com.intel.analytics.bigdl.nn.HardSigmoid.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.{DenseTensorApply, Tensor, TensorFunc6}
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import scala.reflect.ClassTag
/**
* Apply Segment-wise linear approximation of sigmoid.
* Faster than sigmoid
* ⎧ 0, if x < -2.5
* f(x) = ⎨ 1, if x > 2.5
* ⎩ 0.2 * x + 0.5, otherwise
*/
class HardSigmoid[T: ClassTag]
(implicit ev: TensorNumeric[T]) extends TensorModule[T] {
val minValue = ev.fromType[Double](-2.5)
val maxValue = ev.fromType[Double](2.5)
override def updateOutput(input: Tensor[T]): Tensor[T] = {
output.resizeAs(input)
output.map(input, (out, in) => {
if (ev.isGreater(in, maxValue)) {
ev.fromType[Int](1)
} else if (ev.isGreater(minValue, in)) {
ev.fromType[Int](0)
} else {
ev.fromType[Double](0.2 * ev.toType[Double](in) + 0.5)
}
})
}
override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
require(input.isSameSizeAs(gradOutput),
"Input should have the same size as gradOutput" +
s"input size(${input.dim()}) gradOutput size(${gradOutput.dim()})")
gradInput.resizeAs(input)
val func = new TensorFunc6[T] {
override def apply(data1: Array[T], offset1: Int, data2: Array[T],
offset2: Int, data3: Array[T], offset3: Int): Unit = {
if (ev.isGreater(data3(offset3), maxValue)
|| ev.isGreater(minValue, data3(offset3))) {
data1(offset1) = ev.fromType[Double](0)
} else {
data1(offset1) = ev.times(data2(offset2), ev.fromType[Double](0.2))
}
}
}
DenseTensorApply.apply3[T](gradInput, gradOutput, input, func)
gradInput
}
}
object HardSigmoid {
def apply[T : ClassTag]()(implicit ev: TensorNumeric[T]): HardSigmoid[T] = new HardSigmoid[T]()
}