diff options
Diffstat (limited to 'candle-book/src/guide/hello_world.md')
| -rw-r--r-- | candle-book/src/guide/hello_world.md | 40 |
1 files changed, 20 insertions, 20 deletions
diff --git a/candle-book/src/guide/hello_world.md b/candle-book/src/guide/hello_world.md index fc4af0e1..b5b8d7b4 100644 --- a/candle-book/src/guide/hello_world.md +++ b/candle-book/src/guide/hello_world.md @@ -6,7 +6,7 @@ Open `src/main.rs` and fill in this content: ```rust # extern crate candle_core; -use candle_core::{DType, Device, Result, Tensor}; +use candle_core::{Device, Result, Tensor}; struct Model { first: Tensor, @@ -25,11 +25,11 @@ fn main() -> Result<()> { // Use Device::new_cuda(0)?; to use the GPU. let device = Device::Cpu; - let first = Tensor::zeros((784, 100), DType::F32, &device)?; - let second = Tensor::zeros((100, 10), DType::F32, &device)?; + let first = Tensor::randn(0f32, 1.0, (784, 100), &device)?; + let second = Tensor::randn(0f32, 1.0, (100, 10), &device)?; 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)?; println!("Digit {digit:?} digit"); @@ -50,7 +50,7 @@ the classical `Linear` layer. We can do as such ```rust # extern crate candle_core; -# use candle_core::{DType, Device, Result, Tensor}; +# use candle_core::{Device, Result, Tensor}; struct Linear{ weight: Tensor, bias: Tensor, @@ -80,7 +80,7 @@ This will change the model running code into a new function ```rust # extern crate candle_core; -# use candle_core::{DType, Device, Result, Tensor}; +# use candle_core::{Device, Result, Tensor}; # struct Linear{ # weight: Tensor, # bias: Tensor, @@ -110,15 +110,15 @@ fn main() -> Result<()> { let device = Device::cuda_if_available(0)?; // Creating a dummy model - let weight = Tensor::zeros((784, 100), DType::F32, &device)?; - let bias = Tensor::zeros((100, ), DType::F32, &device)?; + let weight = Tensor::randn(0f32, 1.0, (784, 100), &device)?; + let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?; let first = Linear{weight, bias}; - let weight = Tensor::zeros((100, 10), DType::F32, &device)?; - let bias = Tensor::zeros((10, ), DType::F32, &device)?; + let weight = Tensor::randn(0f32, 1.0, (100, 10), &device)?; + let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?; let second = Linear{weight, bias}; 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)?; // Inference on the model let digit = model.forward(&dummy_image)?; @@ -146,7 +146,7 @@ And rewrite our examples using it ```rust # extern crate candle_core; # extern crate candle_nn; -use candle_core::{DType, Device, Result, Tensor}; +use candle_core::{Device, Result, Tensor}; use candle_nn::{Linear, Module}; struct Model { @@ -167,15 +167,15 @@ fn main() -> Result<()> { let device = Device::Cpu; // This has changed (784, 100) -> (100, 784) ! - let weight = Tensor::zeros((100, 784), DType::F32, &device)?; - let bias = Tensor::zeros((100, ), DType::F32, &device)?; + let weight = Tensor::randn(0f32, 1.0, (100, 784), &device)?; + let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?; let first = Linear::new(weight, Some(bias)); - let weight = Tensor::zeros((10, 100), DType::F32, &device)?; - let bias = Tensor::zeros((10, ), DType::F32, &device)?; + let weight = Tensor::randn(0f32, 1.0, (10, 100), &device)?; + let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?; let second = Linear::new(weight, Some(bias)); 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)?; println!("Digit {digit:?} digit"); @@ -188,8 +188,8 @@ 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: -- [For PyTorch users](./guide/cheatsheet.md) -- [Running existing models](./inference/README.md) -- [Training models](./training/README.md) +- [For PyTorch users](../guide/cheatsheet.md) +- [Running existing models](../inference/inference.md) +- [Training models](../training/training.md) |