Added CNN MNIST

NN_Arch.py

@@ -1,9 +1,10 @@
 import torch
 import torch.nn as nn
 import torch.nn.Functional as F
-# MLP Arch with 1 Hidden layer
 
 
+# MLP Arch with 1 Hidden layer
+
 class MLP(nn.Module):
     def __init__(self, input_dim, hidden, out_dim):
 
@@ -21,3 +22,27 @@ class MLP(nn.Module):
         x = self.relu(x)
         x = self.linear2(x)
         return self.softmax(x)
+
+# CNN Arch for MNIST
+
+
+class CNN_Mnist(nn.Module):
+    def __init__(self, args):
+
+        super(CNN_Mnist, self).__init__()
+        self.conv1 = nn.Conv2d(args.num_channels, 10, kernel_size=5)
+        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
+        self.dropout_2d = nn.Dropout2d()
+        self.fc1 = nn.Linear(320, 50)
+        self.fc2 = nn.Linear(50, args.num_classes)
+
+    def forward(self, x):
+        x = F.max_pool2d(self.conv1(x), 2)
+        x = F.relu(x)
+        x = F.max_pool2d(nn.Dropout2d(self.conv2(x)), 2)
+        x = F.relu(x)
+        x = x.view(-1, x.shape[1]*x.shape[2]*x.shape[3])
+        x = F.relu(self.fc1(x))
+        x = F.dropout(x, training=self.training)
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)