## ----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)