Training and testing

Training and testing#

mpol.training.train_to_dirty_image(model, imager, robust=0.5, learn_rate=100, niter=1000)[source]#

Train against a dirty image of the observed visibilities using a loss function of the mean squared error between the RML model image pixel fluxes and the dirty image pixel fluxes. Useful for initializing a separate RML optimization loop at a reasonable starting image.

Parameters:

  • model (torch.nn.Module object) – A neural network module; instance of the mpol.precomposed.GriddedNet class.

  • imager (mpol.gridding.DirtyImager object) – Instance of the mpol.gridding.DirtyImager class.

  • robust (float, default=0.5) – Robust weighting parameter used to create a dirty image.

  • learn_rate (float, default=100) – Learning rate for optimization loop

  • niter (int, default=1000) – Number of iterations for optimization loop

Returns:

model – The input model updated with the state of the training to the dirty image

Return type:

torch.nn.Module object