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

## ----eval=FALSE---------------------------------------------------------------
# devtools::install_github("rstudio/tfdatasets")

## ----eval=FALSE---------------------------------------------------------------
# library(tfdtasets)
# install_tensorflow()

## -----------------------------------------------------------------------------
# library(tfdatasets)
# 
# # create specification for parsing records from an example file
# iris_spec <- csv_record_spec("iris.csv")
# 
# # read the dataset
# dataset <- text_line_dataset("iris.csv", record_spec = iris_spec)
# 
# # take a glimpse at the dataset
# str(dataset)

## -----------------------------------------------------------------------------
# # provide colum names and types explicitly
# iris_spec <- csv_record_spec(
#   names = c("SepalLength", "SepalWidth", "PetalLength", "PetalWidth", "Species"),
#   types = c("double", "double", "double", "double", "integer"),
#   skip = 1
# )
# 
# # read the datset
# dataset <- text_line_dataset("iris.csv", record_spec = iris_spec)

## -----------------------------------------------------------------------------
# mtcars_spec <- csv_record_spec("mtcars.csv", types = "dididddiiii")

## -----------------------------------------------------------------------------
# dataset <- text_line_dataset("iris.csv", record_spec = iris_spec, parallel_records = 4)

## -----------------------------------------------------------------------------
# dataset <- text_line_dataset("iris.csv", record_spec = iris_spec, parallel_records = 4) %>%
#   dataset_batch(128) %>%
#   dataset_prefetch(1)

## -----------------------------------------------------------------------------
# # Creates a dataset that reads all of the examples from two files, and extracts
# # the image and label features.
# filenames <- c("/var/data/file1.tfrecord", "/var/data/file2.tfrecord")
# dataset <- tfrecord_dataset(filenames) %>%
#   dataset_map(function(example_proto) {
#     features <- list(
#       image = tf$FixedLenFeature(shape(), tf$string),
#       label = tf$FixedLenFeature(shape(), tf$int32)
#     )
#     tf$parse_single_example(example_proto, features)
#   })

## -----------------------------------------------------------------------------
# filenames <- c("/var/data/file1.tfrecord", "/var/data/file2.tfrecord")
# dataset <- tfrecord_dataset(filenames, num_parallel_reads = 4)

## ----eval=FALSE---------------------------------------------------------------
# library(tfdatasets)
# 
# record_spec <- sql_record_spec(
#   names = c("disp", "drat", "vs", "gear", "mpg", "qsec", "hp", "am", "wt",  "carb", "cyl"),
#   types = c(tf$float64, tf$int32, tf$float64, tf$int32, tf$float64, tf$float64,
#             tf$float64, tf$int32, tf$int32, tf$int32, tf$int32)
# )
# 
# dataset <- sqlite_dataset(
#   "data/mtcars.sqlite3",
#   "select * from mtcars",
#   record_spec
# )
# 
# dataset

## -----------------------------------------------------------------------------
# dataset <- dataset %>%
#   dataset_map(function(record) {
#     record$Species <- tf$one_hot(record$Species, 3L)
#     record
#   })

## -----------------------------------------------------------------------------
# dataset <- dataset %>%
#   dataset_map(num_parallel_calls = 4, function(record) {
#     record$Species <- tf$one_hot(record$Species, 3L)
#     record
#   })

## -----------------------------------------------------------------------------
# dataset <- dataset %>%
#   dataset_map(num_parallel_calls = 4, function(record) {
#     record$Species <- tf$one_hot(record$Species, 3L)
#     record
#   }) %>%
#   datset_prefetch(1)

## -----------------------------------------------------------------------------
# dataset <- dataset %>%
#   dataset_map_and_batch(batch_size = 128, function(record) {
#     record$Species <- tf$one_hot(record$Species, 3L)
#     record
#   }) %>%
#   datset_prefetch(1)

## -----------------------------------------------------------------------------
# dataset <- csv_dataset("mtcars.csv") %>%
#   dataset_filter(function(record) {
#     record$mpg >= 20
# })
# 
# dataset <- csv_dataset("mtcars.csv") %>%
#   dataset_filter(function(record) {
#     record$mpg >= 20 & record$cyl >= 6L
#   })

## -----------------------------------------------------------------------------
# mtcars_dataset <- text_line_dataset("mtcars.csv", record_spec = mtcars_spec) %>%
#   dataset_prepare(x = c(mpg, disp), y = cyl)
# 
# iris_dataset <- text_line_dataset("iris.csv", record_spec = iris_spec) %>%
#   dataset_prepare(x = -Species, y = Species)

## -----------------------------------------------------------------------------
# mtcars_dataset <- text_line_dataset("mtcars.csv", record_spec = mtcars_spec) %>%
#   dataset_prepare(cyl ~ mpg + disp)

## -----------------------------------------------------------------------------
# mtcars_dataset <- text_line_dataset("mtcars.csv", record_spec = mtcars_spec) %>%
#   dataset_prepare(cyl ~ mpg + disp, batch_size = 16)

## -----------------------------------------------------------------------------
# dataset <- dataset %>%
#   dataset_shuffle(1000) %>%
#   dataset_repeat(10) %>%
#   dataset_batch(128) %>%

## -----------------------------------------------------------------------------
# dataset <- dataset %>%
#   dataset_shuffle_and_repeat(buffer_size = 1000, count = 10) %>%
#   dataset_batch(128)

## -----------------------------------------------------------------------------
# dataset <- dataset %>%
#   dataset_map_and_batch(batch_size = 128, function(record) {
#     record$Species <- tf$one_hot(record$Species, 3L)
#     record
#   }) %>%
#   dataset_prefetch(1)

## -----------------------------------------------------------------------------
# dataset <- dataset %>%
#   dataset_map_and_batch(batch_size = 128, function(record) {
#     record$Species <- tf$one_hot(record$Species, 3L)
#     record
#   }) %>%
#   dataset_prefetch_to_device("/gpu:0")

## -----------------------------------------------------------------------------
# dataset <- text_line_dataset("mtcars.csv", record_spec = mtcars_spec) %>%
#   dataset_filter(function(record) {
#     record$mpg >= 20 & record$cyl >= 6L
#   }) %>%
#   dataset_shuffle_and_repeat(buffer_size = 1000, count = 10) %>%
#   dataset_prepare(cyl ~ mpg + disp, batch_size = 128) %>%
#   dataset_prefetch(1)

## -----------------------------------------------------------------------------
# model %>% fit(
#   x_train, y_train,
#   epochs = 30,
#   batch_size = 128
# )

## -----------------------------------------------------------------------------
# library(keras3)
# library(tfdatasets)
# 
# batch_size = 128
# steps_per_epoch = 500
# 
# # function to read and preprocess mnist dataset
# mnist_dataset <- function(filename) {
#   dataset <- tfrecord_dataset(filename) %>%
#     dataset_map(function(example_proto) {
# 
#       # parse record
#       features <- tf$parse_single_example(
#         example_proto,
#         features = list(
#           image_raw = tf$FixedLenFeature(shape(), tf$string),
#           label = tf$FixedLenFeature(shape(), tf$int64)
#         )
#       )
# 
#       # preprocess image
#       image <- tf$decode_raw(features$image_raw, tf$uint8)
#       image <- tf$cast(image, tf$float32) / 255
# 
#       # convert label to one-hot
#       label <- tf$one_hot(tf$cast(features$label, tf$int32), 10L)
# 
#       # return
#       list(image, label)
#     }) %>%
#     dataset_repeat() %>%
#     dataset_shuffle(1000) %>%
#     dataset_batch(batch_size, drop_remainder = TRUE) %>%
#     dataset_prefetch(1)
# }
# 
# model <- keras_model_sequential() %>%
#   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')
# 
# model %>% compile(
#   loss = 'categorical_crossentropy',
#   optimizer = optimizer_rmsprop(),
#   metrics = c('accuracy')
# )
# 
# history <- model %>% fit(
#   mnist_dataset("mnist/train.tfrecords"),
#   steps_per_epoch = steps_per_epoch,
#   epochs = 20,
#   validation_data = mnist_dataset("mnist/validation.tfrecords"),
#   validation_steps = steps_per_epoch
# )
# 
# score <- model %>% evaluate(
#   mnist_dataset("mnist/test.tfrecords"),
#   steps = steps_per_epoch
# )
# 
# print(score)

## -----------------------------------------------------------------------------
# dataset <- text_line_dataset("mtcars.csv", record_spec = mtcars_spec) %>%
#   dataset_prepare(cyl ~ mpg + disp) %>%
#   dataset_shuffle(20) %>%
#   dataset_batch(5)
# 
# iter <- as_iterator(dataset)
# next_batch <- iter_next(iter)
# next_batch

## -----------------------------------------------------------------------------
# dataset <- read_files("data/*.csv", text_line_dataset, record_spec = mtcars_spec,
#                       parallel_files = 4, parallel_interleave = 16) %>%
#   dataset_prefetch(5000) %>%
#   dataset_shuffle_and_repeat(buffer_size = 1000, count = 3) %>%
#   dataset_batch(128)

## -----------------------------------------------------------------------------
# # command line flags for training script (shard info is passed by training
# # supervisor that executes the script)
# FLAGS <- flags(
#   flag_integer("num_shards", 1),
#   flag_integer("shard_index", 1)
# )
# 
# # forward shard info to read_files
# dataset <- read_files("data/*.csv", text_line_dataset, record_spec = mtcars_spec,
#                       parallel_files = 4, parallel_interleave = 16,
#                       num_shards = FLAGS$num_shards, shard_index = FLAGS$shard_index) %>%
#   dataset_shuffle_and_repeat(buffer_size = 1000, count = 3) %>%
#   dataset_batch(128) %>%
#   dataset_prefetch(1)