{
 "cells": [
  {
   "cell_type": "code",
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   "id": "742dd25e",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import os\n",
    "import urllib.request\n",
    "import sys\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6376949e",
   "metadata": {
    "execution": {
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     "end_time": "2025-04-09T14:57:44.374160",
     "exception": false,
     "start_time": "2025-04-09T14:57:36.472475",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import torch.nn.functional as f\n",
    "import torch.optim as optim\n",
    "\n",
    "import torchvision\n",
    "from torchvision.transforms import v2\n",
    "from torchvision import models\n",
    "from torchvision.models import resnet50, ResNet50_Weights\n",
    "import torch.optim.lr_scheduler as lr_scheduler\n",
    "\n",
    "import random\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c2c2efd2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-09T14:57:44.384169Z",
     "iopub.status.busy": "2025-04-09T14:57:44.383727Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('/kaggle/input/fakenews-fakedit/Dataset/df_balanced_resized.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cd73260e",
   "metadata": {
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   },
   "outputs": [
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       "      <th>clean_title</th>\n",
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       "      <th>6_way_label</th>\n",
       "      <th>binary_label</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BruxellesBlonde</td>\n",
       "      <td>sooooo satisfying</td>\n",
       "      <td>1.537968e+09</td>\n",
       "      <td>i.redd.it</td>\n",
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       "      <th>1</th>\n",
       "      <td>korkow</td>\n",
       "      <td>the chips fell out of this chocolate chip cookie</td>\n",
       "      <td>1.384738e+09</td>\n",
       "      <td>i.imgur.com</td>\n",
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       "      <td>kyletheheroman</td>\n",
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       "      <td>1.551516e+09</td>\n",
       "      <td>i.redd.it</td>\n",
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       "      <td>it almost seems to smile back</td>\n",
       "      <td>1.522871e+09</td>\n",
       "      <td>i.redd.it</td>\n",
       "      <td>True</td>\n",
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       "      <td>https://preview.redd.it/yf1j0rvq4yp01.jpg?widt...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>212</td>\n",
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       "      <td>0.98</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>allhundredyears</td>\n",
       "      <td>this tape deck has seen some shit</td>\n",
       "      <td>1.426363e+09</td>\n",
       "      <td>imgur.com</td>\n",
       "      <td>True</td>\n",
       "      <td>2z1tha</td>\n",
       "      <td>https://external-preview.redd.it/QFHuRDeu_v1cm...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>spyder_19</td>\n",
       "      <td>saw an interesting sign at the store so i got it</td>\n",
       "      <td>1.564682e+09</td>\n",
       "      <td>i.redd.it</td>\n",
       "      <td>True</td>\n",
       "      <td>ckrv3k</td>\n",
       "      <td>https://preview.redd.it/ketwpycjmvd31.jpg?widt...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>32</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Not3Shabby</td>\n",
       "      <td>inherent paradox scrabeck</td>\n",
       "      <td>1.489213e+09</td>\n",
       "      <td>imgur.com</td>\n",
       "      <td>True</td>\n",
       "      <td>5yrdcs</td>\n",
       "      <td>https://external-preview.redd.it/g3nNRMvtW8FCT...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.83</td>\n",
       "      <td>1</td>\n",
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       "      <th>7</th>\n",
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       "      <td>gop senate candidate kelli ward complained mcc...</td>\n",
       "      <td>1.535296e+09</td>\n",
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       "      <td>True</td>\n",
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       "      <td>worldnews_SS</td>\n",
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       "      <td>1.458731e+09</td>\n",
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       "      <th>9</th>\n",
       "      <td>todayilearned_SS</td>\n",
       "      <td>til pegi europes video game in only to turn to...</td>\n",
       "      <td>1.486354e+09</td>\n",
       "      <td>nytimes.com</td>\n",
       "      <td>True</td>\n",
       "      <td>5sc05y</td>\n",
       "      <td>https://external-preview.redd.it/PL4PyhChdGdhF...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>5</td>\n",
       "      <td>subredditsimulator</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             author                                        clean_title  \\\n",
       "0   BruxellesBlonde                                  sooooo satisfying   \n",
       "1            korkow   the chips fell out of this chocolate chip cookie   \n",
       "2    kyletheheroman              has anyone seen a square cloud before   \n",
       "3        FledPotato                      it almost seems to smile back   \n",
       "4   allhundredyears                  this tape deck has seen some shit   \n",
       "5         spyder_19   saw an interesting sign at the store so i got it   \n",
       "6        Not3Shabby                          inherent paradox scrabeck   \n",
       "7          mikewall  gop senate candidate kelli ward complained mcc...   \n",
       "8      worldnews_SS  christians fleeing iraqs mosul region to provi...   \n",
       "9  todayilearned_SS  til pegi europes video game in only to turn to...   \n",
       "\n",
       "    created_utc        domain  hasImage      id  \\\n",
       "0  1.537968e+09     i.redd.it      True  9j2gd1   \n",
       "1  1.384738e+09   i.imgur.com      True  1qv2m9   \n",
       "2  1.551516e+09     i.redd.it      True  awfsgw   \n",
       "3  1.522871e+09     i.redd.it      True  89ssvs   \n",
       "4  1.426363e+09     imgur.com      True  2z1tha   \n",
       "5  1.564682e+09     i.redd.it      True  ckrv3k   \n",
       "6  1.489213e+09     imgur.com      True  5yrdcs   \n",
       "7  1.535296e+09  newsweek.com      True  9ag51l   \n",
       "8  1.458731e+09  engadget.com      True  4bm0cs   \n",
       "9  1.486354e+09   nytimes.com      True  5sc05y   \n",
       "\n",
       "                                           image_url linked_submission_id  \\\n",
       "0  https://preview.redd.it/bjlftqsj3lo11.jpg?widt...                  NaN   \n",
       "1  https://external-preview.redd.it/ESPM-tTuK3wer...                  NaN   \n",
       "2  https://preview.redd.it/trqkt1rx4oj21.jpg?widt...                  NaN   \n",
       "3  https://preview.redd.it/yf1j0rvq4yp01.jpg?widt...                  NaN   \n",
       "4  https://external-preview.redd.it/QFHuRDeu_v1cm...                  NaN   \n",
       "5  https://preview.redd.it/ketwpycjmvd31.jpg?widt...                  NaN   \n",
       "6  https://external-preview.redd.it/g3nNRMvtW8FCT...                  NaN   \n",
       "7  https://external-preview.redd.it/3c79PddPA_og2...                  NaN   \n",
       "8  https://external-preview.redd.it/xNKcWQsoGpWr3...                  NaN   \n",
       "9  https://external-preview.redd.it/PL4PyhChdGdhF...                  NaN   \n",
       "\n",
       "   num_comments  score             subreddit  upvote_ratio  6_way_label  \\\n",
       "0           3.0     15     mildlyinteresting          0.75            0   \n",
       "1           4.0      8  misleadingthumbnails          0.59            2   \n",
       "2           6.0      4     mildlyinteresting          0.60            0   \n",
       "3           9.0    212            pareidolia          0.98            2   \n",
       "4           2.0      6            pareidolia          0.88            2   \n",
       "5           2.0     32     mildlyinteresting          0.87            0   \n",
       "6           0.0      4       fakealbumcovers          0.83            1   \n",
       "7          15.0    243           nottheonion          0.95            0   \n",
       "8          20.0     10    subredditsimulator          0.92            3   \n",
       "9          61.0      5    subredditsimulator          0.86            3   \n",
       "\n",
       "   binary_label  \n",
       "0             0  \n",
       "1             1  \n",
       "2             0  \n",
       "3             1  \n",
       "4             1  \n",
       "5             0  \n",
       "6             1  \n",
       "7             0  \n",
       "8             1  \n",
       "9             1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
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   "execution_count": 5,
   "id": "722e2299",
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   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "! pip install bert-serving-server\n",
    "! pip install bert-serving-client\n",
    "! pip install torch transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "09e8dffc",
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   "outputs": [
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      "text/plain": [
       "BertModel(\n",
       "  (embeddings): BertEmbeddings(\n",
       "    (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "    (position_embeddings): Embedding(512, 768)\n",
       "    (token_type_embeddings): Embedding(2, 768)\n",
       "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (encoder): BertEncoder(\n",
       "    (layer): ModuleList(\n",
       "      (0-11): 12 x BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSdpaSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          (intermediate_act_fn): GELUActivation()\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (pooler): BertPooler(\n",
       "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "    (activation): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import BertModel, BertTokenizer\n",
    "\n",
    "# Load pre-trained BERT model and tokenizer\n",
    "model_name = 'bert-base-uncased'\n",
    "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
    "bert_model = BertModel.from_pretrained(model_name, output_hidden_states = True)\n",
    "\n",
    "# Put the model in evaluation mode, which turns off dropout regularization which is used in training.\n",
    "bert_model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "123c3656",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-09T14:58:16.256413Z",
     "iopub.status.busy": "2025-04-09T14:58:16.255949Z",
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     "shell.execute_reply": "2025-04-09T14:58:16.259038Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_bert_embedding(text):\n",
    "    # Tokenize input text and get token IDs and attention mask\n",
    "    inputs = tokenizer.encode_plus(text, add_special_tokens = True, return_tensors='pt', max_length=80, truncation=True, padding='max_length')\n",
    "\n",
    "    return inputs['input_ids'].squeeze(0), inputs['attention_mask'].squeeze(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f2f68f26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-09T14:58:16.271870Z",
     "iopub.status.busy": "2025-04-09T14:58:16.271629Z",
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     "shell.execute_reply": "2025-04-09T14:58:16.342176Z"
    },
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     "duration": 0.078097,
     "end_time": "2025-04-09T14:58:16.344324",
     "exception": false,
     "start_time": "2025-04-09T14:58:16.266227",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "df_train, df_test = train_test_split(df, test_size=0.2, stratify=df[\"binary_label\"])\n",
    "df_test, df_val = train_test_split(df_test, test_size=0.5, stratify=df_test[\"binary_label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ae5603c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-09T14:58:16.355488Z",
     "iopub.status.busy": "2025-04-09T14:58:16.355259Z",
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     "shell.execute_reply": "2025-04-09T14:58:16.360587Z"
    },
    "papermill": {
     "duration": 0.01337,
     "end_time": "2025-04-09T14:58:16.362806",
     "exception": false,
     "start_time": "2025-04-09T14:58:16.349436",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class FakedditDataset(Dataset):\n",
    "    def __init__(self, df, text_field=\"clean_title\", label_field=\"binary_label\", image_id=\"id\"):\n",
    "        self.df = df.reset_index(drop=True)\n",
    "        self.text_field = text_field\n",
    "        self.label_field = label_field\n",
    "        self.image_id = image_id\n",
    "\n",
    "        self.img_size = 256\n",
    "        # Using the pre-calculated ImageNet mean and std values for normalization\n",
    "        self.mean, self.std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n",
    "\n",
    "        self.transform_func = v2.Compose(\n",
    "                [   v2.Resize(256),\n",
    "                    v2.ToImage(),\n",
    "                    v2.ToDtype(torch.float32, scale=True),\n",
    "                    v2.Normalize(self.mean, self.std)\n",
    "                    ])\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        text = str(self.df.at[index, self.text_field])\n",
    "        label = self.df.at[index, self.label_field]\n",
    "        img_path = \"/kaggle/input/fakenews-fakedit/Dataset/images/\" + self.df.at[index, self.image_id] + \".jpg\"\n",
    "\n",
    "        image = Image.open(img_path)\n",
    "        img = self.transform_func(image)\n",
    "\n",
    "        input_ids, attention_mask = get_bert_embedding(text)\n",
    "\n",
    "        return input_ids, attention_mask, label, img\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b01687f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-09T14:58:16.373613Z",
     "iopub.status.busy": "2025-04-09T14:58:16.373405Z",
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     "shell.execute_reply": "2025-04-09T14:58:16.385182Z"
    },
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     "duration": 0.01933,
     "end_time": "2025-04-09T14:58:16.387149",
     "exception": false,
     "start_time": "2025-04-09T14:58:16.367819",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_dataset = FakedditDataset(df_train)\n",
    "test_dataset = FakedditDataset(df_test)\n",
    "val_dataset = FakedditDataset(df_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3ea3e972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-09T14:58:16.398504Z",
     "iopub.status.busy": "2025-04-09T14:58:16.398275Z",
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     "shell.execute_reply": "2025-04-09T14:58:16.706508Z"
    },
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     "duration": 0.316775,
     "end_time": "2025-04-09T14:58:16.709280",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1643\n",
      "torch.Size([32, 80]) torch.Size([32, 80]) torch.Size([32]) torch.Size([32, 3, 256, 256])\n"
     ]
    }
   ],
   "source": [
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=True)\n",
    "\n",
    "print(len(train_loader))\n",
    "\n",
    "# Verifying dataset was created accurately\n",
    "input_ids, attention_mask, label, img = next(iter(train_loader))\n",
    "print(input_ids.shape, attention_mask.shape, label.shape, img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2bbb61c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-09T14:58:16.721642Z",
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     "shell.execute_reply": "2025-04-09T14:58:16.725731Z"
    },
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     "duration": 0.012021,
     "end_time": "2025-04-09T14:58:16.727524",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class EarlyStopping:\n",
    "    def __init__(self, patience=4, verbose=False, delta=0):\n",
    "        self.patience = patience\n",
    "        self.verbose = verbose\n",
    "        self.counter = 0\n",
    "        self.best_loss = None\n",
    "        self.early_stop = False\n",
    "        self.delta = delta\n",
    "\n",
    "    def __call__(self, val_loss):\n",
    "        if self.best_loss is None:\n",
    "            self.best_loss = val_loss\n",
    "        elif val_loss > self.best_loss + self.delta:\n",
    "            self.counter += 1\n",
    "            if self.verbose:\n",
    "                print(f\"EarlyStopping counter: {self.counter} out of {self.patience}\")\n",
    "            if self.counter >= self.patience:\n",
    "                self.early_stop = True\n",
    "        else:\n",
    "            self.best_loss = val_loss\n",
    "            self.counter = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f2584bec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-09T14:58:16.738341Z",
     "iopub.status.busy": "2025-04-09T14:58:16.738140Z",
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     "shell.execute_reply": "2025-04-09T14:58:16.742634Z"
    },
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     "duration": 0.012462,
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     "exception": false,
     "start_time": "2025-04-09T14:58:16.732558",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 0 1 1]\n",
      "Unique labels: [0 1]\n"
     ]
    }
   ],
   "source": [
    "labels = df_train[\"binary_label\"].values  \n",
    "\n",
    "print(labels)\n",
    "print(\"Unique labels:\", np.unique(labels))\n",
    "# assert set(np.unique(labels)).issubset({0, 1}), \"🔥 Labels must be 0 or 1 for binary classification!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "99e961ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-09T14:58:16.757348Z",
     "iopub.status.busy": "2025-04-09T14:58:16.757133Z",
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     "shell.execute_reply": "2025-04-09T14:58:16.761219Z"
    },
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     "duration": 0.012382,
     "end_time": "2025-04-09T14:58:16.763299",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SelfAttentionFusion(nn.Module):\n",
    "    def __init__(self, embed_dim):\n",
    "        super().__init__()\n",
    "        self.attn = nn.Linear(embed_dim * 2, 2)\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "\n",
    "    def forward(self, x_text, x_img):\n",
    "        stacked = torch.stack([x_text, x_img], dim=1)  # (B, 2, D)\n",
    "        attn_weights = self.softmax(self.attn(torch.cat([x_text, x_img], dim=1))).unsqueeze(2)  # (B, 2, 1)\n",
    "        fused = (attn_weights * stacked).sum(dim=1)  # (B, D)\n",
    "        return fused"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "09b3f58a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-09T14:58:16.774550Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class BERTResNetClassifier(nn.Module):\n",
    "    def __init__(self, num_classes=2):\n",
    "        super(BERTResNetClassifier, self).__init__()\n",
    "\n",
    "        self.num_classes = num_classes\n",
    "\n",
    "        # Image model\n",
    "        self.image_model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V1)\n",
    "        self.fc_image = nn.Linear(1000, 512)\n",
    "        self.drop_img = nn.Dropout(p=0.3)\n",
    "\n",
    "        # Text model\n",
    "        self.text_model = BertModel.from_pretrained(\"bert-base-uncased\")\n",
    "        self.fc_text = nn.Linear(self.text_model.config.hidden_size, 512)\n",
    "        self.drop_text = nn.Dropout(p=0.3)\n",
    "\n",
    "        # Attention-based fusion\n",
    "        self.fusion = SelfAttentionFusion(embed_dim=512)\n",
    "\n",
    "        # Final classification\n",
    "        self.fc_final = nn.Linear(512, num_classes)\n",
    "\n",
    "    def forward(self, image, text_input_ids, text_attention_mask):\n",
    "        # Image path\n",
    "        x_img = self.image_model(image)\n",
    "        x_img = self.drop_img(x_img)\n",
    "        x_img = self.fc_image(x_img)\n",
    "\n",
    "        # Text path\n",
    "        x_text_last_hidden = self.text_model(\n",
    "            input_ids=text_input_ids,\n",
    "            attention_mask=text_attention_mask,\n",
    "            return_dict=False\n",
    "        )[0][:, 0, :]\n",
    "        x_text = self.drop_text(x_text_last_hidden)\n",
    "        x_text = self.fc_text(x_text)\n",
    "\n",
    "        # Fusion\n",
    "        x_fused = self.fusion(x_text, x_img)\n",
    "\n",
    "        # Final classifier\n",
    "        logits = self.fc_final(x_fused)\n",
    "        return logits\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "40348647",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-09T14:58:16.792735Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.utils import compute_class_weight\n",
    "# import torch_xla.core.xla_model as xm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b8ad064a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-09T14:58:16.808136Z",
     "iopub.status.busy": "2025-04-09T14:58:16.807923Z",
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     "shell.execute_reply": "2025-04-09T14:58:18.650416Z"
    },
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     "exception": false,
     "start_time": "2025-04-09T14:58:16.801804",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n",
      "100%|██████████| 97.8M/97.8M [00:00<00:00, 172MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 2 GPUs!\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(device)\n",
    "model = BERTResNetClassifier(num_classes=2)\n",
    "\n",
    "if torch.cuda.device_count() > 1:\n",
    "    print(f\"Using {torch.cuda.device_count()} GPUs!\")\n",
    "    model = nn.DataParallel(model)\n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bafa7e70",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-09T14:58:18.666026Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "\n",
    "class FocalLoss(nn.Module):\n",
    "    def __init__(self, alpha=None, gamma=2.0, reduction='mean'):\n",
    "        super(FocalLoss, self).__init__()\n",
    "        self.alpha = alpha\n",
    "        self.gamma = gamma\n",
    "        self.reduction = reduction\n",
    "\n",
    "    def forward(self, inputs, targets):\n",
    "        ce_loss = F.cross_entropy(inputs, targets, weight=self.alpha, reduction='none')\n",
    "        pt = torch.exp(-ce_loss)  # Prob of correct class\n",
    "        focal_loss = ((1 - pt) ** self.gamma) * ce_loss\n",
    "\n",
    "        if self.reduction == 'mean':\n",
    "            return focal_loss.mean()\n",
    "        elif self.reduction == 'sum':\n",
    "            return focal_loss.sum()\n",
    "        else:\n",
    "            return focal_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9f205502",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-04-09T14:58:18.684572Z",
     "iopub.status.busy": "2025-04-09T14:58:18.684346Z",
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    },
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     "end_time": "2025-04-09T14:58:18.704195",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "\n",
    "class_weights = compute_class_weight(class_weight='balanced', classes=np.unique(labels), y=labels)\n",
    "class_weights = torch.tensor(class_weights, dtype=torch.float).to(device)\n",
    "\n",
    "criterion = FocalLoss(alpha=class_weights, gamma=2.0)\n",
    "# criterion = nn.CrossEntropyLoss(weight=class_weights)\n",
    "# optimizer = torch.optim.Adam(model.parameters(), lr=1e-5, weight_decay=1e-4)\n",
    "optimizer = torch.optim.Adam([\n",
    "    {'params': model.module.text_model.parameters(), 'lr': 1e-5},\n",
    "    {'params': model.module.image_model.parameters(), 'lr': 5e-5},\n",
    "    {'params': list(model.module.fc_image.parameters()) +\n",
    "               list(model.module.fc_text.parameters()) +\n",
    "               list(model.module.fusion.parameters()) +\n",
    "               list(model.module.fc_final.parameters()), 'lr': 1e-4}\n",
    "], weight_decay=1e-5)\n",
    "\n",
    "scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=0, min_lr=1e-6, verbose=True)\n",
    "num_epochs = 20\n",
    "\n",
    "def train_model(model, train_loader, val_loader, criterion, optimizer, scheduler, num_epochs):\n",
    "    early_stopping = EarlyStopping(patience=5, verbose=True)\n",
    "    \n",
    "  # Training loop\n",
    "    for epoch in range(num_epochs):\n",
    "        model.train()\n",
    "        running_loss = 0.0\n",
    "\n",
    "        for input_ids, attention_mask, label, img in train_loader:\n",
    "            input_ids = input_ids.to(device)\n",
    "            attention_mask = attention_mask.to(device)\n",
    "            label = label.to(device)\n",
    "            img = img.to(device)\n",
    "                \n",
    "            optimizer.zero_grad()\n",
    "\n",
    "          # Forward pass\n",
    "            outputs = model(img, input_ids, attention_mask)\n",
    "            loss = criterion(outputs, label)\n",
    "\n",
    "          # Backward pass and optimization\n",
    "            loss.backward()\n",
    "            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
    "            optimizer.step()\n",
    "\n",
    "            running_loss += loss.item()* img.size(0)\n",
    "            \n",
    "       # Validating model and ensuring loss is decreasing     \n",
    "        model.eval()\n",
    "        val_loss = 0.0\n",
    "        correct_preds = 0\n",
    "        with torch.no_grad():\n",
    "            for input_ids, attention_mask, label, img in val_loader:\n",
    "                input_ids = input_ids.to(device)\n",
    "                attention_mask = attention_mask.to(device)\n",
    "                label = label.to(device)\n",
    "                img = img.to(device)\n",
    "    \n",
    "                outputs = model(img, input_ids, attention_mask)\n",
    "                loss = criterion(outputs, label)\n",
    "                val_loss += loss.item() * img.size(0)\n",
    "\n",
    "                _, preds = torch.max(outputs, 1)\n",
    "                correct_preds += torch.sum(preds == label)\n",
    "\n",
    "        val_loss = val_loss / len(val_loader.dataset)\n",
    "        accuracy = correct_preds.double() / len(val_loader.dataset)\n",
    "        scheduler.step(val_loss)\n",
    "        print(f'Epoch {epoch+1}/{num_epochs}, Training Loss: {running_loss/len(train_loader.dataset):.4f}, Validation Loss: {val_loss:.4f}, Accuracy: {accuracy:.4f}')\n",
    "\n",
    "        # Early stopping\n",
    "        early_stopping(val_loss)\n",
    "        if early_stopping.early_stop:\n",
    "            print(\"Early stopping triggered. Stopping training.\")\n",
    "            break\n",
    "\n",
    "    torch.save(model.state_dict(), '/kaggle/working/model2.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bec89fae",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "\n",
    "def evaluate_model(model, test_loader, criterion):\n",
    "    model.eval()\n",
    "    val_losses = []\n",
    "    correct_preds = 0\n",
    "\n",
    "    all_preds = []\n",
    "    all_labels = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for input_ids, attention_mask, label, img in test_loader:\n",
    "            input_ids = input_ids.to(device)\n",
    "            attention_mask = attention_mask.to(device)\n",
    "            label = label.to(device)\n",
    "            img = img.to(device)\n",
    "\n",
    "            outputs = model(\n",
    "                  image = img,\n",
    "                  text_input_ids = input_ids,\n",
    "                  text_attention_mask = attention_mask\n",
    "            )\n",
    "\n",
    "            # Final Softmax layer returns class predictions per sample in batch\n",
    "            # Highest probability value resembles class prediction and is assigned to preds variable\n",
    "            _, preds = torch.max(outputs, dim=1)\n",
    "            #print(outputs)\n",
    "\n",
    "            # Loss is calculated by applying Cross Entropy Loss\n",
    "            val_loss = criterion(outputs, label)\n",
    "\n",
    "            # Counting correct model predictions and incrementing correct prediction count\n",
    "            correct_preds += torch.sum(preds == label)\n",
    "            print(preds, label)\n",
    "\n",
    "            # Appending current loss per batch to list of losses per epoch\n",
    "            val_losses.append(val_loss.item())\n",
    "            \n",
    "            all_preds.extend(preds.cpu().numpy())\n",
    "            all_labels.extend(label.cpu().numpy())\n",
    "            \n",
    "\n",
    "    accuracy = float((correct_preds.double() / len(df_test)) * 100)\n",
    "    precision = precision_score(all_labels, all_preds, average='weighted')\n",
    "    recall = recall_score(all_labels, all_preds, average='weighted')\n",
    "\n",
    "    print(\"\\nAccuracy: \", accuracy)\n",
    "    print(\"Precision: \", precision)\n",
    "    print(\"Recall: \", recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "73760f46",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20, Training Loss: 0.1160, Validation Loss: 0.0951, Accuracy: 0.8409\n",
      "Epoch 2/20, Training Loss: 0.0839, Validation Loss: 0.0968, Accuracy: 0.8478\n",
      "EarlyStopping counter: 1 out of 5\n",
      "Epoch 3/20, Training Loss: 0.0541, Validation Loss: 0.1117, Accuracy: 0.8513\n",
      "EarlyStopping counter: 2 out of 5\n",
      "Epoch 4/20, Training Loss: 0.0336, Validation Loss: 0.1642, Accuracy: 0.8517\n",
      "EarlyStopping counter: 3 out of 5\n",
      "Epoch 5/20, Training Loss: 0.0198, Validation Loss: 0.2515, Accuracy: 0.8484\n",
      "EarlyStopping counter: 4 out of 5\n",
      "Epoch 6/20, Training Loss: 0.0152, Validation Loss: 0.2819, Accuracy: 0.8484\n",
      "EarlyStopping counter: 5 out of 5\n",
      "Early stopping triggered. Stopping training.\n",
      "\n",
      "\n",
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      "        1, 1, 0, 1, 0, 0, 1, 0], device='cuda:0')\n",
      "tensor([0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0,\n",
      "        1, 0, 1, 1, 0, 1, 1, 1], device='cuda:0') tensor([0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n",
      "        0, 1, 1, 1, 0, 1, 1, 1], device='cuda:0')\n",
      "tensor([0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n",
      "        0, 1, 0, 0, 1, 0, 1, 1], device='cuda:0') tensor([0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0,\n",
      "        0, 1, 0, 0, 1, 0, 1, 1], device='cuda:0')\n",
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      "        0, 1, 1, 0, 0, 1, 0, 1], device='cuda:0')\n",
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      "        1, 1, 0, 1, 0, 1, 1, 1], device='cuda:0')\n",
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      "        0, 1, 1, 0, 1, 1, 0, 0], device='cuda:0') tensor([1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
      "        1, 1, 1, 0, 1, 0, 0, 0], device='cuda:0')\n",
      "tensor([0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0,\n",
      "        1, 1, 0, 1, 1, 1, 1, 1], device='cuda:0') tensor([0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0,\n",
      "        1, 1, 0, 1, 1, 0, 0, 1], device='cuda:0')\n",
      "tensor([1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1,\n",
      "        1, 0, 1, 1, 0, 1, 0, 0], device='cuda:0') tensor([0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1,\n",
      "        1, 0, 1, 1, 0, 1, 0, 0], device='cuda:0')\n",
      "tensor([0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
      "        0, 0, 0, 0, 1, 1, 0, 0], device='cuda:0') tensor([0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1,\n",
      "        0, 0, 0, 0, 1, 1, 0, 0], device='cuda:0')\n",
      "tensor([0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
      "        1, 1, 0, 0, 1, 0, 0, 1], device='cuda:0') tensor([1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
      "        1, 1, 0, 0, 1, 0, 0, 1], device='cuda:0')\n",
      "tensor([0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "        0, 0, 0, 0, 1, 1, 0, 0], device='cuda:0') tensor([0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
      "        0, 1, 1, 0, 1, 1, 0, 0], device='cuda:0')\n",
      "tensor([1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,\n",
      "        1, 0, 0, 1, 0, 0, 1, 0], device='cuda:0') tensor([1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,\n",
      "        0, 0, 0, 1, 0, 1, 1, 0], device='cuda:0')\n",
      "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n",
      "        1, 0, 0, 0, 1, 1, 1, 1], device='cuda:0') tensor([0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
      "        1, 0, 0, 0, 1, 1, 1, 1], device='cuda:0')\n",
      "tensor([1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
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      "        1, 0, 0, 1, 0, 0, 1, 0], device='cuda:0')\n",
      "tensor([1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,\n",
      "        1, 0, 0, 1, 1, 0, 1, 1], device='cuda:0') tensor([1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0,\n",
      "        1, 0, 1, 1, 1, 0, 1, 1], device='cuda:0')\n",
      "tensor([0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
      "        0, 0, 1, 1, 0, 1, 1, 1], device='cuda:0') tensor([0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,\n",
      "        1, 0, 0, 1, 0, 1, 1, 0], device='cuda:0')\n",
      "tensor([0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0,\n",
      "        1, 1, 1, 1, 1, 1, 0, 0], device='cuda:0') tensor([0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
      "        1, 1, 1, 1, 1, 1, 0, 0], device='cuda:0')\n",
      "tensor([1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0,\n",
      "        1, 1, 1, 0, 1, 0, 0, 0], device='cuda:0') tensor([1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0,\n",
      "        1, 1, 0, 0, 1, 1, 1, 0], device='cuda:0')\n",
      "tensor([1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1,\n",
      "        0, 1, 1, 0, 0, 0, 1, 0], device='cuda:0') tensor([1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
      "        0, 0, 0, 0, 0, 0, 1, 0], device='cuda:0')\n",
      "tensor([1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0,\n",
      "        0, 0, 0, 0, 0, 1, 0, 0], device='cuda:0') tensor([1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1,\n",
      "        0, 0, 0, 0, 0, 1, 0, 0], device='cuda:0')\n",
      "tensor([1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1,\n",
      "        0, 0, 1, 1, 0, 1, 1, 0], device='cuda:0') tensor([1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,\n",
      "        1, 0, 1, 1, 1, 1, 1, 0], device='cuda:0')\n",
      "tensor([0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1,\n",
      "        0, 0, 0, 0, 0, 1, 1, 1], device='cuda:0') tensor([1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0,\n",
      "        0, 0, 1, 0, 0, 1, 1, 1], device='cuda:0')\n",
      "tensor([0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1,\n",
      "        0, 1, 0, 0, 0, 1, 0, 1], device='cuda:0') tensor([1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1,\n",
      "        0, 1, 0, 1, 1, 1, 0, 1], device='cuda:0')\n",
      "tensor([1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0,\n",
      "        0, 1, 1, 1, 0, 0, 1, 1], device='cuda:0') tensor([1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0,\n",
      "        0, 0, 1, 1, 0, 0, 1, 1], device='cuda:0')\n",
      "tensor([1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0,\n",
      "        0, 0, 1, 0, 1, 0, 0, 1], device='cuda:0') tensor([1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "        0, 1, 1, 0, 0, 0, 0, 1], device='cuda:0')\n",
      "tensor([1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
      "        1, 1, 1, 0, 0, 1, 0, 1], device='cuda:0') tensor([1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0,\n",
      "        1, 1, 1, 1, 0, 1, 0, 0], device='cuda:0')\n",
      "tensor([1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0,\n",
      "        0, 0, 0, 1, 1, 0, 0, 0], device='cuda:0') tensor([1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
      "        0, 0, 0, 1, 1, 0, 0, 0], device='cuda:0')\n",
      "tensor([1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
      "        1, 1, 0, 1, 1, 1, 0, 0], device='cuda:0') tensor([1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
      "        0, 1, 0, 1, 1, 1, 0, 0], device='cuda:0')\n",
      "tensor([0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1,\n",
      "        1, 0, 0, 0, 0, 0, 1, 0], device='cuda:0') tensor([0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0,\n",
      "        0, 0, 0, 1, 0, 0, 0, 0], device='cuda:0')\n",
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      "tensor([1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0,\n",
      "        1, 0, 0, 0, 1, 1, 1, 0], device='cuda:0') tensor([1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1,\n",
      "        1, 0, 0, 0, 1, 1, 1, 0], device='cuda:0')\n",
      "tensor([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
      "        1, 0, 0, 1, 1, 0, 1, 0], device='cuda:0') tensor([0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
      "        1, 0, 0, 1, 1, 0, 1, 0], device='cuda:0')\n",
      "tensor([0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1,\n",
      "        0, 0, 0, 0, 0, 0, 1, 1], device='cuda:0') tensor([0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1,\n",
      "        0, 0, 1, 0, 0, 0, 1, 1], device='cuda:0')\n",
      "tensor([1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
      "        0, 1, 0, 1, 0, 0, 0, 0], device='cuda:0') tensor([0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
      "        0, 1, 0, 1, 0, 1, 0, 0], device='cuda:0')\n",
      "tensor([1, 0, 0, 1, 1, 1, 0, 0, 1], device='cuda:0') tensor([1, 0, 1, 1, 1, 0, 0, 0, 1], device='cuda:0')\n",
      "\n",
      "Accuracy:  85.61424874410108\n",
      "Precision:  0.8561740091595716\n",
      "Recall:  0.8561424874410108\n"
     ]
    }
   ],
   "source": [
    "train_model(model, train_loader,val_loader, criterion, optimizer, scheduler, num_epochs)\n",
    "print(\"\\n\")\n",
    "evaluate_model(model, test_loader, criterion)"
   ]
  },
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   "id": "afc77f76",
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    "torch.save(model, \"/kaggle/working/model.pt\")\n",
    "torch.save(model.state_dict(), \"/kaggle/working/state_dict.pth\")    "
   ]
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