diff --git a/models/dcgan.py b/models/dcgan.py
index 1dd8dbf..f821c89 100644
--- a/models/dcgan.py
+++ b/models/dcgan.py
@@ -10,35 +10,35 @@ class DCGAN_D(nn.Module):
 
         main = nn.Sequential()
         # input is nc x isize x isize
-        main.add_module('initial.conv.{0}-{1}'.format(nc, ndf),
+        main.add_module('initial:{0}-{1}:conv'.format(nc, ndf),
                         nn.Conv2d(nc, ndf, 4, 2, 1, bias=False))
-        main.add_module('initial.relu.{0}'.format(ndf),
+        main.add_module('initial:{0}:relu'.format(ndf),
                         nn.LeakyReLU(0.2, inplace=True))
         csize, cndf = isize / 2, ndf
 
         # Extra layers
         for t in range(n_extra_layers):
-            main.add_module('extra-layers-{0}.{1}.conv'.format(t, cndf),
+            main.add_module('extra-layers-{0}:{1}:conv'.format(t, cndf),
                             nn.Conv2d(cndf, cndf, 3, 1, 1, bias=False))
-            main.add_module('extra-layers-{0}.{1}.batchnorm'.format(t, cndf),
+            main.add_module('extra-layers-{0}:{1}:batchnorm'.format(t, cndf),
                             nn.BatchNorm2d(cndf))
-            main.add_module('extra-layers-{0}.{1}.relu'.format(t, cndf),
+            main.add_module('extra-layers-{0}:{1}:relu'.format(t, cndf),
                             nn.LeakyReLU(0.2, inplace=True))
 
         while csize > 4:
             in_feat = cndf
             out_feat = cndf * 2
-            main.add_module('pyramid.{0}-{1}.conv'.format(in_feat, out_feat),
+            main.add_module('pyramid:{0}-{1}:conv'.format(in_feat, out_feat),
                             nn.Conv2d(in_feat, out_feat, 4, 2, 1, bias=False))
-            main.add_module('pyramid.{0}.batchnorm'.format(out_feat),
+            main.add_module('pyramid:{0}:batchnorm'.format(out_feat),
                             nn.BatchNorm2d(out_feat))
-            main.add_module('pyramid.{0}.relu'.format(out_feat),
+            main.add_module('pyramid:{0}:relu'.format(out_feat),
                             nn.LeakyReLU(0.2, inplace=True))
             cndf = cndf * 2
             csize = csize / 2
 
         # state size. K x 4 x 4
-        main.add_module('final.{0}-{1}.conv'.format(cndf, 1),
+        main.add_module('final:{0}-{1}:conv'.format(cndf, 1),
                         nn.Conv2d(cndf, 1, 4, 1, 0, bias=False))
         self.main = main
 
@@ -65,36 +65,36 @@ class DCGAN_G(nn.Module):
 
         main = nn.Sequential()
         # input is Z, going into a convolution
-        main.add_module('initial.{0}-{1}.convt'.format(nz, cngf),
+        main.add_module('initial:{0}-{1}:convt'.format(nz, cngf),
                         nn.ConvTranspose2d(nz, cngf, 4, 1, 0, bias=False))
-        main.add_module('initial.{0}.batchnorm'.format(cngf),
+        main.add_module('initial:{0}:batchnorm'.format(cngf),
                         nn.BatchNorm2d(cngf))
-        main.add_module('initial.{0}.relu'.format(cngf),
+        main.add_module('initial:{0}:relu'.format(cngf),
                         nn.ReLU(True))
 
         csize, cndf = 4, cngf
         while csize < isize//2:
-            main.add_module('pyramid.{0}-{1}.convt'.format(cngf, cngf//2),
+            main.add_module('pyramid:{0}-{1}:convt'.format(cngf, cngf//2),
                             nn.ConvTranspose2d(cngf, cngf//2, 4, 2, 1, bias=False))
-            main.add_module('pyramid.{0}.batchnorm'.format(cngf//2),
+            main.add_module('pyramid:{0}:batchnorm'.format(cngf//2),
                             nn.BatchNorm2d(cngf//2))
-            main.add_module('pyramid.{0}.relu'.format(cngf//2),
+            main.add_module('pyramid:{0}:relu'.format(cngf//2),
                             nn.ReLU(True))
             cngf = cngf // 2
             csize = csize * 2
 
         # Extra layers
         for t in range(n_extra_layers):
-            main.add_module('extra-layers-{0}.{1}.conv'.format(t, cngf),
+            main.add_module('extra-layers-{0}:{1}:conv'.format(t, cngf),
                             nn.Conv2d(cngf, cngf, 3, 1, 1, bias=False))
-            main.add_module('extra-layers-{0}.{1}.batchnorm'.format(t, cngf),
+            main.add_module('extra-layers-{0}:{1}:batchnorm'.format(t, cngf),
                             nn.BatchNorm2d(cngf))
-            main.add_module('extra-layers-{0}.{1}.relu'.format(t, cngf),
+            main.add_module('extra-layers-{0}:{1}:relu'.format(t, cngf),
                             nn.ReLU(True))
 
-        main.add_module('final.{0}-{1}.convt'.format(cngf, nc),
+        main.add_module('final:{0}-{1}:convt'.format(cngf, nc),
                         nn.ConvTranspose2d(cngf, nc, 4, 2, 1, bias=False))
-        main.add_module('final.{0}.tanh'.format(nc),
+        main.add_module('final:{0}:tanh'.format(nc),
                         nn.Tanh())
         self.main = main
 
@@ -114,31 +114,31 @@ class DCGAN_D_nobn(nn.Module):
         main = nn.Sequential()
         # input is nc x isize x isize
         # input is nc x isize x isize
-        main.add_module('initial.conv.{0}-{1}'.format(nc, ndf),
+        main.add_module('initial:{0}-{1}:conv'.format(nc, ndf),
                         nn.Conv2d(nc, ndf, 4, 2, 1, bias=False))
-        main.add_module('initial.relu.{0}'.format(ndf),
+        main.add_module('initial:{0}:conv'.format(ndf),
                         nn.LeakyReLU(0.2, inplace=True))
         csize, cndf = isize / 2, ndf
 
         # Extra layers
         for t in range(n_extra_layers):
-            main.add_module('extra-layers-{0}.{1}.conv'.format(t, cndf),
+            main.add_module('extra-layers-{0}:{1}:conv'.format(t, cndf),
                             nn.Conv2d(cndf, cndf, 3, 1, 1, bias=False))
-            main.add_module('extra-layers-{0}.{1}.relu'.format(t, cndf),
+            main.add_module('extra-layers-{0}:{1}:relu'.format(t, cndf),
                             nn.LeakyReLU(0.2, inplace=True))
 
         while csize > 4:
             in_feat = cndf
             out_feat = cndf * 2
-            main.add_module('pyramid.{0}-{1}.conv'.format(in_feat, out_feat),
+            main.add_module('pyramid:{0}-{1}:conv'.format(in_feat, out_feat),
                             nn.Conv2d(in_feat, out_feat, 4, 2, 1, bias=False))
-            main.add_module('pyramid.{0}.relu'.format(out_feat),
+            main.add_module('pyramid:{0}:relu'.format(out_feat),
                             nn.LeakyReLU(0.2, inplace=True))
             cndf = cndf * 2
             csize = csize / 2
 
         # state size. K x 4 x 4
-        main.add_module('final.{0}-{1}.conv'.format(cndf, 1),
+        main.add_module('final:{0}-{1}:conv'.format(cndf, 1),
                         nn.Conv2d(cndf, 1, 4, 1, 0, bias=False))
         self.main = main
 
@@ -164,30 +164,30 @@ class DCGAN_G_nobn(nn.Module):
             tisize = tisize * 2
 
         main = nn.Sequential()
-        main.add_module('initial.{0}-{1}.convt'.format(nz, cngf),
+        main.add_module('initial:{0}-{1}:convt'.format(nz, cngf),
                         nn.ConvTranspose2d(nz, cngf, 4, 1, 0, bias=False))
-        main.add_module('initial.{0}.relu'.format(cngf),
+        main.add_module('initial:{0}:relu'.format(cngf),
                         nn.ReLU(True))
 
         csize, cndf = 4, cngf
         while csize < isize//2:
-            main.add_module('pyramid.{0}-{1}.convt'.format(cngf, cngf//2),
+            main.add_module('pyramid:{0}-{1}:convt'.format(cngf, cngf//2),
                             nn.ConvTranspose2d(cngf, cngf//2, 4, 2, 1, bias=False))
-            main.add_module('pyramid.{0}.relu'.format(cngf//2),
+            main.add_module('pyramid:{0}:relu'.format(cngf//2),
                             nn.ReLU(True))
             cngf = cngf // 2
             csize = csize * 2
 
         # Extra layers
         for t in range(n_extra_layers):
-            main.add_module('extra-layers-{0}.{1}.conv'.format(t, cngf),
+            main.add_module('extra-layers-{0}:{1}:conv'.format(t, cngf),
                             nn.Conv2d(cngf, cngf, 3, 1, 1, bias=False))
-            main.add_module('extra-layers-{0}.{1}.relu'.format(t, cngf),
+            main.add_module('extra-layers-{0}:{1}:relu'.format(t, cngf),
                             nn.ReLU(True))
 
-        main.add_module('final.{0}-{1}.convt'.format(cngf, nc),
+        main.add_module('final:{0}-{1}:convt'.format(cngf, nc),
                         nn.ConvTranspose2d(cngf, nc, 4, 2, 1, bias=False))
-        main.add_module('final.{0}.tanh'.format(nc),
+        main.add_module('final:{0}:tanh'.format(nc),
                         nn.Tanh())
         self.main = main