Categorical Accuracy Strategy
Summary
The CategoricalAccuracyStrategy
is used to view a snapshot
of images in the dataset being used in the training session that match
a boolean criterion. To simplify things in the model construction,
this strategy can print images whose output is true
, images
whose output is false
, or all images. A canonical use-case
is to print the images that are (in)correctly categorized by a
classification model. The number of images output is limited by a
user-provided parameter or until no more matches are found.
Note
The name of this class erroneously suggests a rather narrow use-case. We are looking to change the name in a future release of LBANN. In fact, this strategy can take as input any boolean layer, not just categorical accuracy layers.
Arguments
categorical_accuracy_layer_name
(string): The name of the boolean layer to be used to determine matches. A Python Front-End layer’s name can be accessed via thename
attribute. A common use-case is the name of aCategoricalAccuracy
layer that has been added to a model.match_type
(lbann.CategoricalAccuracyStrategy.MatchType
): Criterion for selecting images to output. Possible values are:NOMATCH
Output images corresponding to
false
values.MATCH
Output images corresponding to
true
values.ALL
Output all images.
The default value is
NOMATCH
.num_images_per_epoch
(uint): The maximum number of images to output per epoch. The default value is 10.
Usage
See the usage example as part of the CallbackSummarizeImages documentation.