jason asked . 2021-07-27

How to change a subset of ANN weights while keep others weights unchanged?

I am using the neural network toolbox 2012a in my project. I have created a feed-forward-net with 2 layers(inputs are not counted as a layer as conventionalized in the users' guide), and I want to update some of the input weights (IW{1,1}) while keep other input weights in IW{1,1} and the first-to-second-layer weights(LW{2,1}) fixed. To be short, I want to change a subset of IW{1,1} while remain all the other weights fixed. Let me refer this as my optimal goal here.

If the optimal goal is impossible, a sub-optimal goal is also acceptable. That is,update the entire IW{1,1} and keep the whole LW{2,1} fixed.

I already figured out how to achieve the sub-optimal goal. My solution is to use the command 'adapt' and set the learning rate to 0 for LW{2,1}. But I do not like this solution since 'adapt' is an over-simplified function lacking parameters and features(eg. min-grad, plotperform, etc.) of other training functions/algorithms(eg. trainlm,traingd,etc.) Therefore it is harder to control the training process and check on the results.

So, first, I want to know if there is a way to achieve the optimal goal rather than the sub-optimal.

Second, if the optimal goal is not possible (besides composing everything from scratch), I wonder if I can achieve the sub-optimal goal by taking advantage of some training functions instead of using 'adpat'. I have already looked through 'trainlm' and 'traingd' but I do not think they are helpful to either of my goals.

I will really appreciate it if anyone can help me with this issue.

neural network , training , update a sub-set o...

Expert Answer

Kshitij Singh answered . 2024-12-21 17:09:29

First, let me clarify my train of thought. I was comparing training continuously using net.trainParam.epochs = 100 with training 10 consecutive times in a loop using net.trainParam.epochs = 10 ( or, say, 100 consecutive times in a loop using net.trainParam.epochs = 1). To eliminate complications, do not train with a validation set. For example, train candidates using net.divideFcn = ''. Then use a holdout validation set to choose the best designs.

There is a way to obtain the same result ( I am pretty sure that I did it yrs ago with the 2004 MATLAB 6.5 version of NEWFF). Given the same initial weights at epoch 0, the results will be the same at epoch 10. However, when the second example starts the 11th epoch, it has to call TRAIN again. When TRAIN starts again, it is not in the same state that it would have been in the 11th epoch of the continuous training example.

The task then is to quantify the state of TRAIN at the close of epoch 10 and to guarantee that it is in that state after it is called at the beginning of epoch 11.

Extending this strategy you can interrupt training at any time and assign your specified weights. However, now I understand that you would like some of those weights to remain fixed throughout further training.

Currently, the only way to do that is to keep assigning that same fixed weight thoughout traiing. Whether the assignments are made every epoch or every few epochs would have to be determined by trial and error.

I have performed 40 experiments using MATLAB's simplefit_dataset. There were 10 random weight initializations of 1-4-1 nets for each of the following 4 scenarios:

1. NEWFIT (calls NEWFF) continuous training with the default net.trainParam.epochs = 1000

2. NEWFIT WHILE-LOOP training with net.trainParam.epochs = 1

3. FITNET (calls FEEDFORWARDNET) continuous.

4. FITNET WHILE-LOOP

The 4 MSE results for each of the 10 random weight initializations were in agreement. However, I have not yet compared final weights.

In order to further understand the problem I may obtain 1-3-1 designs to get a wider scatter of results.

Not satisfied with the answer ?? ASK NOW

Recent Matlab Projects

Classification of Covid and Non-Covid Lungs CT-Scan using Deep Learning with MATLAB

Matlab simulation on Wind Energy system

Frequently Asked Questions

MATLAB offers tools for real-time AI applications, including Simulink for modeling and simulation. It can be used for developing algorithms and control systems for autonomous vehicles, robots, and other real-time AI systems.

MATLAB Online™ provides access to MATLAB® from your web browser. With MATLAB Online, your files are stored on MATLAB Drive™ and are available wherever you go. MATLAB Drive Connector synchronizes your files between your computers and MATLAB Online, providing offline access and eliminating the need to manually upload or download files. You can also run your files from the convenience of your smartphone or tablet by connecting to MathWorks® Cloud through the MATLAB Mobile™ app.

Yes, MATLAB provides tools and frameworks for deep learning, including the Deep Learning Toolbox. You can use MATLAB for tasks like building and training neural networks, image classification, and natural language processing.

MATLAB and Python are both popular choices for AI development. MATLAB is known for its ease of use in mathematical computations and its extensive toolbox for AI and machine learning. Python, on the other hand, has a vast ecosystem of libraries like TensorFlow and PyTorch. The choice depends on your preferences and project requirements.

You can find support, discussion forums, and a community of MATLAB users on the MATLAB website, Matlansolutions forums, and other AI-related online communities. Remember that MATLAB's capabilities in AI and machine learning continue to evolve, so staying updated with the latest features and resources is essential for effective AI development using MATLAB.

Without any hesitation the answer to this question is NO. The service we offer is 100% legal, legitimate and won't make you a cheater. Read and discover exactly what an essay writing service is and how when used correctly, is a valuable teaching aid and no more akin to cheating than a tutor's 'model essay' or the many published essay guides available from your local book shop. You should use the work as a reference and should not hand over the exact copy of it.