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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 |