diff options
Diffstat (limited to 'cli/app/search/search_class.py')
| -rw-r--r-- | cli/app/search/search_class.py | 8 |
1 files changed, 5 insertions, 3 deletions
diff --git a/cli/app/search/search_class.py b/cli/app/search/search_class.py index f7b2136..105fc9f 100644 --- a/cli/app/search/search_class.py +++ b/cli/app/search/search_class.py @@ -96,6 +96,8 @@ def find_nearest_vector(sess, generator, opt_fp_in, opt_dims, out_images, out_la 'truncation': input_trunc, }) + target = tf.compat.v1.placeholder(tf.float32, shape=(batch_size, img_size, img_size, num_channels)) + ## clip the Z encoding opt_clip = 1.0 @@ -104,10 +106,10 @@ def find_nearest_vector(sess, generator, opt_fp_in, opt_dims, out_images, out_la tf.random.uniform([batch_size, z_dim], minval=-opt_clip, maxval=opt_clip), input_z) clip_latent = tf.assign(input_z, clipped_encoding) - target = tf.compat.v1.placeholder(tf.float32, shape=(batch_size, img_size, img_size, num_channels)) - ## normalize the Y encoding - normalized_labels = tf.nn.l2_normalize(input_y) + # normalized_labels = tf.nn.l2_normalize(input_y) + # tf.reduce_mean(tf.abs(encoding - gen_encoding)) + normalized_labels = input_y / tf.reduce_sum(input_y) clip_labels = tf.assign(input_y, normalized_labels) ## if computing Feature loss, use these encoders |