使用 factorMatrix 设置线性回归 - DL4j

Question

I have a factorMatrix of type INDArray which is xIn

It contains of 5 columns [A, B, C, D, E]

and another INDArray with 1 column [y] which is yIn

For the ones who are familiar with MatLab I could use now:

modelTrain = fitlm([XInSample yInSample] , 'linear') and then 

retPredictionRegress = predict(modelTrain , XInSample);

I have issues to setup DL4J with xIn and yInd even when I found something that claims to be helpful - but at least not to me.

Could anyone please bring me on track?

Answer 1

Linear regression in dl4j is just a neural network with a specific loss function and output later type. This follows how you would do linear regression in any other deep learning framework. This concept/idea isn't really specific to dl4j itself. It's following conventions you'd find in any instantation of the regression problem with a more general purpose framework.

I see you commented here as well: DL4J linear regression

Replying to your "at least not helpful to me", do you mind clarifying what you have issue with in the question? That would help a bit.

Answering your question, you'd just declare a neural network with your 5 inputs and declare how many ever inputs you have. You're allowed to declare more than 1 output if you find the objective to be suitable. In your case it's 1 output.

An example of this would be:

 //Create the network
        int numInput = 5;
        int numOutputs = 1;
        int nHidden = 10;
        MultiLayerNetwork net = new MultiLayerNetwork(new NeuralNetConfiguration.Builder()
                .seed(seed)
                .weightInit(WeightInit.XAVIER)
                .updater(new Nesterovs(learningRate, 0.9))
                .list()
                .layer(0, new DenseLayer.Builder().nIn(numInput).nOut(nHidden)
                        .activation(Activation.TANH) //Change this to RELU and you will see the net learns very well very quickly
                        .build())
                .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.MSE)
                        .activation(Activation.IDENTITY)
                        .nIn(nHidden).nOut(numOutputs).build())
                .build()
        );

The above snippet comes from the dl4j examples.

In this case, you would be working on tuning the neural network with the appropriate output. I would suggest doing some broader reading on the topic if you want to understand the relationship between neural nets and regression.