| 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 |
