fixed typo

tutorials/03-advanced/variational_auto_encoder/main.py

@@ -37,7 +37,7 @@ class VAE(nn.Module):
             nn.Linear(h_dim, image_size),
             nn.Sigmoid())
     
-    def reparametrize(self, mu, log_var):
+    def reparameterize(self, mu, log_var):
         """"z = mean + eps * sigma where eps is sampled from N(0, 1)."""
         eps = to_var(torch.randn(mu.size(0), mu.size(1)))
         z = mu + eps * torch.exp(log_var/2)    # 2 for convert var to std
@@ -46,7 +46,7 @@ class VAE(nn.Module):
     def forward(self, x):
         h = self.encoder(x)
         mu, log_var = torch.chunk(h, 2, dim=1)  # mean and log variance.
-        z = self.reparametrize(mu, log_var)
+        z = self.reparameterize(mu, log_var)
         out = self.decoder(z)
         return out, mu, log_var