tfmot.clustering.keras.CentroidInitialization
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Specifies how the cluster centroids should be initialized.
LINEAR: Cluster centroids are evenly spaced between the minimum and
maximum values of a given weight tensor.RANDOM: Centroids are sampled using the uniform distribution between the
minimum and maximum weight values in a given layer.DENSITY_BASED: Density-based sampling obtained as follows: first a
cumulative distribution function is built for the weights, then the Y
axis is evenly spaced into as many regions as many clusters we want to
have. After this the corresponding X values are obtained and used to
initialize the clusters centroids.KMEANS_PLUS_PLUS: cluster centroids using the kmeans++ algorithm
Class Variables |
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DENSITY_BASED
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<CentroidInitialization.DENSITY_BASED: 'CentroidInitialization.DENSITY_BASED'>
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KMEANS_PLUS_PLUS
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<CentroidInitialization.KMEANS_PLUS_PLUS: 'CentroidInitialization.KMEANS_PLUS_PLUS'>
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LINEAR
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<CentroidInitialization.LINEAR: 'CentroidInitialization.LINEAR'>
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RANDOM
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<CentroidInitialization.RANDOM: 'CentroidInitialization.RANDOM'>
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Last updated 2023-05-26 UTC.
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