Delete Python

Python

@@ -1,51 +0,0 @@
-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):
-    def __init__(self, num_classes=10):
-        super(TatsukichiHayamaClassifier, self).__init__()
-        # Define your model layers here
-        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
-        self.relu = nn.ReLU()
-        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
-        self.fc1 = nn.Linear(64 * 64 * 64, num_classes)  # Update input size based on your image dimensions
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.relu(x)
-        x = self.pool(x)
-        x = x.view(-1, 64 * 64 * 64)  # Update this size based on your image dimensions
-        x = self.fc1(x)
-        return x
-
-# Load dataset from PyTorch's ImageFolder
-# Adjust the 'root' parameter to the path where your dataset is stored
-train_dataset = datasets.ImageFolder(root="path/to/your/dataset", 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()}')