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| author | Jules Laplace <julescarbon@gmail.com> | 2020-01-08 01:27:53 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2020-01-08 01:27:53 +0100 |
| commit | f86e1d17776f022861ed1799d6f58a7a362bafde (patch) | |
| tree | 4d161f0d87b3a3bcd6881db58d8dd3e8fc2efa1e /cli/app/search | |
| parent | 7c471edbd8e92490bc0cbfd1c8242e75da8c261f (diff) | |
getting latent search updates working...
Diffstat (limited to 'cli/app/search')
| -rw-r--r-- | cli/app/search/search_class.py | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/cli/app/search/search_class.py b/cli/app/search/search_class.py index dc5b10e..6bf6a59 100644 --- a/cli/app/search/search_class.py +++ b/cli/app/search/search_class.py @@ -100,9 +100,9 @@ def find_nearest_vector(sess, generator, opt_fp_in, opt_dims, out_images, out_la ## clip the Z encoding - clipped_encoding = tf.where(tf.abs(latent) >= params.clip, + clipped_encoding = tf.where(tf.abs(input_z) >= params.clip, tf.random.uniform([BATCH_SIZE, Z_DIM], minval=-params.clip, maxval=params.clip), input_z) - clip_latent = tf.assign(latent, clipped_encoding) + clip_latent = tf.assign(input_z, clipped_encoding) target = tf.compat.v1.placeholder(tf.float32, shape=(batch_size, img_size, img_size, num_channels)) |