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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# PKR Currency Classifier Training Notebook"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torchvision.transforms as transforms\n",
"from torchvision import models, datasets\n",
"from torch.utils.data import DataLoader\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load dataset\n",
"transform = transforms.Compose([\n",
" transforms.Resize((224, 224)),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
"])\n",
"\n",
"train_data = datasets.ImageFolder('currency_dataset', transform=transform)\n",
"train_loader = DataLoader(train_data, batch_size=16, shuffle=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Model\n",
"weights = models.MobileNet_V2_Weights.DEFAULT\n",
"model = models.mobilenet_v2(weights=weights)\n",
"model.classifier[1] = nn.Linear(model.last_channel, 2)\n",
"criterion = nn.CrossEntropyLoss()\n",
"optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Train\n",
"model.train()\n",
"for epoch in range(5):\n",
" for images, labels in train_loader:\n",
" optimizer.zero_grad()\n",
" outputs = model(images)\n",
" loss = criterion(outputs, labels)\n",
" loss.backward()\n",
" optimizer.step()\n",
" print(f'Epoch {epoch+1}, Loss: {loss.item()}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Save\n",
"torch.save(model, 'pkr_currency_classifier.pt')"
]
}
],
"metadata": {
"colab": {
"name": "training_notebook.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": ""
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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