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.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[@specialized(Float, Double) T: ClassTag](
implicit ev: TensorNumeric[T]) extends TensorModule[T] {
override def updateOutput(input: Tensor[T]): Tensor[T] = {
output.resizeAs(input)
output.map(input, (_, i) => ev.divide(ev.fromType[Int](1), ev.plus(ev.fromType[Int](1),
ev.exp(ev.negative(i)))))
output
}
override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
gradInput.resizeAs(input)
gradInput.copy(gradOutput)
gradInput.map(output, (g, z) => ev.times(ev.times(g, ev.minus(ev.fromType[Int](1), z)), z))
gradInput
}
override def toString(): String = {
s"nn.Sigmoid"
}
}
object Sigmoid {
def apply[@specialized(Float, Double) T: ClassTag]()
(implicit ev: TensorNumeric[T]) : Sigmoid[T] = {
new Sigmoid[T]()
}
}