com.intel.analytics.bigdl.nn.keras.SpatialDropout1D.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
/**
* Spatial 1D version of Dropout.
* This version performs the same function as Dropout, however it drops
* entire 1D feature maps instead of individual elements. If adjacent frames
* within feature maps are strongly correlated (as is normally the case in
* early convolution layers) then regular dropout will not regularize the
* activations and will otherwise just result in an effective learning rate
* decrease. In this case, SpatialDropout1D will help promote independence
* between feature maps and should be used instead.
* The input of this layer should be 3D.
*
* 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 p Fraction of the input units to drop. Double between 0 and 1.
* @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now.
*/
class SpatialDropout1D[T: ClassTag](
val p: Double = 0.5,
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.SpatialDropout1D(initP = p)
layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object SpatialDropout1D {
def apply[@specialized(Float, Double) T: ClassTag](
p: Double = 0.5,
inputShape: Shape = null)(implicit ev: TensorNumeric[T]): SpatialDropout1D[T] = {
new SpatialDropout1D[T](p, inputShape)
}
}