nn.py 文件源码

def log_likelihood(mu, var, x, muq, varq, a, mask_flat, config):
    if config.out_distr == 'bernoulli':
        log_lik = log_bernoulli(x, mu, eps=1e-6)  # (bs*L, d1*d2)
    elif config.out_distr == 'gaussian':
        log_lik = log_gaussian(x, mu, var)

    log_lik = tf.reduce_sum(log_lik, 1)  # (bs*L, )
    log_lik = tf.multiply(mask_flat, log_lik)
    # TODO: dropout scales the output as input/keep_prob. Issue?
    if config.ll_keep_prob < 1.0:
        log_lik = tf.layers.dropout(log_lik, config.ll_keep_prob)

    # We compute the log-likelihood *per frame*
    num_el = tf.reduce_sum(mask_flat)
    log_px_given_a = tf.truediv(tf.reduce_sum(log_lik), num_el)  # ()

    if config.use_vae:
        log_qa_given_x = tf.reduce_sum(log_gaussian(a, muq, varq), 1)  # (bs*L, )
        log_qa_given_x = tf.multiply(mask_flat, log_qa_given_x)
        log_qa_given_x = tf.truediv(tf.reduce_sum(log_qa_given_x), num_el)  # ()
    else:
        log_qa_given_x = tf.constant(0.0, dtype=tf.float32, shape=())

    LL = log_px_given_a - log_qa_given_x
    return LL, log_px_given_a, log_qa_given_x