torch

Installation

Run:

remotes::install_github("mlverse/torch")

At the first package load additional software will be installed.

Example

Currently this package is only a proof of concept and you can only create a Torch Tensor from an R object. And then convert back from a torch Tensor to an R object.

library(torch)
x <- array(runif(8), dim = c(2, 2, 2))
y <- torch_tensor(x, dtype = torch_float64())
y
#> torch_tensor 
#> (1,.,.) = 
#>   0.5406  0.8648
#>   0.3097  0.9715
#> 
#> (2,.,.) = 
#>   0.1309  0.8992
#>   0.4849  0.1902
#> [ 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} ]

Linear Regression

In the following example we are going to fit a linear regression from scratch using torch’s Autograd.

Note all methods that end with _ (eg. sub_), will modify the tensors in place.

x <- torch_randn(100, 2)
y <- 0.1 + 0.5*x[,1] - 0.7*x[,2]

w <- torch_randn(2, 1, requires_grad = TRUE)
b <- torch_zeros(1, requires_grad = TRUE)

lr <- 0.5
for (i in 1:100) {
  y_hat <- torch_mm(x, w) + b
  loss <- torch_mean((y - y_hat$squeeze(1))^2)
  
  loss$backward()
  
  with_no_grad({
    w$sub_(w$grad*lr)
    b$sub_(b$grad*lr)   
    
    w$grad$zero_()
    b$grad$zero_()
  })
}
print(w)
#> torch_tensor 
#> 1e-09 *
#>  5.2672
#>  -6.7969
#> [ CPUFloatType{2,1} ]
print(b) 
#> torch_tensor 
#> 0.01 *
#> -9.6802
#> [ CPUFloatType{1} ]

Contributing

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