# Auto-Keras: Tuning-free deep learning from R

Sometimes in deep learning, architecture design and hyperparameter tuning pose substantial challenges. Using Auto-Keras, none of these is needed: We start a search procedure and extract the best-performing model. This post presents Auto-Keras in action on the well-known MNIST dataset.

Juan Cruz Rodriguez https://jcrodriguez.rbind.io (FAMAF, Universidad Nacional de Córdoba)https://www.famaf.unc.edu.ar/
2019-04-16

Today, we’re happy to feature a guest post written by Juan Cruz, showing how to use Auto-Keras from R. Juan holds a master’s degree in Computer Science. Currently, he is finishing his master’s degree in Applied Statistics, as well as a Ph.D. in Computer Science, at the Universidad Nacional de Córdoba. He started his R journey almost six years ago, applying statistical methods to biology data. He enjoys software projects focused on making machine learning and data science available to everyone.

In the past few years, artificial intelligence has been a subject of intense media hype. Machine learning, deep learning, and artificial intelligence come up in countless articles, often outside of technology-minded publications. For most any topic, a brief search on the web yields dozens of texts suggesting the application of one or the other deep learning model.

However, tasks such as feature engineering, hyperparameter tuning, or network design, are by no means easy for people without a rich computer science background. Lately, research started to emerge in the area of what is known as Neural Architecture Search (NAS) (Baker et al. 2016; Pham et al. 2018; Zoph and Le 2016; Luo et al. 2018; Liu et al. 2017; Real et al. 2018; Jin, Song, and Hu 2018). The main goal of NAS algorithms is, given a specific tagged dataset, to search for the most optimal neural network to perform a certain task on that dataset. In this sense, NAS algorithms allow the user to not have to worry about any task related to data science engineering. In other words, given a tagged dataset and a task, e.g., image classification, or text classification among others, the NAS algorithm will train several high-performance deep learning models and return the one that outperforms the rest.

Several NAS algorithms were developed on different platforms (e.g. Google Cloud AutoML), or as libraries of certain programming languages (e.g. Auto-Keras, TPOT, Auto-Sklearn). However, for a language that brings together experts from such diverse disciplines as is the R programming language, to the best of our knowledge, there is no NAS tool to this day. In this post, we present the Auto-Keras R package, an interface from R to the Auto-Keras Python library (Jin, Song, and Hu 2018). Thanks to the use of Auto-Keras, R programmers with few lines of code will be able to train several deep learning models for their data and get the one that outperforms the others.

Let’s dive into Auto-Keras!

## Auto-Keras

Note: the Python Auto-Keras library is only compatible with Python 3.6. So make sure this version is currently installed, and correctly set to be used by the reticulate R library.

### Installation

To begin, install the autokeras R package from GitHub as follows:


if (!require("remotes")) {
install.packages("remotes")
}
remotes::install_github("jcrodriguez1989/autokeras")

The Auto-Keras R interface uses the Keras and TensorFlow backend engines by default. To install both the core Auto-Keras library as well as the Keras and TensorFlow backends use the install_autokeras() function:


library("autokeras")
install_autokeras()

This will provide you with default CPU-based installations of Keras and TensorFlow. If you want a more customized installation, e.g. if you want to take advantage of NVIDIA GPUs, see the documentation for install_keras() from the keras R library.

### MNIST Example

We can learn the basics of Auto-Keras by walking through a simple example: recognizing handwritten digits from the MNIST dataset. MNIST consists of 28 x 28 grayscale images of handwritten digits like this:

The dataset also includes labels for each image, telling us which digit it is. For example, the label for the above image is 2.

The MNIST dataset is included with Keras and can be accessed using the dataset_mnist() function from the keras R library. Here we load the dataset, and then create variables for our test and training data:


library("keras")
mnist <- dataset_mnist() # load mnist dataset
c(x_train, y_train) %<-% mnist$train # get train c(x_test, y_test) %<-% mnist$test # and test data

The x data is a 3-d array (images,width,height) of grayscale integer values ranging between 0 to 255.


x_train[1, 14:20, 14:20] # show some pixels from the first image

[,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,]  241  225  160  108    1    0    0
[2,]   81  240  253  253  119   25    0
[3,]    0   45  186  253  253  150   27
[4,]    0    0   16   93  252  253  187
[5,]    0    0    0    0  249  253  249
[6,]    0   46  130  183  253  253  207
[7,]  148  229  253  253  253  250  182

The y data is an integer vector with values ranging from 0 to 9.


n_imgs <- 8
head(y_train, n = n_imgs) # show first 8 labels

[1] 5 0 4 1 9 2 1 3

Each of these images can be plotted in R:


library("ggplot2")
library("tidyr")
# get each of the first n_imgs from the x_train dataset and
# convert them to wide format
mnist_to_plot <-
do.call(rbind, lapply(seq_len(n_imgs), function(i) {
samp_img <- x_train[i, , ] %>%
as.data.frame()
colnames(samp_img) <- seq_len(ncol(samp_img))
data.frame(
img = i,
gather(samp_img, "x", "value", convert = TRUE),
y = seq_len(nrow(samp_img))
)
}))
ggplot(mnist_to_plot, aes(x = x, y = y, fill = value)) + geom_tile() +
scale_fill_gradient(low = "black", high = "white", na.value = NA) +
scale_y_reverse() + theme_minimal() + theme(panel.grid = element_blank()) +
theme(aspect.ratio = 1) + xlab("") + ylab("") + facet_wrap(~img, nrow = 2)

#### Data ready, let’s get the model!

Data pre-processing? Model definition? Metrics, epochs definition, anyone? No, none of them are required by Auto-Keras. For image classification tasks, it is enough for Auto-Keras to be passed the x_train and y_train objects as defined above.

So, to train several deep learning models for two hours, it is enough to run:


# train an Image Classifier for two hours
clf <- model_image_classifier(verbose = TRUE) %>%
fit(x_train, y_train, time_limit = 2 * 60 * 60)

Saving Directory: /tmp/autokeras_ZOG76O
Preprocessing the images.
Preprocessing finished.

Initializing search.
Initialization finished.

+----------------------------------------------+
|               Training model 0               |
+----------------------------------------------+

No loss decrease after 5 epochs.

Saving model.
+--------------------------------------------------------------------------+
|        Model ID        |          Loss          |      Metric Value      |
+--------------------------------------------------------------------------+
|           0            |  0.19463148526847363   |   0.9843999999999999   |
+--------------------------------------------------------------------------+

+----------------------------------------------+
|               Training model 1               |
+----------------------------------------------+

No loss decrease after 5 epochs.

Saving model.
+--------------------------------------------------------------------------+
|        Model ID        |          Loss          |      Metric Value      |
+--------------------------------------------------------------------------+
|           1            |   0.210642946138978    |         0.984          |
+--------------------------------------------------------------------------+

Evaluate it:


clf %>% evaluate(x_test, y_test)

[1] 0.9866


clf %>% final_fit(x_train, y_train, x_test, y_test, retrain = TRUE)

No loss decrease after 30 epochs.

Evaluate the final model:


clf %>% evaluate(x_test, y_test)

[1] 0.9918

And the model can be saved to take it into production with:


clf %>% export_autokeras_model("./myMnistModel.pkl")

### Conclusions

In this post, the Auto-Keras R package was presented. It was shown that, with almost no deep learning knowledge, it is possible to train models and get the one that returns the best results for the desired task. Here we trained models for two hours. However, we have also tried training for 24 hours, resulting in 15 models being trained, to a final accuracy of 0.9928. Although Auto-Keras will not return a model as efficient as one generated manually by an expert, this new library has its place as an excellent starting point in the world of deep learning. Auto-Keras is an open-source R package, and is freely available in https://github.com/jcrodriguez1989/autokeras/.

Although the Python Auto-Keras library is currently in a pre-release version and comes with not too many types of training tasks, this is likely to change soon, as the project it was recently added to the keras-team set of repositories. This will undoubtedly further its progress a lot. So stay tuned, and thanks for reading!

### Reproducibility

To correctly reproduce the results of this post, we recommend using the Auto-Keras docker image by typing:


docker pull jcrodriguez1989/r-autokeras:0.1.0
docker run -it jcrodriguez1989/r-autokeras:0.1.0 /bin/bash

Baker, Bowen, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar. 2016. “Designing Neural Network Architectures Using Reinforcement Learning.” arXiv Preprint arXiv:1611.02167.

Jin, Haifeng, Qingquan Song, and Xia Hu. 2018. “Auto-Keras: An Efficient Neural Architecture Search System.” arXiv Preprint arXiv:1806.10282.

Liu, Hanxiao, Karen Simonyan, Oriol Vinyals, Chrisantha Fernando, and Koray Kavukcuoglu. 2017. “Hierarchical Representations for Efficient Architecture Search.” arXiv Preprint arXiv:1711.00436.

Luo, Renqian, Fei Tian, Tao Qin, Enhong Chen, and Tie-Yan Liu. 2018. “Neural Architecture Optimization.” In Advances in Neural Information Processing Systems, 7816–27.

Pham, Hieu, Melody Y Guan, Barret Zoph, Quoc V Le, and Jeff Dean. 2018. “Efficient Neural Architecture Search via Parameter Sharing.” arXiv Preprint arXiv:1802.03268.

Real, Esteban, Alok Aggarwal, Yanping Huang, and Quoc V Le. 2018. “Regularized Evolution for Image Classifier Architecture Search.” arXiv Preprint arXiv:1802.01548.

Zoph, Barret, and Quoc V Le. 2016. “Neural Architecture Search with Reinforcement Learning.” arXiv Preprint arXiv:1611.01578.

### Corrections

If you see mistakes or want to suggest changes, please create an issue on the source repository.

### Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. Source code is available at https://github.com/jcrodriguez1989/tf_blog_autokeras, unless otherwise noted. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

### Citation

Rodriguez (2019, April 16). RStudio AI Blog: Auto-Keras: Tuning-free deep learning from R. Retrieved from https://blogs.rstudio.com/tensorflow/posts/2019-04-16-autokeras/

BibTeX citation

@misc{rodriguez2019auto-keras:,
author = {Rodriguez, Juan Cruz},
title = {RStudio AI Blog: Auto-Keras: Tuning-free deep learning from R},
url = {https://blogs.rstudio.com/tensorflow/posts/2019-04-16-autokeras/},
year = {2019}
}