diff options
| author | Jules Laplace <julescarbon@gmail.com> | 2020-01-10 19:45:20 +0100 |
|---|---|---|
| committer | Jules Laplace <julescarbon@gmail.com> | 2020-01-10 19:45:20 +0100 |
| commit | 735b26c5b88ae1de1eac134f6d9a626ded33eeac (patch) | |
| tree | 1caecff9a1bee5d2bf570a0f494998a22b91a5ba /cli/app/search/search_dense.py | |
| parent | f462fe520aca4fd15127fd6d0b27e342e2f23a14 (diff) | |
load latents and labels, share interpolation amount
Diffstat (limited to 'cli/app/search/search_dense.py')
| -rw-r--r-- | cli/app/search/search_dense.py | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py index c7cf078..616ba6a 100644 --- a/cli/app/search/search_dense.py +++ b/cli/app/search/search_dense.py @@ -280,7 +280,7 @@ def find_dense_embedding_for_images(params): params.inv_it / params.decay_n, 0.1, staircase=True) else: lrate = tf.constant(params.lr) - trained_params = [encoding] if params.fixed_z else [latent, encoding] + trained_params = [latent, encoding] optimizer = tf.train.AdamOptimizer(learning_rate=lrate, beta1=0.9, beta2=0.999) inv_train_op = optimizer.minimize(inv_loss, var_list=trained_params, global_step=inv_step) @@ -406,12 +406,12 @@ def find_dense_embedding_for_images(params): it += 1 # Save images that are ready. - latent_batch, enc_batch, rec_err_batch = sess.run([latent, encoding, img_rec_err]) - out_lat[out_pos:out_pos+BATCH_SIZE] = latent_batch - out_enc[out_pos:out_pos+BATCH_SIZE] = enc_batch + label_trained, latent_trained, enc_trained, rec_err_trained = sess.run([label, latent, encoding, img_rec_err]) + out_lat[out_pos:out_pos+BATCH_SIZE] = latent_trained + out_enc[out_pos:out_pos+BATCH_SIZE] = enc_trained out_images[out_pos:out_pos+BATCH_SIZE] = image_batch - out_labels[out_pos:out_pos+BATCH_SIZE] = label_batch - out_err[out_pos:out_pos+BATCH_SIZE] = rec_err_batch + out_labels[out_pos:out_pos+BATCH_SIZE] = label_trained + out_err[out_pos:out_pos+BATCH_SIZE] = rec_err_trained gen_images = sess.run(gen_img_orig) images = vs.data2img(gen_images) |