DETM
====

.. py:module:: topmost.models.dynamic.DETM



Module Contents
---------------



.. autoapisummary::


   topmost.models.dynamic.DETM.DETM





.. py: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: :py:obj:`torch.nn.Module`


   The Dynamic Embedded Topic Model. 2019

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


   .. py:attribute:: num_topics
      :value: 50



   .. py:attribute:: num_times


   .. py:attribute:: vocab_size


   .. py:attribute:: eta_hidden_size
      :value: 200



   .. py:attribute:: rho_size
      :value: 300



   .. py:attribute:: enc_drop
      :value: 0.0



   .. py:attribute:: eta_nlayers
      :value: 3



   .. py:attribute:: t_drop


   .. py:attribute:: eta_dropout
      :value: 0.0



   .. py:attribute:: delta
      :value: 0.005



   .. py:attribute:: train_WE
      :value: True



   .. py:attribute:: train_size


   .. py:attribute:: rnn_inp


   .. py:attribute:: device
      :value: 'cpu'



   .. py:attribute:: theta_act
      :value: 'relu'



   .. py:attribute:: mu_q_alpha


   .. py:attribute:: logsigma_q_alpha


   .. py:attribute:: q_theta


   .. py:attribute:: mu_q_theta


   .. py:attribute:: logsigma_q_theta


   .. py:attribute:: q_eta_map


   .. py:attribute:: q_eta


   .. py:attribute:: mu_q_eta


   .. py:attribute:: logsigma_q_eta


   .. py:attribute:: decoder_bn


   .. py:method:: get_activation(act)


   .. py:method:: reparameterize(mu, logvar)

      Returns a sample from a Gaussian distribution via reparameterization.




   .. py:method:: get_kl(q_mu, q_logsigma, p_mu=None, p_logsigma=None)

      Returns KL( N(q_mu, q_logsigma) || N(p_mu, p_logsigma) ).




   .. py:method:: get_alpha()


   .. py:method:: get_eta(rnn_inp)


   .. py:method:: get_theta(bows, times, eta=None)

      Returns the topic proportions.




   .. py:property:: word_embeddings


   .. py:property:: topic_embeddings


   .. py:method:: get_beta(alpha=None)

      Returns the topic matrix eta of shape T x K x V




   .. py:method:: get_NLL(theta, beta, bows)


   .. py:method:: forward(bows, times)


   .. py:method:: init_hidden()

      Initializes the first hidden state of the RNN used as inference network for \eta.