Convolution neural network equations
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Ali Najem
le 14 Juil 2020
Commenté : Mahesh Taparia
le 20 Juil 2020
Hello, all
I hope you doing well.
I know in CNN training data such as (mnist, CIFAR_10 or CIFAR_100 or even ImageNet dataset) there are
feedforward process and then compute the error hence, using backpropagation to minimize that error by updating whole weights in all layers,
however, I need to know how it implements in code for both feedforward and backpropagation progress?
Is there any code to interpret these equations especially equations of updating of all weights?
Thank you for helping..
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Mahesh Taparia
le 18 Juil 2020
Hi
As you know about the feedforward and backpropagation process, for feedforward network we need to create the network architecture. You can refer to this documentation for creation of a sample network. For backpropagation, target is to reduce the loss by finding the optimum weights. In this case the weights are getting updated by the equation:
newWeights=previousWeights-learningRate*derivative of loss wrt weights
In documentation, the direct inbuilt functions are used for backpropagation and weight update. However, you can create the custom functions in MATLAB with the help of the respective equations but inbuilt functions are more optimized.
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Mahesh Taparia
le 20 Juil 2020
Hi
Refer this link for CIFAR data classification. The network architecture is different. Replace the network by inception v3 using 'inceptionv3' function. Refer its documentation here. In this network, the number of classes are 1000, replace the layers with 10 nclasses. For this, use 'replaceLayers' function to replace the last layer with number of classes as 10. Refer this documentation for more information. Hope it will help!
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