AutoKeras is an open source software library for automated machine learning (AutoML). It is developed by DATA Lab at Texas A&M University and community contributors. The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. AutoKeras provides functions to automatically search for architecture and hyperparameters of deep learning models.
Check out the AutoKeras blogpost at the RStudio TensorFlow for R blog.
Install the current released version of {autokeras}
from CRAN:
install.packages("autokeras")
Or install the development version from GitHub:
if (!require("remotes")) { install.packages("remotes") } remotes::install_github("r-tensorflow/autokeras")
Then, use the install_autokeras()
function to install TensorFlow:
library("autokeras") install_autokeras()
autokeras
R package has a configured Docker image.
Steps to run it:
From a bash console:
```{bash eval = FALSE} docker pull jcrodriguez1989/r-autokeras:1.0.0 docker run -it jcrodriguez1989/r-autokeras:1.0.0 /bin/bash
To run the docker image, and share the current folder (in home machine) to the `/data` path (in the docker machine), then do: ```{bash eval = FALSE} docker run -it -v ${PWD}:/data jcrodriguez1989/r-autokeras:1.0.0 /bin/bash ls /data # once when the docker image is running
library("keras") # Get CIFAR-10 dataset, but not preprocessing needed cifar10 <- dataset_cifar10() c(x_train, y_train) %<-% cifar10$train c(x_test, y_test) %<-% cifar10$test
library("autokeras") # Create an image classifier, and train 10 different models clf <- model_image_classifier(max_trials = 10) %>% fit(x_train, y_train)
# And use it to evaluate, predict clf %>% evaluate(x_test, y_test)
clf %>% predict(x_test[1:10, , , ])
# Get the best trained Keras model, to work with the keras R library (keras_model <- export_model(clf))
library("keras") # Get IMDb dataset imdb <- dataset_imdb(num_words = 1000) c(x_train, y_train) %<-% imdb$train c(x_test, y_test) %<-% imdb$test # AutoKeras procceses each text data point as a character vector, # i.e., x_train[[1]] "<START> this film was just brilliant casting..", # so we need to transform the dataset. word_index <- dataset_imdb_word_index() word_index <- c( "<PAD>", "<START>", "<UNK>", "<UNUSED>", names(word_index)[order(unlist(word_index))] ) x_train <- lapply(x_train, function(x) { paste(word_index[x + 1], collapse = " ") }) x_test <- lapply(x_test, function(x) { paste(word_index[x + 1], collapse = " ") }) x_train <- matrix(unlist(x_train), ncol = 1) x_test <- matrix(unlist(x_test), ncol = 1) y_train <- array(unlist(y_train)) y_test <- array(unlist(y_test))
library("autokeras") # Create a text classifier, and train 10 different models clf <- model_text_classifier(max_trials = 10) %>% fit(x_train, y_train)
# And use it to evaluate, predict clf %>% evaluate(x_test, y_test)
clf %>% predict(x_test[1:10])
# Get the best trained Keras model, to work with the keras R library export_model(clf)
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