c6289f5ca0af8af688caba047f14cc8fa2a09f9d,hypergan/graph/graph.py,Graph,discriminator,#Graph#Any#Any#Any#Any#Any#,43

Before Change

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

        else:
            net = linear(net, num_classes, scope="d_fc_end", stddev=0.003)

After Change

            df_class=tf.slice(class_logits, [single_batch_size, 0], [single_batch_size, num_classes-1])
            df_logits=tf.slice(gan_logits, [single_batch_size], [single_batch_size]), 

        return [dr_class,dr_logits,df_class,df_logits, last_layer, d_real, d_fake]


    def split_categories(layer, batch_size, categories):
        start = 0

Instances