Create lennon.py

lennon.py

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+import torch.nn as nn
+
+class LeNNon(nn.Module):
+    def __init__(self):
+        """ Define a CNN architecture used for image classification.
+            This class defines the LeNNon architecture as a PyTorch module
+        """
+        super(LeNNon, self).__init__()
+        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
+        self.fc1 = nn.Linear(32 * 25 * 25, 128)
+        self.fc2 = nn.Linear(128, 10)
+
+    def forward(self, x):
+        """
+        Perform a forward pass through the LeNNon architecture.
+
+        This method applies the convolutional layers, max pooling layers,
+        and fully connected layers to the input tensor x.
+
+        Parameters:
+        -----------
+        x (torch.Tensor): The input tensor.
+
+        Returns:
+        --------
+        torch.Tensor: The output tensor.
+        """
+        x = self.pool(torch.relu(self.conv1(x)))
+        x = self.pool(torch.relu(self.conv2(x)))
+        x = x.view(-1, 32 * 25 * 25)
+        x = torch.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x