Update net.py

typically people use beta to refer to this value.

examples/vae/net.py

@@ -38,14 +38,14 @@ def decode(self, z, sigmoid=True):
         else:
             return h2
 
-    def get_loss_func(self, C=1.0, k=1):
+    def get_loss_func(self, beta=1.0, k=1):
         """Get loss function of VAE.
 
         The loss value is equal to ELBO (Evidence Lower Bound)
         multiplied by -1.
 
         Args:
-            C (int): Usually this is 1.0. Can be changed to control the
+            beta (int): Usually this is 1.0. Can be changed to control the
                 second term of ELBO bound, which works as regularization.
             k (int): Number of Monte Carlo samples used in encoded vector.
         """
@@ -60,7 +60,7 @@ def lf(x):
                     / (k * batchsize)
             self.rec_loss = rec_loss
             self.loss = self.rec_loss + \
-                C * gaussian_kl_divergence(mu, ln_var) / batchsize
+                beta * gaussian_kl_divergence(mu, ln_var) / batchsize
             chainer.report(
                 {'rec_loss': rec_loss, 'loss': self.loss}, observer=self)
             return self.loss