org.deeplearning4j.arbiter.layers.LayerSpace Maven / Gradle / Ivy
/*
*
* * Copyright 2016 Skymind,Inc.
* *
* * 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 org.deeplearning4j.arbiter.layers;
import org.deeplearning4j.arbiter.optimize.parameter.FixedValue;
import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
import org.deeplearning4j.arbiter.util.CollectionUtils;
import org.deeplearning4j.nn.conf.GradientNormalization;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.layers.Layer;
import org.deeplearning4j.nn.weights.WeightInit;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
/**
* Created by Alex on 26/12/2015.
*/
public abstract class LayerSpace implements ParameterSpace {
protected ParameterSpace activationFunction;
protected ParameterSpace weightInit;
protected ParameterSpace biasInit;
protected ParameterSpace dist;
protected ParameterSpace learningRate;
protected ParameterSpace> learningRateAfter;
protected ParameterSpace lrScoreBasedDecay;
protected ParameterSpace l1;
protected ParameterSpace l2;
protected ParameterSpace dropOut;
protected ParameterSpace momentum;
protected ParameterSpace> momentumAfter;
protected ParameterSpace updater;
protected ParameterSpace rho;
protected ParameterSpace rmsDecay;
protected ParameterSpace gradientNormalization;
protected ParameterSpace gradientNormalizationThreshold;
private int numParameters;
@SuppressWarnings("unchecked")
protected LayerSpace(Builder builder){
this.activationFunction = builder.activationFunction;
this.weightInit = builder.weightInit;
this.biasInit = builder.biasInit;
this.dist = builder.dist;
this.learningRate = builder.learningRate;
this.learningRateAfter = builder.learningRateAfter;
this.lrScoreBasedDecay = builder.lrScoreBasedDecay;
this.l1 = builder.l1;
this.l2 = builder.l2;
this.dropOut = builder.dropOut;
this.momentum = builder.momentum;
this.momentumAfter = builder.momentumAfter;
this.updater = builder.updater;
this.rho = builder.rho;
this.rmsDecay = builder.rmsDecay;
this.gradientNormalization = builder.gradientNormalization;
this.gradientNormalizationThreshold = builder.gradientNormalizationThreshold;
numParameters = CollectionUtils.countUnique(collectLeaves());
}
// public abstract L randomLayer();
public List collectLeaves(){
List list = new ArrayList<>();
if(activationFunction != null ) list.addAll(activationFunction.collectLeaves());
if(weightInit != null ) list.addAll(weightInit.collectLeaves());
if(biasInit != null ) list.addAll(biasInit.collectLeaves());
if(dist != null ) list.addAll(dist.collectLeaves());
if(learningRate != null ) list.addAll(learningRate.collectLeaves());
if(learningRateAfter != null ) list.addAll(learningRateAfter.collectLeaves());
if(lrScoreBasedDecay != null ) list.addAll(lrScoreBasedDecay.collectLeaves());
if(l1 != null ) list.addAll(l1.collectLeaves());
if(l2 != null ) list.addAll(l2.collectLeaves());
if(dropOut != null ) list.addAll(dropOut.collectLeaves());
if(momentum != null ) list.addAll(momentum.collectLeaves());
if(momentumAfter != null ) list.addAll(momentumAfter.collectLeaves());
if(updater != null ) list.addAll(updater.collectLeaves());
if(rho != null ) list.addAll(rho.collectLeaves());
if(rmsDecay != null ) list.addAll(rmsDecay.collectLeaves());
if(gradientNormalization != null ) list.addAll(gradientNormalization.collectLeaves());
if(gradientNormalizationThreshold != null ) list.addAll(gradientNormalizationThreshold.collectLeaves());
return list;
}
@Override
public int numParameters() {
return numParameters;
}
@Override
public boolean isLeaf() {
return false;
}
@Override
public void setIndices(int... indices) {
throw new UnsupportedOperationException("Cannot set indices for non-leaf parameter space");
}
protected void setLayerOptionsBuilder(Layer.Builder builder, double[] values){
if(activationFunction != null) builder.activation(activationFunction.getValue(values));
if(weightInit != null) builder.weightInit(weightInit.getValue(values));
if(biasInit != null) builder.biasInit(biasInit.getValue(values));
if(dist != null) builder.dist(dist.getValue(values));
if(learningRate != null) builder.learningRate(learningRate.getValue(values));
if(learningRateAfter != null) builder.learningRateSchedule(learningRateAfter.getValue(values));
if(lrScoreBasedDecay != null) builder.learningRate(lrScoreBasedDecay.getValue(values));
if(l1 != null) builder.l1(l1.getValue(values));
if(l2 != null) builder.l2(l2.getValue(values));
if(dropOut != null) builder.dropOut(dropOut.getValue(values));
if(momentum != null) builder.momentum(momentum.getValue(values));
if(momentumAfter != null) builder.momentumAfter(momentumAfter.getValue(values));
if(updater != null) builder.updater(updater.getValue(values));
if(rho != null) builder.rho(rho.getValue(values));
if(rmsDecay != null) builder.rmsDecay(rmsDecay.getValue(values));
if(gradientNormalization != null) builder.gradientNormalization(gradientNormalization.getValue(values));
if(gradientNormalizationThreshold != null) builder.gradientNormalizationThreshold(gradientNormalizationThreshold.getValue(values));
}
@Override
public String toString() {
return toString(", ");
}
protected String toString(String delim){
StringBuilder sb = new StringBuilder();
if(activationFunction != null) sb.append("activationFunction: ").append(activationFunction).append(delim);
if(weightInit != null) sb.append("weightInit: ").append(weightInit).append(delim);
if(biasInit != null) sb.append("biasInit: ").append(biasInit).append(delim);
if(dist != null) sb.append("dist: ").append(dist).append(delim);
if(learningRate != null) sb.append("learningRate: ").append(learningRate).append(delim);
if(learningRateAfter != null) sb.append("learningRateAfter: ").append(learningRateAfter).append(delim);
if(lrScoreBasedDecay != null) sb.append("lrScoreBasedDecay: ").append(lrScoreBasedDecay).append(delim);
if(l1 != null) sb.append("l1: ").append(l1).append(delim);
if(l2 != null) sb.append("l2: ").append(l2).append(delim);
if(dropOut != null) sb.append("dropOut: ").append(dropOut).append(delim);
if(momentum != null) sb.append("momentum: ").append(momentum).append(delim);
if(momentumAfter != null) sb.append("momentumAfter: ").append(momentumAfter).append(delim);
if(updater != null) sb.append("updater: ").append(updater).append(delim);
if(rho != null) sb.append("rho: ").append(rho).append(delim);
if(rmsDecay != null) sb.append("rmsDecay: ").append(rmsDecay).append(delim);
if(gradientNormalization != null) sb.append("gradientNormalization: ").append(gradientNormalization).append(delim);
if(gradientNormalizationThreshold != null) sb.append("gradientNormalizationThreshold").append(gradientNormalizationThreshold);
String s = sb.toString();
if(s.endsWith(delim)){
//Remove final delimiter
int last = s.lastIndexOf(delim);
return s.substring(0,last);
} else return s;
}
// public abstract static class Builder> {
@SuppressWarnings("unchecked")
public abstract static class Builder {
protected ParameterSpace activationFunction;
protected ParameterSpace weightInit;
protected ParameterSpace biasInit;
protected ParameterSpace dist;
protected ParameterSpace learningRate;
protected ParameterSpace> learningRateAfter;
protected ParameterSpace lrScoreBasedDecay;
protected ParameterSpace l1;
protected ParameterSpace l2;
protected ParameterSpace dropOut;
protected ParameterSpace momentum;
protected ParameterSpace> momentumAfter;
protected ParameterSpace updater;
protected ParameterSpace rho;
protected ParameterSpace rmsDecay;
protected ParameterSpace gradientNormalization;
protected ParameterSpace gradientNormalizationThreshold;
public T activation(String activationFunction){
return activation(new FixedValue(activationFunction));
}
public T activation(ParameterSpace activationFunction){
this.activationFunction = activationFunction;
return (T)this;
}
public T weightInit(WeightInit weightInit){
return (T)weightInit(new FixedValue(weightInit));
}
public T weightInit(ParameterSpace weightInit){
this.weightInit = weightInit;
return (T)this;
}
public T dist(Distribution dist){
return dist(new FixedValue(dist));
}
public T dist(ParameterSpace dist){
this.dist = dist;
return (T)this;
}
public T learningRate(double learningRate){
return learningRate(new FixedValue(learningRate));
}
public T learningRate(ParameterSpace learningRate){
this.learningRate = learningRate;
return (T)this;
}
public T learningRateAfter(Map learningRateAfter){
return learningRateAfter(new FixedValue>(learningRateAfter));
}
public T learningRateAfter(ParameterSpace> learningRateAfter ){
this.learningRateAfter = learningRateAfter;
return (T)this;
}
public T learningRateScoreBasedDecayRate(double lrScoreBasedDecay){
return learningRateScoreBasedDecayRate(new FixedValue(lrScoreBasedDecay));
}
public T learningRateScoreBasedDecayRate(ParameterSpace lrScoreBasedDecay){
this.lrScoreBasedDecay = lrScoreBasedDecay;
return (T)this;
}
public T l1(double l1){
return l1(new FixedValue(l1));
}
public T l1(ParameterSpace l1){
this.l1 = l1;
return (T)this;
}
public T l2(double l2){
return l2(new FixedValue(l2));
}
public T l2(ParameterSpace l2){
this.l2 = l2;
return (T)this;
}
public T dropOut(double dropOut){
return dropOut(new FixedValue(dropOut));
}
public T dropOut(ParameterSpace dropOut){
this.dropOut = dropOut;
return (T)this;
}
public T momentum(double momentum){
return momentum(new FixedValue(momentum));
}
public T momentum(ParameterSpace momentum){
this.momentum = momentum;
return (T)this;
}
public T momentumAfter(Map momentumAfter){
return momentumAfter(new FixedValue>(momentumAfter));
}
public T momentumAfter(ParameterSpace> momentumAfter){
this.momentumAfter = momentumAfter;
return (T)this;
}
public T updater(Updater updater){
return updater(new FixedValue(updater));
}
public T updater(ParameterSpace updater){
this.updater = updater;
return (T)this;
}
public T rho(double rho){
return rho(new FixedValue(rho));
}
public T rho(ParameterSpace rho){
this.rho = rho;
return (T)this;
}
public T rmsDecay(double rmsDecay){
return rmsDecay(new FixedValue(rmsDecay));
}
public T rmsDecay(ParameterSpace rmsDecay){
this.rmsDecay = rmsDecay;
return (T)this;
}
public T gradientNormalization(GradientNormalization gradientNormalization){
return gradientNormalization(new FixedValue(gradientNormalization));
}
public T gradientNormalization(ParameterSpace gradientNormalization){
this.gradientNormalization = gradientNormalization;
return (T)this;
}
public T gradientNormalizationThreshold(double threshold){
return gradientNormalizationThreshold(new FixedValue(threshold));
}
public T gradientNormalizationThreshold(ParameterSpace gradientNormalizationThreshold){
this.gradientNormalizationThreshold = gradientNormalizationThreshold;
return (T)this;
}
public abstract E build();
}
}
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