Benchmark multi-model/multi-view models.

Note

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mmbench.workflow.smcvae.train_smcvae(dataset, datasetdir, outdir, fit_lat_dims=10, beta=1, adam_lr=0.002, n_epochs=10000, host='http://localhost', port=8085)[source]¶

Train the sparse Multi-Channels Variational Auto-Encoder (sMCVAE).

Parameters

dataset : str

the dataset name: euaims or hbn.

datasetdir : str

the path to the dataset associated data.

outdir : str

the destination folder.

fit_lat_dims : int, default 10

the number of latent dimensions.

beta : float, default 1

the loss beta-VAE weight (0.5 for HBN).

adam_lr : float, default 2e-3

the initial learning rate in the ADAM optimizer.

n_epochs : int, default 10000

the number of training epochs.

host : str, default ‘http://localhost’

the host on which visdom is launched.

port : int, default 8085

the port on which the visdom server is launched.