curse_detection_pytorch.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "byAJBMy8i9u3"
      },
      "outputs": [],
      "source": [
        "!pip install transformers datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# library"
      ],
      "metadata": {
        "id": "G4YIOQnMw-GZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
        "from tokenizers import Tokenizer\n",
        "\n",
        "from datasets import load_dataset\n",
        "\n",
        "from sklearn.metrics import multilabel_confusion_matrix\n",
        "from transformers import BertForSequenceClassification, BertConfig\n",
        "\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "from IPython.display import display\n",
        "from typing import List, Dict, Any, Tuple\n",
        "import os\n",
        "os.environ['CUDA_LAUNCH_BLOCKING'] = \"1\"\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
      ],
      "metadata": {
        "id": "lWDbsSFWjFt5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# dataset"
      ],
      "metadata": {
        "id": "XYIxgdEvxBlM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class ApeachDataset(Dataset):\n",
        "    def __init__(self,\n",
        "                 split: str,\n",
        "                 tokenizer: Tokenizer, \n",
        "                 max_length: int = 256,\n",
        "                 padding: str = \"max_length\") -> None:\n",
        "        super().__init__()\n",
        "        dataset = load_dataset(\"jason9693/APEACH\")\n",
        "        texts = dataset[split]['text']\n",
        "        inputs = tokenizer(texts, padding=padding, max_length=max_length, truncation=True, return_tensors=\"pt\")\n",
        "        \n",
        "        self.input_ids = inputs[\"input_ids\"]\n",
        "        self.attention_masks = inputs[\"attention_mask\"]\n",
        "        \n",
        "        labels = dataset[split]['class']\n",
        "        self.labels = torch.tensor(labels, dtype=torch.float32)\n",
        "        \n",
        "    def __len__(self):\n",
        "        return self.input_ids.shape[0]\n",
        "        \n",
        "    def __getitem__(self, index: Any) -> Dict:\n",
        "        return self.input_ids[index], self.attention_masks[index], self.labels[index]\n",
        "\n",
        "    def dataloader(self, **kwargs) -> DataLoader:\n",
        "        return DataLoader(self, **kwargs)"
      ],
      "metadata": {
        "id": "_IaOi7H6jZBS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# load huggingface pretrained model"
      ],
      "metadata": {
        "id": "SaGEGyKxxDon"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "monologg/koelectra-small-v3-discriminator\n",
        "beomi/KcELECTRA-base\n",
        "beomi/kcbert-base\n",
        "beomi/kcbert-large\n",
        "\"\"\"\n",
        "\n",
        "huggingface_model_name = \"beomi/KcELECTRA-base\"\n",
        "labels = ['hate']\n",
        "tokenizer = AutoTokenizer.from_pretrained(huggingface_model_name)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(huggingface_model_name, num_labels=len(labels))"
      ],
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            "20af6bfdbb474f8b809aa48491ac004b",
            "3482a9204b514c34969593cac9af45c8",
            "8aaaa5f5cad14c31b71bb17c3468af08",
            "6edda2397e2148ba8ca3bef194cdee37",
            "0220f1b2991248868b10b5ea39db74d0"
          ]
        },
        "id": "XAJR6Hm8jd_U",
        "outputId": "ac702a6b-6668-4d1c-9dd7-beac1a09cd38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/288 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dbafefae5c4544fbb919f6a737a0d253"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/514 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0427137f35dc4c6fb918e3ca31ec0dda"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.78M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fba7e9571892488190819f0f79e93aa7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd88f217896142c8971c30687272903f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "623c3bddacff47d69dfb708d153b5146"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at beomi/KcELECTRA-base were not used when initializing ElectraForSequenceClassification: ['discriminator_predictions.dense.weight', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense.bias', 'discriminator_predictions.dense_prediction.bias']\n",
            "- This IS expected if you are initializing ElectraForSequenceClassification 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 ElectraForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of ElectraForSequenceClassification were not initialized from the model checkpoint at beomi/KcELECTRA-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.bias', 'classifier.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# dataloader"
      ],
      "metadata": {
        "id": "4D7XljxexG0R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = 64\n",
        "batch_size = 128\n",
        "train_dl = ApeachDataset(\"train\", tokenizer, max_length=max_length).dataloader(batch_size=batch_size)\n",
        "val_dl = ApeachDataset(\"test\", tokenizer, max_length=max_length).dataloader(batch_size=batch_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 137,
          "referenced_widgets": [
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          ]
        },
        "id": "gz6ftBSRj5zg",
        "outputId": "57d6ceba-9ffb-4895-aae9-91ae18f2e6ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset csv (/root/.cache/huggingface/datasets/jason9693___csv/default-13a19883aaaad80d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6fdb98ade5b9459e90c040d59ef4c9ce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset csv (/root/.cache/huggingface/datasets/jason9693___csv/default-13a19883aaaad80d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "21c974e894f14e4f9e83bb9266b2f111"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# hyperparameter"
      ],
      "metadata": {
        "id": "R6p0Q2boxH_T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr = 1e-4\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr = lr)\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ],
      "metadata": {
        "id": "Q0dTgLv7j9nL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_dl)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQwhRgTyx09s",
        "outputId": "41ac68da-315f-409e-dcb8-4e33ca95be78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "62"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "s3aNHV_h0g9o",
        "outputId": "125e2e3e-b962-4369-c8d0-94c593eea37d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.to(device)"
      ],
      "metadata": {
        "id": "CSZxBYFXytOP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 10"
      ],
      "metadata": {
        "id": "KDbZ4U9d1Hhc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# training"
      ],
      "metadata": {
        "id": "LKldHCAVxKP1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.train()\n",
        "\n",
        "for epoch in tqdm(range(1,epochs+1)):\n",
        "    \n",
        "    train_loss = 0.0\n",
        "    train_acc = 0.0\n",
        "    count = 0\n",
        "\n",
        "    for ind,batch in enumerate(tqdm(train_dl),start=1):\n",
        "        \n",
        "        input_ids,attention_masks,labels = batch\n",
        "\n",
        "        input_ids = input_ids.to(device)\n",
        "        attention_masks = attention_masks.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        logits = model(input_ids,attention_masks)\n",
        "        output = logits.logits.sigmoid().squeeze(1)\n",
        "\n",
        "        loss = criterion(output,labels)\n",
        "        train_loss += loss\n",
        "\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        acc = ((output >= 0.5) == labels).sum().item()/(output.size()[0])\n",
        "        train_acc += acc\n",
        "\n",
        "        count += 1\n",
        "\n",
        "        if count % 30 == 0:\n",
        "\n",
        "            print(f'average accuracy: {train_acc/(count)}')\n",
        "            print(f'average loss: {train_loss/(count)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732,
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        "id": "JJvCq-kikELz",
        "outputId": "95ae4d7c-e0ce-4551-f6d3-ead8ed91641a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "fe7886db79b349228b871a8d96e05f3b"
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/62 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average accuracy: 0.4314453125\n",
            "average loss: 4.609428882598877\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/62 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average accuracy: 0.4314453125\n",
            "average loss: 4.609428882598877\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/62 [00:00<?, ?it/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average accuracy: 0.4314453125\n",
            "average loss: 4.609428882598877\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0b9c2b3d4ce44feda5362e3cf728a803"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average accuracy: 0.4314453125\n",
            "average loss: 4.609428882598877\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3c32cfe2891e40e5a692c176ede435fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average accuracy: 0.4314453125\n",
            "average loss: 4.609428882598877\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0506697794f943ed9a0d847aa5527e0c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average accuracy: 0.4314453125\n",
            "average loss: 4.609428882598877\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fca28374481c4a399c50009c51f1f374"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average accuracy: 0.4314453125\n",
            "average loss: 4.609428882598877\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1242ca41d4244f6ebca5af0aca75b57d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average accuracy: 0.4314453125\n",
            "average loss: 4.609428882598877\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/62 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "afacd14920df4cf28544b29593baa2ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average accuracy: 0.4314453125\n",
            "average loss: 4.609428882598877\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/62 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e30abf8242c541389466b502df7a9f53"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average accuracy: 0.4314453125\n",
            "average loss: 4.609428882598877\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DfTgrGjbSoo5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(),'/content/drive/MyDrive/curse_model/model.pth')"
      ],
      "metadata": {
        "id": "yv0NTTqH1AYC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# validation"
      ],
      "metadata": {
        "id": "jR9KdFU2xL58"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.eval()\n",
        "\n",
        "val_loss = 0.0\n",
        "val_acc = 0.0\n",
        "\n",
        "with torch.no_grad():\n",
        "\n",
        "    for ind,batch in enumerate(tqdm(val_dl)):\n",
        "        \n",
        "        input_ids,attention_masks,labels = batch\n",
        "\n",
        "        input_ids = input_ids.to(device)\n",
        "        attention_masks = attention_masks.to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        logits = model(input_ids,attention_masks)\n",
        "        output = logits.logits.sigmoid().squeeze(1)\n",
        "\n",
        "        loss = criterion(output,labels)\n",
        "        val_loss += loss\n",
        "        acc = ((output >= 0.5) == labels).sum().item()/(output.size()[0])\n",
        "        val_acc += acc\n",
        "\n",
        "print(f'average accuracy: {val_acc/(len(val_dl))}')\n",
        "print(f'average loss: {val_loss/(len(val_dl))}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
            "a7812286ac4b4a68810d8bb897ac1c8d",
            "3b146581c40649c1b79c5eb53e42844a",
            "fdd611a38ab14089a25ad29944524982",
            "20acddc27fae4262bb9286086713ec82",
            "b549b1c9e4fb42c1a7e5cda12d5dfe21",
            "5a1fa212e09e4dcbb88383b77b1808f5",
            "79179795e6af4292a4b43a2d575985d2",
            "2c756ff7b83148988e293d3acb8908aa",
            "83af8e6ab5f84e28a0f84215c1272ad1",
            "dd6aecd051194b21bfb89edde380cc68",
            "b8832245f1f640d0a2e53353e3f4f709"
          ]
        },
        "id": "uPTS10lB_w-J",
        "outputId": "88547108-a526-4569-a63c-249927adc18f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/30 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a7812286ac4b4a68810d8bb897ac1c8d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "average accuracy: 0.48125\n",
            "average loss: 309.3230285644531\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels.long().size()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JmmbQ2HamKt4",
        "outputId": "1047926e-267c-48ca-999d-1bbdc28cd730"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([128])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logits.logits.sigmoid().squeeze(1).size()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGnaEmp1kSst",
        "outputId": "4643f044-81b2-4bae-f686-5416660a58d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([128])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "output = logits.logits.sigmoid().squeeze(1)\n",
        "\n",
        "out = output.detach().numpy()\n",
        "\n",
        "print(torch.tensor((output >= 0.5) == labels).sum().item())\n",
        "\n",
        "print(output.size()[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kup84EzPlTsr",
        "outputId": "f86fea96-898c-40cf-8506-709093cecfc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "55\n",
            "128\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-40-328a73a307b5>:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  print(torch.tensor((output >= 0.5) == labels).sum().item())\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "((output >= 0.5) == labels).sum().item()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_15LXrJ9vrVy",
        "outputId": "d517d9bc-ff76-4d89-e767-e8b56706801e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "63"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "IZlcau_PoF-P"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}