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torch

Lifecycle: experimentalTestCRAN statuscudaDiscord

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. See also the full installation guide here.

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,.,.) = #> 0.6192 0.5800#> 0.2488 0.3681#> #> (2,.,.) = #> 0.0042 0.9206#> 0.4388 0.5664#> [ 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+by$backward()
x$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.

About

R Interface to Torch

torch.mlverse.org

Resources

Readme
License

Contributing

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GitHub - royr2/torch: R Interface to Torch · GitHub
Skip to content
BranchesTags
Repository files navigation
torch 
Lifecycle: experimentalTestCRAN statuscudaDiscord
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. See
also the full installation
guide here.
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,.,.) = #> 0.6192 0.5800#> 0.2488 0.3681#> #> (2,.,.) = #> 0.0042 0.9206#> 0.4388 0.5664#> [ 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+by$backward()
x$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.
About
R Interface to Torch
torch.mlverse.org
Resources
Readme
License
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GitHub - royr2/torch: R Interface to Torch · GitHub
Skip to content
BranchesTags
Repository files navigation
torch 
Lifecycle: experimentalTestCRAN statuscudaDiscord
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. See
also the full installation
guide here.
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,.,.) = #> 0.6192 0.5800#> 0.2488 0.3681#> #> (2,.,.) = #> 0.0042 0.9206#> 0.4388 0.5664#> [ 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+by$backward()
x$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.
About
R Interface to Torch
torch.mlverse.org
Resources
Readme
License
Contributing
Contributing
Activity
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - royr2/torch: R Interface to Torch · GitHub
Skip to content
BranchesTags
Repository files navigation
torch 
Lifecycle: experimentalTestCRAN statuscudaDiscord
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. See
also the full installation
guide here.
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,.,.) = #> 0.6192 0.5800#> 0.2488 0.3681#> #> (2,.,.) = #> 0.0042 0.9206#> 0.4388 0.5664#> [ 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+by$backward()
x$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.
About
R Interface to Torch
torch.mlverse.org
Resources
Readme
License
Contributing
Contributing
Activity
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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - royr2/torch: R Interface to Torch · GitHub
Skip to content
BranchesTags
Repository files navigation
torch 
Lifecycle: experimentalTestCRAN statuscudaDiscord
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. See
also the full installation
guide here.
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,.,.) = #> 0.6192 0.5800#> 0.2488 0.3681#> #> (2,.,.) = #> 0.0042 0.9206#> 0.4388 0.5664#> [ 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+by$backward()
x$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.
About
R Interface to Torch
torch.mlverse.org
Resources
Readme
License
Contributing
Contributing
Activity
Stars
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, 'i');
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - royr2/torch: R Interface to Torch · GitHub
Skip to content
BranchesTags
Repository files navigation
torch 
Lifecycle: experimentalTestCRAN statuscudaDiscord
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. See
also the full installation
guide here.
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,.,.) = #> 0.6192 0.5800#> 0.2488 0.3681#> #> (2,.,.) = #> 0.0042 0.9206#> 0.4388 0.5664#> [ 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+by$backward()
x$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.
About
R Interface to Torch
torch.mlverse.org
Resources
Readme
License
Contributing
Contributing
Activity
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var __m = "*";
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GitHub - royr2/torch: R Interface to Torch · GitHub
Skip to content
BranchesTags
Repository files navigation
torch 
Lifecycle: experimentalTestCRAN statuscudaDiscord
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. See
also the full installation
guide here.
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,.,.) = #> 0.6192 0.5800#> 0.2488 0.3681#> #> (2,.,.) = #> 0.0042 0.9206#> 0.4388 0.5664#> [ 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+by$backward()
x$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.
About
R Interface to Torch
torch.mlverse.org
Resources
Readme
License
Contributing
Contributing
Activity
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console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
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})();
})();
GitHub - royr2/torch: R Interface to Torch · GitHub
Skip to content
BranchesTags
Repository files navigation
torch 
Lifecycle: experimentalTestCRAN statuscudaDiscord
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. See
also the full installation
guide here.
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,.,.) = #> 0.6192 0.5800#> 0.2488 0.3681#> #> (2,.,.) = #> 0.0042 0.9206#> 0.4388 0.5664#> [ 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+by$backward()
x$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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R Interface to Torch
torch.mlverse.org
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