what the...

2 files changed, 7 insertions, 8 deletions

diff --git a/cli/app/commands/biggan/extract_dense_vectors.py b/cli/app/commands/biggan/extract_dense_vectors.py
index 05b2c23..018d646 100644
--- a/cli/app/commands/biggan/extract_dense_vectors.py
+++ b/cli/app/commands/biggan/extract_dense_vectors.py
@@ -10,7 +10,7 @@ from app.search.params import timestamp
 @click.command('')
 @click.option('-f', '--folder_id', 'opt_folder_id', type=int,
   help='Folder ID to process')
-@click.option('-ls', '--latent_steps', 'opt_latent_steps', default=100, type=int,
+@click.option('-ls', '--latent_steps', 'opt_latent_steps', default=200, type=int,
   help='Number of optimization iterations')
 @click.option('-ds', '--dense_steps', 'opt_dense_steps', default=2000, type=int,
   help='Number of optimization iterations')
@@ -24,7 +24,7 @@ from app.search.params import timestamp
   help='Normalize labels every N steps')
 @click.option('-feat', '--use_feature_detector', 'opt_use_feature_detector', is_flag=True,
   help='Compute feature loss')
-@click.option('-ll', '--feature_layers', 'opt_feature_layers', default="1a,2a,4a,7a",
+@click.option('-ll', '--feature_layers', 'opt_feature_layers', default="1a,2a,3a,4a,7a",
   help='Feature layers used for loss')
 @click.option('-snap', '--snapshot_interval', 'opt_snapshot_interval', default=20,
   help='Interval to store sample images')
diff --git a/cli/app/search/search_dense.py b/cli/app/search/search_dense.py
index 5c07be1..dcef82f 100644
--- a/cli/app/search/search_dense.py
+++ b/cli/app/search/search_dense.py
@@ -212,20 +212,20 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op
   # Optimizer.
   # --------------------------
   if params.decay_lr:
-    lrate = tf.train.exponential_decay(params.lr, inv_step,
-        params.inv_it / params.decay_n, 0.1, staircase=True)
+    lrate = tf.train.exponential_decay(params.lr, inv_step, params.inv_it, 0.96)
+    # lrate = tf.train.exponential_decay(params.lr, inv_step, params.inv_it / params.decay_n, 0.1, staircase=True)
   else:
     lrate = tf.constant(params.lr)
   
-  # trained_params = [label, latent, encoding]
-  trained_params = [latent, encoding]
+  trained_params = [label, 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)
   reinit_optimizer = tf.variables_initializer(optimizer.variables())
 
-  optimizer_quad = tf.train.AdamOptimizer(learning_rate=params.lr_quad, beta1=0.9, beta2=0.999)
+  optimizer_quad = tf.train.AdamOptimizer(learning_rate=lrate, beta1=0.9, beta2=0.999)
   inv_train_op_quad = optimizer_quad.minimize(inv_loss_quad, var_list=trained_params, global_step=inv_step)
   reinit_optimizer_quad = tf.variables_initializer(optimizer_quad.variables())
 
@@ -401,7 +401,6 @@ def find_dense_embedding_for_images(params, opt_tag="inverse_" + timestamp(), op
   sess.close()
 
 def feature_loss(feature_extractor, opt_feature_layers, BATCH_SIZE, img_a, img_b, y, x, height, width):
-  print("{} {} {} {}".format(y, x, height, width))
   if y is not None:
     img_a = tf.image.crop_to_bounding_box(img_a, y, x, height, width)
     img_b = tf.image.crop_to_bounding_box(img_b, y, x, height, width)