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

## -----------------------------------------------------------------------------
# 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)

## -----------------------------------------------------------------------------
# history_df <- as.data.frame(history)
# str(history_df)

## -----------------------------------------------------------------------------
# # don't show metrics during this run
# history <- model %>% fit(
#   x_train, y_train,
#   epochs = 30, batch_size = 128,
#   view_metrics = FALSE,
#   validation_split = 0.2
# )
# 
# # set global default to never show metrics
# options(keras.view_metrics = FALSE)

## -----------------------------------------------------------------------------
# history <- model %>% fit(
#   x_train, y_train,
#   batch_size = batch_size,
#   epochs = epochs,
#   verbose = 1,
#   callbacks = callback_tensorboard("logs/run_a"),
#   validation_split = 0.2
# )

## -----------------------------------------------------------------------------
# tensorboard("logs/run_a")

## -----------------------------------------------------------------------------
# # launch TensorBoard (data won't show up until after the first epoch)
# tensorboard("logs/run_a")
# 
# # fit the model with the TensorBoard callback
# history <- model %>% fit(
#   x_train, y_train,
#   batch_size = batch_size,
#   epochs = epochs,
#   verbose = 1,
#   callbacks = callback_tensorboard("logs/run_a"),
#   validation_split = 0.2
# )

## -----------------------------------------------------------------------------
# callback_tensorboard(log_dir = "logs/run_b")

## -----------------------------------------------------------------------------
# tensorboard("logs")

## -----------------------------------------------------------------------------
# tensorboard(c("logs/run_a", "logs/run_b"))