README.md

torch

Lifecycle:
experimental R build
status CRAN
status Discord

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.

Installation with Docker

If you would like to install with Docker, please read following document.

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,.,.) = 
#>   0.5955  0.3436
#>   0.4946  0.4344
#> 
#> (2,.,.) = 
#>   0.9322  0.7824
#>   0.6503  0.7516
#> [ 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.



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torch documentation built on Oct. 7, 2021, 9:22 a.m.