mlverse / Torch
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R Interface to Torch
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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.
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,.,.) =
#> 0.9192 0.4962
#> 0.3191 0.3114
#>
#> (2,.,.) =
#> 0.7482 0.7318
#> 0.6661 0.8189
#> [ 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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for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].