# 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)) with open( '{}/wae_loss_{}_{}.pkl'.format(args.resultpath, args.model_type, args.source_data), 'wb') as f: pickle.dump(loss, f) print('\n')
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(), '{}/rae_model_{}_{}.pt'.format(args.resultpath, args.model_type, args.source_data)) torch.save( prior.state_dict(), '{}/rae_prior_{}_{}.pt'.format(args.resultpath, args.model_type, args.source_data)) with open(