nc=args.nc, model_type=args.model_type) src_loaders = load_datasets(args=args) loss = wae.train_model(model, src_loaders['train'], src_loaders['val'], device, args) # conditional generation model.eval() evaluation.sampling(model, device, args.epochs, args, prefix='wae', nrow=4) # t-sne visualization if args.source_data == 'MNIST': evaluation.visualization_tsne(model, src_loaders['val'], device, args, prefix='wae') else: evaluation.visualization_tsne2(model, src_loaders['val'], device, args, prefix='wae') # save models and learning results model = model.to('cpu') torch.save( model.state_dict(), '{}/wae_model_{}_{}.pt'.format(args.resultpath, args.model_type, args.source_data))
prior_list = [z_p_mean, z_p_logvar] for i in range(args.K): evaluation.sampling( model, device, i + 1, args, prefix='rae', prior=[z_p_mean[i, :].unsqueeze(0), z_p_logvar[i, :].unsqueeze(0)], nrow=4) # t-sne visualization if args.source_data == 'MNIST': evaluation.visualization_tsne(model, src_loaders['val'], device, args, prefix='rae', prior=prior_list) else: evaluation.visualization_tsne2(model, src_loaders['val'], device, args, prefix='rae', prior=prior_list) # save models and learning results model = model.to('cpu') prior = prior.to('cpu') torch.save( model.state_dict(),