Implementing a Neural Network

I am trying to create a linear neural network that takes in 11 inputs and gives out 5 outputs.

I've been using the following documentation as reference: https://www.mathworks.com/help/deeplearning/ug/linear-neural-networks.html

Now, x is a 75 by 11 array, of inputs for the training data.

Meanwhile, y is a 5 by 75 array, of true label outputs for the training data.

So far I have tried:

P = x'
T = y
net = linearlayer;
net = configure(net,P,T);
net.IW{11,5}
net.b{1} 

I am getting an error when initializing the weights, what am I doing wrong? Since there is going to be 10 inputs, I assume that there will be 11 weights. Then there should be 1 bias weight.

Overall structure is supposed to be 11 inputs for the input layer, 1 hidden layer, and an output layer of 5 outputs. (Hence, y has 5 rows. Also, hence why x has 11 columns)

I am only trying to set up the linear neural network, not at the point of training it.

Answer 1

[Walter Roberson]

x = rand(75,11);
y = randi(5, 5, 75);
P = x';
T = y;
net = linearlayer;
net = configure(net,P,T);
net.IW

ans = 1×1 cell array
    {5×11 double}

net.IW{1}(5,11)

ans = 0

net.b{1} 

ans = 5×1

0 0 0 0 0

[Tony Stark]

Thank you for your answer, it is really helpful. Although, since there are 11 inputs, shouldn't there be 11 weights? Why is the answer just 0?

[Walter Roberson]

There are 11 scalar weights for each of the 5 outputs, a 5 x 11 array. Asking to look at the 11, 5 configuration is asking to look at the initialization weight for the last step for the last output, a scalar.