- tanhLayer: https://www.mathworks.com/help/releases/R2024b/deeplearning/ref/nnet.cnn.layer.tanhlayer.html
- functionLayer: https://www.mathworks.com/help/deeplearning/ref/nnet.cnn.layer.functionlayer.html
- Deep Learning Functions: https://www.mathworks.com/help/deeplearning/referencelist.html?type=function&listtype=cat&category=index&blocktype=all&capability=&s_tid=CRUX_lftnav
- Discussion on available activation functions: https://www.mathworks.com/matlabcentral/answers/1662520-matlab-activation-function-list
could anyone help me how I can different different activation function for training the model
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As matlab provides reLu layer for performing reLu activation function please help me to know how to perform sine, cosine and tanh activation function.
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Answers (1)
Soumya
on 3 Jun 2025
Edited: Soumya
on 3 Jun 2025
The ‘functionLayer’ from MATLAB's Deep Learning Toolbox enables the implementation of activation functions such as ‘sine’ or cosine’. This function enables the definition of custom activation functions by passing a function handle that operates elementwise on the input data.
For example, a ‘sine’ activation function can be defined as follows using ‘functionLayer’:
sinActivation = functionLayer(@(X) sin(X), 'Name', 'sineActivation');
For ‘tanh’ MATLAB provides an inbuilt ‘tanhLayer’ function which can be directly used into the neural network.
The following resources provide more information on how activation functions can be implemented MATLAB:
I hope this helps!
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