if args.bow_norm:
normalized_data_batch = data_batch / sums
else:
normalized_data_batch = data_batch
theta, _ = model.get_theta(normalized_data_batch)
thetaAvg += theta.sum(0).unsqueeze(0) / args.num_docs_train
weighed_theta = sums * theta
thetaWeightedAvg += weighed_theta.sum(0).unsqueeze(0)
if idx % 100 == 0 and idx > 0:
print('batch: {}/{}'.format(idx, len(indices)))
thetaWeightedAvg = thetaWeightedAvg.squeeze().cpu().numpy() / cnt
print('\nThe 10 most used topics are {}'.format(
thetaWeightedAvg.argsort()[::-1][:10]))
## show topics
beta = model.get_beta()
topic_indices = list(np.random.choice(args.num_topics,
10)) # 10 random topics
print('\n')
for k in range(args.num_topics): #topic_indices:
gamma = beta[k]
top_words = list(gamma.cpu().numpy().argsort()[-args.num_words +
1:][::-1])
topic_words = [vocab[a] for a in top_words]
print('Topic {}: {}'.format(k, topic_words))
if args.train_embeddings:
## show etm embeddings
try:
rho_etm = model.rho.weight.cpu()
except: