acc = eval_particles_acc(particles, val_gen)
print('%s accuracy: %.4f' % (phase, acc))
if phase == 'test':
factor = cmd_args.n_stages * cmd_args.batch_size * cmd_args.stage_len
f_log.write('num_obs %d, loss %.8f\n' % (num_obs, 1.0 - acc))
return 1.0 - acc
if __name__ == '__main__':
random.seed(cmd_args.seed)
np.random.seed(cmd_args.seed)
torch.manual_seed(cmd_args.seed)
db = MnistDataset(cmd_args.data_folder)
flow = build_model(cmd_args, x_dim=db.x_dim, ob_dim=db.ob_dim, ll_func=db.log_likelihood)
ob_net = KernelEmbedNet(db.ob_dim, str(db.ob_dim), cmd_args.nonlinearity, trainable=True).to(DEVICE)
if cmd_args.init_model_dump is not None:
state = torch.load(cmd_args.init_model_dump)
flow.load_state_dict(state)
prior_dist = DiagMvn(mu=[cmd_args.prior_mu] * db.x_dim,
sigma=[cmd_args.prior_sigma] * db.x_dim)
test_locs = [100, 200, 300, 400, 600, 700, 800, 1000, 1300,
1600, 2000, 2600, 3200, 4000, 5100, 6400, 8000, 10000, 12600,
15900, 20000, 25200, 31700, 39900, 50200, 63100, 79500, 100000,
125900, 158500, 199600, 251200, 316300, 398200, 501200, 631000,
794400, 1000000, 1259000, 1584900, 1995300, 2511900,
3162300, 3981100, 5011900, 6309600, 7943300]
torch.manual_seed(cmd_args.seed)
db = TwoGaussDataset(prior_mu=cmd_args.prior_mu,
prior_sigma=cmd_args.prior_sigma,
mu_given=[-1, 2],
l_sigma=1.0,
p=0.5,
partition_sizes={'train': cmd_args.train_samples})
val_db = TwoGaussDataset(
prior_mu=cmd_args.prior_mu,
prior_sigma=cmd_args.prior_sigma,
mu_given=[-1, 2],
l_sigma=1.0,
p=0.5,
partition_sizes={'val': cmd_args.train_samples * cmd_args.num_vals})
flow = build_model(cmd_args, x_dim=2, ob_dim=db.dim)
if cmd_args.init_model_dump is not None:
print('loading', cmd_args.init_model_dump)
flow.load_state_dict(torch.load(cmd_args.init_model_dump))
mvn_dist = DiagMvn(mu=[cmd_args.prior_mu] * cmd_args.gauss_dim,
sigma=[cmd_args.prior_sigma] * cmd_args.gauss_dim)
if cmd_args.phase == 'train':
optimizer = optim.Adam(flow.parameters(),
lr=cmd_args.learning_rate,
weight_decay=cmd_args.weight_decay)
train_global_x_loop(
cmd_args,
lambda x: lm_train_gen(db, x),