Inference Loop
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Inference Loop has 14 facts recorded in Dontopedia across 3 references, with 2 live disagreements.
Mostly:has iteration count(2), contains(2), repeats(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (3)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
containsContains(2)
- Inference Example
ex:inference-example - No Grad Block
ex:no-grad-block
precedesPrecedes(1)
- No Grad Context
ex:no-grad-context
Other facts (14)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Has Iteration Count | 22000 | [2] |
| Has Iteration Count | 22000 | [3] |
| Contains | Tensor Creation | [2] |
| Contains | Model Call | [2] |
| Repeats | Tensor Creation | [2] |
| Repeats | Model Call | [2] |
| Iteration Count | 22000 | [1] |
| Is Part of | Inference Example | [2] |
| Iteration Variable | I | [2] |
| Uses Variable | 22000 | [2] |
| Rdf:type | For Loop | [3] |
| Iterates Over | Range | [3] |
| Has Iterator Variable | i | [3] |
| Has Range | 22000 | [3] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (3)
ctx:claims/beam/8b6abd69-54a1-41b8-bb85-d0b80bff1a3a- full textbeam-chunktext/plain1 KB
doc:beam/8b6abd69-54a1-41b8-bb85-d0b80bff1a3aShow excerpt
loss = criterion(outputs, batch_targets) # Normalize the loss because it is accumulated loss = loss / accumulation_steps # Backward pass loss.backward() # Update wei…
ctx:claims/beam/af924c4f-8579-4b2a-85d1-c042076b09c7- full textbeam-chunktext/plain1 KB
doc:beam/af924c4f-8579-4b2a-85d1-c042076b09c7Show excerpt
loss = loss / accumulation_steps # Backward pass scaler.scale(loss).backward() # Update weights if (i + 1) % accumulation_steps == 0: scaler.step(optimizer) …
ctx:claims/beam/80e4b051-0931-49af-8359-38149d7a6361- full textbeam-chunktext/plain1 KB
doc:beam/80e4b051-0931-49af-8359-38149d7a6361Show excerpt
with profiler.profile(record_shapes=True, use_cuda=True) as prof: with profiler.record_function("model_training"): for i, (batch_inputs, batch_targets) in enumerate(dataloader): with autocast(): # Us…
See also
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