How to use BigDL to calculate MSE values

[gdg1212]

val model = Sequential[Float]()
  .add(Dense(hiddenSize, inputShape = Shape(inputSize)))
  .add(Activation("relu"))
  .add(Dense(1))
val optimizer = Optimizer(model = model,
  sampleRDD = trainingTensor.map(_._2),
  criterion = MSECriterion[Float](),
  batchSize = 100000)


 optimizer
   .setOptimMethod(new Adam(0.001))
   .setEndWhen(Trigger.maxEpoch(100))
    .optimize()

I don't know how to calculate the MSE values of the training and testing sets through the model

[qiuxin2012]

You can add a validation set to evaluate the model during the training, like:
https://github.com/intel-analytics/BigDL-2.x/blob/b4cde76e58c3b97c69424f0c8bbc4c6d08879195/scala/dllib/src/main/scala/com/intel/analytics/bigdl/dllib/models/lenet/Train.scala#L99

And you can use validator to evalute your model after training, like:
https://github.com/intel-analytics/BigDL-2.x/blob/b4cde76e58c3b97c69424f0c8bbc4c6d08879195/scala/dllib/src/test/scala/com/intel/analytics/bigdl/dllib/optim/ValidatorSpec.scala#L57-L60