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

## -----------------------------------------------------------------------------
# parsing_spec <- classifier_parse_example_spec(
#   feature_columns = column_numeric('a'),
#   label_key = 'b',
#   weight_column = 'c'
# )

## -----------------------------------------------------------------------------
# expected_spec <- list(
#   a = tf$python$ops$parsing_ops$FixedLenFeature(reticulate::tuple(1L), dtype = tf$float32),
#   c = tf$python$ops$parsing_ops$FixedLenFeature(reticulate::tuple(1L), dtype = tf$float32),
#   b = tf$python$ops$parsing_ops$FixedLenFeature(reticulate::tuple(1L), dtype = tf$int64)
# )
# 
# # This should be the same as the one we constructed using `classifier_parse_example_spec`
# testthat::expect_equal(parsing_spec, expected_spec)

## -----------------------------------------------------------------------------
# fcs <- feature_columns(...)
# 
# model <- dnn_classifier(
#   n_classes = 1000,
#   feature_columns = fcs,
#   weight_column = 'example-weight',
#   label_vocabulary= c('photos', 'keep', ...),
#   hidden_units = c(256, 64, 16)
# )

## -----------------------------------------------------------------------------
# parsing_spec <- classifier_parse_example_spec(
#   feature_columns = fcs,
#   label_key = 'my-label',
#   label_dtype = tf$string,
#   weight_column = 'example-weight'
# )
# 

## -----------------------------------------------------------------------------
# input_fn_train <- function() {
#   features <- tf$contrib$learn$read_batch_features(
#     file_pattern = train_files,
#     batch_size = batch_size,
#     features = parsing_spec,
#     reader = tf$RecordIOReader)
#   labels <- features[["my-label"]]
#   return(list(features, labels))
# }

## -----------------------------------------------------------------------------
# train(model, input_fn = input_fn_train)