DETM#

Module Contents#

DETM

The Dynamic Embedded Topic Model. 2019

class DETM(vocab_size, num_times, train_size, train_time_wordfreq, num_topics=50, train_WE=True, pretrained_WE=None, en_units=800, eta_hidden_size=200, rho_size=300, enc_drop=0.0, eta_nlayers=3, eta_dropout=0.0, delta=0.005, theta_act='relu', device='cpu')#

Bases: torch.nn.Module

The Dynamic Embedded Topic Model. 2019

Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei

property word_embeddings#

property topic_embeddings#

get_activation(act)#

reparameterize(mu, logvar)#: Returns a sample from a Gaussian distribution via reparameterization.

get_kl(q_mu, q_logsigma, p_mu=None, p_logsigma=None)#: Returns KL( N(q_mu, q_logsigma) || N(p_mu, p_logsigma) ).

get_alpha()#

get_eta(rnn_inp)#

get_theta(bows, times, eta=None)#: Returns the topic proportions.

get_beta(alpha=None)#: Returns the topic matrix eta of shape T x K x V

get_NLL(theta, beta, bows)#

forward(bows, times)#

init_hidden()#: Initializes the first hidden state of the RNN used as inference network for eta.