Add new CNN class for MNIST handling and update model usage in API

src/api.py

@@ -1,22 +1,23 @@
-from fastapi import FastAPI, UploadFile, File
-from PIL import Image
 import torch
+from fastapi import FastAPI, File, UploadFile
+from PIL import Image
 from torchvision import transforms
+
 from main import Net  # Importing Net class from main.py
 
 # Load the model
 model = Net()
-model.load_state_dict(torch.load("mnist_model.pth"))
+model.load_state_dict(torch.load("mnist_cnn_model.pth"))
 model.eval()
 
 # Transform used for preprocessing the image
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize((0.5,), (0.5,))
-])
+transform = transforms.Compose(
+    [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+)
 
 app = FastAPI()
 
+
 @app.post("/predict/")
 async def predict(file: UploadFile = File(...)):
     image = Image.open(file.file).convert("L")

src/cnn.py

@@ -0,0 +1,60 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from torchvision import datasets, transforms
+
+
+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 load_data():
+    transform = transforms.Compose(
+        [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+    )
+
+    trainset = datasets.MNIST(".", download=True, train=True, transform=transform)
+    trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
+    return trainloader
+
+
+def train(model, trainloader):
+    optimizer = optim.SGD(model.parameters(), lr=0.01)
+    criterion = nn.NLLLoss()
+
+    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()
+
+    torch.save(model.state_dict(), "mnist_cnn_model.pth")
+
+
+def main():
+    model = CNN()
+    trainloader = load_data()
+    train(model, trainloader)
+
+
+if __name__ == "__main__":
+    main()

src/main.py

@@ -1,48 +1,34 @@
-from PIL import Image
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from torchvision import datasets, transforms
 from torch.utils.data import DataLoader
-import numpy as np
+from torchvision import datasets, transforms
+
+from cnn import CNN
+from train import train
+
+    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()
+    return model
+
 
 # Step 1: Load MNIST Data and Preprocess
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize((0.5,), (0.5,))
-])
+transform = transforms.Compose(
+    [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+)
 
-trainset = datasets.MNIST('.', download=True, train=True, transform=transform)
+trainset = datasets.MNIST(".", download=True, train=True, transform=transform)
 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)
+model = CNN()
 
 # Step 3: Train the Model
-model = Net()
-optimizer = optim.SGD(model.parameters(), lr=0.01)
-criterion = nn.NLLLoss()
-
-# 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()
-
-torch.save(model.state_dict(), "mnist_model.pth")
+train(model, trainloader)
+torch.save(model.state_dict(), "mnist_cnn_model.pth")

src/train.py

@@ -0,0 +1,19 @@
+import torch.nn as nn
+import torch.optim as optim
+
+
+def train(model, trainloader):
+    optimizer = optim.SGD(model.parameters(), lr=0.01)
+    criterion = nn.NLLLoss()
+
+    epochs = 3
+    for epoch in range(epochs):
+        running_loss = 0
+        for images, labels in trainloader:
+            optimizer.zero_grad()
+            output = model(images)
+            loss = criterion(output, labels)
+            loss.backward()
+            optimizer.step()
+            running_loss += loss.item()
+        print(f"Training loss at epoch {epoch+1}: {running_loss/len(trainloader)}")