com.intel.analytics.zoo.pipeline.api.keras.objectives.MeanSquaredLogarithmicError.scala Maven / Gradle / Ivy

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/*
 * Copyright 2018 Analytics Zoo 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.zoo.pipeline.api.keras.objectives

import com.intel.analytics.bigdl.nn.MeanSquaredLogarithmicCriterion
import com.intel.analytics.bigdl.nn.abstractnn.AbstractCriterion
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric

import scala.reflect.ClassTag

/**
 * It calculates:
 * first_log = K.log(K.clip(y, K.epsilon(), Double.MaxValue) + 1.)
 * second_log = K.log(K.clip(x, K.epsilon(), Double.MaxValue) + 1.)
 * and output K.mean(K.square(first_log - second_log))
 */
class MeanSquaredLogarithmicError[@specialized(Float, Double) T: ClassTag]()
   (implicit ev: TensorNumeric[T]) extends TensorLossFunction[T] {

  override val loss: AbstractCriterion[Tensor[T], Tensor[T], T] =
    MeanSquaredLogarithmicCriterion()
}

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