train a neural network on MNIST in R using Keras

mnist-keras.R

# see here: https://keras.rstudio.com/
# code reproduced here for easy copy-pasting 

# install TF if necessary
devtools::install_github("rstudio/tensorflow")
tensorflow::install_tensorflow()

# load keras
library(keras)

# load mnist from keras
mnist <- dataset_mnist()

# "undo" returned list
x_train <- mnist$train$x
y_train <- mnist$train$y
x_test <- mnist$test$x
y_test <- mnist$test$y

# reshape
x_train <- array_reshape(x_train, c(nrow(x_train), 784))
x_test <- array_reshape(x_test, c(nrow(x_test), 784))

# rescale
x_train <- x_train / 255
x_test <- x_test / 255

# one-hot encoding
y_train <- to_categorical(y_train, 10)
y_test <- to_categorical(y_test, 10)

# define model
model <- keras_model_sequential() 
model %>% 
  layer_dense(units = 256, activation = 'relu', input_shape = c(784)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 128, activation = 'relu') %>%
  layer_dropout(rate = 0.3) %>%
  layer_dense(units = 10, activation = 'softmax')

# compile model
model %>% compile(
  loss = 'categorical_crossentropy',
  optimizer = optimizer_rmsprop(),
  metrics = c('accuracy')
)

# train model
history <- model %>% fit(
  x_train, y_train, 
  epochs = 30, batch_size = 128, 
  validation_split = 0.2
)

# evaluate on test set
model %>% evaluate(x_test, y_test)

# predict on test set
model %>% predict_classes(x_test)