4c60119647c7c5347a6d31265c223137c7ddd896,lib/gan.py,,discriminator,#Any#Any#Any#Any#Any#Any#,54

Before Change

        return class_logits, gan_logits
    num_classes = config["y_dims"]+1
    class_logits, gan_logits = build_logits(net, num_classes)
    return [tf.slice(class_logits, [0, 0], [single_batch_size, num_classes-1]),
                tf.slice(gan_logits, [0], [single_batch_size]),
                tf.slice(class_logits, [single_batch_size, 0], [single_batch_size, num_classes-1]),

After Change

    //net = linear(net, 1, scope="d_proj", stddev=0.03)
    //net = batch_norm(batch_size, name="d_rp_bnend")(net)

    if config["y_dims"] == 1:
        net = linear(net, 1, scope="d_fc_end", stddev=0.003)
        net = tf.reshape(net,  [config["batch_size"]*2, 1])
        class_logits = net
        gan_logits = net

    else:
        num_classes = config["y_dims"]+1
        net = linear(net, num_classes, scope="d_fc_end", stddev=0.003)
        class_logits = tf.slice(net, [0,1], [single_batch_size*2,num_classes-1])
        gan_logits = tf.squeeze(tf.slice(net, [0,0], [single_batch_size*2,1]))

    return [tf.slice(class_logits, [0, 0], [single_batch_size, num_classes-1]),
                tf.slice(gan_logits, [0], [single_batch_size]),
                tf.slice(class_logits, [single_batch_size, 0], [single_batch_size, num_classes-1]),
                tf.slice(gan_logits, [single_batch_size], [single_batch_size]), 

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