com.intel.analytics.bigdl.nn.tf.Log1p.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.tf
import com.intel.analytics.bigdl.nn.abstractnn.AbstractModule
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(952324213749625368L)
class Log1p[T: ClassTag, D: ClassTag] (implicit ev: TensorNumeric[T], ev2: TensorNumeric[D])
extends AbstractModule[Tensor[D], Tensor[D], T] {
output = Tensor[D]()
gradInput = Tensor[D]()
private val buffer: Tensor[D] = Tensor[D]()
override def updateOutput(input: Tensor[D]): Tensor[D] = {
output.resizeAs(input)
.copy(input)
.log1p()
output
}
override def updateGradInput(input: Tensor[D], gradOutput: Tensor[D]): Tensor[D] = {
buffer.resizeAs(input)
buffer.copy(input).add(ev2.fromType[Double](1.0))
gradInput.resizeAs(input)
.fill(ev2.fromType[Double](1.0))
.cdiv(buffer)
.cmul(gradOutput)
gradInput
}
override def getClassTagNumerics() : (Array[ClassTag[_]], Array[TensorNumeric[_]]) = {
(Array[ClassTag[_]](scala.reflect.classTag[T], scala.reflect.classTag[D]),
Array[TensorNumeric[_]](ev, ev2))
}
}
object Log1p {
def apply[T: ClassTag, D: ClassTag]()
(implicit ev: TensorNumeric[T], ev2: TensorNumeric[D]) : Log1p[T, D] = {
new Log1p[T, D]()
}
}