com.intel.analytics.bigdl.nn.KLDCriterion.scala Maven / Gradle / Ivy

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/*
 * 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.AbstractCriterion
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.{T, Table}

import scala.reflect.ClassTag

/**
 * Computes the KL-divergence of the input normal distribution to a standard normal distribution.
 * The input has to be a table. The first element of input is the mean of the distribution,
 * the second element of input is the log_variance of the distribution. The input distribution is
 * assumed to be diagonal.
 *
 * The mean and log_variance are both assumed to be two dimensional tensors. The first dimension are
 * interpreted as batch. The output is the average/sum of each observation.
 */
class KLDCriterion[@specialized(Float, Double) T: ClassTag](
            sizeAverage: Boolean = true)(
  implicit ev: TensorNumeric[T]) extends AbstractCriterion[Table, Tensor[T], T] {

  @transient
  private var mean: Tensor[T] = null
  @transient
  private var logVar: Tensor[T] = null
  @transient
  private var vars: Tensor[T] = null

  override def updateOutput(input: Table, target: Tensor[T]): T = {

    if (mean == null) mean = Tensor[T]()
    if (logVar == null) logVar = Tensor[T]()
    if (vars == null) vars = Tensor[T]()

    mean.resizeAs(input[Tensor[T]](1)).copy(input(1))
    logVar.resizeAs(input[Tensor[T]](2)).copy(input(2))

    val batchSize = if (sizeAverage) mean.size(1) else 1

    //  Appendix B from VAE paper: -0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
    mean.pow(ev.fromType(2))
    vars.resizeAs(logVar).copy(logVar).exp()
    logVar.add(ev.one).add(ev.fromType(-1), mean).add(ev.fromType(-1), vars)

    output = ev.times(ev.fromType(-0.5 / batchSize), logVar.sum())
    output
  }

  override def updateGradInput(input: Table, target: Tensor[T]): Table = {
    if (!gradInput.contains(1)) gradInput(1) = Tensor()
    if (!gradInput.contains(2)) gradInput(2) = Tensor()

    val batchSize = if (sizeAverage) input[Tensor[T]](1).size(1) else 1

    // d_L/d_mu = mu
    gradInput[Tensor[T]](1).resizeAs(input(1)).copy(input(1)).mul(ev.fromType(1.0 / batchSize))
    // d_L/d_sigma = 0.5*(exp(log_sq_sigma)-1)
    gradInput[Tensor[T]](2).resizeAs(input(2)).copy(input(2))
    gradInput[Tensor[T]](2).exp().add(ev.fromType(-1)).mul(ev.fromType(0.5 / batchSize))

    gradInput
  }
}

object KLDCriterion {
  def apply[@specialized(Float, Double) T: ClassTag](sizeAverage: Boolean = true)(
    implicit ev: TensorNumeric[T]): KLDCriterion[T] = {
    new KLDCriterion[T](sizeAverage = sizeAverage)
  }
}