Upload 1134_252_159.py

1134_252_159.py

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+# -*- coding: utf-8 -*-
+"""1134.252.159
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Y4HZHRme1jvjdQROi8EQ1S0PdzDBeh9K
+"""
+
+!pip install torch torchvision numpy opencv-python
+
+from torchvision import datasets, transforms
+
+transform = transforms.Compose([
+    transforms.Resize((128, 128)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+])
+
+# Insert your 'path_to_train_data'
+train_dataset = datasets.ImageFolder(root='path_to_train_data', transform=transform)
+train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
+
+import torch.nn as nn
+import torchvision.models as models
+
+model = models.resnet18(pretrained=True)
+num_features = model.fc.in_features
+model.fc = nn.Linear(num_features, num_classes)  # num_classes should be the number of individuals in your dataset
+
+import torch.optim as optim
+
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+for epoch in range(num_epochs):
+    for inputs, labels in train_loader:
+        optimizer.zero_grad()
+        outputs = model(inputs)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+
+model.eval()
+correct = 0
+total = 0
+with torch.no_grad():
+    for inputs, labels in test_loader:
+        outputs = model(inputs)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+accuracy = 100 * correct / total
+print(f'Accuracy: {accuracy}%')
+
+model.eval()
+img = Image.open('path_to_image')
+img = transform(img).unsqueeze(0)  # Add batch dimension
+output = model(img)
+_, predicted = torch.max(output, 1)
+print(f'Predicted Class: {predicted.item()}')