In this tutorial, we built LeNet-5, an early convolutional neural network (CNN), from scratch using PyTorch. This guide takes you through the complete process, starting with an overview of LeNet-5’s architecture, and continuing into data preparation and model implementation.

First, we explored the architecture of LeNet-5, designed for recognizing handwritten digits. This model has a specific structure with five layers, including two convolutional layers followed by three fully connected layers. The input to the model is a grayscale image sized 32×32 pixels.

Next, we utilized the MNIST dataset, a standard benchmark in the field of machine learning for handwritten digit recognition. We used the Torchvision library to conveniently load and preprocess the dataset, ensuring that it was formatted correctly for our model.

After preparing the data, we implemented the LeNet-5 architecture in PyTorch. This involved defining the necessary layers and specifying essential hyperparameters, such as learning rate, batch size, optimizer, and loss function. Each of these components plays a crucial role in how well the model can learn from the data.

We proceeded to train the model on the MNIST dataset. During training, the model’s accuracy improved steadily, confirming that LeNet-5 is an effective architecture for this task. The final evaluation showed that our model achieved around 98.8% accuracy on the test dataset, showcasing its strong performance.

Through this project, we not only learned how to build and train the LeNet-5 model but also gained a deeper understanding of how CNNs operate, how to handle data, and the importance of model evaluation. This foundational knowledge will prove valuable as you advance to more complex architectures and challenging image recognition tasks in the future.

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