Tutorials
This tutorial provides guidelines for using customized loss function in network construction.
R Xgboost Loss Function
Model Training Example
Let's begin with a small regression example. We can build and train a regression model with the following code:
In mathematical optimization and decision theory, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some 'cost' associated with the event. An optimization problem seeks to minimize a loss function. An objective function is either a loss function or its negative (in specific domains. I was trying to creating a loss function below. Where tts is the total sum of squares and x is values 1-100 and t is a given y hat. W0+W1 is supposedly par(0,1) but I'm having issues with getting. As you see in this example, you used categoricalcrossentropy loss function for the multi-class classification problem of determining whether an iris is of type versicolor, virginica or setosa. However, note that if you would have had a binary-class classification problem, you should have made use of the binarycrossentropy loss function.
Besides the
LinearRegressionOutput
, we also provide LogisticRegressionOutput
and MAERegressionOutput
. However, this might not be enough for real-world models. You can provide your own loss function by using mx.symbol.MakeLoss
when constructing the network.How to Use Your Own Loss Function
We still use our previous example, but this time we use
mx.symbol.MakeLoss
to minimize the (pred-label)^2
R Loss Functions
120 free spins real cash. Then we can train the network just as usual.
We should get very similar results because we are actually minimizing the same loss function. However, the result is quite different.
This is because output of
mx.symbol.MakeLoss
is the gradient of loss with respect to the input data. We can get the real prediction as below.![Loss Loss](/uploads/1/3/8/8/138859976/364049595.png)
We have provided many operations on the symbols. An example of
|pred-label|
can be found below.