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| author | Jules Laplace <julescarbon@gmail.com> | 2020-01-11 12:14:36 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2020-01-11 12:14:36 +0100 |
| commit | 1cda1678bf95559d60e6104fe0e9f9e8d8afb5f2 (patch) | |
| tree | ae66d09737ca1c1538f07fb1385ac891cdc4d48d | |
| parent | 59a7d975f7eecc454acefb5eb7c2ab8cd8480331 (diff) | |
batch size
| -rw-r--r-- | cli/app/search/search_dense.py | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py index 6cc03ce..382ad79 100644 --- a/cli/app/search/search_dense.py +++ b/cli/app/search/search_dense.py @@ -234,7 +234,7 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op layer_name = feature_layer_names[layer] gen_feat = gen_feat_ex[layer_name] target_feat = target_feat_ex[layer_name] - feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [batch_size, -1]) + feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1]) feat_loss += tf.reduce_mean(feat_square_diff) / len(opt_feature_layers) img_feat_err += tf.reduce_mean(feat_square_diff, axis=1) / len(opt_feature_layers) |