forward

as donto believed on

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

forward has 45 facts recorded in Dontopedia across 8 references, with 9 live disagreements.

45 facts·25 predicates·8 sources·9 in dispute

Mostly:rdf:type(5), applies(5), calls(4)

Inbound mentions (5)

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.

`isUsedIn`Is Used in(2)

Fc1ex:fc1
Fc2ex:fc2

`contains-function`Contains Function(1)

Scriptex:script

`implementedInFunction`Implemented in Function(1)

Position Dependent Normalizationex:position-dependent-normalization

`matchesByteExactly`Matches Byte Exactly(1)

Step Functionex:step-function

Other facts (42)

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.

42 facts

Predicate	Value	Ref
Rdf:type	Neural Network Forward Pass	[3]
Rdf:type	Forward Method	[4]
Rdf:type	Neural Network Forward Method	[5]
Rdf:type	Neural Network Method	[7]
Rdf:type	Forward Pass	[8]
Applies	Torch Relu	[4]
Applies	Relu Activation	[6]
Applies	First Layer	[7]
Applies	Second Layer	[7]
Applies	Relu Activation	[7]
Calls	Fc1	[4]
Calls	Bn1	[4]
Calls	Dropout	[4]
Calls	Fc2	[4]
Returns	X	[4]
Returns	Fc2 Output	[6]
Returns	Second Layer Output	[7]
Applied to	Fc1	[3]
Applied to	Fc2	[3]
Has Parameter	Self Parameter	[4]
Has Parameter	X Parameter	[4]
Executes	First Layer	[7]
Executes	Second Layer	[7]
Calls Layer	Fc1 Layer	[8]
Calls Layer	Fc2 Layer	[8]
Depends on Sync Iteration	True	[1]
Computes Cumsum on Line582	True	[1]
Activation Function	Torch.relu	[3]
Has Activation	Re Lu	[4]
Applies Batch Normalization	Bn1	[4]
Applies Dropout	Dropout	[4]
Final Operation	Fc2	[4]
Applies Activation Before Dropout	true	[4]
Passes Through	Fc2 Output	[4]
Has Parameter	X	[5]
Transforms	X	[5]
Applies Activation After	Fc1	[5]
Applies Layer After Activation	Fc2	[5]
Applies Activation	torch.relu	[7]
Execution Order	first-then-second	[7]
Parameter	Input Tensor X	[8]
Operation	Relu Activation	[8]

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.

45 undated

—

dependsOnSyncIterationblah/watt-activation/part-110

ex:true

—

computesCumsumOnLine582blah/watt-activation/part-110

ex:true

—

labelblah/watt-activation/107

forward()

—

typebeam/40cdfaf4-9269-4589-895a-5336c29a6561

ex:NeuralNetworkForwardPass

—

activationFunctionbeam/40cdfaf4-9269-4589-895a-5336c29a6561

ex:torch.relu

—

appliedTobeam/40cdfaf4-9269-4589-895a-5336c29a6561

ex:fc1

—

appliedTobeam/40cdfaf4-9269-4589-895a-5336c29a6561

ex:fc2

—

typebeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

ex:ForwardMethod

—

labelbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

forward

—

hasActivationbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

ex:ReLU

—

appliesBatchNormalizationbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

ex:bn1

—

appliesDropoutbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

ex:dropout

—

callsbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

ex:fc1

—

callsbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

ex:bn1

—

callsbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

ex:dropout

—

callsbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

ex:fc2

—

appliesbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

ex:torch-relu

—

has-parameterbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

ex:self-parameter

—

has-parameterbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

ex:x-parameter

—

final-operationbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

ex:fc2

—

returnsbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

ex:x

—

applies-activation-before-dropoutbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

true

—

passes-throughbeam/2739fb08-c4fc-4bb6-b143-e05bc2133eae

ex:fc2-output

—

typebeam/f6bdd424-985a-4eea-a1d8-a4f7ec22cc5b

ex:NeuralNetworkForwardMethod

—

hasParameterbeam/f6bdd424-985a-4eea-a1d8-a4f7ec22cc5b

ex:x

—

transformsbeam/f6bdd424-985a-4eea-a1d8-a4f7ec22cc5b

ex:x

—

appliesActivationAfterbeam/f6bdd424-985a-4eea-a1d8-a4f7ec22cc5b

ex:fc1

—

appliesLayerAfterActivationbeam/f6bdd424-985a-4eea-a1d8-a4f7ec22cc5b

ex:fc2

—

appliesbeam/58f12238-1846-4fee-9e47-8a6406dd05a7

ex:relu-activation

—

returnsbeam/58f12238-1846-4fee-9e47-8a6406dd05a7

ex:fc2-output

—

typebeam/ed89dfcd-55c3-4faf-8d48-dae86a9a5011

ex:NeuralNetworkMethod

—

labelbeam/ed89dfcd-55c3-4faf-8d48-dae86a9a5011

forward

—

appliesActivationbeam/ed89dfcd-55c3-4faf-8d48-dae86a9a5011

torch.relu

—

appliesbeam/ed89dfcd-55c3-4faf-8d48-dae86a9a5011

ex:first-layer

—

appliesbeam/ed89dfcd-55c3-4faf-8d48-dae86a9a5011

ex:second-layer

—

appliesbeam/ed89dfcd-55c3-4faf-8d48-dae86a9a5011

ex:relu-activation

—

executesbeam/ed89dfcd-55c3-4faf-8d48-dae86a9a5011

ex:first-layer

—

executesbeam/ed89dfcd-55c3-4faf-8d48-dae86a9a5011

ex:second-layer

—

executionOrderbeam/ed89dfcd-55c3-4faf-8d48-dae86a9a5011

first-then-second

—

returnsbeam/ed89dfcd-55c3-4faf-8d48-dae86a9a5011

ex:second-layer-output

—

typebeam/a028f532-cbf7-455e-a47b-43e8b3c5a1d2

ex:ForwardPass

—

parameterbeam/a028f532-cbf7-455e-a47b-43e8b3c5a1d2

ex:input-tensor-x

—

operationbeam/a028f532-cbf7-455e-a47b-43e8b3c5a1d2

ex:relu-activation

—

callsLayerbeam/a028f532-cbf7-455e-a47b-43e8b3c5a1d2

ex:fc1-layer

—

callsLayerbeam/a028f532-cbf7-455e-a47b-43e8b3c5a1d2

ex:fc2-layer

References (8)

8 references

[1]Part 1102 facts
ctx:discord/blah/watt-activation/part-110
[2]1071 fact
ctx:discord/blah/watt-activation/107
- full textwatt-activation-107
  text/plain2 KBdoc:agent/watt-activation-107/7f46d3a6-a9e5-4ccd-b3ab-17637a2a1afc
  Show excerpt
  [2026-03-08 20:49] xenonfun: performance impact? ⏺ Negligible. The current forward already does heavy _gated_cumsum on (B,T,H,K,d_h) tensors. The fix just adds one extra cumsum on a (T,K) ones tensor to get position-dependent effective cou…
[3]40cdfaf4 9269 4589 895a 5336c29a65614 facts
ctx:claims/beam/40cdfaf4-9269-4589-895a-5336c29a6561
- full textbeam-chunk
  text/plain1 KBdoc:beam/40cdfaf4-9269-4589-895a-5336c29a6561
  Show excerpt
  - Integrate the audit process into your CI/CD pipeline to ensure continuous compliance. By following these improvements, you can ensure a more thorough and effective compliance auditing process that covers all necessary GDPR aspects. [Tur…
[4]2739fb08 C4fc 4bb6 B143 E05bc2133eae16 facts
ctx:claims/beam/2739fb08-c4fc-4bb6-b143-e05bc2133eae
- full textbeam-chunk
  text/plain1 KBdoc:beam/2739fb08-c4fc-4bb6-b143-e05bc2133eae
  Show excerpt
```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error class MyMod…
[5]F6bdd424 985a 4eea A1d8 A4f7ec22cc5b5 facts
ctx:claims/beam/f6bdd424-985a-4eea-a1d8-a4f7ec22cc5b
- full textbeam-chunk
  text/plain1 KBdoc:beam/f6bdd424-985a-4eea-a1d8-a4f7ec22cc5b
  Show excerpt
  def forward(self, x): x = torch.relu(self.fc1(x)) x = self.fc2(x) return x # Initialize scorer, optimizer, and loss function scorer = ComplexityScorer() optimizer = optim.Adam(scorer.parameters(), lr=1e-5) loss_…
[6]58f12238 1846 4fee 9e47 8a6406dd05a72 facts
ctx:claims/beam/58f12238-1846-4fee-9e47-8a6406dd05a7
- full textbeam-chunk
  text/plain1 KBdoc:beam/58f12238-1846-4fee-9e47-8a6406dd05a7
  Show excerpt
  - **Cons**: Requires tuning of the weight decay parameter. ### 5. **AdaBelief** - **Description**: AdaBelief is a recent optimizer that modifies the adaptive learning rate scheme of Adam to better align with the curvature of the loss…
[7]Ed89dfcd 55c3 4faf 8d48 Dae86a9a501110 facts
ctx:claims/beam/ed89dfcd-55c3-4faf-8d48-dae86a9a5011
[8]A028f532 Cbf7 455e A47b 43e8b3c5a1d25 facts
ctx:claims/beam/a028f532-cbf7-455e-a47b-43e8b3c5a1d2
- full textbeam-chunk
  text/plain1 KBdoc:beam/a028f532-cbf7-455e-a47b-43e8b3c5a1d2
  Show excerpt
  Ensure that data loading is efficient and does not become a bottleneck. ### 4. Asynchronous Execution Use asynchronous execution to overlap computation and data transfer, leading to better performance. ### 5. CUDA Streams For GPU utilizat…

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forward

Inbound mentions (5)

isUsedInIs Used in(2)

contains-functionContains Function(1)

implementedInFunctionImplemented in Function(1)

matchesByteExactlyMatches Byte Exactly(1)

Other facts (42)

Timeline

References (8)

See also

Keep researching

`isUsedIn`Is Used in(2)

`contains-function`Contains Function(1)

`implementedInFunction`Implemented in Function(1)

`matchesByteExactly`Matches Byte Exactly(1)