com.intel.analytics.bigdl.nn.Sigmoid.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.{IdentityOutputShape, TensorModule}
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import scala.reflect.ClassTag
/**
* Applies the Sigmoid function element-wise to the input Tensor,
* thus outputting a Tensor of the same dimension.
* Sigmoid is defined as: f(x) = 1 / (1 + exp(-x))
*/
@SerialVersionUID(6855417348268610044L)
class Sigmoid[T: ClassTag](
implicit ev: TensorNumeric[T]) extends TensorModule[T] {
private val buffer: Tensor[T] = Tensor[T]()
override def updateOutput(input: Tensor[T]): Tensor[T] = {
output.resizeAs(input).fill(ev.one)
buffer.resizeAs(input).copy(input).mul(ev.fromType(-1))
buffer.exp().add(ev.one)
output.cdiv(buffer)
output
}
override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
updateGradInputInternal(output, gradOutput)
}
private[bigdl] def updateGradInputInternal(output: Tensor[T],
gradOutput: Tensor[T]): Tensor[T] = {
gradInput.resizeAs(gradOutput).copy(gradOutput)
buffer.resizeAs(gradOutput)
buffer.fill(ev.one).sub(output)
gradInput.cmul(output).cmul(buffer)
gradInput
}
override def clearState(): this.type = {
super.clearState()
buffer.set()
this
}
}
object Sigmoid {
def apply[T: ClassTag]()
(implicit ev: TensorNumeric[T]) : Sigmoid[T] = {
new Sigmoid[T]()
}
}