updates to tensorflow code and processing additionsHEADmaster

1 files changed, 9 insertions, 4 deletions

diff --git a/Code/d_model.py b/Code/d_model.py
index 1345ceb..02b882d 100644
--- a/Code/d_model.py
+++ b/Code/d_model.py
@@ -89,8 +89,8 @@ class DiscriminatorModel:
                                                         name='train_op')
 
                 # add summaries to visualize in TensorBoard
-                loss_summary = tf.scalar_summary('loss_D', self.global_loss)
-                self.summaries = tf.merge_summary([loss_summary])
+                loss_summary = tf.summary.scalar('loss_D', self.global_loss)
+                self.summaries = tf.summary.merge([loss_summary])
 
     def build_feed_dict(self, input_frames, gt_output_frames, generator):
         """
@@ -123,14 +123,19 @@ class DiscriminatorModel:
         for scale_num in xrange(self.num_scale_nets):
             scale_net = self.scale_nets[scale_num]
 
+            broken = 0
             # resize gt_output_frames
             scaled_gt_output_frames = np.empty([batch_size, scale_net.height, scale_net.width, 3])
             for i, img in enumerate(gt_output_frames):
                 # for skimage.transform.resize, images need to be in range [0, 1], so normalize to
                 # [0, 1] before resize and back to [-1, 1] after
                 sknorm_img = (img / 2) + 0.5
-                resized_frame = resize(sknorm_img, [scale_net.height, scale_net.width, 3])
-                scaled_gt_output_frames[i] = (resized_frame - 0.5) * 2
+                try:
+                    resized_frame = resize(sknorm_img, [scale_net.height, scale_net.width, 3])
+                    scaled_gt_output_frames[i-broken] = (resized_frame - 0.5) * 2
+                except:
+                    broken += 1
+            #print str(broken) + " " + "broken images"
 
             # combine with resized gt_output_frames to get inputs for prediction
             scaled_input_frames = np.concatenate([g_scale_preds[scale_num],