Use traditional learning rate decaying rather than fast-restarts, works better when training continuously adapting GAN.

enhance.py

@@ -418,22 +418,17 @@ class NeuralEnhancer(object):
             self.imsave('valid/%03i_pixels.png' % i, scald[i])
             self.imsave('valid/%03i_reprod.png' % i, repro[i])
 
-    def decay_with_restart(self):
-        l_min, l_max, l_mult = 1E-7, 1E-3, 0.5
-        t_cur, t_i, t_mult = 10, 10, 1
+    def decay_learning_rate(self):
+        l_r, t_cur = args.learning_rate, 0
 
         while True:
-            yield l_min + 0.5 * (l_max - l_min) * (1.0 + math.cos(t_cur / t_i * math.pi))
+            yield l_r if t_cur > 0 else l_r * 0.1
             t_cur += 1
-
-            if t_cur > t_i:
-                t_cur, t_i = 0, int(t_i * t_mult)
-                l_max = max(l_max * l_mult, 1e-12)
-                l_min = max(l_min * l_mult, 1e-8)
+            if t_cur % args.learning_period == 0: l_r *= args.learning_decay
 
     def train(self):
         images = np.zeros((args.batch_size, 3, args.batch_resolution, args.batch_resolution), dtype=np.float32)
-        learning_rate = self.decay_with_restart()
+        learning_rate = self.decay_learning_rate()
         try:
             running, start = None, time.time()
             for epoch in range(args.epochs):
@@ -450,8 +445,8 @@ class NeuralEnhancer(object):
                     total = total + losses if total is not None else losses
                     l = np.sum(losses)
                     assert not np.isnan(losses).any()
-                    running = l if running is None else running * 0.9 + 0.1 * l
-                    print('↑' if l > running else '↓', end=' ', flush=True)
+                    running = l if running is None else running * 0.95 + 0.05 * l
+                    print('↑' if l > running else '↓', end='', flush=True)
 
                 orign, scald, repro = self.model.predict(images)
                 self.show_progress(orign, scald, repro)
@@ -468,6 +463,7 @@ class NeuralEnhancer(object):
                     print('  - adversary mode: generator engaging discriminator.')
                     self.model.adversary_weight.set_value(args.adversary_weight)
                     running = None
+
         except KeyboardInterrupt:
             pass