def __init__(self, grammar, device, hparams=stgs.VAE_HPARAMS):
"""
Load trained encoder/decoder and grammar model
:param grammar: A nas_grammar.Grammar object
:param hparams: dict, hyperparameters for the VAE and the grammar model
"""
self._grammar = grammar
self.device = device
self.hp = hparams
self.max_len = self.hp['max_len']
self._productions = self._grammar.GCFG.productions()
self._prod_map = make_prod_map(grammar.GCFG)
self._parser = nltk.ChartParser(grammar.GCFG)
self._tokenize = make_tokenizer(grammar.GCFG)
self._n_chars = len(self._productions)
self._lhs_map = grammar.lhs_map
self.vae = NA_VAE(self.hp)
self.vae.eval()
def __init__(self,
cfg,
min_sample_depth,
max_sample_depth,
batch_size=256,
seed=0):
"""
:param cfg: An nltk.CFG object
:param min_sample_depth:
:param max_sample_depth:
:param batch_size:
"""
super().__init__()
random.seed(seed)
self.cfg = cfg
self.bsz = batch_size
self.tokenizer = make_tokenizer(cfg)
self.prod_map = make_prod_map(cfg)
self.weighted_sampling = stgs.VAE_HPARAMS['weighted_sampling']
self.temp = stgs.VAE_HPARAMS['temperature']
self.min_sample_depth, self.max_sample_depth = min_sample_depth, max_sample_depth # min and max lengths of the
# sequences to sample
if self.weighted_sampling:
len_range = max_sample_depth - min_sample_depth
self.probs = np.array([
np.exp(self.temp * n / len_range)
for n in range(min_sample_depth, max_sample_depth + 1)
])
self.probs /= self.probs.sum()
self.max_len = stgs.VAE_HPARAMS[
'max_len'] # max possible length of a sequence
self.lay_symb = stgs.VAE_HPARAMS[
'layer_symbol'] # symbol representing a new layer
self.n_chars = len(cfg.productions())
print(f'Grammar with {self.n_chars} productions.')
self.sents = []
