MATLAB: NN training process

computer visiondeep learningneural networks

why mini batch accuracy (value) graph of training is goes down during training process?

Best Answer

Hi,

Mini batch accuracy should likely to increase with no. of epochs.

But for your case, there can be of multiple reasons behind this:

Mini-batch size
Learning rate
cost function.
Network Architechture
Quality of data and lot more.

It would be better if you provide more information about the NN model you are using.

Moreover, You can also refer to this link : https://www.mathworks.com/matlabcentral/answers/484326-mini-batch-loss-and-accuracy-trends?s_tid=answers_rc1-2_p2_MLT

If your case is similar like that.

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MATLAB: Dropout Layer Before Fully connected Layer

Hi Abdussalam,

Yes, you can use Dropout layer before the fully connected layer, Dropout is just a regularization technique for preventing overfitting in the network, it can be applied anywhere regardless of FC or Conv but again it is generally recommended to use it after FC layer because they are the ones with the greater number of parameter and thus they are likely to excessively co-adapting themselves causing Overfitting.

However, it’s a stochastic regularization technique, you can really place it everywhere. Usually placed on the layer with a greater number of parameters, but no denies you from applying anywhere.

MATLAB: Deep learning_alexnet

You have specified the image as single channel in your code. just change it to 3 channels (RGB).

 imageInputLayer([imageSize 1],....

 imageInputLayer([imageSize 3],....

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