Testing of CNN - (Convolutional Neural Network in PyTorch)

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Syntax

model.eval()
with torch.no_grad():
    for inputs, labels in testloader:
        inputs, labels = inputs.to(device), labels.to(device)
        outputs = model.forward(inputs)
        predicted = torch.max(outputs.data, 1)[1]

Example

model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for inputs, labels in testloader:
        inputs, labels = inputs.to(device), labels.to(device)
        outputs = model.forward(inputs)
        predicted = torch.max(outputs.data, 1)[1]
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

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

Output

Accuracy of the network on the test images: 90%

Explanation

In order to test a Convolutional Neural Network (CNN) in PyTorch, the model.eval() method needs to be called to set the model in evaluation mode. After that, a with torch.no_grad() block is used to turn off the gradient calculations, which saves memory and speeds up computations.

Next, a loop is run over the test data, which feeds the inputs to the model and produces the output. The predicted results can be obtained by applying the torch.max() method on the output tensor, which returns the predicted index of the output tensor.

Lastly, accuracy is calculated by comparing the predicted results with the actual labels and counting the correct predictions.

Use

The testing of a CNN is crucial before deploying the model in production. It helps to ensure that the model is performing well and is giving accurate results. By testing the model, you can identify any errors in the model and improve its performance.

Important Points

• The model needs to be set in evaluation mode using the model.eval() method before testing.
• The torch.no_grad() block is used to turn off the gradients for memory optimization.
• Accuracy can be calculated by comparing the predicted results with the actual labels.

Summary

In summary, the testing of a CNN in PyTorch requires setting the model in evaluation mode and turning off gradients using torch.no_grad(). Accuracy can be calculated by comparing the predicted results with the actual labels. Testing is an essential step in the process of developing a powerful and accurate CNN model.