com.intel.analytics.zoo.pipeline.api.keras.layers.PReLU.scala Maven / Gradle / Ivy
/*
* Copyright 2018 Analytics Zoo 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.zoo.pipeline.api.keras.layers
import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, IdentityOutputShape}
import com.intel.analytics.bigdl.nn.keras.KerasLayer
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.Shape
import com.intel.analytics.zoo.pipeline.api.Net
import com.intel.analytics.zoo.pipeline.api.keras.layers.utils.KerasUtils
import scala.reflect.ClassTag
/**
* Applies parametric ReLU, where parameter varies the slope of the negative part.
*
* f(x) = max(0, x) + a * min(0, x)
*
* Notice: Please don't use weight decay on this.
*
* 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).
*
* Remark: This layer is from Torch and wrapped in Keras style.
*
* @param nOutputPlane Input map number. Default is 0,
* which means using PReLU in shared version and has only one parameter.
* @param inputShape A Single Shape, does not include the batch dimension.
* @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now.
*/
class PReLU[T: ClassTag](
val nOutputPlane: Int = 0,
val inputShape: Shape = null)(implicit ev: TensorNumeric[T])
extends KerasLayer[Tensor[T], Tensor[T], T](KerasUtils.addBatch(inputShape))
with IdentityOutputShape with Net {
override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = {
val layer = com.intel.analytics.bigdl.nn.PReLU(nOutputPlane)
layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object PReLU {
def apply[@specialized(Float, Double) T: ClassTag](
nOutputPlane: Int = 0,
inputShape: Shape = null)(implicit ev: TensorNumeric[T]): PReLU[T] = {
new PReLU[T](nOutputPlane, inputShape)
}
}