search

1 files changed, 13 insertions, 44 deletions

diff --git a/cli/app/commands/biggan/search.py b/cli/app/commands/biggan/search.py
index ec4b0c1..d1e1a0a 100644
--- a/cli/app/commands/biggan/search.py
+++ b/cli/app/commands/biggan/search.py
@@ -104,9 +104,8 @@ def cli(ctx, opt_fp_in, opt_dims):
   # module = hub.Module('https://tfhub.dev/deepmind/biggan-256/2')
   # module = hub.Module('https://tfhub.dev/deepmind/biggan-512/2')
 
-  inputs = {}
-  for k, v in module.get_input_info_dict().items():
-    inputs[k] = tf.compat.v1.placeholder(v.dtype, v.get_shape().as_list(), k, trainable=True)
+  inputs = {k: tf.compat.v1.placeholder(v.dtype, v.get_shape().as_list(), k)
+          for k, v in module.get_input_info_dict().items()}
   input_z = inputs['z']
   input_y = inputs['y']
   input_trunc = inputs['truncation']
@@ -180,57 +179,27 @@ def cli(ctx, opt_fp_in, opt_dims):
   start_im = imgrid(imconvert_uint8(phi_start), cols=5)
   imwrite(join(app_cfg.DIR_OUTPUTS, fp_frames, 'frame_0000_start.png'), start_im)
 
-  cost_op = tf.losses.mean_squared_error(output, phi_target)
-  train_op = tf.train.AdamOptimizer(lr_z).minimize(cost_op)
-
   try:
     for i in range(1000):
       feed_dict = {input_z: z, input_y: y, input_trunc: truncation}
-      
       grad_z, grad_y = sess.run([dc_dz, dc_dy], feed_dict=feed_dict)
-
-      #with tf.GradientTape(watch_accessed_variables=False, persistent=True) as g:
-      #  g.watch(input_z)
-      #  g.watch(input_y)
-      #cost = tf.reduce_sum(tf.pow(output - phi_target, 2))
-      #dc_dz = g.gradient(cost, input_z)
-      #dc_dy = g.gradient(cost, input_y)
-
-      #optimizer.apply_gradients([[dc_dz, input_z], [dc_dy, input_y]])
-      #optimizer.apply_gradients([[grad_z, input_z], [grad_y, input_y]])
-      print("________")
-      #print(z[0][0:10])
-      #print(grad_y[0])
       z -= grad_z * lr_z
       y -= grad_y * lr_y
 
-      # decay/attenuate learning rate to 0.05 of the original over 1000 frames
-      if i > 100:
-        lr_z *= 0.997
-      if i > 500:
-        lr_y *= 0.999
+      lr_z *= 0.997
+      lr_y *= 0.999
+
+      if i % 30 == 0:
+        lr_y *= 1.002
+        y = np.clip(y, 0, 1)
+        for j in range(batch_size):
+          y[j] /= y[j].sum()
+      if i > 200 and i % 100 == 0:
+        mean = np.mean(y, axis=0)
+        y = y / 2 + mean / 2
 
       indices = np.logical_or(z <= -2*truncation, z >= +2*truncation)
       z[indices] = np.random.randn(np.count_nonzero(indices))
-      #print(z[0][0:10])
-      if i < 100:
-        if i % 30 == 0:
-          lr_z *= 1.002
-          y = np.clip(y, 0, 1)
-          for j in range(batch_size):
-            y[j] /= y[j].sum()
-      elif i < 300:
-        if i % 50 == 0:
-          lr_z *= 1.001
-          y = np.clip(y, 0, 1)
-          for j in range(batch_size):
-            y[j] /= y[j].sum()
-      elif i < 600:
-        if i % 60 == 0:
-          y = np.clip(y, 0, 1)
-      else:
-        if i % 100 == 0:
-          y = np.clip(y, 0, 1)
 
       feed_dict = {input_z: z, input_y: y, input_trunc: truncation}
       phi_guess = sess.run(output, feed_dict=feed_dict)