GitHub - mlverse/torch: R Interface to Torch (original) (raw)

torch

Installation

torch can be installed from CRAN with:

install.packages("torch")

You can also install the development version with:

remotes::install_github("mlverse/torch")

At the first package load additional software will be installed. See also the full installation guide here.

Examples

You can create torch tensors from R objects with the torch_tensorfunction and convert them back to R objects with as_array.

library(torch) x <- array(runif(8), dim = c(2, 2, 2)) y <- torch_tensor(x, dtype = torch_float64()) y #> torch_tensor #> (1,.,.) = #> 0.6192 0.5800 #> 0.2488 0.3681 #> #> (2,.,.) = #> 0.0042 0.9206 #> 0.4388 0.5664 #> [ CPUDoubleType{2,2,2} ] identical(x, as_array(y)) #> [1] TRUE

Simple Autograd Example

In the following snippet we let torch, using the autograd feature, calculate the derivatives:

x <- torch_tensor(1, requires_grad = TRUE) w <- torch_tensor(2, requires_grad = TRUE) b <- torch_tensor(3, requires_grad = TRUE) y <- w * x + b y$backward() x$grad #> torch_tensor #> 2 #> [ CPUFloatType{1} ] w$grad #> torch_tensor #> 1 #> [ CPUFloatType{1} ] b$grad #> torch_tensor #> 1 #> [ CPUFloatType{1} ]

Contributing

No matter your current skills it’s possible to contribute to torchdevelopment. See the contributing guide for more information.