vae_gumbel_softmax.py 文件源码

def decoder(tau, logits_y):
    y = tf.reshape(gumbel_softmax(logits_y, tau, hard=False), 
                   [-1, FLAGS.num_cat_dists, FLAGS.num_classes])

    # Generative model p(x|y), i.e. the decoder (shape=(batch_size, 200))
    net = slim.stack(slim.flatten(y), 
                     slim.fully_connected, 
                     [256, 512])

    logits_x = slim.fully_connected(net, 
                                    784, 
                                    activation_fn=None)

    # (shape=(batch_size, 784))
    p_x = bernoulli(logits=logits_x)

    return p_x