From 46089e2fe7d6c0af2a90c03e657e87ae3c14951e Mon Sep 17 00:00:00 2001
From: "sweep-nightly[bot]"
 <131841235+sweep-nightly[bot]@users.noreply.github.com>
Date: Tue, 24 Oct 2023 04:14:32 +0000
Subject: [PATCH 1/3] feat: Add CNN class and training function in cnn.p

---
 src/cnn.py | 31 +++++++++++++++++++++++++++++++
 1 file changed, 31 insertions(+)
 create mode 100644 src/cnn.py

diff --git a/src/cnn.py b/src/cnn.py
new file mode 100644
index 0000000..da739a0
--- /dev/null
+++ b/src/cnn.py
@@ -0,0 +1,31 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+class CNN(nn.Module):
+    def __init__(self):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=5)
+        self.fc1 = nn.Linear(4*4*64, 1024)
+        self.fc2 = nn.Linear(1024, 10)
+
+    def forward(self, x):
+        x = nn.functional.relu(self.conv1(x))
+        x = nn.functional.max_pool2d(x, 2)
+        x = nn.functional.relu(self.conv2(x))
+        x = nn.functional.max_pool2d(x, 2)
+        x = x.view(-1, 4*4*64)
+        x = nn.functional.relu(self.fc1(x))
+        x = self.fc2(x)
+        return nn.functional.log_softmax(x, dim=1)
+
+def train_model(model, dataloader, criterion, optimizer, epochs=3):
+    for epoch in range(epochs):
+        for images, labels in dataloader:
+            optimizer.zero_grad()
+            output = model(images)
+            loss = criterion(output, labels)
+            loss.backward()
+            optimizer.step()
+    return model

From ea205a59557c7c8a820406a76fd17a488a935b83 Mon Sep 17 00:00:00 2001
From: "sweep-nightly[bot]"
 <131841235+sweep-nightly[bot]@users.noreply.github.com>
Date: Tue, 24 Oct 2023 04:16:01 +0000
Subject: [PATCH 2/3] Sandbox run src/cnn.py

---
 src/cnn.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/src/cnn.py b/src/cnn.py
index da739a0..2494387 100644
--- a/src/cnn.py
+++ b/src/cnn.py
@@ -1,13 +1,12 @@
-import torch
 import torch.nn as nn
-import torch.optim as optim
+
 
 class CNN(nn.Module):
     def __init__(self):
         super(CNN, self).__init__()
         self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
         self.conv2 = nn.Conv2d(32, 64, kernel_size=5)
-        self.fc1 = nn.Linear(4*4*64, 1024)
+        self.fc1 = nn.Linear(4 * 4 * 64, 1024)
         self.fc2 = nn.Linear(1024, 10)
 
     def forward(self, x):
@@ -15,13 +14,14 @@ def forward(self, x):
         x = nn.functional.max_pool2d(x, 2)
         x = nn.functional.relu(self.conv2(x))
         x = nn.functional.max_pool2d(x, 2)
-        x = x.view(-1, 4*4*64)
+        x = x.view(-1, 4 * 4 * 64)
         x = nn.functional.relu(self.fc1(x))
         x = self.fc2(x)
         return nn.functional.log_softmax(x, dim=1)
 
+
 def train_model(model, dataloader, criterion, optimizer, epochs=3):
-    for epoch in range(epochs):
+    for _epoch in range(epochs):
         for images, labels in dataloader:
             optimizer.zero_grad()
             output = model(images)

From 9357842ed4cc1c3d112a3d5dedab063f92c0b1fa Mon Sep 17 00:00:00 2001
From: "sweep-nightly[bot]"
 <131841235+sweep-nightly[bot]@users.noreply.github.com>
Date: Tue, 24 Oct 2023 04:17:08 +0000
Subject: [PATCH 3/3] feat: Updated src/main.py

---
 src/main.py | 13 ++++---------
 1 file changed, 4 insertions(+), 9 deletions(-)

diff --git a/src/main.py b/src/main.py
index 243a31e..55b663f 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, train_model
 
 # Step 1: Load MNIST Data and Preprocess
 transform = transforms.Compose([
@@ -31,18 +32,12 @@ 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()
+criterion = nn.CrossEntropyLoss()
 
 # Training loop
 epochs = 3
-for epoch in range(epochs):
-    for images, labels in trainloader:
-        optimizer.zero_grad()
-        output = model(images)
-        loss = criterion(output, labels)
-        loss.backward()
-        optimizer.step()
+model = train_model(model, trainloader, criterion, optimizer, epochs)
 
 torch.save(model.state_dict(), "mnist_model.pth")
\ No newline at end of file