dnrops
/
burn
mirror of https://github.com/tracel-ai/burn.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

correct typos

This commit is contained in:
louisfd 2023-08-13 19:12:24 -04:00
parent 4d6c9e4b5c
commit fd5d4da531
2 changed files with 4 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
burn-book/src/guide/README.md
View File

@ -7,7 +7,7 @@ For clarity, we sometimes omit imports in our code snippets. For more details, p
## Key Learnings
* Creating a projet
* Creating a project
* Creating neural network models
* Importing and preparing datasets
* Training models on data

6
burn-book/src/guide/model.md
View File

@ -2,7 +2,7 @@
The first step is to create a project and add the different Burn dependencies.
In a `Cargo.toml` file, add the `burn`, `burn-wgpu`, `burn-dataset`, `burn-autodiff` and `burn-train`.
Note that the `serde` dependancy is necessary for serialization and is mandatory for the time being.
Note that the `serde` dependency is necessary for serialization and is mandatory for the time being.
```toml
[package]
@ -64,7 +64,7 @@ This is important because you can extend the functionalities of a specific backe
You can also change backend during runtime, for instance to compute training metrics on a cpu backend while using a gpu one only to train the model.
In our example, the backend in use will be determined later on.
Next, we need to instanciate the model for training.
Next, we need to instantiate the model for training.
```rust , ignore
#[derive(Config, Debug)]
@ -95,7 +95,7 @@ When creating a custom neural network module, it is often a good idea to create
This allows you to define default values for your network, thanks to the `Config` attribute.
The benefit of this attribute is that it makes the configuration serializable, enabling you to painlessly save your model hyperparameters, enhancing your experimentation process.
Note that a constructor will automatically be generated for your configuration, which will take as input values for the parameter which do not have default values: `let config = ModelConfig::new(num_classes, hidden_size);`.
The default values can be overriden easily with builder-like methods: (e.g `config.with_dropout(0.2);`)
The default values can be overridden easily with builder-like methods: (e.g `config.with_dropout(0.2);`)
The first implementation block is related to the initialization method.
As we can see, all fields are set using the configuration of the corresponding neural network underlying module.