diff --git a/run_bgan_semi.py b/run_bgan_semi.py
index 41c93a7..6c0415a 100755
--- a/run_bgan_semi.py
+++ b/run_bgan_semi.py
@@ -133,7 +133,7 @@ def b_dcgan(dataset, args):
 
     session = get_session()
     tf.set_random_seed(args.random_seed)
-    # due to how much the TF code sucks all functions take fixed batch_size at all times
+
     dcgan = BDCGAN_Semi(x_dim, z_dim, dataset_size, batch_size=batch_size, J=args.J, J_d=args.J_d, M=args.M,
                         num_layers=args.num_layers,
                         lr=args.lr, optimizer=args.optimizer, gf_dim=args.gf_dim, 
@@ -179,16 +179,13 @@ def b_dcgan(dataset, args):
 
         ### compute disc losses
         batch_z = np.random.uniform(-1, 1, [batch_size, z_dim, dcgan.num_gen])
-        disc_info = session.run(optimizer_dict["disc_semi"] + dcgan.d_losses, # + [dcgan.d_probs] + [dcgan.d_hh],
+        disc_info = session.run(optimizer_dict["disc_semi"] + dcgan.d_losses,
                                 feed_dict={dcgan.labeled_inputs: labeled_image_batch,
                                            dcgan.labels: labels,
                                            dcgan.inputs: image_batch,
                                            dcgan.z: batch_z,
                                            dcgan.d_semi_learning_rate: learning_rate})
-
-        d_losses = disc_info[num_disc:num_disc*2]
-
-        #print disc_info[num_disc*2:num_disc*3][0][:, 0]
+        d_losses = [d_ for d_ in disc_info if d_ is not None]
 
         ### compute generative losses
         batch_z = np.random.uniform(-1, 1, [batch_size, z_dim, dcgan.num_gen])