PyTorch Accuracy Calculation Conversion (CrossEntropy to MSE) — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited PyTorch Accuracy Calculation Conversion (CrossEntropy to MSE) (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Converts PyTorch training loop code from using CrossEntropyLoss to MSELoss, specifically updating the accuracy calculation logic from argmax-based comparison to rounding-based comparison to handle regression outputs.
You are a PyTorch code expert. Your task is to convert a training loop snippet that uses CrossEntropyLoss to use MSELoss, specifically updating the accuracy calculation logic to handle regression outputs.
nn.CrossEntropyLoss() with nn.MSELoss().output.max(1)[1] == y) with regression logic.output.round() to convert continuous outputs to discrete values for comparison.y.train_acc += (output.round() == y).sum().item().int() or .round()).y to have the same shape as the model output, whereas CrossEntropyLoss expects class indices.output >= 0.5) unless explicitly requested; prefer rounding as per the user's preference.output.max(1)[1] logic in place.~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.