org.deeplearning4j.nn.conf.layers.PReLULayer Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.deeplearning4j.nn.conf.layers;
import lombok.*;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.ParamInitializer;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.params.PReLUParamInitializer;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.Collection;
import java.util.Map;
/**
* Parametrized Rectified Linear Unit (PReLU)
*
* {@code f(x) = alpha * x for x < 0, f(x) = x for x >= 0}
*
* alpha has the same shape as x and is a learned parameter.
*
* @author Max Pumperla
*/
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
public class PReLULayer extends BaseLayer {
private long[] inputShape = null;
private long[] sharedAxes = null;
private int nIn;
private int nOut;
private PReLULayer(Builder builder) {
super(builder);
this.inputShape = builder.inputShape;
this.sharedAxes = builder.sharedAxes;
initializeConstraints(builder);
}
@Override
public Layer instantiate(NeuralNetConfiguration conf, Collection trainingListeners,
int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
org.deeplearning4j.nn.layers.feedforward.PReLU ret = new org.deeplearning4j.nn.layers.feedforward.PReLU(conf, networkDataType);
ret.setListeners(trainingListeners);
ret.setIndex(layerIndex);
ret.setParamsViewArray(layerParamsView);
Map paramTable = initializer().init(conf, layerParamsView, initializeParams);
ret.setParamTable(paramTable);
ret.setConf(conf);
return ret;
}
@Override
public InputType getOutputType(int layerIndex, InputType inputType) {
if (inputType == null) {
throw new IllegalStateException("Invalid input type: null for layer name \"" + getLayerName() + "\"");
}
return inputType;
}
@Override
public void setNIn(InputType inputType, boolean override) {
// not needed
}
@Override
public InputPreProcessor getPreProcessorForInputType(InputType inputType) {
// None needed
return null;
}
@Override
public boolean isPretrainParam(String paramName) {
return false;
}
@Override
public ParamInitializer initializer() {
return PReLUParamInitializer.getInstance(inputShape, sharedAxes);
}
@Override
public LayerMemoryReport getMemoryReport(InputType inputType) {
InputType outputType = getOutputType(-1, inputType);
val numParams = initializer().numParams(this);
val updaterStateSize = (int) getIUpdater().stateSize(numParams);
return new LayerMemoryReport.Builder(layerName, PReLULayer.class, inputType, outputType)
.standardMemory(numParams, updaterStateSize).workingMemory(0, 0, 0, 0)
.cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS).build();
}
@NoArgsConstructor
@Getter
@Setter
public static class Builder extends FeedForwardLayer.Builder {
/**
* Explicitly set input shape of incoming activations so that parameters can be initialized properly. This
* explicitly excludes the mini-batch dimension.
*
*/
private long[] inputShape = null;
/**
* Set the broadcasting axes of PReLU's alpha parameter.
*
* For instance, given input data of shape [mb, channels, height, width], setting axes to [2,3] will set alpha
* to shape [channels, 1, 1] and broadcast alpha across height and width dimensions of each channel.
*
*/
private long[] sharedAxes = null;
/**
* Explicitly set input shape of incoming activations so that parameters can be initialized properly. This
* explicitly excludes the mini-batch dimension.
*
* @param shape shape of input data
*/
public Builder inputShape(long... shape) {
this.setInputShape(shape);
return this;
}
/**
* Set the broadcasting axes of PReLU's alpha parameter.
*
* For instance, given input data of shape [mb, channels, height, width], setting axes to [2,3] will set alpha
* to shape [channels, 1, 1] and broadcast alpha across height and width dimensions of each channel.
*
* @param axes shared/broadcasting axes
* @return Builder
*/
public Builder sharedAxes(long... axes) {
this.setSharedAxes(axes);
return this;
}
@Override
@SuppressWarnings("unchecked")
public PReLULayer build() {
return new PReLULayer(this);
}
}
}