# 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) } training slayer v740 by bokundev high quality
def __len__(self): return len(self.data) # Initialize model, optimizer, and loss function model
# Load dataset and create data loader dataset = MyDataset(data, labels) data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True) # Initialize model
# Define a custom dataset class class MyDataset(Dataset): def __init__(self, data, labels): self.data = data self.labels = labels
# Set hyperparameters num_classes = 8 input_dim = 128 batch_size = 32 epochs = 10 lr = 1e-4
