Add CNN class and integrate it into main.py and api.py (✓ Sandbox Passed)

src/cnn.py

@@ -0,0 +1,48 @@
+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.pool = nn.MaxPool2d(2, 2)
+ self.fc1 = nn.Linear(64 * 4 * 4, 128)
+ self.fc2 = nn.Linear(128, 10)
+
+ def forward(self, x):
+ x = self.pool(nn.functional.relu(self.conv1(x)))
+ x = self.pool(nn.functional.relu(self.conv2(x)))
+ x = x.view(-1, 64 * 4 * 4)
+ x = nn.functional.relu(self.fc1(x))
+ x = self.fc2(x)
+ return nn.functional.log_softmax(x, dim=1)
+
+ def train_cnn(self, trainloader, epochs=3):
+ optimizer = optim.SGD(self.parameters(), lr=0.01)
+ criterion = nn.NLLLoss()
+
+ for epoch in range(epochs):
+ for images, labels in trainloader:
+ optimizer.zero_grad()
+ output = self(images)
+ loss = criterion(output, labels)
+ loss.backward()
+ optimizer.step()
+
+ torch.save(self.state_dict(), "mnist_cnn_model.pth")
+
+
+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)
+
+cnn = CNN()
+cnn.train_cnn(trainloader)