diff --git a/DCGAN-PyTorch/01-DCGAN-PL.ipynb b/DCGAN-PyTorch/01-DCGAN-PL.ipynb
index 5e6a5f4dee89ca1fd0fcbd72141c01bbc33c4e7d..d3687e449d70fa5ff984345f1da4f321817e8f06 100644
--- a/DCGAN-PyTorch/01-DCGAN-PL.ipynb
+++ b/DCGAN-PyTorch/01-DCGAN-PL.ipynb
@@ -80,7 +80,7 @@
     "latent_dim          = 128\n",
     "    \n",
     "generator_class     = 'Generator_2'\n",
-    "discriminator_class = 'Discriminator_1'    \n",
+    "discriminator_class = 'Discriminator_2'    \n",
     "    \n",
     "scale               = .01\n",
     "epochs              = 5\n",
@@ -170,7 +170,7 @@
    "source": [
     "print('\\nInstantiation :\\n')\n",
     "generator     = Generator_2(latent_dim=latent_dim, data_shape=data_shape)\n",
-    "discriminator = Discriminator_1(latent_dim=latent_dim, data_shape=data_shape)\n",
+    "discriminator = Discriminator_2(latent_dim=latent_dim, data_shape=data_shape)\n",
     "\n",
     "print('\\nFew tests :\\n')\n",
     "z = torch.randn(batch_size, latent_dim)\n",
@@ -296,7 +296,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "gan = GAN.load_from_checkpoint('./run/SHEEP3/models/last.ckpt')"
+    "gan = GAN.load_from_checkpoint('./run/SHEEP3/models/last-v1.ckpt')"
    ]
   },
   {
diff --git a/DCGAN-PyTorch/modules/Discriminators.py b/DCGAN-PyTorch/modules/Discriminators.py
index 5c6334b254b66ae677b5d57a46541922a33d9569..86c2f706115d86ef5ce835d4d6b61ab750107c8e 100644
--- a/DCGAN-PyTorch/modules/Discriminators.py
+++ b/DCGAN-PyTorch/modules/Discriminators.py
@@ -19,7 +19,7 @@ class Discriminator_1(nn.Module):
     
         super().__init__()
         self.img_shape = data_shape
-        print('init discriminator       : ',data_shape,' to sigmoid')
+        print('init discriminator 1     : ',data_shape,' to sigmoid')
 
         self.model = nn.Sequential(
 
@@ -38,4 +38,48 @@ class Discriminator_1(nn.Module):
         # img_flat = img.view(img.size(0), -1)
         validity = self.model(img)
 
+        return validity
+
+
+
+
+class Discriminator_2(nn.Module):
+
+    def __init__(self, latent_dim=None, data_shape=None):
+    
+        super().__init__()
+        self.img_shape = data_shape
+        print('init discriminator 2     : ',data_shape,' to sigmoid')
+
+        self.model = nn.Sequential(
+
+            nn.Conv2d(1, 32, kernel_size = 3, stride = 2, padding = 1),
+            nn.ReLU(),
+            nn.BatchNorm2d(32),
+            nn.Dropout2d(0.25),
+
+            nn.Conv2d(32, 64, kernel_size = 3, stride = 1, padding = 1),
+            nn.ReLU(),
+            nn.BatchNorm2d(64),
+            nn.Dropout2d(0.25),
+
+            nn.Conv2d(64, 128, kernel_size = 3, stride = 1, padding = 1),
+            nn.ReLU(),
+            nn.BatchNorm2d(128),
+            nn.Dropout2d(0.25),
+
+            nn.Conv2d(128, 256, kernel_size = 3, stride = 2, padding = 1),
+            nn.ReLU(),
+            nn.BatchNorm2d(256),
+            nn.Dropout2d(0.25),
+
+            nn.Flatten(),
+            nn.Linear(12544, 1),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, img):
+        img_nchw = img.permute(0, 3, 1, 2) # from NHWC to NCHW
+        validity = self.model(img_nchw)
+
         return validity
\ No newline at end of file
diff --git a/DCGAN-PyTorch/modules/Generators.py b/DCGAN-PyTorch/modules/Generators.py
index 755b592df2b347f80c48452290afb724d7a3b4e4..70e523d747187daf5cd1acf38d9ea32cd2f44cad 100644
--- a/DCGAN-PyTorch/modules/Generators.py
+++ b/DCGAN-PyTorch/modules/Generators.py
@@ -70,14 +70,14 @@ class Generator_2(nn.Module):
             nn.UpsamplingNearest2d( scale_factor=2 ),
             # nn.UpsamplingBilinear2d( scale_factor=2 ),
             nn.Conv2d( 64,128, (3,3), stride=(1,1), padding=(1,1) ),
-            nn.BatchNorm2d(128),
             nn.ReLU(),
+            nn.BatchNorm2d(128),
 
             nn.UpsamplingNearest2d( scale_factor=2 ),
             # nn.UpsamplingBilinear2d( scale_factor=2 ),
             nn.Conv2d( 128,256, (3,3), stride=(1,1), padding=(1,1)),
-            nn.BatchNorm2d(256),
             nn.ReLU(),
+            nn.BatchNorm2d(256),
 
             nn.Conv2d( 256,1, (5,5), stride=(1,1), padding=(2,2)),
             nn.Sigmoid()