# Parsing Utilities

## Overview

Parsing utilities are a set of functions that helps generate parsing spec for tf$parse_example to be used with estimators. If users keep data in tf$Example format, they need to call tf$parse_example with a proper feature spec. There are two main things that these utility functions help: • Users need to combine parsing spec of features with labels and weights (if any) since they are all parsed from same tf$Example instance. The utility functions combine these specs.

• It is difficult to map expected label by a estimator such as dnn_classifier to corresponding tf$parse_example spec. The utility functions encode it by getting related information from users (key, dtype). ## Example output of parsing spec parsing_spec <- classifier_parse_example_spec( feature_columns = column_numeric('a'), label_key = 'b', weight_column = 'c' ) For the above example, classifier_parse_example_spec would return the following: expected_spec <- list( a = tf$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)

## Example usage with a classifier

Firstly, define features transformations and initiailize your classifier similar to the following:

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)
)

Next, create the parsing configuration for tf$parse_example using classifier_parse_example_spec and the feature columns fcs we have just defined: parsing_spec <- classifier_parse_example_spec( feature_columns = fcs, label_key = 'my-label', label_dtype = tf$string,
weight_column = 'example-weight'
)

This label configuration tells the classifier the following:

• weights are retrieved with key ‘example-weight’
• label is string and can be one of the following c('photos', 'keep', ...)
• integer id for label ‘photos’ is 0, ‘keep’ is 1, etc

Then define your input function with the help of read_batch_features that reads the batches of features from files in tf$Example format with the parsing configuration parsing_spec we just defined: input_fn_train <- function() { features <- tf$contrib$learn$read_batch_features(
file_pattern = train_files,
batch_size = batch_size,
features = parsing_spec,
}
Finally we can train the model using the training input function parsed by classifier_parse_example_spec:
train(model, input_fn = input_fn_train)