models_mnist/generator.py [88:106]:
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      word_vecs = tf.reduce_sum(attention * self.rnn.embedded_input_seq, axis=1)
      size = [params['max_dec_len'], None, params['text_embed_size']]
      word_vecs.set_shape(size)
      outputs['attention'] = attention
      outputs['ques_attended'] = word_vecs
      #outputs['ques_attended'] = self.rnn.word_vecs

      # log probability of each generated sequence
      outputs['log_seq_prob'] = tf.reduce_sum(
                                  tf.log(self.rnn.token_probs + 1e-10), axis=0)
      outputs['ques_prog_loss'] = tf.reduce_mean(-outputs['log_seq_prob'])
      q_output = tf.transpose(self.rnn.encoder_outputs, perm=[1, 0, 2])
      q_output = support.last_relevant(q_output, inputs['ques_len'])
      # bloat the first two dimensions
      q_output = tf.expand_dims(q_output, axis=0)
      outputs['ques_enc'] = tf.expand_dims(q_output, axis=0)

      # keep track of inputs actually used
      used_inputs.extend(['ques', 'ques_len', 'prog_gt'])
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models_vd/generator.py [112:130]:
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      word_vecs = tf.reduce_sum(attention * self.rnn.embedded_input_seq, axis=1)
      size = [params['max_dec_len'], None, params['text_embed_size']]
      word_vecs.set_shape(size)
      outputs['attention'] = attention
      outputs['ques_attended'] = word_vecs
      #outputs['ques_attended'] = self.rnn.word_vecs

      # log probability of each generated sequence
      outputs['log_seq_prob'] = tf.reduce_sum(
                                  tf.log(self.rnn.token_probs + 1e-10), axis=0)
      outputs['ques_prog_loss'] = tf.reduce_mean(-outputs['log_seq_prob'])
      q_output = tf.transpose(self.rnn.encoder_outputs, perm=[1, 0, 2])
      q_output = support.last_relevant(q_output, inputs['ques_len'])
      # bloat the first two dimensions
      q_output = tf.expand_dims(q_output, axis=0)
      outputs['ques_enc'] = tf.expand_dims(q_output, axis=0)

      # keep track of inputs actually used
      used_inputs.extend(['ques', 'ques_len', 'prog_gt'])
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