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## ----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)
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