add neuron netowrk learning

machine_learning/neuron_network.py

@@ -0,0 +1,71 @@
+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 16, 3)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(16, 32, 3)
+        self.fc1 = nn.Linear(32 * 14 * 14, 128)
+        self.fc2 = nn.Linear(128, 5) 
+
+    def forward(self, x):
+        x = self.pool(torch.relu(self.conv1(x)))
+        x = self.pool(torch.relu(self.conv2(x)))
+        x = x.view(-1, 32 * 14 * 14)
+        x = torch.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+def main() -> None:
+    transform = transforms.Compose([
+        transforms.Resize((64, 64)),
+        transforms.ToTensor(),
+        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+    ])
+
+    path = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
+    path = os.path.join(path, 'garbage_photos/train_set')
+    trainset = torchvision.datasets.ImageFolder(root=path, transform=transform)
+    trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True, num_workers=2)
+
+    net = Net()
+
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
+
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    net.to(device)
+
+    for epoch in range(10):
+        running_loss = 0.0
+        for i, data in enumerate(trainloader, 0):
+            inputs, labels = data[0].to(device), data[1].to(device)
+        
+            optimizer.zero_grad()
+            outputs = net(inputs)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+
+            running_loss += loss.item()
+            if i % 200 == 199:  
+                print(f'Epoch: {epoch + 1}, Batch: {i + 1}, Loss: {running_loss / 200:.3f}')
+                running_loss = 0.0
+
+    torch.save({
+        'model_state_dict': net.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'epoch': epoch,
+        'loss': loss,
+    }, 'model.pt')
+
+    print("Uczenie zakończone.")
+
+if __name__ == "__main__":
+    main()