| import torch.nn as nn | |
| import torch | |
| 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 |