torch

Lifecycle: experimental R build status CRAN status

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.

Examples

You can create torch tensors from R objects with the torch_tensor function 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,.,.) = 
#>   0.1512  0.8540
#>   0.3250  0.3191
#> 
#> (2,.,.) = 
#>   0.8256  0.1999
#>   0.1343  0.4721
#> [ 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 torch development. See the contributing guide for more information.