Deep learning: Setting the middle-level parameters of the MATLAB deep learning toolbox

Deep learning: Setting the middle-level parameters of the MATLAB deep learning toolbox

Deep learning plays an important role in the fields of computer science and artificial intelligence, and it has achieved remarkable results in many fields. MATLAB provides a powerful toolbox, the Deep Learning Toolbox, which enables researchers and engineers using deep learning to easily build and train deep neural networks.

When using MATLAB Deep Learning Toolbox to build a neural network, we can customize the network architecture by setting the parameters of the layer. Each layer has a specific set of parameters that control the layer's behavior and functionality. In this article, we will delve into how to set the parameters of the middle layer of MATLAB Deep Learning Toolbox.

First, let us consider a common deep learning layer—Fully Connected Layer. Fully connected layers play a key role in neural networks, which fully connect the input data with each neuron and calculate it through weights and biases. In MATLAB Deep Learning Toolbox, we can use the fullyConnectedLayer function to create a fully connected layer and set its parameters.

Here is an example code to create a fully connected layer:

numClasses = 10;
fcLayer = fullyConnectedLayer(numClasses)