diff --git a/models/dcgan.py b/models/dcgan.py
index 314820e..6bbb011 100644
--- a/models/dcgan.py
+++ b/models/dcgan.py
@@ -8,38 +8,37 @@ class DCGAN_D(nn.Container):
         self.ngpu = ngpu
         assert isize % 16 == 0, "isize has to be a multiple of 16"
 
-        main = nn.Sequential(
-            # input is nc x isize x isize
-            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
-            nn.LeakyReLU(0.2, inplace=True),
-        )
-        i, csize, cndf = 2, isize / 2, ndf
+        main = nn.Sequential()
+        # input is nc x isize x isize
+        main.add_module('initial.conv.{0}-{1}'.format(nc, ndf),
+                        nn.Conv2d(nc, ndf, 4, 2, 1, bias=False))
+        main.add_module('initial.relu.{0}'.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(str(i),
+            main.add_module('extra-layers-{0}.{1}.conv'.format(t, cndf),
                             nn.Conv2d(cndf, cndf, 3, 1, 1, bias=False))
-            main.add_module(str(i+1),
+            main.add_module('extra-layers-{0}.{1}.batchnorm'.format(t, cndf),
                             nn.BatchNorm2d(cndf))
-            main.add_module(str(i+2),
+            main.add_module('extra-layers-{0}.{1}.relu'.format(t, cndf),
                             nn.LeakyReLU(0.2, inplace=True))
-            i += 3
 
         while csize > 4:
             in_feat = cndf
             out_feat = cndf * 2
-            main.add_module(str(i),
+            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(str(i+1),
+            main.add_module('pyramid.{0}.batchnorm'.format(out_feat),
                             nn.BatchNorm2d(out_feat))
-            main.add_module(str(i+2),
+            main.add_module('pyramid.{0}.relu'.format(out_feat),
                             nn.LeakyReLU(0.2, inplace=True))
-            i+=3
             cndf = cndf * 2
             csize = csize / 2
 
         # state size. K x 4 x 4
-        main.add_module(str(i),
+        main.add_module('final.{0}-{1}.conv'.format(cndf, 1),
                         nn.Conv2d(cndf, 1, 4, 1, 0, bias=False))
         self.main = main
 
@@ -63,38 +62,39 @@ class DCGAN_G(nn.Container):
             cngf = cngf * 2
             tisize = tisize * 2
 
-        main = nn.Sequential(
-            # input is Z, going into a convolution
-            nn.ConvTranspose2d(nz, cngf, 4, 1, 0, bias=False),
-            nn.BatchNorm2d(cngf),
-            nn.ReLU(True),
-        )
+        main = nn.Sequential()
+        # input is Z, going into a convolution
+        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),
+                        nn.BatchNorm2d(cngf))
+        main.add_module('initial.{0}.relu'.format(cngf),
+                        nn.ReLU(True))
 
-        i, csize, cndf = 3, 4, cngf
+        csize, cndf = 4, cngf
         while csize < isize//2:
-            main.add_module(str(i),
-                nn.ConvTranspose2d(cngf, cngf//2, 4, 2, 1, bias=False))
-            main.add_module(str(i+1),
+            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),
                             nn.BatchNorm2d(cngf//2))
-            main.add_module(str(i+2),
+            main.add_module('pyramid.{0}.relu'.format(cngf//2),
                             nn.ReLU(True))
-            i += 3
             cngf = cngf // 2
             csize = csize * 2
 
         # Extra layers
         for t in range(n_extra_layers):
-            main.add_module(str(i),
+            main.add_module('extra-layers-{0}.{1}.conv'.format(t, cngf),
                             nn.Conv2d(cngf, cngf, 3, 1, 1, bias=False))
-            main.add_module(str(i+1),
+            main.add_module('extra-layers-{0}.{1}.batchnorm'.format(t, cngf),
                             nn.BatchNorm2d(cngf))
-            main.add_module(str(i+2),
+            main.add_module('extra-layers-{0}.{1}.relu'.format(t, cngf),
                             nn.ReLU(True))
-            i += 3
 
-        main.add_module(str(i),
+        main.add_module('final.{0}-{1}.convt'.format(cngf, nc),
                         nn.ConvTranspose2d(cngf, nc, 4, 2, 1, bias=False))
-        main.add_module(str(i+1), nn.Tanh())
+        main.add_module('final.{0}.tanh'.format(nc),
+                        nn.Tanh())
         self.main = main
 
     def forward(self, input):
@@ -110,34 +110,34 @@ class DCGAN_D_nobn(nn.Container):
         self.ngpu = ngpu
         assert isize % 16 == 0, "isize has to be a multiple of 16"
 
-        main = nn.Sequential(
-            # input is nc x isize x isize
-            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
-            nn.LeakyReLU(0.2, inplace=True),
-        )
-        i, csize, cndf = 2, isize / 2, ndf
+        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),
+                        nn.Conv2d(nc, ndf, 4, 2, 1, bias=False))
+        main.add_module('initial.relu.{0}'.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(str(i),
+            main.add_module('extra-layers-{0}.{1}.conv'.format(t, cndf),
                             nn.Conv2d(cndf, cndf, 3, 1, 1, bias=False))
-            main.add_module(str(i+1),
+            main.add_module('extra-layers-{0}.{1}.relu'.format(t, cndf),
                             nn.LeakyReLU(0.2, inplace=True))
-            i += 2
 
         while csize > 4:
             in_feat = cndf
             out_feat = cndf * 2
-            main.add_module(str(i),
+            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(str(i+1),
+            main.add_module('pyramid.{0}.relu'.format(out_feat),
                             nn.LeakyReLU(0.2, inplace=True))
-            i+=2
             cndf = cndf * 2
             csize = csize / 2
 
         # state size. K x 4 x 4
-        main.add_module(str(i),
+        main.add_module('final.{0}-{1}.conv'.format(cndf, 1),
                         nn.Conv2d(cndf, 1, 4, 1, 0, bias=False))
         self.main = main
 
@@ -161,33 +161,32 @@ class DCGAN_G_nobn(nn.Container):
             cngf = cngf * 2
             tisize = tisize * 2
 
-        main = nn.Sequential(
-            # input is Z, going into a convolution
-            nn.ConvTranspose2d(nz, cngf, 4, 1, 0, bias=False),
-            nn.ReLU(True),
-        )
+        main = nn.Sequential()
+        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),
+                        nn.ReLU(True))
 
-        i, csize, cndf = 3, 4, cngf
+        csize, cndf = 4, cngf
         while csize < isize//2:
-            main.add_module(str(i),
-                nn.ConvTranspose2d(cngf, cngf//2, 4, 2, 1, bias=False))
-            main.add_module(str(i+1),
+            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),
                             nn.ReLU(True))
-            i += 2
             cngf = cngf // 2
             csize = csize * 2
 
         # Extra layers
         for t in range(n_extra_layers):
-            main.add_module(str(i),
+            main.add_module('extra-layers-{0}.{1}.conv'.format(t, cngf),
                             nn.Conv2d(cngf, cngf, 3, 1, 1, bias=False))
-            main.add_module(str(i+1),
+            main.add_module('extra-layers-{0}.{1}.relu'.format(t, cngf),
                             nn.ReLU(True))
-            i += 2
 
-        main.add_module(str(i),
+        main.add_module('final.{0}-{1}.convt'.format(cngf, nc),
                         nn.ConvTranspose2d(cngf, nc, 4, 2, 1, bias=False))
-        main.add_module(str(i+1), nn.Tanh())
+        main.add_module('final.{0}.tanh'.format(nc),
+                        nn.Tanh())
         self.main = main
 
     def forward(self, input):