GAN bug fix.

ragavvenkatesan · Mar 5, 2017 · 34f2ed8 · 34f2ed8
1 parent 4587d9c
commit 34f2ed8
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Showing 2 changed files with 5 additions and 5 deletions.
diff --git a/yann/modules/optimizer.py b/yann/modules/optimizer.py
@@ -230,18 +230,18 @@ def create_updates(self, params, verbose = 1):
             elif self.optimizer_type == 'adam':
                 """ Kingma, Diederik, and Jimmy Ba. "Adam: A method for stochastic optimization."
                      arXiv preprint arXiv:1412.6980 (2014)."""
-                if not self.momentum_type == '_adam':
+                if not self.momentum_type == 'adam':
                     if verbose >= 3 and not self.momentum_type == 'false':
                         print "... ADAM doesn't need explicit momentum. Momentum is removed."
-                    self.momentum_type = '_adam'
+                    self.momentum_type = 'adam'
 
 
                 current_acc_2 = b1 * acc_2 + (1-b1) * gradient
                 current_acc_1 = b2 * acc_1 + (1-b2) * T.sqr( gradient )
                 self.updates[acc_2] = current_acc_2
                 self.updates[acc_1] = current_acc_1
 
-            if self.momentum_type == '_adam':
+            if self.momentum_type == 'adam':
                 self.updates[velocity] = a * current_acc_2 / (T.sqrt(current_acc_1) +
                                                                                  fudge_factor)
 

diff --git a/yann/special/gan.py b/yann/special/gan.py
@@ -404,9 +404,9 @@ def print_status (self, epoch , verbose = 2):
             raise Exception(" Cook first then run this.")
 
         if len(discriminator_probability) < self.batches2train * self.mini_batches_per_batch[0]:
-            print(".. Discriminator Real Sigmoid D(x)   : " + str(discriminator_probability[-1]))
+            print(".. Discriminator Sigmoid D(x)   : " + str(discriminator_probability[-1]))
         else:
-            print(".. Discriminator Real Sigmoid D(x)   : " + str(numpy.mean(\
+            print(".. Discriminator Sigmoid D(x)   : " + str(numpy.mean(\
              discriminator_probability[-1 * self.batches2train * self.mini_batches_per_batch[0]:])))
 
         if len(generator_probability) < self.batches2train * self.mini_batches_per_batch[0]: