Fix #61: remove entropy term on InfoGAN as it is a constant

Author: Agustinus Kristiadi

GAN/infogan/infogan_pytorch.py

@@ -137,8 +137,7 @@ def sample_c(size):
     Q_c_given_x = Q(G_sample)
 
     crossent_loss = torch.mean(-torch.sum(c * torch.log(Q_c_given_x + 1e-8), dim=1))
-    ent_loss = torch.mean(-torch.sum(c * torch.log(c + 1e-8), dim=1))
-    mi_loss = crossent_loss + ent_loss
+    mi_loss = crossent_loss
 
     mi_loss.backward()
     Q_solver.step()

GAN/infogan/infogan_tensorflow.py

@@ -101,8 +101,7 @@ def plot(samples):
 G_loss = -tf.reduce_mean(tf.log(D_fake + 1e-8))
 
 cross_ent = tf.reduce_mean(-tf.reduce_sum(tf.log(Q_c_given_x + 1e-8) * c, 1))
-ent = tf.reduce_mean(-tf.reduce_sum(tf.log(c + 1e-8) * c, 1))
-Q_loss = cross_ent + ent
+Q_loss = cross_ent
 
 D_solver = tf.train.AdamOptimizer().minimize(D_loss, var_list=theta_D)
 G_solver = tf.train.AdamOptimizer().minimize(G_loss, var_list=theta_G)