Introduction to using R for deep learning using the Keras Library

1. Whoa, That’s Deep Man
An introduction to deep learning in R
Martin Eastwood

2. ARTIFICIAL INTELLIGENCE
Broad field of study dedicated to simulating
human intelligence using machines
MACHINE LEARNING
Algorithms that can learn without
being explicitly programmed
DEEP LEARNING
Use of multi-layered neural
networks to learn data
representations
What is
Deep learning?

3. Manual Feature
Extraction /
transformation
ML Classifier
algorithm
It’s a cat!
Neural
network
layer
Neural
network
layer
Neural
network
layer
Neural
network
layer
Machine Learning Vs Deep Learning
Low-level
features
mid-level
features
higher-level
features
High-level
features

4. No more feature engineering*
* Maybe just a little bit…

5. But aren’t neural networks old, what’s
changed?
We now have
much larger
data sets for
training models
Commodity
computers
and GPUs are
much more
powerful
New ways
of handling
exploding
and vanishing
gradients

6. Facebook Friend Tagging

8. Google Pixel Ear Buds

9. Deep Learning Libraries in R
•MXNet
•darch
•deepnet
•h20
•deepwater

11. > devtools::install_github("rstudio/keras")
> library(keras)
> install_keras()

12. Sentiment Analysis Using Keras
Sentiment Review
<chr> <chr>
1 I don't know why I like this movie so well, but I never get tired of watching it.
0 You'd better choose Paul Verhoeven's even if you have watched it.
1 This is the definitive movie version of Hamlet.
0 Long, boring, blasphemous. Never have I been so glad to see ending credits roll.
0 Ming The Merciless does a little Bardwork and a movie most foul!
0 I wouldn't rent this one even on dollar rental night.

13. > model <- keras_model_sequential()
> model %>%
layer_embedding(input_dim=max_features, output_dim=128) %>%
layer_lstm(units=64, dropout=0.25, recurrent_dropout=0.25) %>%
layer_dense(units=1, activation='sigmoid')
Define the Keras Model

14. model %>%
compile(loss=‘binary_crossentropy',
optimizer='adam',
metrics='accuracy')
model %>%
fit(x_train,
y_train,
batch_size=batch_size,
epochs=10,
validation_data=list(x_test, y_test))
Train the Keras Model

15. Training History

16. > predictions <- predict_classes(model, x_test)
> table(y_test, predictions)
0 1
0 10773 1727
1 2911 9589
> mean(y_test == predictions)
[1] 0.81448
Predicting Sentiment

17. • Keras now provides access to Tensorflow, CNTK and
Theano through R
• Can run models using either GPU / CPU
• Lots of pre-trained Keras models available
Conclusions

18. Questions