https://github.com/andrie/tensorflowr
Docker repository containing deep learning for R: RStudio, tensorflow and keras
https://github.com/andrie/tensorflowr
Last synced: about 1 month ago
JSON representation
Docker repository containing deep learning for R: RStudio, tensorflow and keras
- Host: GitHub
- URL: https://github.com/andrie/tensorflowr
- Owner: andrie
- License: mit
- Created: 2017-04-15T05:23:19.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-06-19T16:20:54.000Z (almost 8 years ago)
- Last Synced: 2025-02-14T13:16:55.425Z (3 months ago)
- Size: 9.77 KB
- Stars: 7
- Watchers: 3
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# tensorflowr: Docker repository with deep learning for R.
image | description | from |size | metrics | build status
---------------- | ----------------------------------------- | ---- | ------ | ------- | --------------
[reticulate](https://hub.docker.com/r/andrie/reticulate) | R-3.3.3, RStudio, Python 3.4.2, Anaconda and the `reticulate` package | [rocker/rstudio:3.3.3](https://hub.docker.com/r/rocker/rstudio/) | [](https://microbadger.com/images/andrie/reticulate) | [](https://hub.docker.com/r/andrie/reticulate) | [](https://hub.docker.com/r/andrie/reticulate/builds)
[tensorflowr](https://hub.docker.com/r/andrie/tensorflowr) | Adds tensorflow and keras, installed in python virtualenv and conda environment | [andrie/reticulate](https://hub.docker.com/r/andrie/reticulate) | [](https://microbadger.com/images/andrie/tensorflowr) | [](https://hub.docker.com/r/andrie/tensorflowr) | [](https://hub.docker.com/r/andrie/tensorflowr/builds)
# Repositories
## andrie/reticulate
This repository will be useful to test any R code that connects to Python using the `reticulate` package. The repository uses [rocker/rstudio:3.3.3](https://hub.docker.com/r/rocker/rstudio/) as the base, and adds:
* Python 3.4.2
* Anaconda
* R packages for:
- Typical development tools, including `devtools`, `roxygen2` and `rmarkdown`
- `Rcpp`
- `reticulate`, an interface layer between R and python, installed from [CRAN](https://cran.r-project.org/package=reticulate)
## andrie/tensorflowr
This repository builds two environments that contain `tensorflow` ([tensorflow.org](https://www.tensorflow.org/)) and `keras` ([keras.io](https://keras.io/)):
* Python virtual environment, containing:
- At location `/tensorflow`
- Python 3.4.2
- `tensorflow`, `keras` and `h5py`
- Activate this environment using
```bash
source /tensorflow/bin/activate
```
- A CRAN mirror pointing to a static MRAN snapshot of 2017-06-01
* Anaconda environment ([conda env](https://conda.io/docs/using/envs.html)) containing:
- conda environment `tensorflow`
- Python 3.4.2
- `tensorflow`, `keras` and `h5py`
- Activate this environment using
```bash
source activate tensorflow
```
* The R package `reticulate` (available on [CRAN](https://cran.r-project.org/web/packages/reticulate/index.html)) communicates between R and python.
* The `reticulate` package needs to know where python is installed, so the repository writes environment variables into the `Renviron` file to configure `reticulate` correctly:
```r
TENSORFLOW_PYTHON = "/tensorflow/bin/python"
RETICULATE_PYTHON = "/tensorflow/bin/python"
```
# Docker instructions
## Pull
To pull and build the image, use:
```
docker pull andrie/tensorflowr
```
## Run
Since the repository contains `rocker/rstudio`, you can run RStudio in your web browser by pointing to [https://localhost:8787]([https://localhost:8787) if you map the ports. The following line creates a container and names it `tensorflowr`, so you can easily refer to this later.
```
docker run -d --name tensorflowr -p 8787:8787 andrie/tensorflowr
```
## Exec
To execute code inside the running container:
```
docker exec -ti tensorflowr bash
```
# Hello world
## tensorflow
To test `tensorflow`, try the `Hallo world` example from the `tensorflow` R package:
```r
library(tensorflow)
sess = tf$Session()
hello <- tf$constant('Hello, TensorFlow!')
sess$run(hello)
```
## keras
To test `keras`, try the code from the `kerasR` [vignette](https://cran.r-project.org/web/packages/kerasR/vignettes/introduction.html):
```r
library(kerasR)
mod <- Sequential()
mod$add(Dense(units = 50, input_shape = 13))
mod$add(Activation("relu"))
mod$add(Dense(units = 1))
keras_compile(mod, loss = 'mse', optimizer = RMSprop())
boston <- load_boston_housing()
X_train <- scale(boston$X_train)
Y_train <- boston$Y_train
X_test <- scale(boston$X_test)
Y_test <- boston$Y_test
keras_fit(mod, X_train, Y_train,
batch_size = 32, epochs = 200,
verbose = 1, validation_split = 0.1)
pred <- keras_predict(mod, normalize(X_test))
sd(as.numeric(pred) - Y_test) / sd(Y_test)
```
# License
© Andrie de Vries
[](https://opensource.org/licenses/MIT)