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Customizing the Neural Network model and details in nnstart

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I am not so experienced in Matlab and it's tools. I am trying to learn essential apps with details.
I know nnstart (formerly nntool) and how to create a neural network, then train, and then get the results, following the instructions.
But I could not see the details screens (or code parts) that I can change/update the details of my neural network. The application screen implements everything and I cannot intervene in the details. Neither from app "Neural Networks Fitting" window, nor codes (I already cannot see each code).
Just for example, I want to build/update/change the weights, activation functions (selecting one or entering my own activation functions), neuron counts, input alingment etc. , also want to be able to break during the iterations and code breaks in some parts. Additionally draw a neural network visual scheme as well, if possible. Then I will be able to see the details during the implementation.
Is there a way in Neural network Fitting (at nnstart command) that I can get the control ?
Thank you
  1 Comment
musa
musa on 12 May 2024
I have found an another tool named Deep Network Designer. Looks like this tool allows to manage some parts of your new neural network. Not sure I can do everything or not.

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Answers (1)

Pratyush Swain
Pratyush Swain on 24 May 2024
Hi musa,
nnstart and its associated GUI tools (like the Neural Networks Fitting Tool) offer a straightforward path to creating and training neural networks, the depth of customization and interaction you can have with your model's internals is limited, such as adjusting weights, activation functions.
The Deep Network Designer allows for the drag-and-drop construction of neural network layers, enabling you to visually build and customize your network architecture. This includes selecting different types of layers, adjusting their parameters.
For more information, you can refer to following resources:
While it focuses on structural design (layer types, connections, etc.), for detailed adjustments like custom weights or activation functions, you may need to export the designed network and modify these properties programmatically.
Here's how you can perform customization of activation functions and manual initialization of weights of a simple feedforward Network in MATLAB:
% Create a feedforward network with 10 neurons in the hidden layer
net = feedforwardnet(10);
% Use the logistic sigmoid activation function for the first hidden layer
net.layers{1}.transferFcn = 'logsig';
% Use softmax activation function for the output layer
net.layers{2}.transferFcn = 'softmax';
% Randomly initialize input weights
net.IW{1,1} = rand(size(net.IW{1,1}));
% Set all biases in the first hidden layer to 1
net.b{1} = ones(size(net.b{1}));
Hope this helps.

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