Tecdoc — Motornummer

def __len__(self): return len(self.engine_numbers)

# Assume we have a dataset of engine numbers and corresponding labels/features class EngineDataset(Dataset): def __init__(self, engine_numbers, labels): self.engine_numbers = engine_numbers self.labels = labels

model = EngineModel(num_embeddings=1000, embedding_dim=128) tecdoc motornummer

def forward(self, engine_number): embedded = self.embedding(engine_number) out = torch.relu(self.fc(embedded)) out = self.output_layer(out) return out

for epoch in range(10): for batch in data_loader: engine_numbers_batch = batch["engine_number"] labels_batch = batch["label"] optimizer.zero_grad() outputs = model(engine_numbers_batch) loss = criterion(outputs, labels_batch) loss.backward() optimizer.step() print(f'Epoch {epoch+1}, Loss: {loss.item()}') This example demonstrates a basic approach. The specifics—like model architecture, embedding usage, and preprocessing—will heavily depend on the nature of your dataset and the task you're trying to solve. The success of this approach also hinges on how well the engine numbers correlate with the target features or labels. def __len__(self): return len(self

class EngineModel(nn.Module): def __init__(self, num_embeddings, embedding_dim): super(EngineModel, self).__init__() self.embedding = nn.Embedding(num_embeddings, embedding_dim) self.fc = nn.Linear(embedding_dim, 128) # Assuming the embedding_dim is 128 or adjust self.output_layer = nn.Linear(128, 1) # Adjust based on output dimension

def __getitem__(self, idx): engine_number = self.engine_numbers[idx] label = self.labels[idx] return {"engine_number": engine_number, "label": label} class EngineModel(nn

# Training criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=0.001)