vae_gumbel_softmax.py 文件源码

def encoder(x): 
    # Variational posterior q(y|x), i.e. the encoder (shape=(batch_size, 200))
    net = slim.stack(x, 
                     slim.fully_connected, 
                     [512, 256])

    # Unnormalized logits for number of classes (N) seperate K-categorical distributions
    logits_y = tf.reshape(slim.fully_connected(net, 
                                               FLAGS.num_classes*FLAGS.num_cat_dists, 
                                               activation_fn=None), 
                          [-1, FLAGS.num_cat_dists])

    q_y = tf.nn.softmax(logits_y)
    log_q_y = tf.log(q_y + 1e-20)

    return logits_y, q_y, log_q_y