Run:
remotes::install_github("mlverse/torch")
At the first package load additional software will be installed.
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
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} ]
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} ]
No matter your current skills it’s possible to contribute to torch
development. See the contributing guide for more information.