com.intel.analytics.bigdl.nn.SoftMin.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.TensorModule
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import scala.concurrent.Future
import scala.reflect.ClassTag
/**
* Applies the SoftMin function to an n-dimensional input Tensor, rescaling them so that the
* elements of the n-dimensional output Tensor lie in the range (0,1) and sum to 1.
* Softmin is defined as: f_i(x) = exp(-x_i - shift) / sum_j exp(-x_j - shift)
* where shift = max_i(-x_i).
*/
@SerialVersionUID(- 8738615460960887232L)
class SoftMin[T: ClassTag]()(implicit ev: TensorNumeric[T]) extends TensorModule[T] {
@transient
private var results: Array[Future[Unit]] = null
@transient
private var minInput : Tensor[T] = null
override def updateOutput(input: Tensor[T]): Tensor[T] = {
val (nFrame, stride) = if (input.nDimension() == 1) {
(1, 1)
} else if (input.nDimension() == 2) {
(input.size(1), 1)
} else if (input.nDimension() == 3) {
(1, input.size(2) * input.size(3))
} else {
(input.size(1), input.size(3) * input.size(4))
}
if (results == null || results.length != nFrame * stride) {
results = new Array[Future[Unit]](nFrame * stride)
}
output.resizeAs(input)
if (null == minInput) {
minInput = input.clone().mul(ev.fromType[Int](-1))
} else {
minInput.resizeAs(input).copy(input).mul(ev.fromType[Int](-1))
}
SoftMax.updateOutput[T](minInput, output, results)
output
}
override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
gradInput.resizeAs(output)
SoftMax.updateGradInput[T](minInput, gradOutput, gradInput, output, results)
gradInput.mul(ev.fromType[Int](-1))
gradInput
}
}
object SoftMin {
def apply[@specialized(Float, Double) T: ClassTag]()
(implicit ev: TensorNumeric[T]) : SoftMin[T] = {
new SoftMin[T]()
}
}