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| author | Jules Laplace <julescarbon@gmail.com> | 2019-12-15 00:05:37 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2019-12-15 00:05:37 +0100 |
| commit | de724b765de257e07e0ed2547d4990a20957d2bc (patch) | |
| tree | 2825f778fcd7f3154b9751a634c924d0e7798f1e | |
| parent | 3ca730b3f0d109ffb8673d5f266159e752fa44a4 (diff) | |
7a 6b 5a 7c
| -rw-r--r-- | inversion/image_inversion_inception.py | 18 |
1 files changed, 9 insertions, 9 deletions
diff --git a/inversion/image_inversion_inception.py b/inversion/image_inversion_inception.py index bb883e7..f9d1c79 100644 --- a/inversion/image_inversion_inception.py +++ b/inversion/image_inversion_inception.py @@ -229,14 +229,14 @@ if params.features: gen_feat = gen_feat_ex["InceptionV3/Mixed_6b"] target_feat = target_feat_ex["InceptionV3/Mixed_6b"] feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1]) - feat_loss += tf.reduce_mean(feat_square_diff) * 0.3 - img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.3 + feat_loss += tf.reduce_mean(feat_square_diff) * 0.16 + img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.16 - # gen_feat = gen_feat_ex["InceptionV3/Mixed_5a"] - # target_feat = target_feat_ex["InceptionV3/Mixed_5a"] - # feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1]) - # feat_loss += tf.reduce_mean(feat_square_diff) - # img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) + gen_feat = gen_feat_ex["InceptionV3/Mixed_5a"] + target_feat = target_feat_ex["InceptionV3/Mixed_5a"] + feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1]) + feat_loss += tf.reduce_mean(feat_square_diff) * 0.16 + img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.16 # gen_feat = gen_feat_ex["InceptionV3/Mixed_7b"] # target_feat = target_feat_ex["InceptionV3/Mixed_7b"] @@ -247,8 +247,8 @@ if params.features: gen_feat = gen_feat_ex["InceptionV3/Mixed_7c"] target_feat = target_feat_ex["InceptionV3/Mixed_7c"] feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1]) - feat_loss += tf.reduce_mean(feat_square_diff) * 0.2 - img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.2 + feat_loss += tf.reduce_mean(feat_square_diff) * 0.17 + img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) * 0.17 else: feat_loss = tf.constant(0.0) |