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ML / CUDA

assertion `t >= 0 && t < n_classes` failed

PyTorch / C++ training loops, classification

Training or inference aborts with an assertion about class index bounds.

What it means (root cause)

A label/index falls outside [0, n_classes). Usually off-by-one, 1-based labels, or a class count mismatch between model head and data.

The symptom developers actually see: Training or inference aborts with an assertion about class index bounds.

How to avoid it: Add a dataset unit test that asserts label bounds before training.

Step-by-step fix

Concrete, ordered steps from the dataset. Apply them in order; each line is a verified action, not generic advice.

  1. 1Audit label ranges: min/max of targets must be within [0, n_classes-1].
  2. 2Convert 1-based labels to 0-based, or set n_classes = max_label + 1.
  3. 3Verify the final layer's out_features matches the number of classes exactly.

Where this error appears

Languages, frameworks, and runtimes where this error is observed (from the 2026 DevFixPro error dataset, retrieved 2026-08-29):

PyTorch / C++ training loopsclassification

PyTorch training or inference in Python or embedded C++. The assertion fires at the loss/label boundary, never mid-forward-pass.

How to prevent it & common questions

Practical prevention plus the questions developers ask most about this error.

What does "assertion `t >= 0 && t < n_classes` failed" mean?

A label/index falls outside [0, n_classes). Usually off-by-one, 1-based labels, or a class count mismatch between model head and data.

Which environments are affected by assertion `t >= 0 && t < n_classes` failed?

PyTorch / C++ training loops, classification

How do I fix assertion `t >= 0 && t < n_classes` failed?

Audit label ranges: min/max of targets must be within [0, n_classes-1]. Convert 1-based labels to 0-based, or set n_classes = max_label + 1. Verify the final layer's out_features matches the number of classes exactly.

How do I prevent assertion `t >= 0 && t < n_classes` failed?

Add a dataset unit test that asserts label bounds before training.

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● Data updated 2026-08-29

Sources & attribution

  • Error records aggregated from Google Search Console query gaps (2026-05-21~2026-08-18) for devfixpro.com, plus official framework docs (MDN, Node.js docs, Python docs, Go.dev, Rust book, Oracle Java docs, Docker docs, npm docs). Source dataset retrieved 2026-08-29. License: CC BY 4.0 — attribute DevFixPro (devfixpro.com).
  • Official reference: https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html
  • All cause, environment, fix and prevention text on this page is taken verbatim from the DevFixPro 2026 error dataset. DevFixPro does not invent root causes or fixes.
How this assertion `t >= 0 && t < n_classes` failed page is built

Each error page is generated from a single record in the DevFixPro 2026 error dataset. The meaning, root cause, environments, fix steps, prevention, and official references are copied verbatim from that dataset and its official-doc sources; related-error links are computed from the error's category and explicit peer list. No root cause or fix is invented. The retrieval date for this dataset is 2026-08-29.

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