PyTorch LeNet Testing for CIFAR-10

Testing a LeNet model for the CIFAR-10 dataset in PyTorch involves evaluating the trained model on the test set of CIFAR-10 images. This guide covers the syntax, example, output, explanation, use cases, important points, and a summary of testing a LeNet model for CIFAR-10 in PyTorch.

Syntax

import torch
import torch.nn as nn
from torch.utils.data import DataLoader

# Assuming 'LeNet' is the trained LeNet model and 'test_loader' is the DataLoader for test data

# Set the model to evaluation mode
LeNet.eval()

with torch.no_grad():
    for inputs, targets in test_loader:
        outputs = LeNet(inputs)
        # Perform evaluation and analysis

Example

import torch
import torch.nn as nn
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR10
from lenet import LeNet  # Assuming you have a LeNet model implementation

# Load the trained LeNet model (assuming 'lenet.pth' exists)
lenet_model = LeNet()
lenet_model.load_state_dict(torch.load('lenet.pth'))

# Set the model to evaluation mode
lenet_model.eval()

# Define data transformations
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# Create the CIFAR-10 test dataset
test_dataset = CIFAR10(root='./data', train=False, download=True, transform=transform)

# Create a DataLoader for the test dataset
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

# Evaluate the model on the test data
correct_predictions = 0
total_samples = 0

with torch.no_grad():
    for inputs, targets in test_loader:
        outputs = lenet_model(inputs)
        _, predicted = torch.max(outputs, 1)
        total_samples += targets.size(0)
        correct_predictions += (predicted == targets).sum().item()

accuracy = correct_predictions / total_samples
print(f'Test Accuracy: {accuracy * 100:.2f}%')

Output

The output of the example would be the test accuracy of the LeNet model on the CIFAR-10 test set.

Explanation

• The lenet_model.eval() sets the LeNet model to evaluation mode, disabling features like dropout.
• The loop over the test data calculates model predictions (outputs) and compares them with ground truth labels to compute accuracy.

Use

• Testing a LeNet model on CIFAR-10 allows assessing its performance on unseen images.
• It helps in understanding how well the model has generalized to the test set.

Important Points

• Ensure that the model is loaded with the correct state dictionary from the trained LeNet model.
• CIFAR-10 test data should be preprocessed similarly to the training data.

Summary

Testing a LeNet model for CIFAR-10 in PyTorch involves setting the model to evaluation mode and evaluating its performance on the test set. By analyzing the test accuracy, you can gain insights into the model's ability to generalize to unseen images and make informed decisions about its deployment or further improvement.