toxic_classification_RoBert.ipynb

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        "<a href=\"https://colab.research.google.com/github/levi3001/cs470_term_project_toxic_classification/blob/main/toxic_classification_RoBert.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "YvSrOZ_hv_SS"
      },
      "outputs": [],
      "source": [
        "try:\n",
        "    %tensorflow_version 2.x\n",
        "except Exception:\n",
        "    pass\n",
        "\n",
        "import os\n",
        "import random\n",
        "import re\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import re\n",
        "from tqdm.auto import tqdm\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "kW7k_ppx2MN9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e869dc8-9dce-449e-9eb5-4c955af8aa1a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8SmmsG-0ELQA",
        "outputId": "0258e7a1-5d88-46da-d99f-526d198ae921"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  drive/MyDrive/jigsaw-toxic-comment-classification-challenge.zip\n",
            "  inflating: data/sample_submission.csv.zip  \n",
            "  inflating: data/test.csv.zip       \n",
            "  inflating: data/test_labels.csv.zip  \n",
            "  inflating: data/train.csv.zip      \n"
          ]
        }
      ],
      "source": [
        "!unzip  drive/MyDrive/jigsaw-toxic-comment-classification-challenge.zip -d data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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        },
        "id": "S_adDNelIIjt",
        "outputId": "7b621adc-0bea-4dc9-ae68-827abc7c6e24"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "                      id  ... identity_hate\n",
              "0       0000997932d777bf  ...             0\n",
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      "source": [
        "train_data=pd.read_csv('data/train.csv.zip')\n",
        "train_data"
      ]
    },
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              "      <td>I don't anonymously edit articles at all.</td>\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                      id                                       comment_text\n",
              "0       00001cee341fdb12  Yo bitch Ja Rule is more succesful then you'll...\n",
              "1       0000247867823ef7  == From RfC == \\n\\n The title is fine as it is...\n",
              "2       00013b17ad220c46  \" \\n\\n == Sources == \\n\\n * Zawe Ashton on Lap...\n",
              "3       00017563c3f7919a  :If you have a look back at the source, the in...\n",
              "4       00017695ad8997eb          I don't anonymously edit articles at all.\n",
              "...                  ...                                                ...\n",
              "153159  fffcd0960ee309b5  . \\n i totally agree, this stuff is nothing bu...\n",
              "153160  fffd7a9a6eb32c16  == Throw from out field to home plate. == \\n\\n...\n",
              "153161  fffda9e8d6fafa9e  \" \\n\\n == Okinotorishima categories == \\n\\n I ...\n",
              "153162  fffe8f1340a79fc2  \" \\n\\n == \"\"One of the founding nations of the...\n",
              "153163  ffffce3fb183ee80  \" \\n :::Stop already. Your bullshit is not wel...\n",
              "\n",
              "[153164 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "test_data=pd.read_csv('data/test.csv.zip')\n",
        "test_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "v1YrXfT7Jj51",
        "outputId": "e5a81a26-e83b-4ec1-cd20-4761718ddd02"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-35461c39-c47d-4ed9-a16b-8ad6a434e739')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      </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",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
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              "          document.querySelector('#df-35461c39-c47d-4ed9-a16b-8ad6a434e739 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-35461c39-c47d-4ed9-a16b-8ad6a434e739');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "  "
            ],
            "text/plain": [
              "                      id  toxic  severe_toxic  ...  threat  insult  identity_hate\n",
              "0       00001cee341fdb12     -1            -1  ...      -1      -1             -1\n",
              "1       0000247867823ef7     -1            -1  ...      -1      -1             -1\n",
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              "4       00017695ad8997eb     -1            -1  ...      -1      -1             -1\n",
              "...                  ...    ...           ...  ...     ...     ...            ...\n",
              "153159  fffcd0960ee309b5     -1            -1  ...      -1      -1             -1\n",
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              "153163  ffffce3fb183ee80     -1            -1  ...      -1      -1             -1\n",
              "\n",
              "[153164 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "test_label=pd.read_csv('data/test_labels.csv.zip')\n",
        "test_label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "zko0SVimxJTu"
      },
      "outputs": [],
      "source": [
        "import string\n",
        "def remove_punc(text):\n",
        "  return text.translate(str.maketrans('', '', string.punctuation))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "TyW58BcSz0C3"
      },
      "outputs": [],
      "source": [
        "#https://www.kaggle.com/gtskyler/toxic-comments-bert\n",
        "def clean_text(text):\n",
        "    text = text.lower()\n",
        "    text = re.sub(r\"what's\", \"what is \", text)\n",
        "    text = re.sub(r\"\\'s\", \" \", text)\n",
        "    text = re.sub(r\"\\'ve\", \" have \", text)\n",
        "    text = re.sub(r\"can't\", \"cannot \", text)\n",
        "    text = re.sub(r\"n't\", \" not \", text)\n",
        "    text = re.sub(r\"i'm\", \"i am \", text)\n",
        "    text = re.sub(r\"\\'re\", \" are \", text)\n",
        "    text = re.sub(r\"\\'d\", \" would \", text)\n",
        "    text = re.sub(r\"\\'ll\", \" will \", text)\n",
        "    text = re.sub(r\"\\'scuse\", \" excuse \", text)\n",
        "    text = re.sub('\\W', ' ', text)\n",
        "    text = re.sub('\\s+', ' ', text)\n",
        "    text = text.strip(' ')\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "YdAh3l9vMl84"
      },
      "outputs": [],
      "source": [
        "#train_data['comment_text']=train_data['comment_text'].map(lambda text: clean_text(text))\n",
        "#test_data['comment_text']=test_data['comment_text'].map(lambda text: clean_text(text))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "PEmUrpIxJn03"
      },
      "outputs": [],
      "source": [
        "train_data['comment_texxxt'] = train_data['comment_text'].map(lambda text: clean_text(text)).map(lambda text: remove_punc(text))\n",
        "test_data['comment_texxxt'] = test_data['comment_text'].map(lambda text: clean_text(text)).map(lambda text: remove_punc(text))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "TWz5R58kMev0"
      },
      "outputs": [],
      "source": [
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "JTT6g-dYKHjC"
      },
      "outputs": [],
      "source": [
        "train_values_x = train_data['comment_texxxt'].values\n",
        "train_values_y = train_data[[\"toxic\",\"severe_toxic\",\"obscene\",\"threat\",\"insult\",\"identity_hate\"]].values\n",
        "test_x= test_data['comment_texxxt'].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "1iTVDUBJhdIW"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Bf6ka8N59WDW"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Ivn6Er4y9Z8q"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "CNXxgHGX9lHl"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "XU7z7hjk9omY"
      },
      "outputs": [],
      "source": [
        "train_x, val_x, train_y, val_y = train_test_split(train_values_x, train_values_y, test_size=0.2, random_state=30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oGlEnXDJ9nIn",
        "outputId": "73c6f5ed-fc0f-440b-b37c-a608514bc827"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['you had the image name right but you left a blank width parameter that apparently prevented the image from showing',\n",
              "       'aksungur hi nico you have created the article aksungur please see my note in the discussion page talk have a good day',\n",
              "       'samaleks if you can prove that i am from that douchebag forum called skyscrapercity or that i am a meat puppet please go ahead please be aware that i will continue to revert false propaganda and correct information on pages if you revert my corrected information that turns out to be war so you are the one starting the war',\n",
              "       ...,\n",
              "       'national playing fields association just thought you might like to see how with the help of the npfa the page has grown since your initial creation i am sure there is still far more to do',\n",
              "       're booth swarbrick and whalen sorry i posted that request was so long ago that now i can scarcely remember what i was after once upon a time i think the larkin page made reference to these books in passing and i was hoping to get more details at any rate i imagine these books should be used by our page over to you or someone like you instinct',\n",
              "       'dont listen to anonmoos'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "train_x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hMLRTu3-ED3m",
        "outputId": "a07391b1-13b7-4623-e5b9-9d3986311c17"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['the syrian army is now advancing on aleppo with russian air cover this article is inherently unstable the article is also edited appropriately as the war progresses which just means it is unstable',\n",
              "       'oppose there is zero benefit to readers particularly mobile users from ambiguous titles justin brown defensive lineman born 1982 gridiron football defensive end justin brown wide receiver born 1991 american football wide receiver and justin brown aquanaut american professional aquanaut are clearly titled per wp criteria at the moment leave well alone',\n",
              "       'file skin of our teeth handbill jpg you have added a comment asking for the addition of a copyright status however i do not see any warnings attached to that image so i do not understand what is wrong with what i have already attached it is a low res version of a promotional item which comes under fair use it is also a photograph of something i own so it is also covered under fair use from that angle tell me that you think i need to add that is not already covered',\n",
              "       ...,\n",
              "       'really it not so much selflessness as just a general opinion that people put too much stock in who a person is and what they have done in their life i would much prefer to judge others and have others judge me by my contributions both good and bad when i had a short biography on my userpage i think people treated me differently because of my age and my background and i never really liked that',\n",
              "       'june 2007 please stop if you continue to ignore our policies by introducing inappropriate pages such as sauce face to wikipedia you will be blocked',\n",
              "       'the biography of robert dallenbach lacks any credible sources all the articles related to the pillar of fire church are irrelevant to this encyclopedia and should be deleted one biography of orland wolfram states that he founded the mision biblica where are the documented sources someone in the pillar of fire has learned how to bypass wiki standards carol penrod the last phrase contains misspelled english they probably meant the founder of the church wiki editors where are you carol penrod'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "val_x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "x5pS3KURLAWm"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "nCJqoqGADe1U"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zd6Y_KhEYlZ",
        "outputId": "f65ae87a-5896-4d30-cecd-2c2538ef84f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.14.1-py3-none-any.whl (3.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.4 MB 5.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.2)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
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            "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",
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          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "0lWfEbHQ0bD8"
      },
      "outputs": [],
      "source": [
        "from transformers import TFBertModel, RobertaConfig, RobertaTokenizerFast, TFAutoModel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
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        "outputId": "f037742c-ac51-4aef-faf5-9ca8822ae82d"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "74fa9220fa2546cdbb7eab19b856c5f3",
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cd1c5131ef354ec3992158b18cd3ff1e",
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at distilroberta-base were not used when initializing TFRobertaModel: ['lm_head']\n",
            "- This IS expected if you are initializing TFRobertaModel 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 TFRobertaModel 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 TFRobertaModel were initialized from the model checkpoint at distilroberta-base.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFRobertaModel for predictions without further training.\n"
          ]
        }
      ],
      "source": [
        "# choose RoBERT model\n",
        "\n",
        "\n",
        "model_name= 'distilroberta-base'\n",
        "#model_name='roberat-base'\n",
        "\n",
        "#max length of sentences\n",
        "max_length = 128\n",
        "\n",
        "#load transformers config \n",
        "config = RobertaConfig.from_pretrained(model_name)\n",
        "\n",
        "\n",
        "\n",
        "#load RoBERTa tokenizer\n",
        "tokenizer = RobertaTokenizerFast.from_pretrained(pretrained_model_name_or_path = model_name, config = config)\n",
        "\n",
        "#load RoBERTa model\n",
        "robert = TFAutoModel.from_pretrained(model_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "9r83ZW06Bpsl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a76ac4c6-40d2-41df-c1f7-9b401486fea3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PreTrainedTokenizerFast(name_or_path='distilroberta-base', vocab_size=50265, model_max_len=512, is_fast=True, padding_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'sep_token': '</s>', 'pad_token': '<pad>', 'cls_token': '<s>', 'mask_token': AddedToken(\"<mask>\", rstrip=False, lstrip=True, single_word=False, normalized=False)})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "HwAFbEvHHbYc"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "2nHWPrIyGScS"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "Nu_BejTtGYFZ"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "iUyJ4tDm8SaE"
      },
      "outputs": [],
      "source": [
        "train_x = tokenizer(text=list(train_x), add_special_tokens=True, max_length=max_length,\n",
        "    truncation=True, padding=True, return_tensors='tf', return_token_type_ids = True, return_attention_mask = True,\n",
        "    verbose = True)\n",
        "val_x= tokenizer(text=list(val_x), add_special_tokens=True, max_length=max_length,\n",
        "    truncation=True, padding=True, return_tensors='tf', return_token_type_ids = True, return_attention_mask = True,\n",
        "    verbose = True)\n",
        "test_x = tokenizer(text=list(test_x), add_special_tokens=True, max_length=max_length,\n",
        "    truncation=True, padding=True, return_tensors='tf', return_token_type_ids = True, return_attention_mask = True,\n",
        "    verbose = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "BbRlH6IzhrXR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1bbfc231-ee6d-4d07-e4f6-76c61cb88b9c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': <tf.Tensor: shape=(127656, 128), dtype=int32, numpy=\n",
            "array([[    0,  6968,    56, ...,     1,     1,     1],\n",
            "       [    0,  6629,  1545, ...,     1,     1,     1],\n",
            "       [    0, 33243,  1627, ...,     1,     1,     1],\n",
            "       ...,\n",
            "       [    0, 11535,   816, ...,     1,     1,     1],\n",
            "       [    0,   241, 13939, ...,     1,     1,     1],\n",
            "       [    0,   417,  2533, ...,     1,     1,     1]], dtype=int32)>, 'token_type_ids': <tf.Tensor: shape=(127656, 128), dtype=int32, numpy=\n",
            "array([[0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       ...,\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(127656, 128), dtype=int32, numpy=\n",
            "array([[1, 1, 1, ..., 0, 0, 0],\n",
            "       [1, 1, 1, ..., 0, 0, 0],\n",
            "       [1, 1, 1, ..., 0, 0, 0],\n",
            "       ...,\n",
            "       [1, 1, 1, ..., 0, 0, 0],\n",
            "       [1, 1, 1, ..., 0, 0, 0],\n",
            "       [1, 1, 1, ..., 0, 0, 0]], dtype=int32)>}\n",
            "{'input_ids': <tf.Tensor: shape=(31915, 128), dtype=int32, numpy=\n",
            "array([[    0,   627, 13550, ...,     1,     1,     1],\n",
            "       [    0, 10223,  3876, ...,     1,     1,     1],\n",
            "       [    0, 21710,  3024, ...,     1,     1,     1],\n",
            "       ...,\n",
            "       [    0, 20982,    24, ...,     1,     1,     1],\n",
            "       [    0,   267,  4438, ...,     1,     1,     1],\n",
            "       [    0,   627, 24613, ...,     1,     1,     1]], dtype=int32)>, 'token_type_ids': <tf.Tensor: shape=(31915, 128), dtype=int32, numpy=\n",
            "array([[0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       ...,\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(31915, 128), dtype=int32, numpy=\n",
            "array([[1, 1, 1, ..., 0, 0, 0],\n",
            "       [1, 1, 1, ..., 0, 0, 0],\n",
            "       [1, 1, 1, ..., 0, 0, 0],\n",
            "       ...,\n",
            "       [1, 1, 1, ..., 0, 0, 0],\n",
            "       [1, 1, 1, ..., 0, 0, 0],\n",
            "       [1, 1, 1, ..., 0, 0, 0]], dtype=int32)>}\n",
            "{'input_ids': <tf.Tensor: shape=(153164, 128), dtype=int32, numpy=\n",
            "array([[    0,  9839, 32594, ...,     1,     1,     1],\n",
            "       [    0,  7761,   910, ...,     1,     1,     1],\n",
            "       [    0,    29, 39412, ...,     1,     1,     1],\n",
            "       ...,\n",
            "       [    0, 36557, 33523, ...,     1,     1,     1],\n",
            "       [    0,  1264,     9, ...,     1,     1,     1],\n",
            "       [    0,  8287,   416, ...,     1,     1,     1]], dtype=int32)>, 'token_type_ids': <tf.Tensor: shape=(153164, 128), dtype=int32, numpy=\n",
            "array([[0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       ...,\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0],\n",
            "       [0, 0, 0, ..., 0, 0, 0]], dtype=int32)>, 'attention_mask': <tf.Tensor: shape=(153164, 128), dtype=int32, numpy=\n",
            "array([[1, 1, 1, ..., 0, 0, 0],\n",
            "       [1, 1, 1, ..., 0, 0, 0],\n",
            "       [1, 1, 1, ..., 0, 0, 0],\n",
            "       ...,\n",
            "       [1, 1, 1, ..., 0, 0, 0],\n",
            "       [1, 1, 1, ..., 0, 0, 0],\n",
            "       [1, 1, 1, ..., 0, 0, 0]], dtype=int32)>}\n"
          ]
        }
      ],
      "source": [
        "print(train_x)\n",
        "print(val_x)\n",
        "print(test_x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "s2wUnVMcOelQ"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers import Input, Dropout, Dense, GlobalAveragePooling1D"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "6XItSiAW0SFY"
      },
      "outputs": [],
      "source": [
        "def build_classifier_model():\n",
        "    input_ids = Input(shape=(max_length,), name='input_ids', dtype='int32')\n",
        "    attention_mask = Input(shape=(max_length,), name='attention_mask', dtype='int32') \n",
        "    inputs = {'input_ids': input_ids, 'attention_mask': attention_mask}\n",
        "    seq_output = robert(inputs)[0]\n",
        "    x=tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128))(seq_output)\n",
        "    x=tf.keras.layers.Dropout(0.2)(x)\n",
        "    y=tf.keras.layers.Dense(6,name='outputs')(x)\n",
        "    model= tf.keras.Model(inputs, y)\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "g9tvMcaj9sy0"
      },
      "outputs": [],
      "source": [
        "model = build_classifier_model()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "I_0YDLNQihZB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "outputId": "fb62962c-bf18-445a-f174-4defaa3d72aa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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RTk9ISBg4cKBAIDAwMHB0dCwqKqIoqrq6evny5VZWVnw+38nJSXbKHXt7+2fPnvXs2ZMQIjv1ym+//ebk5FRSUkL+d6oclt6kl27cuLFbt266uroikYj+Zsj872pd1ZD9n+KysjIdHR1ra2uleyY5OZmpTysOHoqi3NzcBAKBRCKhP1ZVVS1cuDAwMNDJyYmp7ZkzZ7788kuKoq5du0bvim3btind4NKlS7W0tOLj48eOHSsSiczNzfft28cyJOgv26NHj1axU1iWyu58uXpGRUXp6ekJBIKTJ0+OHTvWwMCgc+fOR48eZdatrq5et25dt27d+Hx+hw4dbGxsnJ2dFf/hbvDgwVeuXKEoyt3dnRDy66+/yi6ttxSWQVVZWUkXrWyw/J/w8HCBQCAUCvPy8oKDgzt16vT48WP23aL0OFKaqGIz5TpdcQwoPTrYd47cRuS6j73r693tShv7zTffaGtrl5WVKR0/VEPOS0oPKxYsx4jShrOciBpxmqp3d7FTpflKd52K44Q+USv10Ucf0XVQ7Dvq7/O/RCKh/+XrP//5D50u9/+5TRlILFcfVXz00UeKU+XImTBhgkAgqKyspD+OHj3aysqKWfrjjz8SQm7evNmgcmkBAQGxsbH035WVlbq6ujNmzKgrsyrljh07tnPnzrW1tezlsl+wKPWd86k6OkjpOZO9zhp4p9eOjwum+Zo5PpvvQiA7I0pubq6NjY22tjYzWZDSvTpjxgx6+3Z2dvfu3aMoys/PTyAQiESiU6dOsRdHT1PJ4XD69etHD5KFCxfS7Tpw4ABLS2WPMvZBW1e1le4uVYY0pcEDGNdxlut4u+xxVXZIW7wfbuv9QlHUwYMH+/fvz+Px9PT0bGxsVq9e3TLflTBONHCcqHjpp5TF/RC4b88QuG9nFA9gRStWrNDV1T106FBhYeGDBw/69etnYmKSm5tLL506dSpzO0tR1KZNm4jM7aynp6fsrby/v7++vv6jR48qKipSU1Ppt2xlZ2c3YlNnz541MDBYvXq1Ks1s1sD9L7/8smnTproySCSSCxcu2NjYjB49urS0lMkTFxe3cuXKgoKCt2/fDho0iI5dlpaWikSi8PDw8vLy3NzciRMn0s3/9ttveTze8ePH3717t2TJEi0trT/++IMp69mzZ/RUPEFBQcz2PTw8Dhw4oBi4Z+/NpUuXcjic77///t27dxKJJCoqSvZOuq5qyH7Dod+5OmDAAKW7Li8vjxDSpUsX+mNrDR6Koo4cOUIIYSbFO3fu3M8//3zt2jVCSFpaGp04Z86c8+fP03/T0zgwdwZyG6Tn/fzll18KCwsLCgrGjRvH4/FkQwlyY+bQoUOEkIULF6rSKexL5YI4cvVkKlZUVJSfnz9kyBB9fX3mdQjr1q3T1tY+deqURCK5e/dux44dhw8fLtdlWVlZpqam1dXVTLUVIwvspbAMqr/++osQMmjQIKo+dBHz5s3btm3bxIkT//zzT5bdovQ4quvgUr2Zcp0u97Guo4N958htRK776j1a69pyXY21s7Pr1auXbKPkxk+DzkuKh1W9PVjXMaI4bllORI07TbF3BAsVm6901ylmYzmZs1PsO+rv8z9FUTdu3NDS0rK1taWvMnLfFho9kJpSYVq9gfuysjIDA4PAwEAmJSEhgcvlbt26tbi4+OHDhz179pSdBFx1L1686NWrV01NDf3x2bNnhBBnZ+fhw4fTwcoePXps376d+aqjSrmhoaFEtYmY2S9Yajznsw942XMme4U1806vXm30uKA0e3w234VANnBfVVXl6ekpG3yva696enpqa2u/fPmSyTllyhRVJjWurq62tbW1tramL+60+fPnR0REsLSUUjjK2Aet0mqz33Kw09gBjOs4S/XaZY+ruEPa4v1wm+6XiIgIQsj69evfvn1bUFDw448/0u9ja+7vSnXBOGndcaL6pR+B+/cLAvftTL2Be4lEIhQK6TnBab///jshhImYN/TrnGz48o8//iAyz6Gr9+ucLLUH7uV+d1UM3MtlcHJy+umnn5gnCuWEhYURQvLz8+m5U+TebVJeXq6np8d0gUQi4fF4AQEBTFnPnj0rLCzU19c3MjKiHyHPyMiwtLSsrKyUC9yz96ZEItHT0xs1ahSzVPYrFks1ZL/h0H06cuRIpS2trKwkhJiYmNAfW3HwlJSUCASCmTNn0h8XLFgglUpra2s7derE/BtHv379mEumKoF75oVd9AzpDx8+ZDLIvpz2+PHjHTt2NDMze/HiBaVCp7AfgKoE7pmK0V+Nnjx5Qn8cOHDghx9+yGz566+/1tLSkhul69evnz59Ov13UVERj8cTiUTMfyrUWwr7oLpz5w4h5NNPP6XqI1cE+25RehwpTWxQM1luQFmODvYuYLkBrbfrWbastLGlpaUcDsfNzU02keUVfOznJcXKs2M/RuQazjJmGn2aYu8IFo1oPrPrKJXHCTulfUfJfFugKIqeGG3u3LnU/35baMpAanSFGfUG7pcuXdqtWze5ib9lZ1qztLR8/vy56iUy5s6dKzuwU1JSCCGjRo26fv3627dvCwsLFy9eTAg5fPiw6uXu37+fEHLw4MF6S2e5YKnxnK/6gGfXRu/02u5xQWnw+JSj3gsBc7qWSqWTJ0++cOECs4hlr16+fJkQwtybFRUVde3aVTYWz4KOcDH/2VBWVmZtba30GVLZ87bqgfu6qs1+y8FOYwcwruMs1WuXPS6rrh1CtcH7YRVpZr9UVVUZGhqOGDGCSamuro6MjFQsS+3flVSBcdLy40T1Sz/By2kB2rHU1NTS0tIBAwYwKQMHDtTV1VXxTXHsBgwYoKen9/jx46ZvqoXJfuG5evUqSwapVPrixYv58+cHBgZ+8MHAqEH+AAAgAElEQVQHb968UczM5XIJITU1NXZ2dmZmZtOmTVu5cmVmZia9NC0tTSKRMC8oEwgE5ubmcjtNLBZPmTLl3bt30dHRhJCIiIiAgADmv8kY7L355MkTiUTi6uqqtMmqVIMQQs/fWtds/gUFBYQQkUikdGmDNHHwCIXC8ePH01foyspKHR0dHR0dDofj6elJT3P/6NGjvn370l3TUPRaUqlUNrGoqIjD4YjF4nnz5o0bN+7333+nXx3M3inqPQDpIcFUrKKigpKZyb2mpobL5crOPU0IOXr06MSJE+m/RSLR6NGji4uLT506pWIp7INKKBQSmbmtVce+W5QeR0oTm9JMWSoeHUShCxrdRvYtK20sfRvN8iYPOeznpSZSeozQ2MeMWk5TpCEd0YjmM7uu0dWTo0rfrV27tnv37lFRUb/99ptselMGUqMrrKITJ07Exsb+/PPPshN/L126dPfu3b/88ktpaenTp09dXFw+/vhj5tW1KsrJyTl9+rSfnx+TQs+237t3bxcXF2NjY7FYvGrVKrFYzLzVQ5Vy6S6g/3WsQWQvWGo856urg9ronV7bPS40eXzKaY4LQU1NzZQpU8zMzMaOHcsksuzVkSNHduvWbf/+/fQdS3R0tK+vr9y9Sl1mzpwpFouZ+X8PHz78+eefK70Lreu8za6uaqtrd2nmAKbhOk7epx5X8QBpE/fDjaA5/fLgwYPCwkLZF7Bpa2vPmzdPMafavyupAuOk5cdJoy/9BC+nBWhP6AgsHV9jGBoaskz71SA8Hu/169dq2VRrGT58OMurSHR0dDp37jx9+vTNmzenpaWtX7+eTj937tzw4cNNTU15PN6iRYvoRIFAcOXKlcGDB69bt87Ozs7X17e8vLysrIwQsmzZMs7fsrKyFGOd9Ctqd+3aVVhYGBcXN3v2bMXKsPfmixcvCCGmpqZKG6JiNWxsbLhcbl0Xj9zcXEJI165d69pdDdLEwePr65ubm/vHH3+cP3+e+fY4adKk+/fvZ2RknD9/3sfHRy31pNG/5VRXV7948WL//v02NjZ0OnunNOsBOG7cuLt37546daq8vPzOnTsnT5787LPPZL8MP3z4MCUlxc3Njen0M2fOEELox6xUwT6obG1t+Xw+PWFOg7DvFqXHkdJEdTVTxaNDjW1kp7SxFRUV5O/wUF1UPy81vmH1YR8zajlNNYiKzVe661SsHv3gsFKDBg0ihKjSd3w+/8CBAxwO56uvvpKtYVMGUnPsT0Z0dPSGDRsSEhLol7fTXr16FR4e/vXXX48cOVJfX79Lly579uzJycmhH3FVXXh4+KxZs2RfWGphYUEIkf3tXFdX18bGJiMjQ/VyBQIB+bs7Goq5YKnxnK+uDtLMO712fFxo8vgkzX8hmDt3bnp6+q5dux49esQksuxVDocze/bsp0+f/vLLL4SQgwcPMhPf10soFH799dc3btygH2/cuXNnYGAgs1SV8za7uqqtdHfVO6QVaeYArtd7eB1vlz3eiAOkTdwPt+l+KS4upouod/vq/a7EAuOE0SrjpCmXfgTuAdoP+sIgd1opLCy0tLRs+salUqm6NqX5nJycCCH0t5Ts7GwPDw9zc/Pbt28XFRWFh4cz2Xr37n3mzJmcnJyQkJCYmJjNmzfTt7bMjJy0mzdvym3f2dl50KBBv//+u7+/v5eXl5GRkWId2HuT/g5Jz2ajSMVq8Pn8IUOGvHz5kp6wVQ79+7PsYwKN1vTBM378eAMDg9OnTycmJg4dOpROHDx4sIWFxfHjx+/cuTNixIim17Ne7J3SrAfgypUrR44c6efnJxKJJk6c6O3tvWfPHtkMR44cmTx5smyPFxQUCASCS5cu0b/B1It9UPF4vDFjxrx58+b69euKSwsKCmbOnKl0xXp3i+JxVFeiWpqp4tHRIE3sesXG0jd2LE/BNOi81MhWqYB9zKjlNNVQ9TafZdepUj2hUFjXv7XeunWL/H1TXu8TTB9//HFwcHB6evqaNWuYxKYMpGban4SQbdu2HT58+MqVK506dZJNT09Pr6mpkU0UiUTGxsapqamqbzw3N/fo0aP0j9kMoVDYtWtX2UAhIaS6ulosFqteblVVFfm7OxpE9oK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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "tf.keras.utils.plot_model(model, show_shapes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "IAE3h8lY97FH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "4c024b93-d587-4c2e-9aee-458060d04d0f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['tf_roberta_model/roberta/pooler/dense/kernel:0', 'tf_roberta_model/roberta/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_roberta_model/roberta/pooler/dense/kernel:0', 'tf_roberta_model/roberta/pooler/dense/bias:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss`argument?\n",
            " 129/3990 [..............................] - ETA: 17:03 - loss: 0.1865 - accuracy: 0.6965"
          ]
        },
        {
          "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-26-470765782853>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m      validation_data=({'input_ids': val_x['input_ids'], 'attention_mask': val_x['attention_mask']},\n\u001b[1;32m      6\u001b[0m     {'outputs': val_y}),\n\u001b[0;32m----> 7\u001b[0;31m     batch_size = 32, )\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   1219\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   1220\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-> 1221\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   1222\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   1223\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    434\u001b[0m     \"\"\"\n\u001b[1;32m    435\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--> 436\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    437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\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    293\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    294\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--> 295\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    296\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    297\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    314\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    315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\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    317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\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    352\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    353\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--> 354\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    355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    356\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   1030\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\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-> 1032\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   1033\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\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   1102\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   1103\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-> 1104\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   1105\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   1106\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    552\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\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[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 554\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    555\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    556\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    867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 869\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    870\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    871\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    867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m   return pack_sequence_as(\n\u001b[0;32m--> 869\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    870\u001b[0m       expand_composites=expand_composites)\n\u001b[1;32m    871\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    548\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\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    549\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--> 550\u001b[0;31m       \u001b[0mx\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    551\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mx\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[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mt\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[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   1147\u001b[0m     \"\"\"\n\u001b[1;32m   1148\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-> 1149\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   1150\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   1151\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   1113\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   1114\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-> 1115\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   1116\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   1117\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: "
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "model.compile(loss = tf.keras.losses.BinaryCrossentropy(from_logits=True), optimizer =Adam(learning_rate=1e-5, decay=1e-6) \\\n",
        "              , metrics = ['accuracy'])\n",
        "model.fit(x={'input_ids': train_x['input_ids'], 'attention_mask': train_x['attention_mask']},\n",
        "    y={'outputs': train_y}, epochs = 2, \n",
        "     validation_data=({'input_ids': val_x['input_ids'], 'attention_mask': val_x['attention_mask']},\n",
        "    {'outputs': val_y}),\n",
        "    batch_size = 32, )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mmds7XPG-N0O"
      },
      "outputs": [],
      "source": [
        "predicted = tf.math.sigmoid(model.predict({'input_ids': test_x['input_ids'], 'attention_mask': test_x['attention_mask']}, batch_size = 200))\n",
        "predict = np.hstack((test_data.id[:, np.newaxis], predicted))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "awmWtWKEHnby"
      },
      "outputs": [],
      "source": [
        "subm = pd.DataFrame(predict, columns = ['id', 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'])\n",
        "subm.to_csv('subm.csv', index = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QpYvoSrLIIU3"
      },
      "outputs": [],
      "source": [
        "subm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YprL4uu8IKRH"
      },
      "outputs": [],
      "source": [
        "predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l35URXkXwNK2"
      },
      "outputs": [],
      "source": [
        "test_x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hUZnM-lJI1JY"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "name=  [ 'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']\n",
        "values= [sum(train_data[name[i]]>0) for i in range(len(name))]\n",
        "plt.bar(name, values)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ptqlgzpekjv1"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ucOjEk3dbbNw"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
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