An example of training MNIST in R using Keras.

keras-mnist.R

## From: https://tensorflow.rstudio.com/keras/
library(keras)

mnist <- dataset_mnist()
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
y_train <- to_categorical(y_train, 10)
y_test <- to_categorical(y_test, 10)

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'))

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

plot(history)

model %>% evaluate(x_test, y_test)
model %>% predict_classes(x_test)