We provide an overview of the tools that enable deep learning in R, including packages such as tensorflown keras, and tfestimators. Demos are included to show the API. We also discuss the latest features.

1. R interface for Tensorflow
#sdss2019
Kevin Kuo @kevinykuo

2. Artificial
intelligence?

3. Artificial
Intelligence?
Getty Images

4. Getty Images
Artificial
Intelligence?

5. Rule #1 of AI:

6. Rule #1 of AI:
Someone talk to you about AI
with a straight face? They
tryna hustle ya.

7. ELI5
TensorFlow?

8. ELI5 a stats
undergrad
TensorFlow

9. What’s a tensor?

10. What’s a tensor?
What’s flowing?

12. Tensors & Ops
Tensors are just
multidimensional
arrays

13. Tensors & Ops
Tensors are just
multidimensional
arrays
You apply Ops
(transformations
) to Tensors to
get more Tensors

15. Deep Learning?
Neural networks?

16. Find a function F
such that Y ≈ f(x)

17. Find a function F
such that Y ≈ f(x)
(at least some of the
time)

18. Find a function F
such that Y ≈ f(x)
(at least some of the
time, hopefully)

20. Really just
straightforward
matrix algebra

28. Resources

29. tensorflow
https://www.tensorflow.org/guide/extend/ar
chitecture

30. tensorflow
tf.keras

31. tensorflow
tf.keras
library(keras)

32. library(tfdatasets)
library(tfprobability)
library(tensorflow)

35. GLM is a neural net!
We’re doing AI!

37. Proof (by example).

38. library(keras)
library(tfprobability)
model <- keras_model_sequential() %>%
layer_dense(units = 1, input_shape = 1,
name = "parameters") %>%
layer_lambda(f = k_exp) %>%
layer_distribution_lambda(tfd_poisson)

39. library(keras)
library(tfprobability)
model <- keras_model_sequential() %>%
layer_dense(units = 1, input_shape = 1,
name = "parameters") %>%
layer_lambda(f = k_exp) %>%
layer_distribution_lambda(tfd_poisson)
neg_loglik <- function(y_true, y_pred) {
- tfd_log_prob(y_pred, y_true)
}
model %>%
compile(optimizer = optimizer_sgd(lr = 0.1),
loss = neg_loglik)

40. model <- keras_model_sequential() %>%
layer_dense(units = 1, input_shape = 1,
name = "parameters") %>%
layer_lambda(f = k_exp) %>%
layer_distribution_lambda(tfd_poisson)
neg_loglik <- function(y_true, y_pred) {
- tfd_log_prob(y_pred, y_true)
}
model %>%
compile(optimizer = optimizer_sgd(lr = 0.1),
loss = neg_loglik)
model %>%
fit(x, y, epochs = 15)

41. > parameters <- model %>%
+ get_layer("parameters") %>%
+ (function(x) x$get_weights())
> parameters
[[1]]
[,1]
[1,] 1.985788
[[2]]
[1] 1.090006

42. > glm(y ~ x, family = poisson())
Call: glm(formula = y ~ x, family = poisson())
Coefficients:
(Intercept) x
1.090 1.986
Degrees of Freedom: 99 Total (i.e. Null); 98 Residual
Null Deviance: 359
Residual Deviance: 90.1 AIC: 484.2

43. > parameters <- model %>%
+ get_layer("parameters") %>%
+ (function(x) x$get_weights())
> parameters
[[1]]
[,1]
[1,] 1.985788
[[2]]
[1] 1.090006

45. model <- keras_model_sequential() %>%
layer_dense(units = 8675309, input_shape = 1,
name = "parameters") %>%
layer_dense(units = 8675309) %>%
layer_lambda(f = k_exp) %>%
layer_distribution_lambda(tfd_poisson)
NOW YOU HAVE A DEEP NEURAL NET

46. model <- keras_model_sequential() %>%
layer_dense(units = 8675309, input_shape = 1,
name = "parameters") %>%
layer_dense_variational(
units = 1,
make_posterior_fn = posterior_mean_field,
make_prior_fn = prior_trainable,
kl_weight = 1 / n_rows,
activation = "linear"
) %>%
layer_lambda(f = k_exp) %>%
layer_distribution_lambda(tfd_poisson)
NOW YOUR MODEL IS A RANDOM
VARIABLE ZOMG

47. model <- keras_model_sequential() %>%
layer_dense(units = 8675309, input_shape = 1,
name = "parameters") %>%
layer_dense_variational(
units = 2,
make_posterior_fn = posterior_mean_field,
make_prior_fn = prior_trainable,
kl_weight = 1 / n_rows,
activation = "linear"
) %>%
layer_distribution_lambda(function(t) {
tfd_normal(t[,1], k_softplus(t[,2]))
})
HETEROSCEDASTIC ERRORS OMGWTFBBQ

48. model <- keras_model_sequential() %>%
____ %>%
____ %>%
____
model %>%
compile(optimizer = ____,
loss = ____)
THE POSSIBILITIES ARE ENDLESS

49. model <- keras_model_sequential() %>%
____ %>%
____ %>%
____
model %>%
compile(optimizer = ____,
loss = ____)
THE POSSIBILITIES ARE ENDLESS

50. Why should I give a
___ about deep
learning?

52. Enabling new
applications?

53. 1.123%
Percentage of data scientists who
work with image data on the job

54. Things neural nets can help with
- Images (yeah, sometimes we get them)
- Natural language / Time series
- High dimensional categorical predictors
- Multi-task learning, i.e. when we want to predict
multiple outputs with a single model
- Combining different types of inputs into the same model
- Making predictions on devices without docker containers
(e.g. your phone)

55. Resources
● https://tensorflow.rstudio.com/
● https://keras.rstudio.com/
● https://blogs.rstudio.com/tensorflow/
● https://github.com/rstudio/tfprobability