def forward(self, x): x = self.encoder(x) x = self.decoder(x) return x
# Define a custom dataset class class MyDataset(Dataset): def __init__(self, data, labels): self.data = data self.labels = labels training slayer v740 by bokundev high quality
Slayer V7.4.0 Developer: Bokundev Task: Training a high-quality model def forward(self, x): x = self
# Load dataset and create data loader dataset = MyDataset(data, labels) data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True) labels) data_loader = DataLoader(dataset
# Initialize model, optimizer, and loss function model = SlayerV7_4_0(num_classes, input_dim) optimizer = optim.Adam(model.parameters(), lr=lr) criterion = nn.CrossEntropyLoss()
def __getitem__(self, idx): data = self.data[idx] label = self.labels[idx] return { 'data': torch.tensor(data), 'label': torch.tensor(label) }