com.intel.analytics.bigdl.nn.keras.GaussianNoise.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.keras
import com.intel.analytics.bigdl.nn.abstractnn._
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.Shape
import scala.reflect.ClassTag
/**
* Apply additive zero-centered Gaussian noise.
* This is useful to mitigate overfitting (you could see it as a form of random data augmentation).
* Gaussian Noise is a natural choice as corruption process for real valued inputs.
* As it is a regularization layer, it is only active at training time.
*
* When you use this layer as the first layer of a model, you need to provide the argument
* inputShape (a Single Shape, does not include the batch dimension).
*
* @param sigma Double, standard deviation of the noise distribution.
* @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now.
*/
class GaussianNoise[T: ClassTag](
val sigma: Double,
val inputShape: Shape = null)(implicit ev: TensorNumeric[T])
extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape))
with IdentityOutputShape {
override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = {
val layer = com.intel.analytics.bigdl.nn.GaussianNoise(stddev = sigma)
layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object GaussianNoise {
def apply[@specialized(Float, Double) T: ClassTag](
sigma: Double,
inputShape: Shape = null)(implicit ev: TensorNumeric[T]): GaussianNoise[T] = {
new GaussianNoise[T](sigma, inputShape)
}
}