com.intel.analytics.bigdl.nn.MSECriterion.scala Maven / Gradle / Ivy
/*
* Copyright 2016 The BigDL Authors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.intel.analytics.bigdl.nn
import com.intel.analytics.bigdl.nn.abstractnn.TensorCriterion
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.tensor.Tensor
import scala.reflect.ClassTag
/**
* The mean squared error criterion
* e.g. input: a, target: b, total elements: n
* loss(a, b) = 1/n \sum |a_i - b_i|^2
* sizeAverage is true by default to divide the sum of squared error by n
*/
@SerialVersionUID(- 7078521754128606735L)
class MSECriterion[@specialized(Float, Double) T: ClassTag]
(implicit ev: TensorNumeric[T]) extends TensorCriterion[T] {
var sizeAverage = true
override def updateOutput(input: Tensor[T], target: Tensor[T]): T = {
gradInput.resizeAs(input).copy(input)
gradInput.sub(target)
output = gradInput.dot(gradInput)
if (sizeAverage) output = ev.divide(output, ev.fromType[Int](input.nElement))
output
}
override def updateGradInput(input: Tensor[T], target: Tensor[T]): Tensor[T] = {
var norm = ev.fromType[Int](2)
if (sizeAverage) {
norm = ev.fromType[Double](2.0 / input.nElement)
}
gradInput.mul(norm)
gradInput
}
}
object MSECriterion {
def apply[@specialized(Float, Double) T: ClassTag]()
(implicit ev: TensorNumeric[T]) : MSECriterion[T] = {
new MSECriterion[T]()
}
}