Generates Parsing Spec for TensorFlow Example to be Used with Regressors

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 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 regressor such as dnn_regressor to corresponding tf$parse_example spec. This utility encodes it by getting related information from users (key, dtype).

regressor_parse_example_spec(feature_columns, label_key,
  label_dtype = tf$float32, label_default = NULL,
  label_dimension = 1L, weight_column = NULL)

Arguments

feature_columns

An iterable containing all feature columns. All items should be instances of classes derived from _FeatureColumn.

label_key

A string identifying the label. It means tf$Example stores labels with this key.

label_dtype

A tf$dtype identifies the type of labels. By default it is tf$float32.

label_default

used as label if label_key does not exist in given tf$Example. By default default_value is none, which means tf$parse_example will error out if there is any missing label.

label_dimension

Number of regression targets per example. This is the size of the last dimension of the labels and logits Tensor objects (typically, these have shape [batch_size, label_dimension]).

weight_column

A string or a _NumericColumn 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 _NumericColumn, raw tensor is fetched by key weight_column$key, then weight_column$normalizer_fn is applied on it to get weight tensor.

Value

A dict mapping each feature key to a FixedLenFeature or VarLenFeature value.

Raises

  • 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 _FeatureColumn instance.

  • ValueError: If weight_column is not a _NumericColumn instance.

  • ValueError: if label_key is NULL.

See also

Other parsing utilities: classifier_parse_example_spec