Разбор training loop на PyTorch для многоклассовой классификации

A code-review answer should separate correctness from style. Correctness issues include whether Dataset returns objects that the default collate function can batch, whether the model receives tensors rather than custom records, whether labels are passed to the loss, and whether the optimizer is created, zeroed, stepped and tied to model.parameters().

For multiclass classification with CrossEntropyLoss, the model should normally return raw logits, not softmax probabilities, because the loss applies log-softmax internally. Labels should be class indices with the right dtype and range. The training loop should run for multiple epochs, set model.train(), move the batch to the target device in the loop, and have a separate validation loop with model.eval() and no_grad().

Design issues include a weak linear-only image architecture, missing batch_size, no metrics/logging, no validation split, no seed/reproducibility strategy and unclear input-shape assumptions.