nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/loss/LossFunctions.java
public static LossInfo mse(String outputName, SDVariable predictions, SDVariable label, SDVariable weights,
                               Reduction reduction, int... dimensions){
        LossInfo.Builder b = validate("mse", predictions, label, reduction);
        SameDiff sd = predictions.getSameDiff();

        if(weights == null){
            weights = sd.one("mse_loss_weights", SCALAR);
        }

        SDVariable diff = predictions.sub(label);
        String name = (reduction == Reduction.NONE ? outputName : null);
        SDVariable preReduceLoss = sd.square(diff).mul(name, weights);

        return doReduce(sd, outputName, true, b, reduction, preReduceLoss, label, weights, dimensions);
    }
public static LossInfo mse(String outputName, SDVariable predictions, SDVariable label, SDVariable weights,
                               Reduction reduction, int... dimensions){
        LossInfo.Builder b = validate("mse", predictions, label, reduction);
        SameDiff sd = predictions.getSameDiff();

        if(weights == null){
            weights = sd.one("mse_loss_weights", SCALAR);
        }

        SDVariable diff = predictions.sub(label);
        String name = (reduction == Reduction.NONE ? outputName : null);
        SDVariable preReduceLoss = sd.square(diff).mul(name, weights);

        return doReduce(sd, outputName, true, b, reduction, preReduceLoss, label, weights, dimensions);
    }
Mean squared error: L = mean( (predicted - label)^2)

@param outputName  Name of the output SDVariable
@param predictions Predictions variable
@param label       Label variable
@param weights     Weights array. May be null, or any broadcastable shape (with predictions/label arrays).
Note that this is also used for masking (weight of 0 = 'masked out')
@param reduction   Type of reduction to perform for the loss function
@param dimensions  Dimension(s) to apply the loss function on
@return LossInfo - bean with variables etc for the loss function