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Project description
MixtureMapping
Train Gaussian Mixture Mappings
Provides:
- Layers to build tensorflow models to map Gaussian mixtures
- Tools to compute yield values of Gaussian mixtures in complex binning schemes
Example
import mixturemapping as mm
import tensorflow as tf
inMeans = tf.keras.Input(shape=(mixN, inputMixM), name="Means", dtype=dataType)
inStdDevs = tf.keras.Input(shape=(mixN, inputMixM), name="StdDevs", dtype=dataType)
inWeight = tf.keras.Input(shape=(mixN), name="Weights", dtype=dataType)
mapModel = tf.keras.Sequential()
mapModel.add( tf.keras.layers.Dense(40, activation="relu", kernel_regularizer=regularizers.l2(0.001)) )
mapModel.add( tf.keras.layers.Dense(40, activation="relu", kernel_regularizer=regularizers.l2(0.001)) )
mapModel.add( tf.keras.layers.Dense(outputMixM))
y = mapModel(inMeans)
deltaModel = tf.keras.Sequential()
deltaModel.add( tf.keras.layers.Dense(40, activation="relu", kernel_regularizer=regularizers.l2(0.001)) )
deltaModel.add( tf.keras.layers.Dense(40, activation="relu", kernel_regularizer=regularizers.l2(0.001)) )
deltaModel.add( tf.keras.layers.Dense(outputMixM))
yDelta = deltaModel(inMeans)
covALayer = mm.layers.TrainableCovMatrix(outputMixM, name="CovA")
covA = covALayer(inMeans)
mapLayer = mm.layers.GeneralMapping(outputMixM, name="Mapping", dtype=dataType)
newDist = mapLayer({'means': inMeans, 'y':y, 'yDelta':yDelta, 'stdDevs': inStdDevs, 'weights': inWeight, 'covA': covA})
distLayer = mm.layers.Distribution(dtype=dataType, regularize_cov_epsilon=0.95)
dist = distLayer(newDist)
Developement
$ py -m venv env
$ .\env\Scripts\activate
$ pip install -r requirements.txt
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