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def forward(self, x): x = self.embedding(x) x = self.fc1(x) x = self.relu(x) x = self.dropout(x) x = self.fc2(x) return x class CustomDataset(Dataset): def __init__(self, data, labels): self.data = data self.labels = labels def __len__(self): return len(self.data) def __getitem__(self, idx): return self.data[idx], self.labels[idx] # Example data data = torch.randint(0, 1000, (1000,)) labels = torch.randint(0, 10, (1000,)) dataset = CustomDataset(data, labels) dataloader = DataLoader(dataset, batch_size=32, shuffle=True, num_workers=4) model = LanguageEmbeddingModel(vocab_size=1000, embedding_dim=128, hidden_dim=64, output_dim= 10) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min') device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) writer = SummaryWriter() num_epochs = 10 for epoch in range(num_epochs): running_loss = 0.0 model.train() for i, (inputs, labels) in enumerate(dataloader):