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"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DLfuYXOZRLSc",
"outputId": "7bb39bd1-b77c-4b73-a2cc-504307f87a5e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Libraries imported successfully!\n"
]
}
],
"source": [
"# ============================================================\n",
"# Transfer Learning using ResNet on CIFAR-10\n",
"# ============================================================\n",
"# Description : Pretend CIFAR-10 categories are Bengali\n",
"# celebrities — same transfer learning concept\n",
"# Model : ResNet18 pretrained on ImageNet\n",
"# Author : Fatima\n",
"# ============================================================\n",
"\n",
"# ── Import Required Libraries ────────────────────────────────\n",
"import torch # PyTorch\n",
"import torch.nn as nn # neural network\n",
"import torchvision.models as models # pretrained models\n",
"import torchvision.transforms as transforms # image transforms\n",
"from torchvision.datasets import CIFAR10 # practice dataset\n",
"from torch.utils.data import DataLoader # data feeding\n",
"import matplotlib.pyplot as plt # visualization\n",
"\n",
"print(\"Libraries imported successfully!\")"
]
},
{
"cell_type": "code",
"source": [
"# ── Step 2: Load Pretrained ResNet18 ────────────────────────\n",
"# ResNet18 = pretrained on ImageNet (1000 categories)\n",
"# already knows edges, shapes, textures, face patterns\n",
"# we borrow this knowledge for our celebrity task\n",
"\n",
"print(\"Loading pretrained ResNet18...\")\n",
"\n",
"# weights=DEFAULT loads the pretrained ImageNet weights\n",
"model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)\n",
"\n",
"# look at the final layer before we change it\n",
"print(\"\\nOriginal final layer:\")\n",
"print(model.fc)\n",
"\n",
"# ── Step 3: Replace Final Layer ─────────────────────────────\n",
"# original final layer → Linear(512, 1000) = 1000 categories\n",
"# we replace it → Linear(512, 10) = 10 celebrities\n",
"# (in real project → Linear(512, 250) = 250 Bengali celebs)\n",
"\n",
"num_celebrities = 10 # pretending 10 CIFAR categories = 10 celebrities\n",
"\n",
"model.fc = nn.Linear(512, num_celebrities)\n",
"\n",
"print(\"\\nModified final layer:\")\n",
"print(model.fc)\n",
"\n",
"print(\"\\nResNet18 ready for celebrity recognition! ✅\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yk6ClSQrRwz9",
"outputId": "bcf48079-0cc7-4798-adf6-14abc7bdbd69"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Loading pretrained ResNet18...\n",
"Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 44.7M/44.7M [00:00<00:00, 344MB/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Original final layer:\n",
"Linear(in_features=512, out_features=1000, bias=True)\n",
"\n",
"Modified final layer:\n",
"Linear(in_features=512, out_features=10, bias=True)\n",
"\n",
"ResNet18 ready for celebrity recognition! ✅\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# ── Step 3: Load CIFAR-10 Dataset ───────────────────────────\n",
"# CIFAR-10 = 60,000 images, 10 categories\n",
"# we pretend each category = one Bengali celebrity\n",
"# ResNet expects images of size 224x224\n",
"# so we resize CIFAR-10 images from 32x32 to 224x224\n",
"\n",
"print(\"Loading CIFAR-10 dataset...\")\n",
"\n",
"# define image transformations\n",
"transform = transforms.Compose([\n",
" # resize to 224x224 (ResNet expected input size)\n",
" transforms.Resize((224, 224)),\n",
" # convert image to PyTorch tensor\n",
" transforms.ToTensor(),\n",
" # normalize using ImageNet mean and std\n",
" # because ResNet was trained with these values\n",
" transforms.Normalize(\n",
" mean=[0.485, 0.456, 0.406],\n",
" std=[0.229, 0.224, 0.225]\n",
" )\n",
"])\n",
"\n",
"# load training data\n",
"train_dataset = CIFAR10(\n",
" root=\"./data\", # where to save dataset\n",
" train=True, # training set\n",
" download=True, # download if not present\n",
" transform=transform # apply transformations\n",
")\n",
"\n",
"# load test data\n",
"test_dataset = CIFAR10(\n",
" root=\"./data\",\n",
" train=False, # test set\n",
" download=True,\n",
" transform=transform\n",
")\n",
"\n",
"# create dataloaders\n",
"train_loader = DataLoader(\n",
" train_dataset,\n",
" batch_size=32, # 32 images at a time\n",
" shuffle=True # randomize each epoch\n",
")\n",
"\n",
"test_loader = DataLoader(\n",
" test_dataset,\n",
" batch_size=32,\n",
" shuffle=False # no need to shuffle test data\n",
")\n",
"\n",
"print(f\"Training images : {len(train_dataset)}\")\n",
"print(f\"Test images : {len(test_dataset)}\")\n",
"print(f\"Training batches: {len(train_loader)}\")\n",
"print(\"Dataset ready! ✅\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BtdWc1VLShty",
"outputId": "4e5d120c-d273-46bc-99c0-dbfb4c2c154f"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Loading CIFAR-10 dataset...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 170M/170M [41:04<00:00, 69.2kB/s]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Training images : 50000\n",
"Test images : 10000\n",
"Training batches: 1563\n",
"Dataset ready! ✅\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# ── Step 4: Training Loop (Fast Version) ────────────────────\n",
"# use only 500 images instead of 50,000\n",
"# so it runs fast on CPU\n",
"# concept is exactly the same!\n",
"\n",
"from torch.utils.data import Subset\n",
"\n",
"# take only first 500 training images\n",
"small_train = Subset(train_dataset, range(500))\n",
"small_loader = DataLoader(small_train,\n",
" batch_size=32,\n",
" shuffle=True)\n",
"\n",
"# setup\n",
"device = torch.device(\"cpu\")\n",
"print(f\"Using device: {device}\")\n",
"model = model.to(device)\n",
"\n",
"# loss and optimizer\n",
"loss_fn = nn.CrossEntropyLoss()\n",
"optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n",
"\n",
"print(f\"Training on 500 images ({len(small_loader)} batches)\")\n",
"print(\"Starting training...\\n\")\n",
"\n",
"for epoch in range(2):\n",
"\n",
" total_loss = 0\n",
" correct = 0\n",
" total = 0\n",
"\n",
" for batch_idx, (images, labels) in enumerate(small_loader):\n",
"\n",
" images = images.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" # Step 1: forward pass\n",
" predictions = model(images)\n",
"\n",
" # Step 2: calculate loss\n",
" loss = loss_fn(predictions, labels)\n",
"\n",
" # Step 3: backpropagation\n",
" loss.backward()\n",
"\n",
" # Step 4: update weights\n",
" optimizer.step()\n",
"\n",
" # Step 5: reset gradients\n",
" optimizer.zero_grad()\n",
"\n",
" total_loss += loss.item()\n",
" _, predicted = torch.max(predictions, 1)\n",
" correct += (predicted == labels).sum().item()\n",
" total += labels.size(0)\n",
"\n",
" print(f\"Epoch {epoch+1} | Batch {batch_idx+1}/{len(small_loader)} | Loss: {round(loss.item(), 4)}\")\n",
"\n",
" accuracy = round((correct / total) * 100, 2)\n",
" avg_loss = round(total_loss / len(small_loader), 4)\n",
" print(f\"\\nEpoch {epoch+1} Summary:\")\n",
" print(f\" Average Loss : {avg_loss}\")\n",
" print(f\" Accuracy : {accuracy}%\\n\")\n",
"\n",
"print(\"Training complete! ✅\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jTWccloycRsU",
"outputId": "c2cfce1a-9ee0-4c57-f833-3ebbefc49007"
},
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Using device: cpu\n",
"Training on 500 images (16 batches)\n",
"Starting training...\n",
"\n",
"Epoch 1 | Batch 1/16 | Loss: 0.7116\n",
"Epoch 1 | Batch 2/16 | Loss: 1.3489\n",
"Epoch 1 | Batch 3/16 | Loss: 1.1916\n",
"Epoch 1 | Batch 4/16 | Loss: 1.2354\n",
"Epoch 1 | Batch 5/16 | Loss: 1.4737\n",
"Epoch 1 | Batch 6/16 | Loss: 1.1769\n",
"Epoch 1 | Batch 7/16 | Loss: 1.4508\n",
"Epoch 1 | Batch 8/16 | Loss: 1.3606\n",
"Epoch 1 | Batch 9/16 | Loss: 1.7716\n",
"Epoch 1 | Batch 10/16 | Loss: 1.4312\n",
"Epoch 1 | Batch 11/16 | Loss: 1.4026\n",
"Epoch 1 | Batch 12/16 | Loss: 0.9197\n",
"Epoch 1 | Batch 13/16 | Loss: 2.539\n",
"Epoch 1 | Batch 14/16 | Loss: 1.0783\n",
"Epoch 1 | Batch 15/16 | Loss: 1.2675\n",
"Epoch 1 | Batch 16/16 | Loss: 1.628\n",
"\n",
"Epoch 1 Summary:\n",
" Average Loss : 1.3742\n",
" Accuracy : 57.4%\n",
"\n",
"Epoch 2 | Batch 1/16 | Loss: 0.9406\n",
"Epoch 2 | Batch 2/16 | Loss: 0.6462\n",
"Epoch 2 | Batch 3/16 | Loss: 0.4644\n",
"Epoch 2 | Batch 4/16 | Loss: 0.9182\n",
"Epoch 2 | Batch 5/16 | Loss: 0.9893\n",
"Epoch 2 | Batch 6/16 | Loss: 0.7247\n",
"Epoch 2 | Batch 7/16 | Loss: 0.8304\n",
"Epoch 2 | Batch 8/16 | Loss: 0.9865\n",
"Epoch 2 | Batch 9/16 | Loss: 0.7683\n",
"Epoch 2 | Batch 10/16 | Loss: 0.5611\n",
"Epoch 2 | Batch 11/16 | Loss: 0.8494\n",
"Epoch 2 | Batch 12/16 | Loss: 0.9649\n",
"Epoch 2 | Batch 13/16 | Loss: 0.7664\n",
"Epoch 2 | Batch 14/16 | Loss: 1.0289\n",
"Epoch 2 | Batch 15/16 | Loss: 0.7901\n",
"Epoch 2 | Batch 16/16 | Loss: 1.2206\n",
"\n",
"Epoch 2 Summary:\n",
" Average Loss : 0.8406\n",
" Accuracy : 69.8%\n",
"\n",
"Training complete! ✅\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# ── Step 5: Evaluate on Test Data ───────────────────────────\n",
"# test the model on images it has NEVER seen before\n",
"# this tells us if model truly learned or just memorized\n",
"\n",
"from torch.utils.data import Subset\n",
"\n",
"# take 200 test images\n",
"small_test = Subset(test_dataset, range(200))\n",
"small_test_loader = DataLoader(small_test,\n",
" batch_size=32,\n",
" shuffle=False)\n",
"\n",
"# switch model to evaluation mode\n",
"# turns off dropout and batch normalization\n",
"model.eval()\n",
"\n",
"correct = 0\n",
"total = 0\n",
"\n",
"# torch.no_grad() = don't calculate gradients\n",
"# we're just testing, not training\n",
"with torch.no_grad():\n",
" for images, labels in small_test_loader:\n",
"\n",
" images = images.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" # forward pass only\n",
" predictions = model(images)\n",
"\n",
" # get predicted class\n",
" _, predicted = torch.max(predictions, 1)\n",
" correct += (predicted == labels).sum().item()\n",
" total += labels.size(0)\n",
"\n",
"accuracy = round((correct / total) * 100, 2)\n",
"print(f\"Test Accuracy: {accuracy}%\")\n",
"print(f\"Correct: {correct}/{total}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UFj9_RLzlSFA",
"outputId": "933ecf45-afc5-41a6-d90c-410dd5eed1f5"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Test Accuracy: 52.0%\n",
"Correct: 104/200\n"
]
}
]
}
]
} |