Running Loss
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Running Loss has 7 facts recorded in Dontopedia across 3 references, with 1 live disagreement.
Mostly:rdf:type(3), accumulates across(1), initialized to(1)
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.
accumulatesAccumulates(1)
- Loss Tracking
ex:loss-tracking
initializesInitializes(1)
- Epoch Loop
ex:epoch-loop
usesVariableUses Variable(1)
- Calculate Average Loss
ex:calculate-average-loss
Other facts (7)
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 |
|---|---|---|
| Rdf:type | Accumulator Variable | [1] |
| Rdf:type | Accumulator | [2] |
| Rdf:type | Accumulated Metric | [3] |
| Accumulates Across | Batches | [2] |
| Initialized to | 0 | [2] |
| Reset Each Epoch | Epoch Loop | [2] |
| Is Accumulated Over | Batch Iterations | [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/19e4aaf4-f77d-418a-98ab-75fcf4c80784- full textbeam-chunktext/plain1 KB
doc:beam/19e4aaf4-f77d-418a-98ab-75fcf4c80784Show excerpt
running_loss = 0.0 for inputs, targets in dataloader: optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, targets) loss.backward() optimizer.step() running_loss += …
ctx:claims/beam/d722ad53-d442-458e-b561-cab7e12fcbbf- full textbeam-chunktext/plain1 KB
doc:beam/d722ad53-d442-458e-b561-cab7e12fcbbfShow excerpt
optimizer = optim.Adam(model.parameters(), lr=0.001) # Using Adam optimizer scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=5, verbose=True) scaler = GradScaler() try: for epoch in range(100): running…
ctx:claims/beam/d37ddcd2-e87b-45fe-94fd-23a99f3a695e- full textbeam-chunktext/plain1 KB
doc:beam/d37ddcd2-e87b-45fe-94fd-23a99f3a695eShow excerpt
# Calculate average loss for the epoch avg_loss = running_loss / len(data_loader) print(f'Epoch [{epoch + 1}/100], Loss: {avg_loss:.4f}, LR: {optimizer.param_groups[0]["lr"]}') # Step the scheduler s…
See also
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