LeNet is a famous Convolutional Neural Network (CNN) architecture introduced in the late 1990s for recognizing hand-written digits. It comprises multiple Convolutional and Pooling layers followed by fully connected layers. This architecture is often used as a starting point for other image classification tasks.

You can learn more about LeNet here.

LeNet Classifying Handwritten Digits 1/2

Constructing the LeNet architecture link copied

We will construct the LeNet architecture with the help of the Pug Keras layer component.

Next, plug the LeNet architecture into the pug SL component and hit the run button to train the network using the MNIST data set. Finally, connect the trained agent to the prediction component and test the accuracy using the testing dataset from the MNIST component. Training LeNet architecture may take more time due to its deeper network structure.

LeNet Classifying Handwritten Digits 2/2

Conclusion link copied

You now learned how to use the plug-in PUG for a LeNet classification problem in Grasshopper. Here you can find an overview of the script.

Final exercise file

Here you find the final GH script of the tutorial.