# 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(