torch.nn
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
torch.nn has 13 facts recorded in Dontopedia across 5 references, with 1 live disagreement.
Mostly:rdf:type(5), depends on(1), submodule of(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (6)
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.
importsImports(2)
- Latency Reducer Class
ex:latency reducer class - Torch Nn Import
ex:torch-nn-import
aliasesAliases(1)
- Nn Import Alias
ex:nn-import-alias
containsContains(1)
- Torch Library
ex:torch-library
containsImportContains Import(1)
- Code Example
ex:code-example
isImportOfIs Import of(1)
- Torch Nn Import
ex:torch-nn-import
Other facts (9)
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 | Python Submodule | [1] |
| Rdf:type | Python Module | [2] |
| Rdf:type | Python Module | [3] |
| Rdf:type | Python Submodule | [4] |
| Rdf:type | Python Module | [5] |
| Depends on | Torch Module | [3] |
| Submodule of | Torch Module | [3] |
| Part of | Torch Library | [4] |
| Imported in | Example Implementation | [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.
References (5)
ctx:claims/beam/56ec773d-331c-4612-b327-318a1a96426f- full textbeam-chunktext/plain1 KB
doc:beam/56ec773d-331c-4612-b327-318a1a96426fShow excerpt
```python import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset # Example data preparation inputs = torch.randn(3000, 128) # Example input data labels = torch.randn(3000, 1) …
ctx:claims/beam/e544e68c-76b5-4e41-95e3-2d1c8d6c4836- full textbeam-chunktext/plain1 KB
doc:beam/e544e68c-76b5-4e41-95e3-2d1c8d6c4836Show excerpt
- The `model` is created with a dynamic context size. - The `model.summary()` prints the model structure, and `model.predict` tests the model with the padded `input_ids`. By following these steps and using the provided example code, you sh…
ctx:claims/beam/f537c0ec-0996-4601-868a-9cb050537ebdctx:claims/beam/2b55433d-f10b-4ba8-ac07-7b8a156dc333- full textbeam-chunktext/plain1 KB
doc:beam/2b55433d-f10b-4ba8-ac07-7b8a156dc333Show excerpt
- Use tools like `torch.utils.benchmark` to measure and compare the performance of different configurations. ### Example with Error Handling Here's an example with error handling: ```python import torch import torch.nn as nn class Sc…
ctx:claims/beam/0a6354af-a6f7-4051-8cb3-e50345232784
See also
Keep researching
Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.