Mirko_hob asked . 2021-06-30

Crossentropy loss function - What is a good performance goal?

Good Afternoon,

Looking around ANSWER and exploring GOOGLE GROUPS i found this method by Dr. Greg Heath to define a valid training goal for the MSE performance function:

[I,N]=size(x);
[O,N]=size(t);
MSE00a=mean(var(t,0,2));                
Ntrn=floor(0.7*N);                     
Hub=floor((Ntrn-O)/(I+1+O));
MSEgoal=0.01*(Ndof/Ntrneq)*MSE00a;

And i was wondering if there is a similar method to set a Crossentropy reference goal for neural net performance, since i want to experiment different type of loss functions in order to get the best results.

crossentropy , neural networks , performance goal , deep learning , matlab

Expert Answer

Kshitij Singh answered . 2024-12-21 01:32:42

These equations are not necessarily precise.

For example:

data = design + test

design = training + validation

In particular:

Test subset data should not be used to estimate design parameters.

However, since we typically let the training function randomly perform the trn/val/tst division, the separate train/val/tst subsets are not available before training.

That is why I typically design 10 nets for every trial value for the number of hidden nodes.

The cross-entropy operation computes the cross-entropy loss between network predictions and target values for single-label and multi-label classification tasks.

The crossentropy function computes the cross-entropy loss between predictions and targets represented as dlarray data. Using dlarray objects makes working with high dimensional data easier by allowing you to label the dimensions. For example, you can label which dimensions correspond to spatial, time, channel, and batch dimensions using the "S", "T", "C", and "B" labels, respectively. For unspecified and other dimensions, use the "U" label. For dlarray object functions that operate over particular dimensions, you can specify the dimension labels by formatting the dlarray object directly, or by using the DataFormat option.

for example

Create an array of prediction scores for 12 observations over 10 classes.

numClasses = 10;
numObservations = 12;

Y = rand(numClasses,numObservations);
dlY = dlarray(Y,'CB');
dlY = softmax(dlY);

View the size and format of the prediction scores.

size(dlY)

ans = 1×2

    10    12

dims(dlY)

ans = 
'CB'

Create an array of targets encoded as one-hot vectors.

labels = randi(numClasses,[1 numObservations]);
targets = onehotencode(labels,1,'ClassNames',1:numClasses);

View the size of the targets.

size(targets)

ans = 1×2

    10    12

Compute the cross-entropy loss between the predictions and the targets.

loss = crossentropy(dlY,targets)

loss = 
  1x1 dlarray

    2.3343

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