EncodingAccuracy¶
Inheritance Diagram
-
class
ashpy.metrics.gan.
EncodingAccuracy
(classifier, model_selection_operator=None, logdir='/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/v0.2.0/docs/source/log')[source]¶ Bases:
ashpy.metrics.classifier.ClassifierMetric
Generator and Encoder accuracy performance.
Measure the Generator and Encoder performance together, by classifying: G(E(x)), y using a pre-trained classified (on the dataset of x).
Methods
__init__
(classifier[, …])Measure the Generator and Encoder performance together.
update_state
(context)Update the internal state of the metric, using the information from the context object.
Attributes
best_folder
Retrieve the folder used to save the best model when doing model selection.
best_model_sel_file
Retrieve the path to JSON file containing the measured performance of the best model.
logdir
Retrieve the log directory.
metric
Retrieve the
tf.keras.metrics.Metric
object.model_selection_operator
Retrieve the operator used for model selection.
name
Retrieve the metric name.
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__init__
(classifier, model_selection_operator=None, logdir='/home/docs/checkouts/readthedocs.org/user_builds/ashpy/checkouts/v0.2.0/docs/source/log')[source]¶ Measure the Generator and Encoder performance together.
This is done by classifying: G(E(x)), y using a pre-trained classified (on the dataset of x).
- Parameters
classifier (
tf.keras.Model
) – Keras Model to use as a Classifier to measure the accuracy. Generally assumed to be the Inception Model.model_selection_operator (
typing.Callable
) –The operation that will be used when model_selection is triggered to compare the metrics, used by the update_state. Any
typing.Callable
behaving like anoperator
is accepted.Note
Model selection is done ONLY if an operator is specified here.
logdir (str) – Path to the log dir, defaults to a log folder in the current directory.
- Return type
None
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