Step 4 - ResNet Transfer Learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bcdeec36-c8ea-468d-bd2d-8460b70d77e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use pytorch to build a convolutional neural network\n",
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "from torch import optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision.io import read_image\n",
    "from torchvision import transforms\n",
    "from torchmetrics import Accuracy, Precision, F1Score\n",
    "\n",
    "import itertools\n",
    "# from pyimagesearch import config\n",
    "from imutils import paths\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import shutil\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "972ae4b7-5e0b-47b0-827e-1878a1b59a8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "CURRENT_DATASET_PATH = \"dataset-for-classification\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "794fc84a-1ced-4e57-a526-83728ea7ae4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bdbf97a2-e628-4302-a8a3-44134fb9bde3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision import transforms\n",
    "transformer = torchvision.transforms.Compose([\n",
    "    transforms.Resize(size = (224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6fecdbf5-98d9-424f-b515-b9526649d952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10338"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datafolder = torchvision.datasets.ImageFolder(CURRENT_DATASET_PATH, transform = transformer)\n",
    "len(datafolder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "948af561-b550-4d21-a0fd-6c488d0a4c16",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split Dataset\n",
    "import torch.utils.data as data\n",
    "\n",
    "# Random split\n",
    "train_set_size = int(len(datafolder) * 0.8)\n",
    "valid_set_size = len(datafolder) - train_set_size\n",
    "train_set, valid_set = data.random_split(datafolder, [train_set_size, valid_set_size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5c07b45a-c230-4b67-b7d4-d92582443e07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8270, 2068)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_set), len(valid_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f4865097-07bd-40ed-b2f7-e7425490e579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82,\n",
       " ['adho-mukha-svanasana',\n",
       "  'adho-mukha-vriksasana',\n",
       "  'adho-mukha-vrksasana',\n",
       "  'ananda-balasana',\n",
       "  'anjaneyasana',\n",
       "  'ardha-chandrasana',\n",
       "  'ardha-matsyendrasana',\n",
       "  'ardha-pincha-mayurasana',\n",
       "  'ardha-uttanasana',\n",
       "  'astavakrasana',\n",
       "  'baddha-konasana',\n",
       "  'bakasana',\n",
       "  'balasana',\n",
       "  'bharadvajasana-i',\n",
       "  'bhujangasana',\n",
       "  'bhujapidasana',\n",
       "  'bitilasana',\n",
       "  'camatkarasana',\n",
       "  'chakravakasana',\n",
       "  'chaturanga-dandasana',\n",
       "  'dandasana',\n",
       "  'dhanurasana',\n",
       "  'dwi-pada-viparita-dandasana',\n",
       "  'eka-pada-koundinyanasana-i',\n",
       "  'eka-pada-koundinyanasana-ii',\n",
       "  'eka-pada-rajakapotasana',\n",
       "  'eka-pada-rajakapotasana-ii',\n",
       "  'garudasana',\n",
       "  'gomukhasana',\n",
       "  'halasana',\n",
       "  'kapotasana',\n",
       "  'makarasana',\n",
       "  'malasana',\n",
       "  'marjaryasana',\n",
       "  'matsyasana',\n",
       "  'mayurasana',\n",
       "  'natarajasana',\n",
       "  'padmasana',\n",
       "  'paripurna-navasana',\n",
       "  'parivrtta-trikonasana',\n",
       "  'parsva-bakasana',\n",
       "  'pasasana',\n",
       "  'paschimottanasana',\n",
       "  'phalakasana',\n",
       "  'prasarita-padottanasana',\n",
       "  'purvottanasana',\n",
       "  'salabhasana',\n",
       "  'salamba-bhujangasana',\n",
       "  'salamba-sarvangasana',\n",
       "  'salamba-sirsasana',\n",
       "  'savasana',\n",
       "  'setu-bandha-sarvangasana',\n",
       "  'sukhasana',\n",
       "  'supta-baddha-konasana',\n",
       "  'supta-matsyendrasana',\n",
       "  'supta-padangusthasana',\n",
       "  'supta-virasana',\n",
       "  'tadasana',\n",
       "  'tittibhasana',\n",
       "  'tolasana',\n",
       "  'upavistha-konasana',\n",
       "  'urdhva-dhanurasana',\n",
       "  'urdhva-mukha-svanasana',\n",
       "  'urdhva-prasarita-eka-padasana',\n",
       "  'ustrasana',\n",
       "  'utkatasana',\n",
       "  'uttana-shishosana',\n",
       "  'uttanasana',\n",
       "  'utthita-hasta-padangustasana',\n",
       "  'utthita-hasta-padangusthasana',\n",
       "  'utthita-parsvakonasana',\n",
       "  'utthita-trikonasana',\n",
       "  'vajrasana',\n",
       "  'vasisthasana',\n",
       "  'viparita-karani',\n",
       "  'virabhadrasana-i',\n",
       "  'virabhadrasana-ii',\n",
       "  'virabhadrasana-iii',\n",
       "  'virasana',\n",
       "  'vriksasana',\n",
       "  'vrischikasana',\n",
       "  'vrksasana'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = datafolder.classes\n",
    "classes_len = len(classes)\n",
    "classes_len, classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e00f155-2e12-4cb6-af7a-3dee8908b185",
   "metadata": {},
   "outputs": [],
   "source": [
    "img, label = train_set[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55cc7a8d-ce35-402d-990c-e80d75f60bbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([3, 224, 224]), 26)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f8239eff-ab87-41b9-8fc0-39cf4a2629cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 224, 224])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "img_sample = img.permute(1, 2, 0)\n",
    "print(img.shape)\n",
    "plt.imshow(img_sample)\n",
    "plt.title(classes[label]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6776119a-88f2-4b5e-95a3-212ed47e2703",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.models.efficientnet as e\n",
    "import torchvision.models.resnet as r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "26a26aaa-4916-4aa7-822f-bd722fe8516b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-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",
      "/usr/local/lib/python3.10/dist-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=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n",
      "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n",
      "100%|██████████| 97.8M/97.8M [00:03<00:00, 25.7MB/s]\n",
      "/usr/local/lib/python3.10/dist-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=EfficientNet_B0_Weights.IMAGENET1K_V1`. You can also use `weights=EfficientNet_B0_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n",
      "Downloading: \"https://download.pytorch.org/models/efficientnet_b0_rwightman-3dd342df.pth\" to /root/.cache/torch/hub/checkpoints/efficientnet_b0_rwightman-3dd342df.pth\n",
      "100%|██████████| 20.5M/20.5M [00:01<00:00, 19.9MB/s]\n"
     ]
    }
   ],
   "source": [
    "WEIGHTS = r.resnet50(pretrained= True)\n",
    "WEIGHTS2 = e.efficientnet_b0(pretrained= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "279c42fe-821c-45e6-b43b-e483b268a365",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Module.state_dict of 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): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
       ")>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WEIGHTS.state_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "97cdc1c6-17bb-4982-8757-27dabb6f373c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for p in WEIGHTS.parameters():\n",
    "    p.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "152f233c-311c-406f-98d6-30072b66db8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "WEIGHTS.fc = nn.Linear(2048, classes_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dd579620-c5e3-42d9-b2ca-6c9d5ea04312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Linear(in_features=2048, out_features=82, bias=True)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WEIGHTS.fc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d1ee218c-6b20-444f-977d-6f2bebaa17c2",
   "metadata": {
    "scrolled": true
   },
   "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): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=2048, out_features=82, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = WEIGHTS\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "79ebebe0-2b02-491a-a991-a54a2289b371",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "BATCH_SIZE = 32\n",
    "train_loader = DataLoader(dataset=train_set, batch_size=BATCH_SIZE, shuffle=True)\n",
    "test_loader = DataLoader(dataset=valid_set, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "81abb7eb-daf5-4d17-9623-731e5649fc47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up parameters\n",
    "loss_fn = nn.CrossEntropyLoss().to(device)\n",
    "optimizer = optim.Adam(model.parameters(), lr= 0.01)\n",
    "accuracy_fn = Accuracy(task='multiclass', num_classes=len(classes)).to(device)\n",
    "f1 = F1Score(task='multiclass', num_classes=len(classes)).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "529c5490-bf7f-4980-b06d-a5b2cbf911e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.422408103942871 | 0.03125\n",
      "805.8040771484375 | 17.625\n",
      "1213.806884765625 | 51.71875\n",
      " Train Loss: 5.5836 | Train Acc: 0.285\n",
      "8.138978004455566 | 0.847059428691864\n",
      "296.3093566894531 | 50.28456115722656\n",
      "638.3108520507812 | 97.84706115722656\n",
      " Train Loss: 3.2720 | Train Acc: 0.485\n",
      "5.607666492462158 | 1.0160601139068604\n",
      "235.2345733642578 | 61.79730987548828\n",
      "538.9265747070312 | 118.92230987548828\n",
      " Train Loss: 2.8289 | Train Acc: 0.582\n",
      "5.463421821594238 | 1.1761858463287354\n",
      "270.1831970214844 | 61.926185607910156\n",
      "538.8009643554688 | 122.61368560791016\n",
      " Train Loss: 2.7075 | Train Acc: 0.612\n",
      "5.947300910949707 | 1.2681236267089844\n",
      "234.53399658203125 | 66.79937744140625\n",
      "465.1534118652344 | 133.17437744140625\n",
      " Train Loss: 2.3812 | Train Acc: 0.655\n",
      "5.309415817260742 | 1.186018705368042\n",
      "238.85769653320312 | 69.02976989746094\n",
      "489.3805847167969 | 135.40476989746094\n",
      " Train Loss: 2.4679 | Train Acc: 0.667\n",
      "5.877370834350586 | 1.2295515537261963\n",
      "195.70001220703125 | 74.22955322265625\n",
      "392.6170349121094 | 145.69830322265625\n",
      " Train Loss: 2.0753 | Train Acc: 0.715\n",
      "4.708211898803711 | 1.4029650688171387\n",
      "204.00587463378906 | 73.80921936035156\n",
      "410.38421630859375 | 145.49671936035156\n",
      " Train Loss: 2.0565 | Train Acc: 0.721\n",
      "4.4701032638549805 | 1.5018489360809326\n",
      "201.41842651367188 | 74.87684631347656\n",
      "429.939453125 | 147.12684631347656\n",
      " Train Loss: 2.1991 | Train Acc: 0.726\n",
      "4.105210304260254 | 1.538858413696289\n",
      "192.60377502441406 | 76.16385650634766\n",
      "387.009765625 | 151.35134887695312\n",
      " Train Loss: 1.9435 | Train Acc: 0.751\n",
      " Test Loss 0.1216 | Test Acc: 0.93 | F1Score: 0.9285707473754883  \n"
     ]
    }
   ],
   "source": [
    "EPOCHS = 10\n",
    "\n",
    "train_losses, train_acc = 0, 0\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "  for batch, (X, y )in enumerate(train_loader):\n",
    "    X = X.to(device)\n",
    "    y = y.to(device)\n",
    "    model.train()\n",
    "\n",
    "    y_pred =model(X)\n",
    "    print(y_pred)\n",
    "    loss = loss_fn(y_pred, y)\n",
    "    train_acc += accuracy_fn(y_pred.argmax(dim=1), y)\n",
    "    train_losses += loss\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    if batch % 100 == 0:\n",
    "      print(f'{train_losses} | {train_acc}')\n",
    "\n",
    "  train_losses /= len(train_loader)\n",
    "  train_acc /= len(train_loader)\n",
    "  print(f' Train Loss: {train_losses:.4f} | Train Acc: {train_acc:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b8399768-cf10-40fa-9238-aa1c0bb107d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Test Loss 0.1216 | Test Acc: 0.93 | F1Score: 0.9285707473754883  \n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "test_loss, test_acc, test_f1 = 0, 0, 0\n",
    "with torch.inference_mode():\n",
    "    for Xt, yt in test_loader:\n",
    "        Xt = Xt.to(device)\n",
    "        yt= yt.to(device)\n",
    "        test_pred = model(X)\n",
    "        t_loss = loss_fn(test_pred, y)\n",
    "        test_loss += t_loss\n",
    "        test_acc += accuracy_fn(test_pred.argmax(dim=1), y)\n",
    "        test_f1 += f1(test_pred.argmax(dim=1), y)\n",
    "    test_loss /= len(test_loader)\n",
    "    test_acc /= len(test_loader)\n",
    "    test_f1 /= len(test_loader)\n",
    "    \n",
    "    print(f' Test Loss {test_loss:.4f} | Test Acc: {test_acc:.2f} | F1Score: {test_f1}  ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "863d1f93-dce6-4a06-87e9-1e8faf7ad00c",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), \"img_classification-10-eps-WITH_DICT.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "375a26ea-6914-442e-82d5-9c101c8a75d5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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