Trains a simple deep NN on the MNIST dataset.

Gets to 98.40% test accuracy after 20 epochs (there is a lot of margin for parameter tuning). 2 seconds per epoch on a K520 GPU.

library(keras)

# Data Preparation ---------------------------------------------------

batch_size <- 128
num_classes <- 10
epochs <- 30

# The data, shuffled and split between train and test sets
c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset_mnist()

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

# Transform RGB values into [0,1] range
x_train <- x_train / 255
x_test <- x_test / 255

cat(nrow(x_train), 'train samples\n')
cat(nrow(x_test), 'test samples\n')

# Convert class vectors to binary class matrices
y_train <- to_categorical(y_train, num_classes)
y_test <- to_categorical(y_test, num_classes)

# 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')

summary(model)

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

# Training & Evaluation ----------------------------------------------------

# Fit model to data
history <- model %>% fit(
  x_train, y_train,
  batch_size = batch_size,
  epochs = epochs,
  verbose = 1,
  validation_split = 0.2
)

plot(history)

score <- model %>% evaluate(
  x_test, y_test,
  verbose = 0
)

# Output metrics
cat('Test loss:', score[[1]], '\n')
cat('Test accuracy:', score[[2]], '\n')