菜单
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.
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Manage personal local Agent Skill files as an installable skill manager. Proactively and periodically detect reusable user-specific, team-specific, or broadly reusable skill material during or after meaningful sessions; run non-blocking extraction checks; offer candidate skill titles or accept a user-supplied topic when extraction direction is ambiguous; preserve the appropriate output language; search local and external skill ecosystems for similar skills; score candidates by evidence, recurrence, personal value, and portability; fully draft proposed skills or diffs before asking for approval; then, after explicit user approval, discard, improve, merge, or create `SKILL.md` folders.
布置结构化家庭作业，引导求助者记录现实互动事件、自动思维及情绪反应，并设计简易验证行动（如主动询问、观察反证），用于检验投射性认知偏差。适用于已识别出具体非理性信念（如‘别人肯定不喜欢我’）且情绪稳定者。
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基于 SOC 职业分类
| id | 9b068ae5-d85e-4a29-97e3-6494ae1a8eac |
| name | PyTorch Accuracy Calculation Conversion (CrossEntropy to MSE) |
| description | 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. |
| version | 0.1.0 |
| tags | ["pytorch","loss-function","accuracy","code-conversion","regression"] |
| triggers | ["convert accuracy calculation to MSELoss","change CrossEntropyLoss accuracy to MSE","use round for accuracy calculation","PyTorch regression accuracy metric"] |
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.