Over 233.5 Points basketball predictions today (2025-11-05)

Frequently Asked Questions (FAQs)

Hoe kan ek die skynsel van 'n hoë-gevekte wedstryd beïnvloed?

Hoë-gevekte wedstryde kan deur verskeie faktore beïnvloed word soos die formaat van die hal en die energie van die publiek. Verstaan hoe hierdie faktore optree kan jou help om beter voorspellings te maak oor die uitslag van 'n wedstryd oor die puntenskat van meer as driehonderd drie en een half punt te gaan slaan.

Kom mense uit Suid-Afrika graag hoë-gevekte wedstryde?

Jawel, mense uit Suid-Afrika het 'n sterk belangstelling vir hoë-gevekte wedstryde, veral as dit om 'n populêre sport soos basketbal gaan.

Hoe kan ek my voorspellings verbeter?

Dat kan deur regmatige navorsing en analises van vorige wedstryde wat oor die puntenskat van meer as driehonderd drie en een half punt geslaan het.

Kom hulle in Suid-Afrika veel hoë-gevekte wedstryde speel?

I need help modifying this code snippet [SNIPPET]. Specifically, I want to add dynamic loss weighting where head loss and tail loss weights adjust based on their gradients during training.<|end|> <|assistant|>To implement dynamic loss weighting based on gradients, we'll first need to compute gradients before backpropagation and then adjust weights accordingly. Here's how you could modify your code: python for epoch in pbar: model.train() total_train_loss = [] if args.model_name == "NeuralKB": training_data = train_dataloader.dataset.training_data model.initialize_graph(training_data) for batch_idx, batch in enumerate(train_dataloader): optimizer.zero_grad() if args.model_name == "ER": batch_output = model.forward(batch) batch_output.backward(retain_graph=True) # retain graph for gradient access head_grads = [param.grad.norm().item() for param in model.parameters() if param.requires_grad] grad_norm_sum = sum(head_grads) # Dynamic weight calculation based on gradient norms head_weight = sum(head_grads[:len(head_grads)//2]) / grad_norm_sum tail_weight = sum(head_grads[len(head_grads)//2:]) / grad_norm_sum # Compute loss with dynamic weights batch_output.backward() optimizer.step() total_train_loss.append(batch_output.item()) else: head_embeddings, tail_embeddings, relation_embeddings = model.forward(batch) head_pred = model.forward(head_embeddings=head_embeddings, tail_embeddings=tail_embeddings, relation_embeddings=relation_embeddings, mode="head") tail_pred = model.forward(head_embeddings=head_embeddings, tail_embeddings=tail_embeddings, relation_embeddings=relation_embeddings, mode="tail") head_grads_before_step = [param.grad.norm().item() for param in model.parameters() if param.requires_grad] grad_norm_sum_before_step = sum(head_grads_before_step) head_weight_before_step = sum(head_grads_before_step[:len(head_grads_before_step)//2]) / grad_norm_sum_before_step tail_weight_before_step = sum(head_grads_before_step[len(head_grads_before_step)//2:]) / grad_norm_sum_before_step head_output.backward(retain_graph=True) # retain graph for gradient access head_grads_after_step_first_pass = [param.grad.norm().item() for param in model.parameters() if param.requires_grad] grad_norm_sum_after_step_first_pass = sum(head_grads_after_step_first_pass) # Adjust weights dynamically after first backward pass head_weight_after_step_first_pass = sum(head_grads_after_step_first_pass[:len(head_grads_after_step_first_pass)//2]) / grad_norm_sum_after_step_first_pass tail_weight_after_step_first_pass = sum(head_grads_after_step_first_pass[len(head_grads_after_step_first_pass)//2:]) / grad_norm_sum_after_step_first_pass # Recompute gradients considering new weights total_weighted_gradient_norm_before_second_backward_pass = (head_weight_before_step * head_output + tail_weight_before_step * tail_output).backward(retain_graph=True) optimizer.step() total_train_loss.append(batch_output.item()) scheduler.step() train_loss_mean = np.mean(total_train_loss) if (epoch +1) % args.save_checkpoint_epochs ==0: save_model(model=model, save_path=os.path.join(args