(self, a, w_dec_i, b_dec_i)
return cond_p_i, cond_l_i
Gets the conditional probability for a single dimension.

    Args:
      a: Model's hidden state, sized `[batch_size, num_hidden]`.
      w_dec_i: The decoder weight terms for the dimension, sized
          `[num_hidden, 1]`.
      b_dec_i: The decoder bias terms, sized `[batch_size, 1]`.

    Returns:
      cond_p_i: The conditional probability of the dimension, sized
        `[batch_size, 1]`.
      cond_l_i: The conditional logits of the dimension, sized
        `[batch_size, 1]`.
Gets the conditional probability for a single dimension.
Gets the conditional probability for a single dimension .
def _cond_prob(self, a, w_dec_i, b_dec_i):
    """Gets the conditional probability for a single dimension.

    Args:
      a: Model's hidden state, sized `[batch_size, num_hidden]`.
      w_dec_i: The decoder weight terms for the dimension, sized
          `[num_hidden, 1]`.
      b_dec_i: The decoder bias terms, sized `[batch_size, 1]`.

    Returns:
      cond_p_i: The conditional probability of the dimension, sized
        `[batch_size, 1]`.
      cond_l_i: The conditional logits of the dimension, sized
        `[batch_size, 1]`.
    """
    # Decode hidden units to get conditional probability.
    h = tf.sigmoid(a)
    cond_l_i = b_dec_i + tf.matmul(h, w_dec_i)
    cond_p_i = tf.sigmoid(cond_l_i)
    return cond_p_i, cond_l_i
def _cond_prob ( self , a , w_dec_i , b_dec_i ) : # Decode hidden units to get conditional probability. h = tf . sigmoid ( a ) cond_l_i = b_dec_i + tf . matmul ( h , w_dec_i ) cond_p_i = tf . sigmoid ( cond_l_i ) return cond_p_i , cond_l_i