Tag: bayesian

Keras tuner Bayesian Optmization graph error

bayesian conv-neural-network keras keras-tuner python

I am trying to optimize a convolutional neural network with Bayesian Optimization algorithm provided in keras tuner library. When I perform the line: tuner_cnn.search(datagen.flow(X_trainRusReshaped,Y_trainRusHot), epochs=50, batch_size=256) I encounter this error: InvalidArgumentError: Graph execution error One-Hot-Encode y_train and y_test as the following: I defined my model builder like that: perform the tuner search: I also tried to do: But it does

How to use `Dirichlet Process Gaussian Mixture Model` in Scikit-learn? (n_components?)

bayesian machine-learning python scikit-learn statistics

My understanding of “an infinite mixture model with the Dirichlet Process as a prior distribution on the number of clusters” is that the number of clusters is determined by the data as they converge to a certain amount of clusters. This R Implementation https://github.com/jacobian1980/ecostates decides on the number of clusters in this way. Although, the R implementation uses a Gibbs