reg_scale was the problem

Author: carpedm20

config.py

@@ -36,7 +36,7 @@ def add_argument_group(name):
 train_arg.add_argument('--is_train', type=str2bool, default=True, help='')
 train_arg.add_argument('--optimizer', type=str, default='rmsprop', help='')
 train_arg.add_argument('--max_step', type=int, default=2000, help='')
-train_arg.add_argument('--reg_scale', type=float, default=0.01, help='')
+train_arg.add_argument('--reg_scale', type=float, default=0.5, help='')
 train_arg.add_argument('--initial_K_d', type=int, default=200, help='')
 train_arg.add_argument('--initial_K_g', type=int, default=1000, help='')
 train_arg.add_argument('--K_d', type=int, default=1, help='')

layers.py

@@ -58,7 +58,7 @@ def conv2d(inputs, num_outputs, kernel_size, stride,
       inputs, num_outputs, kernel_size,
       stride, activation_fn=activation_fn, 
       weights_initializer=weights_initializer,
-      biases_initializer=tf.zeros_initializer, scope=scope, **kargv)
+      biases_initializer=tf.zeros_initializer(dtype=tf.float32), scope=scope, **kargv)
   if name:
     scope = "{}/{}".format(name, scope)
   _update_dict(layer_dict, scope, outputs)

model.py

@@ -138,8 +138,8 @@ def log_loss(logits, label, name):
             self.refiner_loss, [self.regularization_loss], "reg_loss")
 
     self.refiner_summary = tf.summary.merge([
-        tf.summary.image("synthetic_images", self.x),
-        tf.summary.image("refined_images", self.denormalized_R_x),
+        tf.summary.image("synthetic_images", self.x, max_outputs=7),
+        tf.summary.image("refined_images", self.denormalized_R_x, max_outputs=7),
         tf.summary.scalar("refiner/realism_loss",
                           tf.reduce_mean(self.realism_loss)),
         tf.summary.scalar("refiner/regularization_loss",

trainer.py

@@ -49,10 +49,10 @@ def train(self):
                              save_model_secs=self.checkpoint_secs,
                              global_step=self.model.discrim_step)
 
-    config = tf.ConfigProto(allow_soft_placement=True)
-    config.gpu_options.allow_growth = True
+    sess_config = tf.ConfigProto(allow_soft_placement=True)
+    sess_config.gpu_options.allow_growth = True
 
-    sess = sv.prepare_or_wait_for_session(config=config)
+    sess = sv.prepare_or_wait_for_session(config=sess_config)
 
     print("[*] Training starts...")
     self._summary_writer = None