def run_reverse_ais(model,
obs,
state,
num_steps,
sigma,
hdim,
L,
epsilon,
data,
prior,
schedule=None):
if schedule is None:
schedule = ais.sigmoid_schedule(num_steps)
path = AISPath(model,
obs,
1,
sigma,
hdim,
L,
epsilon,
data,
prior,
init_state=state)
lld = ais.reverse_ais(path, schedule, sigma)
return lld
def run_ais(model,
obs,
num_samples,
num_steps,
sigma,
hdim,
L,
epsilon,
prior,
schedule=None,
recog=None):
if schedule is None:
schedule = ais.sigmoid_schedule(num_steps)
if recog:
obs = obs.reshape(obs.shape[0], 3 * 32 * 32)
obs_rep = np.tile(obs, [num_samples, 1])
rec_net = utils.load_recog(50, recog)
state = rec_net(obs_rep)
else:
state = None
path = AISPath(model,
obs,
num_samples,
sigma,
hdim,
L,
epsilon,
prior,
init_state=state)
lld = ais.ais(path, schedule, sigma)
return lld
def run_ais(model,
obs,
num_samples,
num_steps,
sigma,
hdim,
L,
epsilon,
data,
schedule=None,
init_state=None,
mask=np.array([[0, 1]]),
temp_range=(-4, 4)):
if schedule is None:
schedule = ais.sigmoid_schedule(num_steps, temp_range)
path = AISPath(model,
obs,
num_samples,
sigma,
hdim,
L,
epsilon,
data,
mask,
init_state=init_state)
lld = ais.ais(path, schedule, sigma)
return lld
def run_ais(model,
obs,
num_samples,
num_steps,
hdim,
L,
epsilon,
schedule=None,
init_state=None,
save_dir='.',
box=(60, 100, 60, 100),
temp_range=(-4, 4)):
if schedule is None:
schedule = ais.sigmoid_schedule(num_steps, temp_range)
path = AISPath(model,
obs,
num_samples,
hdim,
L,
epsilon,
box=box,
init_state=init_state)
lld = ais.ais(path, schedule, save_dir)
return lld
def run_ais(model,
obs,
num_samples,
num_steps,
sigma,
hdim,
L,
epsilon,
data,
prior,
schedule=None):
if schedule is None:
schedule = ais.sigmoid_schedule(num_steps)
mean = None
log_sigma = None
## prior:recog <--If using the recognition nets to predict initial AIS chain.
if prior[:5] == 'recog':
'load encoder net to predict initial dist...'
obs = obs.reshape(obs.shape[0], 784)
obs_rep = np.tile(obs, [num_samples, 1])
if prior[5] == '1':
rec_net = utils.load_encoder('vae', 'c4000', eval_np=True)
elif prior[5] == '2':
rec_net = utils.load_encoder('iwae', '50', eval_np=True)
state, mean, log_sigma = rec_net(obs_rep)
else:
state = None
path = AISPath(model,
obs,
num_samples,
sigma,
hdim,
L,
epsilon,
data,
prior,
init_state=state,
recog_mean=mean,
recog_log_sigma=log_sigma)
lld = ais.ais(path, schedule, sigma)
return lld