R Interfaces to Python Keras Package

February 11, 2018
By

(This article was first published on S+/R – Yet Another Blog in Statistical Computing, and kindly contributed to R-bloggers)

Keras is a popular Python package to do the prototyping for deep neural networks with multiple backends, including TensorFlow, CNTK, and Theano. Currently, there are two R interfaces that allow us to use Keras from R through the reticulate package. While the keras R package is able to provide a flexible and feature-rich API, the kerasR R package is more convenient and computationally efficient. For instance, in the below example mimicking the Python code shown in https://statcompute.wordpress.com/2017/01/02/dropout-regularization-in-deep-neural-networks, the kerasR package is at least 10% faster than the keras package in terms of the computing time.

df <- read.csv("credit_count.txt")
Y <- matrix(df[df$CARDHLDR == 1, ]$DEFAULT)
X <- scale(df[df$CARDHLDR == 1, ][3:14])
set.seed(2018)
rows <- sample(1:nrow(Y), nrow(Y) - 2000)
Y1 <- Y[rows, ]
Y2 <- Y[-rows, ]
X1 <- X[rows, ]
X2 <- X[-rows, ]

### USE KERAS PACKAGE (https://keras.rstudio.com) ###

library(keras)
dnn1 % 
  ### DEFINE THE INPUT LAYER ###
  layer_dense(units = 50, activation = 'relu', input_shape = ncol(X), kernel_constraint = constraint_maxnorm(4)) %>% 
  layer_dropout(rate = 0.2, seed = 1) %>% 
  ### DEFINE THE 1ST HIDDEN LAYER ###
  layer_dense(units = 20, activation = 'relu', kernel_constraint = constraint_maxnorm(4)) %>% 
  layer_dropout(rate = 0.2, seed = 1) %>% 
  ### DEFINE THE 2ND HIDDEN LAYER ###
  layer_dense(units = 20, activation = 'relu', kernel_constraint = constraint_maxnorm(4)) %>% 
  layer_dropout(rate = 0.2, seed = 1) %>% 
  layer_dense(units = 1, activation = 'sigmoid') %>% 
  compile(loss = 'binary_crossentropy', optimizer = 'sgd', metrics = c('accuracy'))

dnn1 %>% fit(X1, Y1, batch_size = 50, epochs = 20, verbose = 0, validation_split = 0.3)
pROC::roc(as.numeric(Y2), as.numeric(predict_proba(dnn1, X2)))

### USE KERAS PACKAGE (https://github.com/statsmaths/kerasR) ###

library(kerasR)
dnn2 <- Sequential()
### DEFINE THE INPUT LAYER ###
dnn2$add(Dense(units = 50, input_shape = ncol(X), activation = 'relu', kernel_constraint = max_norm(4)))
dnn2$add(Dropout(rate = 0.2, seed = 1))
### DEFINE THE 1ST HIDDEN LAYER ###
dnn2$add(Dense(units = 20, activation = 'relu', kernel_constraint = max_norm(4)))
dnn2$add(Dropout(rate = 0.2, seed = 1))
### DEFINE THE 2ND HIDDEN LAYER ###
dnn2$add(Dense(units = 20, activation = 'relu', kernel_constraint = max_norm(4)))
dnn2$add(Dropout(rate = 0.2, seed = 1))
dnn2$add(Dense(units = 1, activation = 'sigmoid'))
keras_compile(dnn2,  loss = 'binary_crossentropy', optimizer = 'sgd', metrics = 'accuracy')

keras_fit(dnn2, X1, Y1, batch_size = 50, epochs = 20, verbose = 0, validation_split = 0.3)
pROC::roc(as.numeric(Y2), as.numeric(keras_predict_proba(dnn2, X2)))

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