Dontopedia

Weight Update Process

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

Weight Update Process has 25 facts recorded in Dontopedia across 5 references, with 3 live disagreements.

25 facts·15 predicates·5 sources·3 in dispute

Mostly:executes(8), rdf:type(2), requires(2)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (12)

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)

governsGoverns(2)

appliesApplies(1)

causesCauses(1)

describesDescribes(1)

enablesEnables(1)

followsFollows(1)

occurs-beforeOccurs Before(1)

precedesPrecedes(1)

purposePurpose(1)

Other facts (24)

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.

24 facts
PredicateValueRef
ExecutesOptimizer Step[3]
ExecutesOptimizer Zero Grad[3]
ExecutesOptimizer Step[4]
ExecutesScaler Update[4]
ExecutesZero Grad[4]
ExecutesScaler Step[5]
ExecutesScaler Update[5]
ExecutesZero Gradient[5]
Rdf:typeTraining Process[2]
Rdf:typeTraining Step[5]
RequiresI Plus 1 Mod Accumulation[4]
RequiresBackward Pass[5]
UsesAdam[1]
ConditionI Plus 1 Mod Accumulation[3]
Conditional onAccumulation Steps Complete[3]
Performed byOptimizer[3]
Has ConditionI Plus 1 Mod Accumulation[4]
FollowsBackward Pass[4]
Is Part ofTraining Loop[4]
AppliesGradients[4]
Uses VariableAccumulation Steps[4]
Triggers AfterAccumulation Check[5]
Results inModel Parameter Update[5]
Occurs AfterBackward Pass[5]

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.

25 undated
—
usesbeam/f266ef67-57dd-4b1f-b9ab-661effb75c4b
ex:Adam
—
typebeam/1a9575d4-0f05-41b2-a8bf-3a9f1dd9dcb9
ex:TrainingProcess
—
labelbeam/1a9575d4-0f05-41b2-a8bf-3a9f1dd9dcb9
Weight Update Process
—
conditionbeam/8b6abd69-54a1-41b8-bb85-d0b80bff1a3a
ex:i-plus-1-mod-accumulation
—
executesbeam/8b6abd69-54a1-41b8-bb85-d0b80bff1a3a
ex:optimizer-step
—
executesbeam/8b6abd69-54a1-41b8-bb85-d0b80bff1a3a
ex:optimizer-zero-grad
—
conditional-onbeam/8b6abd69-54a1-41b8-bb85-d0b80bff1a3a
ex:accumulation-steps-complete
—
performed-bybeam/8b6abd69-54a1-41b8-bb85-d0b80bff1a3a
ex:optimizer
—
hasConditionbeam/af924c4f-8579-4b2a-85d1-c042076b09c7
ex:i_plus_1_mod_accumulation
—
followsbeam/af924c4f-8579-4b2a-85d1-c042076b09c7
ex:backward-pass
—
isPartOfbeam/af924c4f-8579-4b2a-85d1-c042076b09c7
ex:training-loop
—
requiresbeam/af924c4f-8579-4b2a-85d1-c042076b09c7
ex:i_plus_1_mod_accumulation
—
appliesbeam/af924c4f-8579-4b2a-85d1-c042076b09c7
ex:gradients
—
executesbeam/af924c4f-8579-4b2a-85d1-c042076b09c7
ex:optimizer_step
—
executesbeam/af924c4f-8579-4b2a-85d1-c042076b09c7
ex:scaler_update
—
executesbeam/af924c4f-8579-4b2a-85d1-c042076b09c7
ex:zero_grad
—
usesVariablebeam/af924c4f-8579-4b2a-85d1-c042076b09c7
ex:accumulation_steps
—
triggers-afterbeam/2bacfc08-73f1-4c21-88e8-d07ff734da09
ex:accumulation-check
—
executesbeam/2bacfc08-73f1-4c21-88e8-d07ff734da09
ex:scaler-step
—
executesbeam/2bacfc08-73f1-4c21-88e8-d07ff734da09
ex:scaler-update
—
executesbeam/2bacfc08-73f1-4c21-88e8-d07ff734da09
ex:zero-gradient
—
typebeam/2bacfc08-73f1-4c21-88e8-d07ff734da09
ex:TrainingStep
—
resultsInbeam/2bacfc08-73f1-4c21-88e8-d07ff734da09
ex:model-parameter-update
—
occurs-afterbeam/2bacfc08-73f1-4c21-88e8-d07ff734da09
ex:backward-pass
—
requiresbeam/2bacfc08-73f1-4c21-88e8-d07ff734da09
ex:backward-pass

References (5)

5 references
  1. [1]F266ef67 57dd 4b1f B9ab 661effb75c4b1 fact
    ctx:claims/beam/f266ef67-57dd-4b1f-b9ab-661effb75c4b
  2. [2]1a9575d4 0f05 41b2 A8bf 3a9f1dd9dcb92 facts
    ctx:claims/beam/1a9575d4-0f05-41b2-a8bf-3a9f1dd9dcb9
    • full textbeam-chunk
      text/plain1 KBdoc:beam/1a9575d4-0f05-41b2-a8bf-3a9f1dd9dcb9
      Show excerpt
      - **Description**: Coefficient for L2 norm of the weights. - **Range**: Typically between \(10^{-6}\) and \(10^{-2}\). - **Example Values**: \(1e-6\), \(1e-5\), \(1e-4\), \(1e-3\), \(1e-2\). - **Dropout Rate** - **De…
  3. [3]8b6abd69 54a1 41b8 Bb85 D0b80bff1a3a5 facts
    ctx:claims/beam/8b6abd69-54a1-41b8-bb85-d0b80bff1a3a
    • full textbeam-chunk
      text/plain1 KBdoc:beam/8b6abd69-54a1-41b8-bb85-d0b80bff1a3a
      Show excerpt
      loss = criterion(outputs, batch_targets) # Normalize the loss because it is accumulated loss = loss / accumulation_steps # Backward pass loss.backward() # Update wei…
  4. [4]Af924c4f 8579 4b2a 85d1 C042076b09c79 facts
    ctx:claims/beam/af924c4f-8579-4b2a-85d1-c042076b09c7
    • full textbeam-chunk
      text/plain1 KBdoc:beam/af924c4f-8579-4b2a-85d1-c042076b09c7
      Show excerpt
      loss = loss / accumulation_steps # Backward pass scaler.scale(loss).backward() # Update weights if (i + 1) % accumulation_steps == 0: scaler.step(optimizer) …
  5. [5]2bacfc08 73f1 4c21 88e8 D07ff734da098 facts
    ctx:claims/beam/2bacfc08-73f1-4c21-88e8-d07ff734da09
    • full textbeam-chunk
      text/plain914 Bdoc:beam/2bacfc08-73f1-4c21-88e8-d07ff734da09
      Show excerpt
      # Backward pass scaler.scale(loss).backward() # Update weights if (i + 1) % accumulation_steps == 0: scaler.step(optimizer) …

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