If users keep data in TensorFlow Example format, they need to call tf$parse_example
with a proper feature spec. There are two main things that this utility
helps:
Users need to combine parsing spec of features with labels and
weights (if any) since they are all parsed from same tf$Example
instance.
This utility combines these specs.
It is difficult to map expected label by
a classifier such as dnn_classifier
to corresponding tf$parse_example
spec.
This utility encodes it by getting related information from users (key,
dtype).
classifier_parse_example_spec(feature_columns, label_key,
label_dtype = tf$int64, label_default = NULL, weight_column = NULL)
An iterable containing all feature columns. All items
should be instances of classes derived from _FeatureColumn
.
A string identifying the label. It means tf$Example
stores
labels with this key.
A tf$dtype
identifies the type of labels. By default it
is tf$int64
. If user defines a label_vocabulary
, this should be set as
tf$string
. tf$float32
labels are only supported for binary
classification.
used as label if label_key does not exist in given
tf$Example
. An example usage: let's say label_key
is 'clicked' and
tf$Example
contains clicked data only for positive examples in following
format key:clicked, value:1
. This means that if there is no data with key
'clicked' it should count as negative example by setting
label_deafault=0
. Type of this value should be compatible with
label_dtype
.
A string or a numeric column created by
column_numeric()
defining feature column representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example. If it is a string, it is
used as a key to fetch weight tensor from the features
. If it is a
numeric column, raw tensor is fetched by key weight_column$key
, then
weight_column$normalizer_fn
is applied on it to get weight tensor.
A dict mapping each feature key to a FixedLenFeature
or
VarLenFeature
value.
ValueError: If label is used in feature_columns
.
ValueError: If weight_column is used in feature_columns
.
ValueError: If any of the given feature_columns
is not a feature column instance.
ValueError: If weight_column
is not a numeric column instance.
ValueError: if label_key is NULL
.
Other parsing utilities: regressor_parse_example_spec