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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.TensorModule
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric

import scala.reflect.ClassTag

/**
 * The [[Log]] module applies a log transformation to the input data
 */
@SerialVersionUID(- 5175095570714684226L)
class Log[T: ClassTag] (implicit ev: TensorNumeric[T])
  extends TensorModule[T] {
  override def updateOutput(input: Tensor[T]): Tensor[T] = {
    output.resizeAs(input)
      .copy(input)
      .log()
    output
  }
  override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
    gradInput.resizeAs(input)
      .fill(ev.fromType[Double](1.0))
      .cdiv(input)
      .cmul(gradOutput)

    gradInput
  }
}

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




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