File size: 3,003 Bytes
2894987 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.models as models
from torchvision import datasets
from torch.utils.data import DataLoader
import os
# Define directories
structured_dataset_path = "C:\\Users\\srira\\OneDrive\\Desktop\\AI_PROJ\\structured_data"
train_dir = os.path.join(structured_dataset_path, "train")
val_dir = os.path.join(structured_dataset_path, "val")
# Define transformations
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
# Load dataset
train_dataset = datasets.ImageFolder(root=train_dir, transform=transform)
val_dataset = datasets.ImageFolder(root=val_dir, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
# Load pretrained model
model = models.resnet50(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, len(train_dataset.classes))
# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
def train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=10):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
for epoch in range(num_epochs):
model.train()
running_loss = 0.0
correct = 0
total = 0
for images, labels in train_loader:
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
_, predicted = outputs.max(1)
total += labels.size(0)
correct += predicted.eq(labels).sum().item()
train_acc = 100 * correct / total
val_acc = evaluate_model(model, val_loader, device)
print(f"Epoch {epoch+1}/{num_epochs}, Loss: {running_loss/len(train_loader):.4f}, Train Acc: {train_acc:.2f}%, Val Acc: {val_acc:.2f}%")
return model
def evaluate_model(model, val_loader, device):
model.eval()
correct = 0
total = 0
with torch.no_grad():
for images, labels in val_loader:
images, labels = images.to(device), labels.to(device)
outputs = model(images)
_, predicted = outputs.max(1)
total += labels.size(0)
correct += predicted.eq(labels).sum().item()
return 100 * correct / total
# Train the model
trained_model = train_model(model, train_loader, val_loader, criterion, optimizer, num_epochs=10)
# Save the model
torch.save(trained_model.state_dict(), "smart_recycling_model.pth")
|