Neural Network Validation - ( Image Recognition with PyTorch )

Heading h2

Syntax

correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))

Example

correct = 0
total = 0
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))

Output

Accuracy of the network on the 10000 test images: 72 %

Explanation

Validation or testing is a crucial step in the process of building a neural network. The purpose of validation is to test the network's performance on unseen data and ensure that it has not overfit the training data.

In this example, we are using PyTorch to build an image recognition neural network. After training the network, we use a validation set to check its accuracy on unseen data. We calculate the number of correct predictions made by the network and divide it by the total number of test images to get the accuracy of the network.

Use

Validation is an essential step in building any machine learning model, neural networks included. You can use validation to check the accuracy of your model on unseen data and ensure that it is not overfitting on the training data.

Important Points

• Validation is the process of testing a neural network on unseen data
• Validation ensures that the network is not overfitting on the training data
• Validation is an essential step to check the accuracy of a machine learning model

Summary

In conclusion, validation is a crucial step in the process of building a neural network. It allows us to test the network's accuracy on unseen data and ensures that it has not overfit the training data. By using PyTorch to build an image recognition neural network, we can easily validate its accuracy on unseen data.