Fix pre/post-training evaluation to use same batch in nn_tutorial #3667
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The tutorial was comparing loss on different batches: - Pre-training: evaluated on first 64 instances (batch 0) - Post-training: evaluated on last batch from training loop This made the comparison misleading as it wasn't measuring improvement on the same data. Changes: - Save the initial batch (xb_initial, yb_initial) after first evaluation - Use the saved initial batch for post-training evaluation - Added clarifying comment about fair comparison - Now both evaluations use the same data (first 64 training instances) This provides an accurate before/after comparison showing the model's improvement on the same batch of data.
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/tutorials/3667
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The tutorial was comparing loss on different batches:
This made the comparison misleading as it wasn't measuring improvement on the same data.
Changes:
This provides an accurate before/after comparison showing the model's improvement on the same batch of data.
Fixes #3666
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