'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:
The conditional probability of the dimension, sized `[batch_size, 1]`.'

def _cond_prob(self, a, w_dec_i, b_dec_i):

    h = tf.sigmoid(a)
    p_cond_i = tf.sigmoid((b_dec_i + tf.matmul(h, w_dec_i)))
    return p_cond_i
