reprocess

1 files changed, 13 insertions, 13 deletions

diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py
index 7c0c728..fe0c1aa 100644
--- a/cli/app/search/search_dense.py
+++ b/cli/app/search/search_dense.py
@@ -253,32 +253,32 @@ def find_dense_embedding_for_images(params):
     gen_feat = gen_feat_ex["InceptionV3/Conv2d_1a_3x3"]
     target_feat = target_feat_ex["InceptionV3/Conv2d_1a_3x3"]
     feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
-    feat_loss = tf.reduce_mean(feat_square_diff) * 0.15
-    img_feat_err = tf.reduce_mean(feat_square_diff, axis=1) * 0.15
+    feat_loss = tf.reduce_mean(feat_square_diff) * 0.25
+    img_feat_err = tf.reduce_mean(feat_square_diff, axis=1) * 0.25
 
     gen_feat = gen_feat_ex["InceptionV3/Conv2d_2a_3x3"]
     target_feat = target_feat_ex["InceptionV3/Conv2d_2a_3x3"]
     feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
-    feat_loss += tf.reduce_mean(feat_square_diff) * 0.15
-    img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.15
+    feat_loss += tf.reduce_mean(feat_square_diff) * 0.25
+    img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.25
 
-    gen_feat = gen_feat_ex["InceptionV3/Conv2d_3b_1x1"]
-    target_feat = target_feat_ex["InceptionV3/Conv2d_3b_1x1"]
-    feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
-    feat_loss += tf.reduce_mean(feat_square_diff) * 0.15
-    img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.15
+    # gen_feat = gen_feat_ex["InceptionV3/Conv2d_3b_1x1"]
+    # target_feat = target_feat_ex["InceptionV3/Conv2d_3b_1x1"]
+    # feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
+    # feat_loss += tf.reduce_mean(feat_square_diff) * 0.25
+    # img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.25
 
     gen_feat = gen_feat_ex["InceptionV3/Conv2d_4a_3x3"]
     target_feat = target_feat_ex["InceptionV3/Conv2d_4a_3x3"]
     feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
-    feat_loss += tf.reduce_mean(feat_square_diff) * 0.15
-    img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.15
+    feat_loss += tf.reduce_mean(feat_square_diff) * 0.25
+    img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.25
 
     gen_feat = gen_feat_ex["InceptionV3/Mixed_7a"]
     target_feat = target_feat_ex["InceptionV3/Mixed_7a"]
     feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
-    feat_loss += tf.reduce_mean(feat_square_diff) * 0.4
-    img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.4
+    feat_loss += tf.reduce_mean(feat_square_diff) * 0.25
+    img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.25
 
   else:
     feat_loss = tf.constant(0.0)