parser.add_argument('--log_filename',
type = str, default = 'train.log')
-parser.add_argument('--download',
- action='store_true', default = False)
-
parser.add_argument('--seed',
type = int, default = 0)
descr = [ s.strip().split(' ') for s in descr ]
l = max([ len(s) for s in descr ])
+ #descr = [ [ '<unk>' ] * (l - len(s)) + s for s in descr ]
descr = [ s + [ '<unk>' ] * (l - len(s)) for s in descr ]
return descr
self.token2id = dict([ (t, n) for n, t in enumerate(tokens) ])
self.id2token = dict([ (n, t) for n, t in enumerate(tokens) ])
+ # Tokenize the train and test sets
t = [ [ self.token2id[u] for u in s ] for s in self.train_descr ]
self.train_input = torch.tensor(t, device = self.device)
t = [ [ self.token2id[u] for u in s ] for s in self.test_descr ]
nb_epochs_finished = 0
if args.no_checkpoint:
- log_string(f'Not trying to load checkpoint.')
+ log_string(f'not trying to load checkpoint.')
else:
try:
nb_epochs_finished = checkpoint['nb_epochs_finished']
model.load_state_dict(checkpoint['model_state'])
optimizer.load_state_dict(checkpoint['optimizer_state'])
- log_string(f'Checkpoint loaded with {nb_epochs_finished} epochs finished.')
+ log_string(f'checkpoint loaded with {nb_epochs_finished} epochs finished.')
except FileNotFoundError:
- log_string('Starting from scratch.')
+ log_string('starting from scratch.')
except:
- log_string('Error when loading the checkpoint.')
+ log_string('error when loading the checkpoint.')
exit(1)
######################################################################
token_probas = token_count / token_count.sum()
h = -torch.xlogy(token_probas, token_probas).sum()
train_set_perplexity = math.exp(h)
-log_string(f'Train set perplexity {train_set_perplexity}')
+log_string(f'train set perplexity {train_set_perplexity}')
for k in range(nb_epochs_finished, nb_epochs):