implement fully connected layer in matlab

Here's a sample code that demonstrates how to implement a fully connected layer in MATLAB using the Neural Network Toolbox:

main.m
% Define the input data
inputs = rand(10, 1);

% Define the size of the fully connected layer
numNodes = 5;

% Create a fully connected layer with random weights and biases
layer = fullyConnectedLayer(numNodes);

% Evaluate the layer on the input data
outputs = layer(inputs);
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In this example, we first define an input data matrix of size 10x1. We then define a fully connected layer with 5 nodes using the fullyConnectedLayer function. This function initializes the layer with random weights and biases.

To evaluate the layer on the input data, we simply pass the input data matrix as an argument to the layer object. The output is a vector of size 5x1, which represents the activations of the nodes in the fully connected layer for the given input.

This is just a basic implementation of a fully connected layer. For more advanced applications, you may want to modify the layer's parameters, such as the learning rate and regularization strength, or implement multiple consecutive fully connected layers to create a deep neural network.