[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.
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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