fully connected layer forward pass weight matrix shape 5x5, bias vector shape 1x25, input shape 25x1, batch size=2 in matlab in matlab in matlab

Assuming that the number of neurons in the current layer is 25, and the number of neurons in the previous layer is 5, the forward pass of a fully connected layer with the given shapes can be implemented in MATLAB as follows:

main.m
% Initialize weights and biases
weights = rand(25, 5); % Weight matrix shape is 25x5
biases = rand(25, 1); % Bias vector shape is 1x25

% Initialize input and perform forward pass
batch_size = 2;
input = rand(5, batch_size); % Input shape is 5x2
output = weights * input + biases; % Output shape will be 25x2
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In this implementation, the weights are randomly initialized with values of shape (25, 5), the biases are randomly initialized with values of shape (25, 1), and the input is randomly initialized with values of shape (5, batch_size).

The forward pass calculation is performed using matrix multiplication of the weights with the input, followed by addition of the biases. The output will have a shape of (25, batch_size), where batch_size is set to 2 in this example.