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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#import libraries\n",
"import torch \n",
"from torchvision import datasets, transforms \n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"from torch.utils.data import DataLoader\n",
"from torchvision.datasets import ImageFolder\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#define the data transforms\n",
"\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",
" ])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"#insert the datasets\n",
"\n",
"train_dataset = ImageFolder('./data/train', transform=transform)\n",
"test_dataset =ImageFolder('./data/test', transform=transform)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# make cnn model\n",
"\n",
"class CNN(nn.Module):\n",
" def __init__(self):\n",
" super(CNN, self).__init__()\n",
" self.conv1 = nn.Conv2d(3, 6, 5)\n",
" self.conv2 = nn.Conv2d(6, 16, 5)\n",
" self.pool = nn.MaxPool2d(2, 2)\n",
" self.fc1 = nn.Linear(16 * 53 * 53, 120)\n",
" self.fc2 = nn.Linear(120, 84)\n",
" self.fc3 = nn.Linear(84, 3)\n",
"\n",
" def forward(self, x):\n",
" x = self.conv1(x)\n",
" x = self.pool(x)\n",
" x = self.conv2(x)\n",
" x = self.pool(x)\n",
" x = x.view(-1, 16 * 53 * 53)\n",
" x = self.fc1(x)\n",
" x = self.fc2(x)\n",
" x = self.fc3(x)\n",
" return x\n",
"\n",
" \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"batch_size = 8\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
"test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"model = CNN()\n",
"loss_function = nn.CrossEntropyLoss()\n",
"optimizer = optim.Adam(model.parameters(), lr=0.001)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch [1/10], Step [1/34], Loss: 1.0981\n",
"Epoch [2/10], Step [1/34], Loss: 1.2921\n",
"Epoch [3/10], Step [1/34], Loss: 0.4883\n",
"Epoch [4/10], Step [1/34], Loss: 0.3408\n",
"Epoch [5/10], Step [1/34], Loss: 0.1063\n",
"Epoch [6/10], Step [1/34], Loss: 0.0406\n",
"Epoch [7/10], Step [1/34], Loss: 0.0009\n",
"Epoch [8/10], Step [1/34], Loss: 0.0066\n",
"Epoch [9/10], Step [1/34], Loss: 0.0009\n",
"Epoch [10/10], Step [1/34], Loss: 0.0012\n"
]
}
],
"source": [
"#Train the model\n",
"\n",
"for epoch in range(10):\n",
" for i, (images, labels) in enumerate(train_loader):\n",
"\n",
" outputs = model(images)\n",
"\n",
" loss = loss_function(outputs, labels)\n",
"\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" if i % 200 == 0:\n",
" print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, 10, i + 1, len(train_loader), loss.item()))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"#iterate over the test data \n",
"\n",
"correct = 0\n",
"total = 0\n",
"for i, (images, labels) in enumerate(test_loader):\n",
" outputs = model(images)\n",
" \n",
" _, predicted = torch.max(outputs.data, 1)\n",
" correct += (predicted == labels).sum().item()\n",
" total += labels.size(0)\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 53.333333333333336%\n"
]
}
],
"source": [
"#calculate the accuracy\n",
"accuracy = 100 * correct / total\n",
"print('Accuracy: {}%' .format(accuracy))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"model_scripted = torch.jit.script(model)\n",
"model_scripted.save('./models/cat_dog_cnn.pt')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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