From 90bbe88f7d4709422a3e1e48d9b987498777632b Mon Sep 17 00:00:00 2001
From: "sweep-nightly[bot]"
 <131841235+sweep-nightly[bot]@users.noreply.github.com>
Date: Fri, 13 Oct 2023 23:09:52 +0000
Subject: [PATCH] feat: Updated src/main.py

---
 src/main.py | 22 +++++++++-------------
 1 file changed, 9 insertions(+), 13 deletions(-)

diff --git a/src/main.py b/src/main.py
index 243a31e..f361f2e 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  # Import the CNN class
 
 # Step 1: Load MNIST Data and Preprocess
 transform = transforms.Compose([
@@ -16,19 +17,14 @@
 trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
 
 # Step 2: Define the PyTorch Model
-class Net(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.fc1 = nn.Linear(28 * 28, 128)
-        self.fc2 = nn.Linear(128, 64)
-        self.fc3 = nn.Linear(64, 10)
-    
-    def forward(self, x):
-        x = x.view(-1, 28 * 28)
-        x = nn.functional.relu(self.fc1(x))
-        x = nn.functional.relu(self.fc2(x))
-        x = self.fc3(x)
-        return nn.functional.log_softmax(x, dim=1)
+# Create an instance of the CNN class
+cnn = CNN()
+
+# Train the CNN
+cnn.train(trainloader, lr=0.001, epochs=10)
+
+# Save the trained model
+cnn.save_model("mnist_model.pth")
 
 # Step 3: Train the Model
 model = Net()