Create Train.py

Train.py

@@ -0,0 +1,36 @@
+import torch
+from torchvision import datasets, transforms
+import torch.nn as nn
+import torch.optim as optim
+
+# Define your model class
+class TatsukichiHayamaClassifier(nn.Module):
+    # ... (your model definition)
+
+# Load dataset from PyTorch's ImageFolder
+train_dataset = datasets.ImageFolder(root="TatsukichiHayamaDataset", transform=transforms.ToTensor())
+
+# Create a DataLoader for training
+dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)
+
+# Create an instance of TatsukichiHayamaClassifier
+your_num_classes = 10  # Adjust this based on your dataset
+model = TatsukichiHayamaClassifier(num_classes=your_num_classes)
+
+# Model, criterion, and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+# Training loop
+num_epochs = 10
+
+for epoch in range(num_epochs):
+    model.train()
+    for images, labels in dataloader:
+        optimizer.zero_grad()
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+
+    print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item()}')