You can put a Softmax in front of CrossEntropyLoss.

2026-05-17 · Source: NLP on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, medium

Summary

Neurarch, a design-time linter for neural network architectures, identifies 17 common structural bugs in PyTorch models before runtime. These rules are categorized into structure, layer ordering, missing patterns, performance inefficiencies, and Transformer-specific issues. For example, it flags placing Softmax before `nn.CrossEntropyLoss` (which applies log-softmax internally), leading to double application and training instability. Other rules catch issues like normalization after activation, deep networks without residual connections, attention layers missing positional encoding, and incorrect Grouped-Query Attention head divisibility. The linter aims to prevent hours of GPU time and debugging by catching errors visible from the architecture graph, which current PyTorch tools only flag at runtime.

Key takeaway

For NLP Engineers and Computer Vision Engineers designing PyTorch models, integrating a design-time linter like Neurarch can significantly reduce debugging time and wasted GPU resources. You should proactively validate your architecture graphs against common structural pitfalls, such as incorrect layer ordering or missing essential components like positional encodings in attention models. This approach catches errors visible from the graph before costly training runs, saving days of iterative debugging.

Key insights

Design-time static analysis for neural networks prevents common architectural bugs before costly runtime.

Principles

• Catch bugs at design time, not runtime
• Static analysis identifies graph-visible errors
• Heuristics guide architectural best practices

Method

The Neurarch linter applies 17 rules across five categories (structure, ordering, pattern, performance, Transformer-specific) to an architecture graph, flagging issues like double Softmax application or missing residuals before model training.

In practice

• Use design-time linters to validate architecture graphs
• Check for Softmax before `nn.CrossEntropyLoss`
• Ensure deep networks have residual connections

Topics

• Neurarch
• Design-time Linter
• Neural Network Architecture
• PyTorch
• Static Analysis

Code references

• neurarch-ai/neurarch-feedback

Best for: NLP Engineer, Computer Vision Engineer, Machine Learning Engineer, AI Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by NLP on Medium.