9c8dc8d6eb3208d8d442311853ca5859b0ac04aa,hypergan/trainers/adam_trainer.py,,run,#Any#,46

Before Change

    _, d_cost, d_log = sess.run([d_optimizer, d_loss, d_log_t])

    // in WGAN paper, values are clipped.  This might not work, and is slow.
    if(config.clipped_discriminator):
        clip = [tf.assign(d,tf.clip_by_value(d, -config.clip_value, config.clip_value))  for d in d_vars]
        sess.run(clip)

    global iteration
    if(d_class_loss is not None):
        _, g_cost,d_fake,d_real,d_class = sess.run([g_optimizer, g_loss, d_fake_loss, d_real_loss, d_class_loss])
        if iteration % 100 == 0:

After Change

    _, d_cost, d_log = sess.run([d_optimizer, d_loss, d_log_t])

    // in WGAN paper, values are clipped.  This might not work, and is slow.
    if(config.d_clipped_weights):
        sess.run(gan.graph.clip)

    global iteration
    if(d_class_loss is not None):
        _, g_cost,d_fake,d_real,d_class = sess.run([g_optimizer, g_loss, d_fake_loss, d_real_loss, d_class_loss])
        if iteration % 100 == 0:

Instances