From e9f94ad031fca9eb94f13f3109aa90b0c81d3e0f Mon Sep 17 00:00:00 2001
From: "sweep-nightly[bot]"
 <131841235+sweep-nightly[bot]@users.noreply.github.com>
Date: Fri, 13 Oct 2023 23:10:36 +0000
Subject: [PATCH 1/2] feat: Added CNN class for handling MNIST dataset

---
 src/cnn.py | 30 ++++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)
 create mode 100644 src/cnn.py

diff --git a/src/cnn.py b/src/cnn.py
new file mode 100644
index 0000000..a9b6209
--- /dev/null
+++ b/src/cnn.py
@@ -0,0 +1,30 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class CNN(nn.Module):
+    """
+    Convolutional Neural Network class for handling MNIST dataset.
+    Inherits from nn.Module.
+    """
+    def __init__(self):
+        """
+        Initialize the layers of the network.
+        """
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.fc1 = nn.Linear(64 * 4 * 4, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        """
+        Define the forward pass of the network.
+        """
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 64 * 4 * 4)
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)

From 3de2bcb76b05f9bf5439143e9c1cfe8317925543 Mon Sep 17 00:00:00 2001
From: "sweep-nightly[bot]"
 <131841235+sweep-nightly[bot]@users.noreply.github.com>
Date: Fri, 13 Oct 2023 23:11:29 +0000
Subject: [PATCH 2/2] feat: Updated src/main.py

---
 src/main.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/main.py b/src/main.py
index 243a31e..ce60cc9 100644
--- a/src/main.py
+++ b/src/main.py
@@ -5,6 +5,7 @@
 from torchvision import datasets, transforms
 from torch.utils.data import DataLoader
 import numpy as np
+from cnn import CNN
 
 # Step 1: Load MNIST Data and Preprocess
 transform = transforms.Compose([
@@ -31,7 +32,7 @@ def forward(self, x):
         return nn.functional.log_softmax(x, dim=1)
 
 # Step 3: Train the Model
-model = Net()
+model = CNN()
 optimizer = optim.SGD(model.parameters(), lr=0.01)
 criterion = nn.NLLLoss()