DBN Implementation Example: Building a Two-Hidden-Layer Neural Network

In this article, we explore the construction of a neural network with two hidden layers. Our network architecture will process 500-dimensional input data and generate 10-dimensional output predictions. The two hidden layers will contain 50 and 20 nodes respectively, implemented using fully connected (dense) layers with appropriate activation functions. The final output layer will utilize the softmax function to compress outputs into probability distributions, enabling probabilistic interpretation of results. Notably, we will train this neural network to enhance its accuracy and performance through iterative optimization. The implementation will involve selecting appropriate loss functions, such as categorical cross-entropy for multi-class classification tasks, to minimize errors during training. We'll demonstrate how to implement backpropagation using frameworks like TensorFlow or PyTorch, including gradient calculation and weight updates. Furthermore, we'll examine various optimization algorithms including Stochastic Gradient Descent (SGD), Adam, and RMSprop, discussing their implementation parameters like learning rates and momentum. The code examples will show how to monitor training progress through metrics like loss convergence and accuracy improvement over epochs.