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

## -----------------------------------------------------------------------------
# library(keras)
# 
# # generate dummy training data
# data <- matrix(rexp(1000*784), nrow = 1000, ncol = 784)
# labels <- matrix(round(runif(1000*10, min = 0, max = 9)), nrow = 1000, ncol = 10)
# 
# # create model
# model <- keras_model_sequential()
# 
# # add layers and compile
# model %>%
#   layer_dense(32, input_shape = c(784)) %>%
#   layer_activation('relu') %>%
#   layer_dense(10) %>%
#   layer_activation('softmax') %>%
#   compile(
#     loss='binary_crossentropy',
#     optimizer = optimizer_sgd(),
#     metrics='accuracy'
#   )
# 
# # fit with callbacks
# model %>% fit(data, labels, callbacks = list(
#   callback_model_checkpoint("checkpoints.h5"),
#   callback_reduce_lr_on_plateau(monitor = "val_loss", factor = 0.1)
# ))

## -----------------------------------------------------------------------------
# library(keras)
# 
# # define custom callback class
# LossHistory <- R6::R6Class("LossHistory",
#   inherit = KerasCallback,
# 
#   public = list(
# 
#     losses = NULL,
# 
#     on_batch_end = function(batch, logs = list()) {
#       self$losses <- c(self$losses, logs[["loss"]])
#     }
# ))
# 
# # define model
# model <- keras_model_sequential()
# 
# # add layers and compile
# model %>%
#   layer_dense(units = 10, input_shape = c(784)) %>%
#   layer_activation(activation = 'softmax') %>%
#   compile(
#     loss = 'categorical_crossentropy',
#     optimizer = 'rmsprop'
#   )
# 
# # create history callback object and use it during training
# history <- LossHistory$new()
# model %>% fit(
#   X_train, Y_train,
#   batch_size=128, epochs=20, verbose=0,
#   callbacks= list(history)
# )
# 
# # print the accumulated losses
# history$losses