index.R
In keras: R Interface to 'Keras'

## ----setup, include=FALSE-----------------------------------------------------
library(keras)
knitr::opts_chunk$set(eval = FALSE)

## ---- eval=FALSE--------------------------------------------------------------
#  install.packages("keras")

## -----------------------------------------------------------------------------
#  devtools::install_github("rstudio/keras")

## ---- eval=FALSE--------------------------------------------------------------
#  install.packages("keras")
#  install_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')
#  )

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

## -----------------------------------------------------------------------------
#  plot(history)

## ---- results = 'hide'--------------------------------------------------------
#  model %>% evaluate(x_test, y_test)

## ---- results = 'hide'--------------------------------------------------------
#  model %>% predict_classes(x_test)