How to use a self-made loss function for a simple Neural Network in Matlab?

21 次查看（过去 30 天）
Wahab riaz
Wahab riaz 2021-12-29
回答： pathakunta 2024-1-26
I have been using
net = feedforwardnet(10) %or
net = fitnet(10)
for my regression problem in Matlab. I am using simple networks with 1 or 2 layers and ReLU activation function (net.transferFcn = 'poslin').
But now, I have to use a self-made custom loss functions instead of 'mse' (mean squared error). Could you please let me know how can I do this.
I have found the following document regarding using custom layers and loss functions:https://www.mathworks.com/help/deeplearning/ug/define-custom-regression-output-layer.html
But this is regarding to complex Neural Networks like CNN. I could not understand how to simplify this for a normal deep neural network.

请先登录，再回答此问题。

回答（2 个）

yanqi liu
yanqi liu 2021-12-30
Ran in:
yes，sir，may be it same on：https://ww2.mathworks.cn/matlabcentral/answers/1618945-how-to-use-a-self-made-loss-function-for-a-simple-neural-net
net=newff([0,1],[5,1],{'tansig','logsig'},'traingd')
Warning: NEWFF used in an obsolete way.
See help for NEWFF to update calls to the new argument list. net = Neural Network name: 'Custom Neural Network' userdata: (your custom info) dimensions: numInputs: 1 numLayers: 2 numOutputs: 1 numInputDelays: 0 numLayerDelays: 0 numFeedbackDelays: 0 numWeightElements: 16 sampleTime: 1 connections: biasConnect: [1; 1] inputConnect: [1; 0] layerConnect: [0 0; 1 0] outputConnect: [0 1] subobjects: input: Equivalent to inputs{1} output: Equivalent to outputs{2} inputs: {1x1 cell array of 1 input} layers: {2x1 cell array of 2 layers} outputs: {1x2 cell array of 1 output} biases: {2x1 cell array of 2 biases} inputWeights: {2x1 cell array of 1 weight} layerWeights: {2x2 cell array of 1 weight} functions: adaptFcn: 'adaptwb' adaptParam: (none) derivFcn: 'defaultderiv' divideFcn: (none) divideParam: (none) divideMode: 'sample' initFcn: 'initlay' performFcn: 'mse' performParam: .regularization, .normalization plotFcns: {'plotperform', 'plottrainstate', 'plotregression'} plotParams: {1x3 cell array of 3 params} trainFcn: 'traingd' trainParam: .showWindow, .showCommandLine, .show, .epochs, .time, .goal, .min_grad, .max_fail, .lr weight and bias values: IW: {2x1 cell} containing 1 input weight matrix LW: {2x2 cell} containing 1 layer weight matrix b: {2x1 cell} containing 2 bias vectors methods: adapt: Learn while in continuous use configure: Configure inputs & outputs gensim: Generate Simulink model init: Initialize weights & biases perform: Calculate performance sim: Evaluate network outputs given inputs train: Train network with examples view: View diagram unconfigure: Unconfigure inputs & outputs
net.performFcn
ans = 'mse'
we can find the default is mse
so，make the self definition function，https://www.mathworks.com/matlabcentral/answers/64319-neural-network-how-to-use-a-custom-performance-function
pathakunta
pathakunta 2024-1-26
I have been using net = feedforwardnet(10) %or net = fitnet(10) for my regression problem in Matlab. I am using simple networks with 1 or 2 layers and ReLU activation function (net.transferFcn = 'poslin'). But now, I have to use a self-made custom loss functions instead of 'mse' (mean squared error). Could you please let me know how can I do this. I have found the following document regarding using custom layers and loss functions:https://www.mathworks.com/help/deeplearning/ug/define-custom-regression-output-layer.html But this is regarding to complex Neural Networks like CNN. I could not understand how to simplify this for a normal deep neural network.

类别

在 Help Center 和 File Exchange 中查找有关 Sequence and Numeric Feature Data Workflows 的更多信息

标签

产品


版本

R2020b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by