yoga_classification_RESNET.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "import torchvision \n",
    "import numpy as np\n",
    "import os \n",
    "import pandas as pd \n",
    "from PIL import Image\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path=\"E:\\\\datasets\\\\yoga pose estimation\\\\\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df=pd.read_pickle(path+\"datasettest_df_yoga.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>eka pada rajakapotasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>savasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ardha pincha mayurasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adho mukha vriksasana</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bhairavasana</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <th>1121</th>\n",
       "      <td>garudasana</td>\n",
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       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>viparita karani</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
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       "      <th>1123</th>\n",
       "      <td>urdhva hastasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>utthita ashwa sanchalanasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>malasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                             Label  \\\n",
       "0          eka pada rajakapotasana   \n",
       "1                         savasana   \n",
       "2          ardha pincha mayurasana   \n",
       "3            adho mukha vriksasana   \n",
       "4                     bhairavasana   \n",
       "...                            ...   \n",
       "1121                    garudasana   \n",
       "1122               viparita karani   \n",
       "1123              urdhva hastasana   \n",
       "1124  utthita ashwa sanchalanasana   \n",
       "1125                      malasana   \n",
       "\n",
       "                                                  Image  \n",
       "0     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "1     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "2     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "3     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "4     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "...                                                 ...  \n",
       "1121  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "1122  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "1123  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "1124  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "1125  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "\n",
       "[1126 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=pd.read_pickle(path+\"datasettrain_yoga.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset ,DataLoader\n",
    "from torchvision import transforms\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bharadvajasana i</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bhujapidasana</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>urdhva mukha svanasana</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>anantasana</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>4496</th>\n",
       "      <td>setu bandha sarvangasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4497</th>\n",
       "      <td>viparita karani</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4498</th>\n",
       "      <td>paripurna navasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4499</th>\n",
       "      <td>setu bandha sarvangasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4500</th>\n",
       "      <td>chaturanga dandasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4501 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Label  \\\n",
       "0      eka pada rajakapotasana   \n",
       "1             bharadvajasana i   \n",
       "2                bhujapidasana   \n",
       "3       urdhva mukha svanasana   \n",
       "4                   anantasana   \n",
       "...                        ...   \n",
       "4496  setu bandha sarvangasana   \n",
       "4497           viparita karani   \n",
       "4498        paripurna navasana   \n",
       "4499  setu bandha sarvangasana   \n",
       "4500      chaturanga dandasana   \n",
       "\n",
       "                                                  Image  \n",
       "0     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "1     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "2     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "3     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "4     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "...                                                 ...  \n",
       "4496  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "4497  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "4498  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "4499  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "4500  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "\n",
       "[4501 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             Label  \\\n",
      "0          eka pada rajakapotasana   \n",
      "1                 bharadvajasana i   \n",
      "2                    bhujapidasana   \n",
      "3           urdhva mukha svanasana   \n",
      "4                       anantasana   \n",
      "...                            ...   \n",
      "5622                    garudasana   \n",
      "5623               viparita karani   \n",
      "5624              urdhva hastasana   \n",
      "5625  utthita ashwa sanchalanasana   \n",
      "5626                      malasana   \n",
      "\n",
      "                                                  Image  \n",
      "0     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
      "1     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
      "2     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
      "3     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
      "4     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
      "...                                                 ...  \n",
      "5622  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
      "5623  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
      "5624  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
      "5625  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
      "5626  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
      "\n",
      "[5627 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "combined_df = pd.concat([train_df, test_df], ignore_index=True)\n",
    "\n",
    "print(combined_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>bharadvajasana i</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bhujapidasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>urdhva mukha svanasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>anantasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5622</th>\n",
       "      <td>garudasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5623</th>\n",
       "      <td>viparita karani</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5624</th>\n",
       "      <td>urdhva hastasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5625</th>\n",
       "      <td>utthita ashwa sanchalanasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5626</th>\n",
       "      <td>malasana</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5627 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "                             Label  \\\n",
       "0          eka pada rajakapotasana   \n",
       "1                 bharadvajasana i   \n",
       "2                    bhujapidasana   \n",
       "3           urdhva mukha svanasana   \n",
       "4                       anantasana   \n",
       "...                            ...   \n",
       "5622                    garudasana   \n",
       "5623               viparita karani   \n",
       "5624              urdhva hastasana   \n",
       "5625  utthita ashwa sanchalanasana   \n",
       "5626                      malasana   \n",
       "\n",
       "                                                  Image  \n",
       "0     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "1     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "2     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "3     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "4     <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "...                                                 ...  \n",
       "5622  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "5623  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "5624  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "5625  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "5626  <PIL.PngImagePlugin.PngImageFile image mode=RG...  \n",
       "\n",
       "[5627 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Cannot index by location index with a non-integer key",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[33], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mcombined_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlabel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ninja\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\indexing.py:1073\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1070\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m   1072\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m-> 1073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ninja\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\indexing.py:1622\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1620\u001b[0m key \u001b[38;5;241m=\u001b[39m item_from_zerodim(key)\n\u001b[0;32m   1621\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_integer(key):\n\u001b[1;32m-> 1622\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot index by location index with a non-integer key\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1624\u001b[0m \u001b[38;5;66;03m# validate the location\u001b[39;00m\n\u001b[0;32m   1625\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_integer(key, axis)\n",
      "\u001b[1;31mTypeError\u001b[0m: Cannot index by location index with a non-integer key"
     ]
    }
   ],
   "source": [
    "combined_df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_df[\"Label\"]=label_encoder.fit_transform(combined_df[\"Label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Label</th>\n",
       "      <th>Image</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>87</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5622</th>\n",
       "      <td>35</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5623</th>\n",
       "      <td>99</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5624</th>\n",
       "      <td>86</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5625</th>\n",
       "      <td>93</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5626</th>\n",
       "      <td>46</td>\n",
       "      <td>&lt;PIL.PngImagePlugin.PngImageFile image mode=RG...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5627 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Label                                              Image\n",
       "0        31  <PIL.PngImagePlugin.PngImageFile image mode=RG...\n",
       "1        17  <PIL.PngImagePlugin.PngImageFile image mode=RG...\n",
       "2        20  <PIL.PngImagePlugin.PngImageFile image mode=RG...\n",
       "3        87  <PIL.PngImagePlugin.PngImageFile image mode=RG...\n",
       "4         4  <PIL.PngImagePlugin.PngImageFile image mode=RG...\n",
       "...     ...                                                ...\n",
       "5622     35  <PIL.PngImagePlugin.PngImageFile image mode=RG...\n",
       "5623     99  <PIL.PngImagePlugin.PngImageFile image mode=RG...\n",
       "5624     86  <PIL.PngImagePlugin.PngImageFile image mode=RG...\n",
       "5625     93  <PIL.PngImagePlugin.PngImageFile image mode=RG...\n",
       "5626     46  <PIL.PngImagePlugin.PngImageFile image mode=RG...\n",
       "\n",
       "[5627 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes=combined_df['Label'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "107"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(107,)\n"
     ]
    }
   ],
   "source": [
    "print(label_encoder.classes_.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe=combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "from torch.utils.data import Dataset\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomDataset(Dataset):\n",
    "    def __init__ (self,dataframe,transform=None):\n",
    "        self.data=dataframe\n",
    "        self.transform=transform\n",
    "    def __len__(self):\n",
    "\n",
    "        return len(self.data)\n",
    "\n",
    "        \n",
    "    def __getitem__(self,index):\n",
    "\n",
    "        label=self.data.iloc[index][\"Label\"]\n",
    "\n",
    "        image=self.data.iloc[index][\"Image\"]\n",
    "\n",
    "        if self.transform:\n",
    "            image=self.transform(image)\n",
    "\n",
    "\n",
    "        return image, label\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),  # RESNET İÇİN\n",
    "    transforms.ToTensor(),  # Görüntüyü tensöre çeviriyoruz\n",
    "    transforms.Normalize(mean=[0.485, 0.456, 0.406],  # Normalizasyon\n",
    "                         std=[0.229, 0.224, 0.225])\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=CustomDataset(dataframe=dataframe,transform=transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "image,label=dataset[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([3, 224, 224])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.CustomDataset at 0x2833149e790>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = dataset.data['Label'].values[:len(dataset)]  \n",
    "\n",
    "indices = list(range(len(dataset)))\n",
    "train_indices, test_indices = train_test_split(indices, test_size=0.2, stratify=labels)\n",
    "\n",
    "train_dataset = torch.utils.data.Subset(dataset, train_indices)\n",
    "test_dataset = torch.utils.data.Subset(dataset, test_indices)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 67,  56,  84,  97,  46,  98,  89,  70,  31,  47,  14,  64,   6,  39,\n",
      "         73,  43, 100,  95,  46,  60,  77,  72,   9,  75,  18,  94,  86,  14,\n",
      "         97,  56,  37,  73], dtype=torch.int32)\n",
      "tensor([ 70,   7,  98,  74,  36,  43,  11,  87,  71, 102,  46, 103,  81,  74,\n",
      "         41,  95,  51,  34,  99,  89,  29,  35,  14,  22,  20,  18,  52, 100,\n",
      "         69,  87,  64,  38], dtype=torch.int32)\n",
      "tensor([ 21,  67,  62,  76,  73,  76,  70, 102,  66,  41,  63,  86,  56,  23,\n",
      "        102,   9,  89,  99,  73,  21,  56,  36,  61,  99,  86,  99,  98,  40,\n",
      "         34,  75,  27,  89], dtype=torch.int32)\n",
      "tensor([ 76, 105,  18,   1,  38,  59,  65,  53,  35,  42, 105,  66,  73,   4,\n",
      "         12,  24,  36,  51,  21,  37,  10,  88,  24,  52,  92,  65,  14,  55,\n",
      "         55,  22,  83,  89], dtype=torch.int32)\n",
      "tensor([ 75,  17,  41,  73,  60,  34,  76,  14,  29,  52,  68,  73,  34,  22,\n",
      "         66,  82,  64,  71,  92,  36,  10,  12,  68, 100,  58,  71,  82,  85,\n",
      "         29,  81,  20,  24], dtype=torch.int32)\n",
      "tensor([ 40,  95,  81,  82,  94,  67,  37,   4,  87,   9,  66,  60,  77,  50,\n",
      "         62,  86,  12,  47, 106,  18,  46,   8,  10,  82,  26,  33,  33,  28,\n",
      "         30,  32,  88,  29], dtype=torch.int32)\n",
      "tensor([101,  36,  42,  57, 105,  52,  97,   3,  22, 101,  79,  41, 104,  71,\n",
      "         81,  11,  70,  23,  83,  49,  91,  78,  32,  19,  11,  95,  85,  43,\n",
      "        100,  78,  45,  94], dtype=torch.int32)\n",
      "tensor([ 27,  85,  61,  52, 105,  65,  19,  68,  60,  81,  24,  21,   9,  12,\n",
      "         14,  92,  53,  98, 104,  64,  74,  70,  79,  97,  85,  68,  67,   7,\n",
      "         25,  72,  89,  67], dtype=torch.int32)\n",
      "tensor([ 68,  86,  89, 106,  95,  64,  98,  92,  45,  31,  87,  45,  59,  56,\n",
      "         38,  45,  89,  56,  93,  91,  45,  63,  37,  90,  92,  32,  82,  39,\n",
      "         12,  12,  81,   9], dtype=torch.int32)\n",
      "tensor([ 22,  21,  34,  25,  78,  92,  37,  84,  40,  98,  56,  37, 102,  73,\n",
      "         85,  45,  82,  66,  11,   9,  74,  65,  99,  66, 104,  91,  56,  26,\n",
      "         67,   6,  96,  14], dtype=torch.int32)\n",
      "tensor([ 14,  92,  12,  85,   7,  18,   9,  70,   3,  36,  50,  85,   5,  96,\n",
      "        104, 100,  19,  20,  62,  14,  44,  60,  89,   5,  25,   8,  74,  26,\n",
      "         21,   9,  78,  32], dtype=torch.int32)\n",
      "tensor([ 14,  89,  49,  83,  37,  23,  78,  24, 106,  22,  95,   8,  10,  76,\n",
      "         74,  42,  18,  88,  79, 103,  74,  70,   3,  94,  58,  82,  75,  44,\n",
      "         28,  10,  61,  88], dtype=torch.int32)\n",
      "tensor([ 33,  22,  22,  66,  46,  26,  88,  63,  14,  84,  60,  93,  85,  60,\n",
      "         38,  17,  27, 105,  62,  77,  21,  63,  63,  75,  64,  85,   8,  14,\n",
      "         20,  36,  83,  22], dtype=torch.int32)\n",
      "tensor([62,  3, 89,  8, 21, 23, 36,  6, 89,  7, 71, 97, 40, 62, 73, 31,  6, 25,\n",
      "        79,  3, 69, 93, 34, 25, 85, 84, 45, 91, 52, 71, 63, 81],\n",
      "       dtype=torch.int32)\n",
      "tensor([  7,  49, 106, 101,  21,  21,  72,  84,  59,  62,  41,  20,  86,  19,\n",
      "         20,  17,   4,  35,  19,  36,  88,  37,  66,  70,  96,  47,   5,  89,\n",
      "         32,   4,  73,  42], dtype=torch.int32)\n",
      "tensor([ 36,  84,  93,  99,  28,  30,   3,  59,  43,   7,  51,   1,   9,   3,\n",
      "         77,  37, 105,   2,  22,  74,  26,  87,  75,  20,  82,  98,   4,  41,\n",
      "         53,  73,  95,  60], dtype=torch.int32)\n",
      "tensor([ 85,  51,  98,  60,  92,  82,  76,  77,  27,   2,  44,  24,  50,  99,\n",
      "         17,  16,  62,  82,  11,  26,  47,  41,  23,   9,  49,  60,  32,   1,\n",
      "        105,  44,  12,  78], dtype=torch.int32)\n",
      "tensor([ 11,  22,  31,  59,  78,  19,  59,  45,  10,  21,  60,  36,  46,  30,\n",
      "         88,  21,  72,  36,  40,  40,  70,  94,  95,  36,  96,  71,  54,  97,\n",
      "        102,  22,  20,  72], dtype=torch.int32)\n",
      "tensor([ 21,  62,  16,   5,  59,  24,  16, 101,  24,  76,  47,  96,  84,  67,\n",
      "         69,  91,  47,  14,  31,  50,  11,  98,  20,  65,  52,  77,  68,   1,\n",
      "         42,  36,  92,  51], dtype=torch.int32)\n",
      "tensor([ 19,  79,  56,  91,   8,  23, 100,  63,   7,  28,  85,  30,   1,  20,\n",
      "         81,  62,  24,   6,  24,  10,  38, 105,  98,  84,  24,  71,  50,  10,\n",
      "        104,  51,  81,  36], dtype=torch.int32)\n",
      "tensor([ 67,  86,  10,  17, 104,  10,  98,  83,   2,   1,  60,  68,   1,  89,\n",
      "        100,  49,  88,  68,   4,  88,  62,  92,  33,  26,  65,  61,  60,  82,\n",
      "         30,  23,  50,  39], dtype=torch.int32)\n",
      "tensor([ 94,  97,  63,  99,  14,  32, 102, 104,  14,  59,  79,  81,  24,  39,\n",
      "          3,  76,  72,  36,  27,  40,  25,  22,  98,  95,  66,  21,  78,  93,\n",
      "         78,  66,  56,   6], dtype=torch.int32)\n",
      "tensor([ 18,  47,  75,  85,  44,  45,  70,  17,  84,  77,  24,  64,   6, 100,\n",
      "         74,  68,  37,  59,  72,  99,  50,  18,   5,  53,  85,  52,  70,  42,\n",
      "         31,  87,  81,  61], dtype=torch.int32)\n",
      "tensor([ 39,  55,  24,  35,  92,  22,  20,  45,  35,  35,  38,  99,  80,  28,\n",
      "         37,  25,  14,  94,  46,  64,  91,  47,  34,  98,   5, 106,  12,  45,\n",
      "         75,  44,  43,  17], dtype=torch.int32)\n",
      "tensor([101,  70,  62,  63, 105,  81,   6,   5,  34,  64,  92, 102,  40,  95,\n",
      "         84,  30,  70,  43,  75,  76, 105,  90,  14,  94,  47,  69,  67,  21,\n",
      "         53,  33,  46,   6], dtype=torch.int32)\n",
      "tensor([105,  76,  57,  22,  82, 102,  66,  23,  51,  88,  50,  19,  89,  99,\n",
      "         37,  31,  93,  14,  96,  21,  89,  79,  60,   8, 104,   3,  75,  99,\n",
      "        102,   4,  96, 104], dtype=torch.int32)\n",
      "tensor([ 76,  99,   4,  77,  69,  73,  68,  32,  66,  70, 101,  95,  11,  12,\n",
      "         95,  20,  73,  81,   6,  95,  11,  12,  22,  50,  86,  40,  54,  40,\n",
      "         84,  23,  66,  60], dtype=torch.int32)\n",
      "tensor([ 54,  82,  24,  59,  89,  25,  76, 102,  56,  64,   2,  14,   7, 103,\n",
      "         96,  56,  84,  69,  50,  41,   5,  63,   9,  31,  28,  24,  50,  14,\n",
      "         30,  85,  63,  87], dtype=torch.int32)\n",
      "tensor([87, 91, 54, 36, 29, 78, 87,  9, 37, 79, 28, 29, 25, 47, 85, 91,  8, 87,\n",
      "        95, 79, 65, 10, 92, 13, 92, 69, 19, 32, 12, 37, 26, 28],\n",
      "       dtype=torch.int32)\n",
      "tensor([ 58,  39,  25,  10,  81,  21,   3,  46,  40,  29,  89,   3,  12,  66,\n",
      "         77,  50,  89,  23,  96,  65,  29,   8,  55, 102,  72,  57,  97, 102,\n",
      "          3,  38,  51,  24], dtype=torch.int32)\n",
      "tensor([35, 42, 89, 35, 24,  4, 83, 20, 14, 60, 21,  4, 66, 72, 86, 57, 22, 19,\n",
      "        51, 25, 81, 63, 57, 88, 38, 47,  3, 89, 46, 85, 11, 32],\n",
      "       dtype=torch.int32)\n",
      "tensor([ 94,  88,  57,  61,  97,  31,  68,  72,  79,  25,  35, 104, 105,  30,\n",
      "         66,  24,  23,  96,  51,  29,  65,  67,  42,  79,  56,   4,  29,  94,\n",
      "         89,  24,  98,  66], dtype=torch.int32)\n",
      "tensor([ 49,  52,  29,  30,  16,  42, 102,  27,  35,  41,  47,  67,  64,  47,\n",
      "          6,   5,  39,  21,  77,  91,   6,  97,  18,  81, 105,  43,  24, 100,\n",
      "         52,  87,  11,  46], dtype=torch.int32)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x,y \u001b[38;5;129;01min\u001b[39;00m train_loader: \n\u001b[0;32m      2\u001b[0m     \u001b[38;5;28mprint\u001b[39m(y)\n",
      "File \u001b[1;32mc:\\Users\\ninja\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:630\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    628\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m    629\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 630\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    631\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    632\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m    633\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m    634\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
      "File \u001b[1;32mc:\\Users\\ninja\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:673\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    671\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    672\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 673\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    674\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m    675\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
      "File \u001b[1;32mc:\\Users\\ninja\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:50\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_collation:\n\u001b[0;32m     49\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__getitems__\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__:\n\u001b[1;32m---> 50\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__getitems__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpossibly_batched_index\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     51\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m         data \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m possibly_batched_index]\n",
      "File \u001b[1;32mc:\\Users\\ninja\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\utils\\data\\dataset.py:420\u001b[0m, in \u001b[0;36mSubset.__getitems__\u001b[1;34m(self, indices)\u001b[0m\n\u001b[0;32m    418\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m indices])  \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[0;32m    419\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 420\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices[idx]] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m indices]\n",
      "File \u001b[1;32mc:\\Users\\ninja\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\utils\\data\\dataset.py:420\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    418\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39m__getitems__([\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindices[idx] \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m indices])  \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[0;32m    419\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 420\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m[\u001b[49m\u001b[43midx\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m indices]\n",
      "Cell \u001b[1;32mIn[16], line 17\u001b[0m, in \u001b[0;36mCustomDataset.__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m     14\u001b[0m image\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39miloc[index][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mImage\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m     16\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform:\n\u001b[1;32m---> 17\u001b[0m     image\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m image, label\n",
      "File \u001b[1;32mc:\\Users\\ninja\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\transforms\\transforms.py:95\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[1;34m(self, img)\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, img):\n\u001b[0;32m     94\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m t \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransforms:\n\u001b[1;32m---> 95\u001b[0m         img \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     96\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
      "File \u001b[1;32mc:\\Users\\ninja\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\transforms\\transforms.py:137\u001b[0m, in \u001b[0;36mToTensor.__call__\u001b[1;34m(self, pic)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, pic):\n\u001b[0;32m    130\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    131\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m    132\u001b[0m \u001b[38;5;124;03m        pic (PIL Image or numpy.ndarray): Image to be converted to tensor.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    135\u001b[0m \u001b[38;5;124;03m        Tensor: Converted image.\u001b[39;00m\n\u001b[0;32m    136\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 137\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_tensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpic\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\ninja\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\transforms\\functional.py:176\u001b[0m, in \u001b[0;36mto_tensor\u001b[1;34m(pic)\u001b[0m\n\u001b[0;32m    174\u001b[0m img \u001b[38;5;241m=\u001b[39m img\u001b[38;5;241m.\u001b[39mpermute((\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m))\u001b[38;5;241m.\u001b[39mcontiguous()\n\u001b[0;32m    175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(img, torch\u001b[38;5;241m.\u001b[39mByteTensor):\n\u001b[1;32m--> 176\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_float_dtype\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdiv(\u001b[38;5;241m255\u001b[39m)\n\u001b[0;32m    177\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    178\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m img\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for x,y in train_loader: \n",
    "    print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset size: 4501\n",
      "Test dataset size: 1126\n"
     ]
    }
   ],
   "source": [
    "print(f\"Train dataset size: {len(train_dataset)}\")\n",
    "print(f\"Test dataset size: {len(test_dataset)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "if torch.cuda.is_available():\n",
    "    device=torch.device(\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "device\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RESNET "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ninja\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "c:\\Users\\ninja\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "import torchvision.models as models\n",
    "\n",
    "model= models.resnet18(pretrained=True)\n",
    "\n",
    "num_classes = 107  \n",
    "model.fc = nn.Linear(model.fc.in_features, num_classes)  # SON KATMANI SINIF SAYISINA GÖRE DEĞİŞTİRİYORUZ\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=512, out_features=107, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=512, out_features=107, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchsummary import summary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 64, 112, 112]           9,408\n",
      "       BatchNorm2d-2         [-1, 64, 112, 112]             128\n",
      "              ReLU-3         [-1, 64, 112, 112]               0\n",
      "         MaxPool2d-4           [-1, 64, 56, 56]               0\n",
      "            Conv2d-5           [-1, 64, 56, 56]          36,864\n",
      "       BatchNorm2d-6           [-1, 64, 56, 56]             128\n",
      "              ReLU-7           [-1, 64, 56, 56]               0\n",
      "            Conv2d-8           [-1, 64, 56, 56]          36,864\n",
      "       BatchNorm2d-9           [-1, 64, 56, 56]             128\n",
      "             ReLU-10           [-1, 64, 56, 56]               0\n",
      "       BasicBlock-11           [-1, 64, 56, 56]               0\n",
      "           Conv2d-12           [-1, 64, 56, 56]          36,864\n",
      "      BatchNorm2d-13           [-1, 64, 56, 56]             128\n",
      "             ReLU-14           [-1, 64, 56, 56]               0\n",
      "           Conv2d-15           [-1, 64, 56, 56]          36,864\n",
      "      BatchNorm2d-16           [-1, 64, 56, 56]             128\n",
      "             ReLU-17           [-1, 64, 56, 56]               0\n",
      "       BasicBlock-18           [-1, 64, 56, 56]               0\n",
      "           Conv2d-19          [-1, 128, 28, 28]          73,728\n",
      "      BatchNorm2d-20          [-1, 128, 28, 28]             256\n",
      "             ReLU-21          [-1, 128, 28, 28]               0\n",
      "           Conv2d-22          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-23          [-1, 128, 28, 28]             256\n",
      "           Conv2d-24          [-1, 128, 28, 28]           8,192\n",
      "      BatchNorm2d-25          [-1, 128, 28, 28]             256\n",
      "             ReLU-26          [-1, 128, 28, 28]               0\n",
      "       BasicBlock-27          [-1, 128, 28, 28]               0\n",
      "           Conv2d-28          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-29          [-1, 128, 28, 28]             256\n",
      "             ReLU-30          [-1, 128, 28, 28]               0\n",
      "           Conv2d-31          [-1, 128, 28, 28]         147,456\n",
      "      BatchNorm2d-32          [-1, 128, 28, 28]             256\n",
      "             ReLU-33          [-1, 128, 28, 28]               0\n",
      "       BasicBlock-34          [-1, 128, 28, 28]               0\n",
      "           Conv2d-35          [-1, 256, 14, 14]         294,912\n",
      "      BatchNorm2d-36          [-1, 256, 14, 14]             512\n",
      "             ReLU-37          [-1, 256, 14, 14]               0\n",
      "           Conv2d-38          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-39          [-1, 256, 14, 14]             512\n",
      "           Conv2d-40          [-1, 256, 14, 14]          32,768\n",
      "      BatchNorm2d-41          [-1, 256, 14, 14]             512\n",
      "             ReLU-42          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-43          [-1, 256, 14, 14]               0\n",
      "           Conv2d-44          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-45          [-1, 256, 14, 14]             512\n",
      "             ReLU-46          [-1, 256, 14, 14]               0\n",
      "           Conv2d-47          [-1, 256, 14, 14]         589,824\n",
      "      BatchNorm2d-48          [-1, 256, 14, 14]             512\n",
      "             ReLU-49          [-1, 256, 14, 14]               0\n",
      "       BasicBlock-50          [-1, 256, 14, 14]               0\n",
      "           Conv2d-51            [-1, 512, 7, 7]       1,179,648\n",
      "      BatchNorm2d-52            [-1, 512, 7, 7]           1,024\n",
      "             ReLU-53            [-1, 512, 7, 7]               0\n",
      "           Conv2d-54            [-1, 512, 7, 7]       2,359,296\n",
      "      BatchNorm2d-55            [-1, 512, 7, 7]           1,024\n",
      "           Conv2d-56            [-1, 512, 7, 7]         131,072\n",
      "      BatchNorm2d-57            [-1, 512, 7, 7]           1,024\n",
      "             ReLU-58            [-1, 512, 7, 7]               0\n",
      "       BasicBlock-59            [-1, 512, 7, 7]               0\n",
      "           Conv2d-60            [-1, 512, 7, 7]       2,359,296\n",
      "      BatchNorm2d-61            [-1, 512, 7, 7]           1,024\n",
      "             ReLU-62            [-1, 512, 7, 7]               0\n",
      "           Conv2d-63            [-1, 512, 7, 7]       2,359,296\n",
      "      BatchNorm2d-64            [-1, 512, 7, 7]           1,024\n",
      "             ReLU-65            [-1, 512, 7, 7]               0\n",
      "       BasicBlock-66            [-1, 512, 7, 7]               0\n",
      "AdaptiveAvgPool2d-67            [-1, 512, 1, 1]               0\n",
      "           Linear-68                  [-1, 107]          54,891\n",
      "================================================================\n",
      "Total params: 11,231,403\n",
      "Trainable params: 11,231,403\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 62.79\n",
      "Params size (MB): 42.84\n",
      "Estimated Total Size (MB): 106.20\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(model,input_size=(3,224,224))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ResNet(\n",
       "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu): ReLU(inplace=True)\n",
       "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "  (layer1): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): BasicBlock(\n",
       "      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): BasicBlock(\n",
       "      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=512, out_features=107, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/10], Loss: 2.1229\n",
      "Epoch [2/10], Loss: 1.1548\n",
      "Epoch [3/10], Loss: 0.7145\n",
      "Epoch [4/10], Loss: 0.4169\n",
      "Epoch [5/10], Loss: 0.2754\n",
      "Epoch [6/10], Loss: 0.2014\n",
      "Epoch [7/10], Loss: 0.1523\n",
      "Epoch [8/10], Loss: 0.1177\n",
      "Epoch [9/10], Loss: 0.1001\n",
      "Epoch [10/10], Loss: 0.1175\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 10\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()  \n",
    "    running_loss = 0.0\n",
    "\n",
    "    for images, labels in train_loader:\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        # Forward pass\n",
    "        outputs = model(images)\n",
    "        labels=labels.long()\n",
    "        #print(outputs.dtype)\n",
    "        #print(labels.dtype)\n",
    "        loss = criterion(outputs, labels)\n",
    "\n",
    "        # Backward pass and optimization\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "    \n",
    "    print(f\"Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader):.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 65.10%\n"
     ]
    }
   ],
   "source": [
    "model.eval()   \n",
    "correct = 0\n",
    "total = 0\n",
    "\n",
    "with torch.no_grad():   \n",
    "    for images, labels in test_loader:\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "print(f'Accuracy: {100 * correct / total:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'resnet18_finetuned_yoga.pth')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'model_checkpoint.pth')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ninja\\AppData\\Local\\Temp\\ipykernel_32916\\603424615.py:8: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.load_state_dict(torch.load('model_checkpoint.pth'))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [21/44], Loss: 0.1516\n",
      "Epoch [22/44], Loss: 0.1140\n",
      "Epoch [23/44], Loss: 0.1134\n",
      "Epoch [24/44], Loss: 0.1469\n",
      "Epoch [25/44], Loss: 0.1262\n",
      "Epoch [26/44], Loss: 0.0920\n",
      "Epoch [27/44], Loss: 0.0844\n",
      "Epoch [28/44], Loss: 0.0802\n",
      "Epoch [29/44], Loss: 0.0899\n",
      "Epoch [30/44], Loss: 0.0772\n",
      "Epoch [31/44], Loss: 0.0965\n",
      "Epoch [32/44], Loss: 0.1136\n",
      "Epoch [33/44], Loss: 0.0929\n",
      "Epoch [34/44], Loss: 0.0561\n",
      "Epoch [35/44], Loss: 0.0395\n",
      "Epoch [36/44], Loss: 0.0422\n",
      "Epoch [37/44], Loss: 0.0350\n",
      "Epoch [38/44], Loss: 0.0344\n",
      "Epoch [39/44], Loss: 0.0306\n",
      "Epoch [40/44], Loss: 0.0319\n",
      "Epoch [41/44], Loss: 0.0333\n",
      "Epoch [42/44], Loss: 0.0334\n",
      "Epoch [43/44], Loss: 0.0303\n",
      "Epoch [44/44], Loss: 0.0309\n"
     ]
    }
   ],
   "source": [
    "\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "model.load_state_dict(torch.load('model_checkpoint.pth'))\n",
    "\n",
    "start_epoch = 20  \n",
    "\n",
    "num_epochs = 24  \n",
    "\n",
    "for epoch in range(start_epoch, start_epoch + num_epochs):\n",
    "    model.train()  \n",
    "    running_loss = 0.0\n",
    "\n",
    "    for images, labels in train_loader:\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "         \n",
    "        outputs = model(images)\n",
    "        labels = labels.long()\n",
    "\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "    \n",
    "    print(f\"Epoch [{epoch+1}/{start_epoch + num_epochs}], Loss: {running_loss/len(train_loader):.4f}\")\n",
    "\n",
    " \n",
    "torch.save(model.state_dict(), 'model_checkpoint.pth')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 73.18%\n"
     ]
    }
   ],
   "source": [
    "model.eval()   \n",
    "correct = 0\n",
    "total = 0\n",
    "\n",
    "with torch.no_grad():   \n",
    "    for images, labels in test_loader:\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "print(f'Accuracy: {100 * correct / total:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "execution_count": null,
   "metadata": {},
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": null,
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