singa::MSE Class Reference

MSE is for mean squared error or squared euclidean distance. More...

#include <loss.h>

Inheritance diagram for singa::MSE:

Collaboration diagram for singa::MSE:

Public Member Functions
Tensor	Forward (int flag, const Tensor &prediction, const Tensor &target) override
	Compute the loss values for each sample/instance given the prediction and the target, which is 0.5/||prediction-target||^2 Users can call Average(const Tensor&) to get the average loss value over all samples in the batch. More...
Tensor	Backward () override
	Compute the gradients of the loss values w.r.t. More...
Public Member Functions inherited from singa::Loss
void	Setup (const string &conf)
virtual void	ToDevice (std::shared_ptr< Device > device)
virtual void	Setup (const LossConf &conf)
	Set meta fields from user configurations.
float	Evaluate (int flag, const Tensor &prediction, const Tensor &target)
	Average loss values for all samples in the mini-batch It calls Forward() internally. More...

Detailed Description

MSE is for mean squared error or squared euclidean distance.

Member Function Documentation

Backward()

Tensor singa::MSE::Backward ( )

overridevirtual

Compute the gradients of the loss values w.r.t.

the prediction, which is (prediction-target)/batchsize

Implements singa::Loss.

Forward()

Tensor singa::MSE::Forward ( int  flag, const Tensor &  prediction, const Tensor &  target  )

overridevirtual

Compute the loss values for each sample/instance given the prediction and the target, which is 0.5/||prediction-target||^2 Users can call Average(const Tensor&) to get the average loss value over all samples in the batch.

Implements singa::Loss.

---

The documentation for this class was generated from the following file:

• /home/moaz/incubator-singa/include/singa/model/loss.h