## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, eval=FALSE)

## -----------------------------------------------------------------------------
# library(keras)
# 
# FLAGS <- flags(
#   flag_integer("dense_units1", 128),
#   flag_numeric("dropout1", 0.4),
#   flag_integer("dense_units2", 128),
#   flag_numeric("dropout2", 0.3)
# )

## -----------------------------------------------------------------------------
# model <- keras_model_sequential() %>%
#   layer_dense(units = FLAGS$dense_units1, activation = 'relu',
#               input_shape = c(784)) %>%
#   layer_dropout(rate = FLAGS$dropout1) %>%
#   layer_dense(units = FLAGS$dense_units2, activation = 'relu') %>%
#   layer_dropout(rate = FLAGS$dropout2) %>%
#   layer_dense(units = 10, activation = 'softmax')

## -----------------------------------------------------------------------------
# model %>% compile(
#   loss = 'categorical_crossentropy',
#   optimizer = optimizer_rmsprop(),
#   metrics = c('accuracy')
# )

## -----------------------------------------------------------------------------
# cloudml_train("mnist_mlp.R", config = "tuning.yml")

## -----------------------------------------------------------------------------
# job_trials("cloudml_2018_01_08_142717956")

## -----------------------------------------------------------------------------
# job_collect("cloudml_2018_01_08_142717956")

## -----------------------------------------------------------------------------
# job_collect("cloudml_2018_01_08_142717956", trials = "all")

## -----------------------------------------------------------------------------
# trials <- job_trials("cloudml_2018_01_08_142717956")
# job_collect("cloudml_2018_01_08_142717956", trials = trials$trialId[1:5])

## -----------------------------------------------------------------------------
# summary <- tf$Summary()
# summary$value$add(tag = "accuracy", simple_value = accuracy)
# summary_writer$add_summary(summary, iteration_number)