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more doc fixes (#804)

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Ssslakter
2023-09-11 02:36:29 +07:00
committed by GitHub
parent 4f18180fc7
commit 18d6db2180
4 changed files with 10 additions and 10 deletions
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4
candle-book/src/SUMMARY.md
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@ -10,10 +10,10 @@
# Reference Guide # Reference Guide
- [Running a model](inference/README.md) - [Running a model](inference/inference.md)
- [Using the hub](inference/hub.md) - [Using the hub](inference/hub.md)
- [Error management](error_manage.md) - [Error management](error_manage.md)
- [Training](training/README.md) - [Training](training/training.md)
- [MNIST](training/mnist.md) - [MNIST](training/mnist.md)
- [Fine-tuning]() - [Fine-tuning]()
- [Serialization]() - [Serialization]()

16
candle-book/src/guide/hello_world.md
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@ -6,7 +6,7 @@ Open `src/main.rs` and fill in this content:
```rust ```rust
# extern crate candle_core; # extern crate candle_core;
use candle_core::{DType, Device, Result, Tensor}; use candle_core::{Device, Result, Tensor};
struct Model { struct Model {
first: Tensor, first: Tensor,
@ -29,7 +29,7 @@ fn main() -> Result<()> {
let second = Tensor::randn(0f32, 1.0, (100, 10), &device)?; let second = Tensor::randn(0f32, 1.0, (100, 10), &device)?;
let model = Model { first, second }; let model = Model { first, second };
let dummy_image = Tensor::zeros((1, 784), DType::F32, &device)?; let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
let digit = model.forward(&dummy_image)?; let digit = model.forward(&dummy_image)?;
println!("Digit {digit:?} digit"); println!("Digit {digit:?} digit");
@ -50,7 +50,7 @@ the classical `Linear` layer. We can do as such
```rust ```rust
# extern crate candle_core; # extern crate candle_core;
# use candle_core::{DType, Device, Result, Tensor}; # use candle_core::{Device, Result, Tensor};
struct Linear{ struct Linear{
weight: Tensor, weight: Tensor,
bias: Tensor, bias: Tensor,
@ -80,7 +80,7 @@ This will change the model running code into a new function
```rust ```rust
# extern crate candle_core; # extern crate candle_core;
# use candle_core::{DType, Device, Result, Tensor}; # use candle_core::{Device, Result, Tensor};
# struct Linear{ # struct Linear{
# weight: Tensor, # weight: Tensor,
# bias: Tensor, # bias: Tensor,
@ -111,7 +111,7 @@ fn main() -> Result<()> {
// Creating a dummy model // Creating a dummy model
let weight = Tensor::randn(0f32, 1.0, (784, 100), &device)?; let weight = Tensor::randn(0f32, 1.0, (784, 100), &device)?;
let bias = Tensor::zeros((100, ), DType::F32, &device)?; let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?;
let first = Linear{weight, bias}; let first = Linear{weight, bias};
let weight = Tensor::randn(0f32, 1.0, (100, 10), &device)?; let weight = Tensor::randn(0f32, 1.0, (100, 10), &device)?;
let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?; let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?;
@ -146,7 +146,7 @@ And rewrite our examples using it
```rust ```rust
# extern crate candle_core; # extern crate candle_core;
# extern crate candle_nn; # extern crate candle_nn;
use candle_core::{DType, Device, Result, Tensor}; use candle_core::{Device, Result, Tensor};
use candle_nn::{Linear, Module}; use candle_nn::{Linear, Module};
struct Model { struct Model {
@ -189,7 +189,7 @@ Feel free to modify this example to use `Conv2d` to create a classical convnet i
Now that we have the running dummy code we can get to more advanced topics: Now that we have the running dummy code we can get to more advanced topics:
- [For PyTorch users](../guide/cheatsheet.md) - [For PyTorch users](../guide/cheatsheet.md)
- [Running existing models](../inference/README.md) - [Running existing models](../inference/inference.md)
- [Training models](../training/README.md) - [Training models](../training/training.md)

0
candle-book/src/inference/README.md → candle-book/src/inference/inference.md
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0
candle-book/src/training/README.md → candle-book/src/training/training.md
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