fully connected layer forward pass only in matlab

To perform a forward pass in a fully connected layer of a neural network, you can use matrix multiplication. Here's how to implement it in MATLAB:

Assuming you have the following inputs:

• x: a matrix of input features of size batch_size x input_size, where batch_size is the number of input examples and input_size is the number of input features per example.
• weights: a matrix of weights of size input_size x output_size, where output_size is the number of neurons in the fully connected layer.
• bias: a vector of biases of size 1 x output_size.

You can perform the forward pass as follows:

main.m
function out = fully_connected_forward(x, weights, bias)
% x: input features
% weights: weight matrix
% bias: bias vector

% perform matrix multiplication
out = x * weights;

% add bias to each row
out = out + bias;

% apply activation function if needed (for example, a ReLU)
out = max(0, out);

end
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This function takes in the input features x, weight matrix weights, and bias vector bias. It first performs matrix multiplication between x and weights. Then it adds the bias vector to each row of the resulting matrix. Finally, it applies an activation function (in this case, a ReLU) if needed. The output is returned as out.