README.Rmd

---
output: github_document
---
 
<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# torch <a href='https://torch.mlverse.org'><img src='man/figures/torch.png' align="right" height="139" /></a>

[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
![R build status](https://github.com/mlverse/torch/workflows/Test/badge.svg)
[![CRAN status](https://www.r-pkg.org/badges/version/torch)](https://CRAN.R-project.org/package=torch)
[![](https://cranlogs.r-pkg.org/badges/torch)](https://cran.r-project.org/package=torch)

## Installation

Run:

```r
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.

```{r}
library(torch)
x <- array(runif(8), dim = c(2, 2, 2))
y <- torch_tensor(x, dtype = torch_float64())
y
identical(x, as_array(y))
```

### Simple Autograd Example

In the following snippet we let torch, using the autograd feature, calculate the derivatives:

```{r}
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
w$grad
b$grad
```

### 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.

```{r, eval=TRUE}
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)
print(b) 
```

## Contributing

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