(대분류)_모두다_증분데이터_사용_이것이_최종임_model_33으로_저장.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KorStats/classification-project/blob/main/(%EB%8C%80%EB%B6%84%EB%A5%98)_%EB%AA%A8%EB%91%90%EB%8B%A4_%EC%A6%9D%EB%B6%84%EB%8D%B0%EC%9D%B4%ED%84%B0_%EC%82%AC%EC%9A%A9_%EC%9D%B4%EA%B2%83%EC%9D%B4_%EC%B5%9C%EC%A2%85%EC%9E%84_model_33%EC%9C%BC%EB%A1%9C_%EC%A0%80%EC%9E%A5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-imvwIZmPVwp",
        "outputId": "d4ff28c8-6a57-47e2-a419-9134e74ecc09"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Successfully installed sacremoses-0.0.49 tokenizers-0.8.1rc1 transformers-3.0.2\n",
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            "Building wheels for collected packages: kobert\n",
            "  Building wheel for kobert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kobert: filename=kobert-0.2.3-py3-none-any.whl size=15674 sha256=efa1079cbc79bb78199723238b66c92dfd226edddfbd52ea511bd848c9317bb3\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-2cvux433/wheels/d3/68/ca/334747dfb038313b49cf71f84832a33372f3470d9ddfd051c0\n",
            "Successfully built kobert\n",
            "Installing collected packages: urllib3, jmespath, pyyaml, botocore, tokenizers, s3transfer, huggingface-hub, transformers, onnxruntime, boto3, kobert\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
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            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
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            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.8.1rc1\n",
            "    Uninstalling tokenizers-0.8.1rc1:\n",
            "      Successfully uninstalled tokenizers-0.8.1rc1\n",
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            "      Successfully uninstalled transformers-3.0.2\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 boto3-1.21.32 botocore-1.24.32 huggingface-hub-0.4.0 jmespath-1.0.0 kobert-0.2.3 onnxruntime-1.8.0 pyyaml-6.0 s3transfer-0.5.2 tokenizers-0.11.6 transformers-4.17.0 urllib3-1.25.11\n",
            "/content/.cache/kobert_v1.zip[██████████████████████████████████████████████████]\n",
            "/content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece[██████████████████████████████████████████████████]\n"
          ]
        }
      ],
      "source": [
        "!pip install mxnet\n",
        "!pip install gluonnlp pandas tqdm\n",
        "!pip install sentencepiece\n",
        "!pip install transformers==3.0.2\n",
        "!pip install torch\n",
        "\n",
        "#깃허브에서 KoBERT 파일 로드\n",
        "!pip install git+https://git@github.com/SKTBrain/KoBERT.git@master\n",
        "import torch\n",
        "from torch import nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import gluonnlp as nlp\n",
        "import numpy as np\n",
        "from tqdm import tqdm, tqdm_notebook\n",
        "#kobert\n",
        "from kobert.utils import get_tokenizer\n",
        "from kobert.pytorch_kobert import get_pytorch_kobert_model\n",
        "\n",
        "#transformers\n",
        "from transformers import AdamW\n",
        "from transformers.optimization import get_cosine_schedule_with_warmup\n",
        "#GPU 사용\n",
        "device = torch.device(\"cuda:0\")\n",
        "#BERT 모델, Vocabulary 불러오기\n",
        "bertmodel, vocab = get_pytorch_kobert_model()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LjIKhkDtPbvA",
        "outputId": "914da80f-e2d0-4b08-b954-5bcf51e89643"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J5hcrkWdPbxW",
        "outputId": "8de2dbd2-2189-426d-ebb4-c057f42cee01"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n",
            " 데이터\t\t   '답안 작성용 파일.csv'     labels.zip\n",
            " 산업분류자동화     dabunru_inc_model_22.pt  'My Drive'\n",
            "'Colab Notebooks'   dabunru_inc_model.pt      val.txt\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "!ln -s /content/gdrive/My\\ Drive/ /mydrive\n",
        "!ls /mydrive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YnCVJyzDPbzg",
        "outputId": "8c2756c8-bb37-4ae0-e530-81d2283498e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  /content/gdrive/My Drive/산업분류자동화/모델 개발용 자료.zip\n",
            "2. 모델개발용자료.txt:  mismatching \"local\" filename (2. ыкиыН╕ъ░Ьы░ЬьЪйьЮРыгМ.txt),\n",
            "         continuing with \"central\" filename version\n",
            "  inflating: /content/dataset/2. 모델개발용자료.txt  \n",
            "Archive:  /content/gdrive/My Drive/산업분류자동화/실습용 자료.zip\n",
            "1. 실습용자료.txt:  mismatching \"local\" filename (1. ьЛдьК╡ьЪйьЮРыгМ.txt),\n",
            "         continuing with \"central\" filename version\n",
            "  inflating: /content/dataset/1. 실습용자료.txt  \n"
          ]
        }
      ],
      "source": [
        "!unzip /content/gdrive/'My Drive'/산업분류자동화/'모델 개발용 자료'.zip -d /content/dataset\n",
        "!unzip /content/gdrive/'My Drive'/산업분류자동화/'실습용 자료'.zip -d /content/dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "clR298QvPb1V"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df_A=pd.read_csv('/content/gdrive/MyDrive/데이터/df_A_inc.csv') #증분데이터 사용\n",
        "df_B=pd.read_csv('/content/gdrive/MyDrive/데이터/df_B_inc.csv') #증분데이터 사용\n",
        "df_C=pd.read_csv('/content/gdrive/MyDrive/데이터/df_C_inc.csv')\n",
        "df_D=pd.read_csv('/content/gdrive/MyDrive/데이터/df_D_inc.csv') #증분데이터 사용\n",
        "df_E=pd.read_csv('/content/gdrive/MyDrive/데이터/df_E_inc.csv') #증분데이터 사용\n",
        "df_F=pd.read_csv('/content/gdrive/MyDrive/데이터/df_F_inc_dain.csv')\n",
        "df_G=pd.read_csv('/content/gdrive/MyDrive/데이터/df_G_inc.csv')\n",
        "df_H=pd.read_csv('/content/gdrive/MyDrive/데이터/spell_check/H_spellcheck.csv')\n",
        "df_I=pd.read_csv('/content/gdrive/MyDrive/데이터/spell_check/I_spell_check.csv')\n",
        "df_J=pd.read_csv('/content/gdrive/MyDrive/데이터/df_J_inc.csv') #증분데이터 사용\n",
        "df_K=pd.read_csv('/content/gdrive/MyDrive/데이터/df_K_inc.csv') #증분데이터 사용\n",
        "df_L=pd.read_csv('/content/gdrive/MyDrive/데이터/df_L_inc.csv')\n",
        "df_M=pd.read_csv('/content/gdrive/MyDrive/데이터/df_M_inc.csv')\n",
        "df_N=pd.read_csv('/content/gdrive/MyDrive/데이터/df_N_inc.csv') #증분데이터 사용\n",
        "df_O=pd.read_csv('/content/gdrive/MyDrive/데이터/df_O_inc.csv') #증분데이터 사용\n",
        "df_P=pd.read_csv('/content/gdrive/MyDrive/데이터/df_P_inc.csv')\n",
        "df_Q=pd.read_csv('/content/gdrive/MyDrive/데이터/df_Q_inc.csv')\n",
        "df_R=pd.read_csv('/content/gdrive/MyDrive/데이터/df_R_inc.csv')\n",
        "df_S=pd.read_csv('/content/gdrive/MyDrive/데이터/spell_check/S_spellcheck.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HNU07nOcQ0YS"
      },
      "outputs": [],
      "source": [
        "df=pd.concat([df_A, df_B, df_C, df_D, df_E, df_F, df_G, df_H, df_I, df_J, df_K, df_L, df_M, df_N, df_O, df_P, df_Q, df_R, df_S])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-vgT6CZ2ROow",
        "outputId": "41f1a510-0e69-43c6-b866-1e5cb3236a27"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "C    830871\n",
              "G    352042\n",
              "I    187425\n",
              "P    107494\n",
              "S    100396\n",
              "H     98038\n",
              "Q     86657\n",
              "M     74308\n",
              "F     73466\n",
              "L     67852\n",
              "K     54098\n",
              "R     52271\n",
              "N     27296\n",
              "J     22852\n",
              "A     13275\n",
              "E     10428\n",
              "O      6380\n",
              "B      5847\n",
              "D      1968\n",
              "Name: digit_1, dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['digit_1'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X9BNjZPDQAcL"
      },
      "source": [
        "**데이터 전처리**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IqjVKJomPb5f"
      },
      "outputs": [],
      "source": [
        "df.loc[(df['digit_1'] == \"A\"), 'digit_1'] = 0  \n",
        "df.loc[(df['digit_1'] == \"B\"), 'digit_1'] = 1  \n",
        "df.loc[(df['digit_1'] == \"C\"), 'digit_1'] = 2  \n",
        "df.loc[(df['digit_1'] == \"D\"), 'digit_1'] = 3  \n",
        "df.loc[(df['digit_1'] == \"E\"), 'digit_1'] = 4  \n",
        "df.loc[(df['digit_1'] == \"F\"), 'digit_1'] = 5  \n",
        "df.loc[(df['digit_1'] == \"G\"), 'digit_1'] = 6  \n",
        "df.loc[(df['digit_1'] == \"H\"), 'digit_1'] = 7  \n",
        "df.loc[(df['digit_1'] == \"I\"), 'digit_1'] = 8  \n",
        "df.loc[(df['digit_1'] == \"J\"), 'digit_1'] = 9  \n",
        "df.loc[(df['digit_1'] == \"K\"), 'digit_1'] = 10  \n",
        "df.loc[(df['digit_1'] == \"L\"), 'digit_1'] = 11 \n",
        "df.loc[(df['digit_1'] == \"M\"), 'digit_1'] = 12  \n",
        "df.loc[(df['digit_1'] == \"N\"), 'digit_1'] = 13  \n",
        "df.loc[(df['digit_1'] == \"O\"), 'digit_1'] = 14  \n",
        "df.loc[(df['digit_1'] == \"P\"), 'digit_1'] = 15  \n",
        "df.loc[(df['digit_1'] == \"Q\"), 'digit_1'] = 16 \n",
        "df.loc[(df['digit_1'] == \"R\"), 'digit_1'] = 17  \n",
        "df.loc[(df['digit_1'] == \"S\"), 'digit_1'] = 18  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NeACK7jmPb7v",
        "outputId": "f155bfee-3348-447b-a242-c2985c0254e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[['벼 맥류 콩류 잡곡류 종자 생산 보급', '0'], ['벼 모판 생산 농가 출하', '0'], ['사업장에서 오이. 고추를 재배 오이. 고추를 판매', '0'], ['볍씨 발아 모종', '0'], ['  벼 재배', '0']]\n"
          ]
        }
      ],
      "source": [
        "data_list = []\n",
        "for q, label in zip(df['text'], df['digit_1'])  :\n",
        "    data = []\n",
        "    data.append(q)\n",
        "    data.append(str(label))\n",
        "\n",
        "    data_list.append(data)\n",
        "\n",
        "print(data_list[:5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UUhz2evRPb94"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split             \n",
        "dataset_train, dataset_test = train_test_split(data_list, test_size=0.1, random_state=0) #추후에 test_size 수정 필요"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d7wPK8GRRaqx"
      },
      "source": [
        "Kobert 입력데이터 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BETvSxMCPb_m"
      },
      "outputs": [],
      "source": [
        "class BERTDataset(Dataset):\n",
        "    def __init__(self, dataset, sent_idx, label_idx, bert_tokenizer, max_len,\n",
        "                 pad, pair):\n",
        "        transform = nlp.data.BERTSentenceTransform(\n",
        "            bert_tokenizer, max_seq_length=max_len, pad=pad, pair=pair)\n",
        "\n",
        "        self.sentences = [transform([i[sent_idx]]) for i in dataset]\n",
        "        self.labels = [np.int32(i[label_idx]) for i in dataset]\n",
        "\n",
        "    def __getitem__(self, i):\n",
        "        return (self.sentences[i] + (self.labels[i], ))\n",
        "\n",
        "    def __len__(self):\n",
        "        return (len(self.labels))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bhhdNTK7Rc-Y"
      },
      "outputs": [],
      "source": [
        "# Setting parameters\n",
        "max_len = 64\n",
        "batch_size = 64\n",
        "warmup_ratio = 0.1\n",
        "num_epochs = 5 #추후 num_epochs 증가할 필요 있음\n",
        "max_grad_norm = 1\n",
        "log_interval = 200\n",
        "learning_rate =  5e-5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7rRowSFRdAn",
        "outputId": "c0e6b2ba-0cb9-4f68-a108-a9eedb460032"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "using cached model. /content/.cache/kobert_news_wiki_ko_cased-1087f8699e.spiece\n"
          ]
        }
      ],
      "source": [
        "#토큰화\n",
        "tokenizer = get_tokenizer()\n",
        "tok = nlp.data.BERTSPTokenizer(tokenizer, vocab, lower=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WySX5NlpRdEv"
      },
      "outputs": [],
      "source": [
        "data_train = BERTDataset(dataset_train, 0, 1, tok, max_len, True, False)\n",
        "data_test = BERTDataset(dataset_test, 0, 1, tok, max_len, True, False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "en97ZxXARdHY"
      },
      "outputs": [],
      "source": [
        "train_dataloader = torch.utils.data.DataLoader(data_train, batch_size=batch_size, num_workers=5)\n",
        "test_dataloader = torch.utils.data.DataLoader(data_test, batch_size=batch_size, num_workers=5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "18XwcKI4RdJq"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "676TTfuiRmCI"
      },
      "source": [
        "Kobert 학습 모델 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7v7PG8iGRk5Q"
      },
      "outputs": [],
      "source": [
        "class BERTClassifier(nn.Module):\n",
        "    def __init__(self,\n",
        "                 bert,\n",
        "                 hidden_size = 768,\n",
        "                 num_classes=19,   ##클래스 수 조정##\n",
        "                 dr_rate=None,\n",
        "                 params=None):\n",
        "        super(BERTClassifier, self).__init__()\n",
        "        self.bert = bert\n",
        "        self.dr_rate = dr_rate\n",
        "                 \n",
        "        self.classifier = nn.Linear(hidden_size , num_classes)\n",
        "        if dr_rate:\n",
        "            self.dropout = nn.Dropout(p=dr_rate)\n",
        "    \n",
        "    def gen_attention_mask(self, token_ids, valid_length):\n",
        "        attention_mask = torch.zeros_like(token_ids)\n",
        "        for i, v in enumerate(valid_length):\n",
        "            attention_mask[i][:v] = 1\n",
        "        return attention_mask.float()\n",
        "\n",
        "    def forward(self, token_ids, valid_length, segment_ids):\n",
        "        attention_mask = self.gen_attention_mask(token_ids, valid_length)\n",
        "        \n",
        "        _, pooler = self.bert(input_ids = token_ids, token_type_ids = segment_ids.long(), attention_mask = attention_mask.float().to(token_ids.device))\n",
        "        if self.dr_rate:\n",
        "            out = self.dropout(pooler)\n",
        "        return self.classifier(out)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ifOA11RbRk7m",
        "outputId": "c55917b6-a718-4760-ef64-673f685d8993"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<torch.utils.data.dataloader.DataLoader at 0x7fdd7b8734d0>"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#BERT 모델 불러오기\n",
        "model = BERTClassifier(bertmodel,  dr_rate=0.5).to(device)\n",
        "\n",
        "#optimizer와 schedule 설정\n",
        "no_decay = ['bias', 'LayerNorm.weight']\n",
        "optimizer_grouped_parameters = [\n",
        "    {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},\n",
        "    {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
        "]\n",
        "\n",
        "optimizer = AdamW(optimizer_grouped_parameters, lr=learning_rate)\n",
        "loss_fn = nn.CrossEntropyLoss()\n",
        "\n",
        "t_total = len(train_dataloader) * num_epochs\n",
        "warmup_step = int(t_total * warmup_ratio)\n",
        "\n",
        "scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=warmup_step, num_training_steps=t_total)\n",
        "\n",
        "#정확도 측정을 위한 함수 정의\n",
        "def calc_accuracy(X,Y):\n",
        "    max_vals, max_indices = torch.max(X, 1)\n",
        "    train_acc = (max_indices == Y).sum().data.cpu().numpy()/max_indices.size()[0]\n",
        "    return train_acc\n",
        "    \n",
        "train_dataloader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:23: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
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            "epoch 4 batch id 30201 loss 0.025115955621004105 train acc 0.9882066198139134\n",
            "epoch 4 batch id 30401 loss 0.031378790736198425 train acc 0.9882179040163153\n",
            "epoch 4 train acc 0.9882288394201191\n"
          ]
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        {
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          "text": [
            "epoch 4 test acc 0.9847993746968753\n"
          ]
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          "text": [
            "epoch 5 batch id 1 loss 0.2609051764011383 train acc 0.96875\n",
            "epoch 5 batch id 201 loss 0.004693792201578617 train acc 0.9916044776119403\n",
            "epoch 5 batch id 401 loss 0.0024091133382171392 train acc 0.9911549251870324\n",
            "epoch 5 batch id 601 loss 0.038612544536590576 train acc 0.9910045757071547\n",
            "epoch 5 batch id 801 loss 0.013016393408179283 train acc 0.990753745318352\n",
            "epoch 5 batch id 1001 loss 0.008754552341997623 train acc 0.9907436313686314\n",
            "epoch 5 batch id 1201 loss 0.011029865592718124 train acc 0.9906458159866778\n",
            "epoch 5 batch id 1401 loss 0.0023982145357877016 train acc 0.9905870806566738\n",
            "epoch 5 batch id 1601 loss 0.12306080758571625 train acc 0.9904942223610244\n",
            "epoch 5 batch id 1801 loss 0.0011191981611773372 train acc 0.9902831760133259\n",
            "epoch 5 batch id 2001 loss 0.06218138709664345 train acc 0.9901767866066966\n",
            "epoch 5 batch id 2201 loss 0.13085384666919708 train acc 0.9900613357564744\n",
            "epoch 5 batch id 2401 loss 0.04076730087399483 train acc 0.9901538421491045\n",
            "epoch 5 batch id 2601 loss 0.08658923208713531 train acc 0.9901239907727797\n",
            "epoch 5 batch id 2801 loss 0.002691414672881365 train acc 0.9900258836129954\n",
            "epoch 5 batch id 3001 loss 0.07371353358030319 train acc 0.990149116961013\n",
            "epoch 5 batch id 3201 loss 0.14434339106082916 train acc 0.9900909871915027\n",
            "epoch 5 batch id 3401 loss 0.002094454364851117 train acc 0.9900672596295207\n",
            "epoch 5 batch id 3601 loss 0.02768414281308651 train acc 0.9900808803110247\n",
            "epoch 5 batch id 3801 loss 0.006114261690527201 train acc 0.9901588397790055\n",
            "epoch 5 batch id 4001 loss 0.036716435104608536 train acc 0.9901860472381905\n",
            "epoch 5 batch id 4201 loss 0.0011402706149965525 train acc 0.9902776124732207\n",
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            "epoch 5 batch id 5401 loss 0.07366093248128891 train acc 0.9903461164599149\n",
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            "epoch 5 batch id 6201 loss 0.0013026173692196608 train acc 0.9904198919529108\n",
            "epoch 5 batch id 6401 loss 0.0011384438257664442 train acc 0.9904092134041556\n",
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            "epoch 5 batch id 6801 loss 0.0011674916604533792 train acc 0.9904287972357007\n",
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            "epoch 5 batch id 9401 loss 0.002149280859157443 train acc 0.990614362833741\n",
            "epoch 5 batch id 9601 loss 0.050730057060718536 train acc 0.9906178392875742\n",
            "epoch 5 batch id 9801 loss 0.03323337808251381 train acc 0.990614796959494\n",
            "epoch 5 batch id 10001 loss 0.0030609294772148132 train acc 0.9906259374062594\n",
            "epoch 5 batch id 10201 loss 0.0009425616590306163 train acc 0.9906519581413586\n",
            "epoch 5 batch id 10401 loss 0.0027677551843225956 train acc 0.9906529420248054\n",
            "epoch 5 batch id 10601 loss 0.03270502761006355 train acc 0.9906229365154231\n",
            "epoch 5 batch id 10801 loss 0.009173139929771423 train acc 0.9906374409776872\n",
            "epoch 5 batch id 11001 loss 0.038165461272001266 train acc 0.9906372147986546\n",
            "epoch 5 batch id 11201 loss 0.058032091706991196 train acc 0.9906328118025176\n",
            "epoch 5 batch id 11401 loss 0.0032965412829071283 train acc 0.9906408977282695\n",
            "epoch 5 batch id 11601 loss 0.06917935609817505 train acc 0.9906433173864322\n",
            "epoch 5 batch id 11801 loss 0.0013606443535536528 train acc 0.9906655156342683\n",
            "epoch 5 batch id 12001 loss 0.19395512342453003 train acc 0.9906622364802933\n",
            "epoch 5 batch id 12201 loss 0.006337280850857496 train acc 0.990680835587247\n",
            "epoch 5 batch id 12401 loss 0.0011765823001042008 train acc 0.9906509555680993\n",
            "epoch 5 batch id 12601 loss 0.018703820183873177 train acc 0.9906765832076819\n",
            "epoch 5 batch id 12801 loss 0.027199409902095795 train acc 0.9907026306538552\n",
            "epoch 5 batch id 13001 loss 0.02898675575852394 train acc 0.9906990327667102\n",
            "epoch 5 batch id 13201 loss 0.006152528803795576 train acc 0.9907050128778123\n",
            "epoch 5 batch id 13401 loss 0.006121704820543528 train acc 0.990694491082755\n",
            "epoch 5 batch id 13601 loss 0.001378218992613256 train acc 0.9907084037938387\n",
            "epoch 5 batch id 13801 loss 0.0007155706407502294 train acc 0.9907275740888342\n",
            "epoch 5 batch id 14001 loss 0.0015417697140946984 train acc 0.9907528926505249\n",
            "epoch 5 batch id 14201 loss 0.07622195780277252 train acc 0.9907367879022604\n",
            "epoch 5 batch id 14401 loss 0.002380065619945526 train acc 0.9907384903826123\n",
            "epoch 5 batch id 14601 loss 0.009972366504371166 train acc 0.9907444267515924\n",
            "epoch 5 batch id 14801 loss 0.018806420266628265 train acc 0.9907660377677184\n",
            "epoch 5 batch id 15001 loss 0.05668071284890175 train acc 0.9907464502366509\n",
            "epoch 5 batch id 15201 loss 0.1650141477584839 train acc 0.9907458802052497\n",
            "epoch 5 batch id 15401 loss 0.0017494678031653166 train acc 0.9907443104343874\n",
            "epoch 5 batch id 15601 loss 0.0032517011277377605 train acc 0.9907618101403756\n",
            "epoch 5 batch id 15801 loss 0.08895640075206757 train acc 0.9907759002594773\n",
            "epoch 5 batch id 16001 loss 0.06702323257923126 train acc 0.9907759671270545\n",
            "epoch 5 batch id 16201 loss 0.0022964973468333483 train acc 0.9907866412567126\n",
            "epoch 5 batch id 16401 loss 0.004419295582920313 train acc 0.9907732379123224\n",
            "epoch 5 batch id 16601 loss 0.003176720580086112 train acc 0.9907874525630986\n",
            "epoch 5 batch id 16801 loss 0.0013342107413336635 train acc 0.9907864487232902\n",
            "epoch 5 batch id 17001 loss 0.11807702481746674 train acc 0.9907762778660079\n",
            "epoch 5 batch id 17201 loss 0.0007053700974211097 train acc 0.9907863278297774\n",
            "epoch 5 batch id 17401 loss 0.013318261131644249 train acc 0.9907934529624735\n",
            "epoch 5 batch id 17601 loss 0.017537813633680344 train acc 0.9908101812397023\n",
            "epoch 5 batch id 17801 loss 0.004259603563696146 train acc 0.9908256558620302\n",
            "epoch 5 batch id 18001 loss 0.004972793161869049 train acc 0.9908364465863008\n",
            "epoch 5 batch id 18201 loss 0.003050179686397314 train acc 0.9908289723092137\n",
            "epoch 5 batch id 18401 loss 0.0013666049344465137 train acc 0.9908318501711864\n",
            "epoch 5 batch id 18601 loss 0.02073860913515091 train acc 0.990832986129778\n",
            "epoch 5 batch id 18801 loss 0.009090017527341843 train acc 0.9908382532843998\n",
            "epoch 5 batch id 19001 loss 0.05282944068312645 train acc 0.9908508104836588\n",
            "epoch 5 batch id 19201 loss 0.008265664801001549 train acc 0.9908541547315244\n",
            "epoch 5 batch id 19401 loss 0.008300239220261574 train acc 0.9908566246585228\n",
            "epoch 5 batch id 19601 loss 0.001299939933232963 train acc 0.9908542612621805\n",
            "epoch 5 batch id 19801 loss 0.09667501598596573 train acc 0.9908566802181707\n",
            "epoch 5 batch id 20001 loss 0.003020478878170252 train acc 0.9908699877506125\n",
            "epoch 5 batch id 20201 loss 0.006861685309559107 train acc 0.9908814848274838\n",
            "epoch 5 batch id 20401 loss 0.046217262744903564 train acc 0.990873609136807\n",
            "epoch 5 batch id 20601 loss 0.002755100140348077 train acc 0.9908856062812484\n",
            "epoch 5 batch id 20801 loss 0.07427844405174255 train acc 0.9908988750540839\n",
            "epoch 5 batch id 21001 loss 0.0054697864688932896 train acc 0.9909096590638541\n",
            "epoch 5 batch id 21201 loss 0.14271654188632965 train acc 0.9909165546436489\n",
            "epoch 5 batch id 21401 loss 0.002348379697650671 train acc 0.9909386535675903\n",
            "epoch 5 batch id 21601 loss 0.02413836680352688 train acc 0.9909292393870655\n",
            "epoch 5 batch id 21801 loss 0.09484201669692993 train acc 0.9909278817485436\n",
            "epoch 5 batch id 22001 loss 0.04017825052142143 train acc 0.99092867937821\n",
            "epoch 5 batch id 22201 loss 0.008349240757524967 train acc 0.9909287588396919\n",
            "epoch 5 batch id 22401 loss 0.12277797609567642 train acc 0.9909246518012589\n",
            "epoch 5 batch id 22601 loss 0.0163548793643713 train acc 0.9909268395203752\n",
            "epoch 5 batch id 22801 loss 0.06244846060872078 train acc 0.9909193949826762\n",
            "epoch 5 batch id 23001 loss 0.06929981708526611 train acc 0.9909215903656363\n",
            "epoch 5 batch id 23201 loss 0.009256791323423386 train acc 0.9909358702211112\n",
            "epoch 5 batch id 23401 loss 0.0013188618468120694 train acc 0.9909398903892996\n",
            "epoch 5 batch id 23601 loss 0.055659543722867966 train acc 0.9909438424219312\n",
            "epoch 5 batch id 23801 loss 0.00468778470531106 train acc 0.9909503539767237\n",
            "epoch 5 batch id 24001 loss 0.011896246112883091 train acc 0.9909580590392068\n",
            "epoch 5 batch id 24201 loss 0.0008858242072165012 train acc 0.9909591804057684\n",
            "epoch 5 batch id 24401 loss 0.005407074466347694 train acc 0.9909609237326339\n",
            "epoch 5 batch id 24601 loss 0.06711313873529434 train acc 0.9909696252184871\n",
            "epoch 5 batch id 24801 loss 0.0029082458931952715 train acc 0.9909756663037781\n",
            "epoch 5 batch id 25001 loss 0.09425783157348633 train acc 0.9909772359105635\n",
            "epoch 5 batch id 25201 loss 0.009397504851222038 train acc 0.990979400619023\n",
            "epoch 5 batch id 25401 loss 0.00998162105679512 train acc 0.9909932187709145\n",
            "epoch 5 batch id 25601 loss 0.015277053229510784 train acc 0.9909976661067927\n",
            "epoch 5 batch id 25801 loss 0.011170129291713238 train acc 0.9910038612844463\n",
            "epoch 5 batch id 26001 loss 0.05819554999470711 train acc 0.9910063555247876\n",
            "epoch 5 batch id 26201 loss 0.013684859499335289 train acc 0.9910040408763024\n",
            "epoch 5 batch id 26401 loss 0.026597773656249046 train acc 0.9910189244725579\n",
            "epoch 5 batch id 26601 loss 0.05482068657875061 train acc 0.991019487049359\n",
            "epoch 5 batch id 26801 loss 0.06431148201227188 train acc 0.9910159602253648\n",
            "epoch 5 batch id 27001 loss 0.010107314214110374 train acc 0.9910269527054554\n",
            "epoch 5 batch id 27201 loss 0.0033213000278919935 train acc 0.9910331881180839\n",
            "epoch 5 batch id 27401 loss 0.005405222065746784 train acc 0.9910342003941462\n",
            "epoch 5 batch id 27601 loss 0.0005314964801073074 train acc 0.9910408590268468\n",
            "epoch 5 batch id 27801 loss 0.0008541112765669823 train acc 0.9910328090716162\n",
            "epoch 5 batch id 28001 loss 0.03384118527173996 train acc 0.9910416145851934\n",
            "epoch 5 batch id 28201 loss 0.17765776813030243 train acc 0.9910392140349633\n",
            "epoch 5 batch id 28401 loss 0.0004830652615055442 train acc 0.9910528018379634\n",
            "epoch 5 batch id 28601 loss 0.0015068997163325548 train acc 0.9910552734170134\n",
            "epoch 5 batch id 28801 loss 0.0013004858046770096 train acc 0.9910522855109197\n",
            "epoch 5 batch id 29001 loss 0.157312273979187 train acc 0.9910633469535534\n",
            "epoch 5 batch id 29201 loss 0.011757386848330498 train acc 0.9910651604397109\n",
            "epoch 5 batch id 29401 loss 0.004139138385653496 train acc 0.9910626976973572\n",
            "epoch 5 batch id 29601 loss 0.06423864513635635 train acc 0.9910681860410121\n",
            "epoch 5 batch id 29801 loss 0.008332137018442154 train acc 0.9910725520955672\n",
            "epoch 5 batch id 30001 loss 0.06862569600343704 train acc 0.9910825889137028\n",
            "epoch 5 batch id 30201 loss 0.022961406037211418 train acc 0.991089388596404\n",
            "epoch 5 batch id 30401 loss 0.011685318313539028 train acc 0.9910889033255486\n",
            "epoch 5 train acc 0.991089178774789\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3396 [00:00<?, ?it/s]"
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          "text": [
            "epoch 5 test acc 0.9850570307628352\n"
          ]
        }
      ],
      "source": [
        "for e in range(num_epochs):\n",
        "    train_acc = 0.0\n",
        "    test_acc = 0.0\n",
        "    model.train()\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(train_dataloader)):\n",
        "        optimizer.zero_grad()\n",
        "        token_ids = token_ids.long().to(device)\n",
        "        segment_ids = segment_ids.long().to(device)\n",
        "        valid_length= valid_length\n",
        "        label = label.long().to(device)\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "        loss = loss_fn(out, label)\n",
        "        loss.backward()\n",
        "        torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)\n",
        "        optimizer.step()\n",
        "        scheduler.step()  # Update learning rate schedule\n",
        "        train_acc += calc_accuracy(out, label)\n",
        "        if batch_id % log_interval == 0:\n",
        "            print(\"epoch {} batch id {} loss {} train acc {}\".format(e+1, batch_id+1, loss.data.cpu().numpy(), train_acc / (batch_id+1)))\n",
        "    print(\"epoch {} train acc {}\".format(e+1, train_acc / (batch_id+1)))\n",
        "    \n",
        "    model.eval()\n",
        "    for batch_id, (token_ids, valid_length, segment_ids, label) in enumerate(tqdm_notebook(test_dataloader)):\n",
        "        token_ids = token_ids.long().to(device)\n",
        "        segment_ids = segment_ids.long().to(device)\n",
        "        valid_length= valid_length\n",
        "        label = label.long().to(device)\n",
        "        out = model(token_ids, valid_length, segment_ids)\n",
        "        test_acc += calc_accuracy(out, label)\n",
        "    print(\"epoch {} test acc {}\".format(e+1, test_acc / (batch_id+1)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ixTuzcNLL_ZD"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FLnwzqNGTG3U"
      },
      "source": [
        "a,b,d,e,o만 증분데이터 acc 0.982008457"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TMQgbwX-RlAN"
      },
      "outputs": [],
      "source": [
        "#학습 모델 저장\n",
        "torch.save(model, 'dabunryu_inc_model_33.pt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jj9GfWhQY7q7"
      },
      "outputs": [],
      "source": [
        "model_save_name='dabunru_inc_model_33.pt'\n",
        "path=F\"/content/gdrive/My Drive/{model_save_name}\"\n",
        "torch.save(model.state_dict(), path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IxMc7oIwHnb9"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "djCyIRhLHniV"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BQdYuddLPWjr"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PZBnmSJ0Qthr"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5diIGci9bort"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "background_execution": "on",
      "collapsed_sections": [],
      "machine_shape": "hm",
      "name": "(대분류) 모두다 증분데이터 사용_이것이 최종임_model_33으로 저장",
      "provenance": [],
      "authorship_tag": "ABX9TyMWpmW4LjL1elF72D77PKF5",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
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