output = bert_output.pooler_output\n",
        "# Add trainable layers on top of frozen layers to adapt the pretrained features on the new data.\n",
        "bi_lstm = tf.keras.layers.Bidirectional(\n",
        "    tf.keras.layers.LSTM(64, return_sequences=True)\n",
        ")(sequence_output)\n",
        "# Applying hybrid pooling approach to bi_lstm sequence output.\n",
        "avg_pool = tf.keras.layers.GlobalAveragePooling1D()(bi_lstm)\n",
        "max_pool = tf.keras.layers.GlobalMaxPooling1D()(bi_lstm)\n",
        "concat = tf.keras.layers.concatenate([avg_pool, max_pool])\n",
        "dropout = tf.keras.layers.Dropout(0.3)(concat)\n",
        "output = tf.keras.layers.Dense(1, activation=\"sigmoid\")(dropout)\n",
        "model = tf.keras.models.Model(\n",
        "    inputs=[input_ids, attention_masks, token_type_ids], outputs=output\n",
        ")\n",
        "\n",
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.00002),\n",
        "    loss=\"binary_crossentropy\",\n",
        "    metrics=[\"acc\"],\n",
        ")\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5MNPfXyx6Px",
        "outputId": "a5ad2b35-9523-4520-b494-251ba63c12d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at bert-base-uncased were not used when initializing TFBertModel: ['mlm___cls', 'nsp___cls']\n",
            "- This IS expected if you are initializing TFBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "All the layers of TFBertModel were initialized from the model checkpoint at bert-base-uncased.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_3\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_ids (InputLayer)         [(None, 128)]        0           []                               \n",
            "                                                                                                  \n",
            " attention_masks (InputLayer)   [(None, 128)]        0           []                               \n",
            "                                                                                                  \n",
            " token_type_ids (InputLayer)    [(None, 128)]        0           []                               \n",
            "                                                                                                  \n",
            " tf_bert_model_3 (TFBertModel)  TFBaseModelOutputWi  109482240   ['input_ids[0][0]',              \n",
            "                                thPoolingAndCrossAt               'attention_masks[0][0]',        \n",
            "                                tentions(last_hidde               'token_type_ids[0][0]']         \n",
            "                                n_state=(None, 128,                                               \n",
            "                                 768),                                                            \n",
            "                                 pooler_output=(Non                                               \n",
            "                                e, 768),                                                          \n",
            "                                 past_key_values=No                                               \n",
            "                                ne, hidden_states=N                                               \n",
            "                                one, attentions=Non                                               \n",
            "                                e, cross_attentions                                               \n",
            "                                =None)                                                            \n",
            "                                                                                                  \n",
            " bidirectional_3 (Bidirectional  (None, 128, 128)    426496      ['tf_bert_model_3[0][0]']        \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " global_average_pooling1d_3 (Gl  (None, 128)         0           ['bidirectional_3[0][0]']        \n",
            " obalAveragePooling1D)                                                                            \n",
            "                                                                                                  \n",
            " global_max_pooling1d_3 (Global  (None, 128)         0           ['bidirectional_3[0][0]']        \n",
            " MaxPooling1D)                                                                                    \n",
            "                                                                                                  \n",
            " concatenate_3 (Concatenate)    (None, 256)          0           ['global_average_pooling1d_3[0][0\n",
            "                                                                 ]',                              \n",
            "                                                                  'global_max_pooling1d_3[0][0]'] \n",
            "                                                                                                  \n",
            " dropout_151 (Dropout)          (None, 256)          0           ['concatenate_3[0][0]']          \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 1)            257         ['dropout_151[0][0]']            \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 109,908,993\n",
            "Trainable params: 109,908,993\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data=[train_data['input_ids'],train_data['token_type_ids'],train_data['attention_mask']]\n",
        "valid_data=[test_data['input_ids'],test_data['token_type_ids'],test_data['attention_mask']]\n",
        "history=model.fit(input_data,train_y,validation_data=(valid_data,test_y),epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "ZKfP76Kq3Ry-",
        "outputId": "c1d80078-c7c9-434f-92dd-71f44f597f66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model_3/bert/pooler/dense/kernel:0', 'tf_bert_model_3/bert/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model_3/bert/pooler/dense/kernel:0', 'tf_bert_model_3/bert/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n",
            "243/243 [==============================] - 459s 2s/step - loss: 0.6840 - acc: 0.5574 - val_loss: 0.5246 - val_acc: 0.8301\n",
            "Epoch 2/10\n",
            "243/243 [==============================] - 433s 2s/step - loss: 0.3643 - acc: 0.8675 - val_loss: 0.2676 - val_acc: 0.8991\n",
            "Epoch 3/10\n",
            "243/243 [==============================] - 432s 2s/step - loss: 0.2393 - acc: 0.9134 - val_loss: 0.2273 - val_acc: 0.9119\n",
            "Epoch 4/10\n",
            "243/243 [==============================] - 432s 2s/step - loss: 0.1574 - acc: 0.9476 - val_loss: 0.2437 - val_acc: 0.9022\n",
            "Epoch 5/10\n",
            " 47/243 [====>.........................] - ETA: 5:21 - loss: 0.1189 - acc: 0.9668"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-57-392f97944086>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0minput_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'input_ids'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'token_type_ids'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'attention_mask'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mvalid_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'input_ids'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'token_type_ids'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'attention_mask'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mhistory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalid_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1387\u001b[0m               \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp_logs\u001b[0m  \u001b[0;31m# No error, now safe to assign to logs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1388\u001b[0m               \u001b[0mend_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_increment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1389\u001b[0;31m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1390\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1391\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    436\u001b[0m     \"\"\"\n\u001b[1;32m    437\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_call_train_batch_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 438\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m    295\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_begin_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_end_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    298\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m       raise ValueError(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_end_hook\u001b[0;34m(self, mode, batch, logs)\u001b[0m\n\u001b[1;32m    316\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 318\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhook_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_batches_for_timing_check\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook_helper\u001b[0;34m(self, hook_name, batch, logs)\u001b[0m\n\u001b[1;32m    354\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    355\u001b[0m       \u001b[0mhook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 356\u001b[0;31m       \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    357\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    358\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_timing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1032\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1033\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1034\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_update_progbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1035\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1036\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1104\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1105\u001b[0m       \u001b[0;31m# Only block async when verbose = 1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1106\u001b[0;31m       \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_to_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1107\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36msync_to_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m    561\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 563\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m    912\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 914\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    915\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    912\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 914\u001b[0;31m       \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    915\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36m_to_single_numpy_or_python_type\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    555\u001b[0m     \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 557\u001b[0;31m       \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    558\u001b[0m     \u001b[0;31m# Strings, ragged and sparse tensors don't have .item(). Return them as-is.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1221\u001b[0m     \"\"\"\n\u001b[1;32m   1222\u001b[0m     \u001b[0;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1223\u001b[0;31m     \u001b[0mmaybe_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1224\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1187\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1188\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1189\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1190\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1191\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data=[train_data['input_ids'],train_data['token_type_ids'],train_data['attention_mask']]\n",
        "valid_data=[test_data['input_ids'],test_data['token_type_ids'],test_data['attention_mask']]\n",
        "history=model.fit(input_data,train_y,validation_data=(valid_data,test_y),epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iz_6T6uCnHp_",
        "outputId": "badacffa-f40e-4b8a-de61-8b7816eaa116"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model_3/bert/pooler/dense/kernel:0', 'tf_bert_model_3/bert/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_bert_model_3/bert/pooler/dense/kernel:0', 'tf_bert_model_3/bert/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n",
            "77/77 [==============================] - 196s 2s/step - loss: 0.9342 - acc: 0.6062 - val_loss: 0.5906 - val_acc: 0.7621\n",
            "Epoch 2/10\n",
            "77/77 [==============================] - 159s 2s/step - loss: 0.6695 - acc: 0.6546 - val_loss: 0.5325 - val_acc: 0.7673\n",
            "Epoch 3/10\n",
            "77/77 [==============================] - 159s 2s/step - loss: 0.6314 - acc: 0.6665 - val_loss: 0.5099 - val_acc: 0.7693\n",
            "Epoch 4/10\n",
            "77/77 [==============================] - 159s 2s/step - loss: 0.6186 - acc: 0.6788 - val_loss: 0.4716 - val_acc: 0.7863\n",
            "Epoch 5/10\n",
            "77/77 [==============================] - 159s 2s/step - loss: 0.6005 - acc: 0.6821 - val_loss: 0.4958 - val_acc: 0.7631\n",
            "Epoch 6/10\n",
            "77/77 [==============================] - 159s 2s/step - loss: 0.5716 - acc: 0.7092 - val_loss: 0.5082 - val_acc: 0.7518\n",
            "Epoch 7/10\n",
            "77/77 [==============================] - 159s 2s/step - loss: 0.5558 - acc: 0.7174 - val_loss: 0.4959 - val_acc: 0.7528\n",
            "Epoch 8/10\n",
            "77/77 [==============================] - 159s 2s/step - loss: 0.5378 - acc: 0.7260 - val_loss: 0.4325 - val_acc: 0.7894\n",
            "Epoch 9/10\n",
            "77/77 [==============================] - 159s 2s/step - loss: 0.5229 - acc: 0.7375 - val_loss: 0.4891 - val_acc: 0.7492\n",
            "Epoch 10/10\n",
            "77/77 [==============================] - 159s 2s/step - loss: 0.5080 - acc: 0.7457 - val_loss: 0.4822 - val_acc: 0.7575\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_accuracy = [.7621, .7673, .7693, .7863, .7631, .7518, .7528, .7894]\n",
        "accuracy = [.6062, .6546, .6665, .6788, .6821, .7092, .7174, .7260]\n",
        "epochs = [1,2,3,4,5,6,7,8]\n",
        "plt.plot(epochs, val_accuracy)\n",
        "plt.plot(epochs, accuracy)\n",
        "plt.title('Model Accuracy vs No. of Epochs')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='center left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "3jV-gRO8zSdN",
        "outputId": "f2eac449-96f4-4878-c132-50f736446838"
      },
      "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": [
        "bert_model.trainable=True\n",
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.000002),\n",
        "    loss=\"binary_crossentropy\",\n",
        "    metrics=[\"acc\"],\n",
        ")"
      ],
      "metadata": {
        "id": "6qpmFn2XYi2i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('/content/drive/MyDrive/IT350/models/BERT_QA_trainable_true/model.h5')"
      ],
      "metadata": {
        "id": "cTP0wkx3ox0U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_data=[test_data['input_ids'],test_data['token_type_ids'],test_data['attention_mask']]\n",
        "model.evaluate(val_data,test_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AopPCI36JkUG",
        "outputId": "5d8a2f63-1f11-444b-8ab1-8995442ba377"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "154/154 [==============================] - 86s 528ms/step - loss: 0.2082 - acc: 0.9174\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.20824430882930756, 0.9174293279647827]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "model=load_model('/content/drive/MyDrive/IT350/models/BERT_QA/model.h5',custom_objects={'TFBertModel':TFBertModel})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GTxkz4Xc3HsY",
        "outputId": "377e08f7-4476-4880-a1fe-e8beb2c7f062"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(history.params)\n",
        "print(history.history.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "icP17yYnrCEM",
        "outputId": "d51e7a65-db1b-415c-a903-020cd0dfc8c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'verbose': 1, 'epochs': 10, 'steps': 77}\n",
            "dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss vs No. of Epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('no. of epochs')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "LgDz8snxgVkm",
        "outputId": "6ca22d58-63c4-4c27-d812-ed678c56dba8"
      },
      "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": [
        ""
      ],
      "metadata": {
        "id": "sqClSEiofWh0"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}