Dontopedia

batch_idx

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

batch_idx has 6 facts recorded in Dontopedia across 3 references, with 1 live disagreement.

6 facts·3 predicates·3 sources·1 in dispute
Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (1)

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.

containsBatchIndexContains Batch Index(1)

Other facts (5)

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.

5 facts
PredicateValueRef
Rdf:typeLoop Variable[1]
Rdf:typeLogging Variable[2]
Rdf:typeInteger[3]
Is Provided byEnumerate[3]
Is Enumeratedtrue[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.

typebeam/f5a5540b-3c9d-4103-85d7-7db7b8ea25d3
ex:LoopVariable
labelbeam/f5a5540b-3c9d-4103-85d7-7db7b8ea25d3
batch_idx
typebeam/d37ddcd2-e87b-45fe-94fd-23a99f3a695e
ex:LoggingVariable
typebeam/b37d3f65-b489-4a88-aa05-62e2c014851e
ex:Integer
isProvidedBybeam/b37d3f65-b489-4a88-aa05-62e2c014851e
ex:enumerate
isEnumeratedbeam/b37d3f65-b489-4a88-aa05-62e2c014851e
true

References (3)

3 references
  1. ctx:claims/beam/f5a5540b-3c9d-4103-85d7-7db7b8ea25d3
  2. ctx:claims/beam/d37ddcd2-e87b-45fe-94fd-23a99f3a695e
    • full textbeam-chunk
      text/plain1 KBdoc:beam/d37ddcd2-e87b-45fe-94fd-23a99f3a695e
      Show 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
  3. ctx:claims/beam/b37d3f65-b489-4a88-aa05-62e2c014851e
    • full textbeam-chunk
      text/plain1 KBdoc:beam/b37d3f65-b489-4a88-aa05-62e2c014851e
      Show excerpt
      import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from torch.cuda.amp import GradScaler, autocast # Initialize PyTorch model model = nn.Sequential( nn.Linear(128, 128)

See also

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