/
FineTuneConfiguration.java
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/
FineTuneConfiguration.java
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package org.deeplearning4j.nn.transferlearning;
import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.Data;
import lombok.NoArgsConstructor;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.dropout.Dropout;
import org.deeplearning4j.nn.conf.dropout.IDropout;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.stepfunctions.StepFunction;
import org.deeplearning4j.nn.conf.weightnoise.IWeightNoise;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.learning.config.IUpdater;
import org.nd4j.shade.jackson.annotation.JsonInclude;
import org.nd4j.shade.jackson.annotation.JsonTypeInfo;
import org.nd4j.shade.jackson.core.JsonProcessingException;
import java.io.IOException;
import java.util.List;
/**
* Created by Alex on 21/02/2017.
*/
@JsonTypeInfo(use = JsonTypeInfo.Id.CLASS, include = JsonTypeInfo.As.PROPERTY, property = "type")
@JsonInclude(JsonInclude.Include.NON_NULL)
@NoArgsConstructor
@AllArgsConstructor
@Data
@Builder(builderClassName = "Builder")
public class FineTuneConfiguration {
protected IActivation activationFn;
protected WeightInit weightInit;
protected Double biasInit;
protected Distribution dist;
protected Double l1;
protected Double l2;
protected Double l1Bias;
protected Double l2Bias;
protected IDropout dropout;
protected IWeightNoise weightNoise;
protected IUpdater iUpdater;
protected IUpdater biasUpdater;
protected Boolean miniBatch;
protected Integer numIterations;
protected Integer maxNumLineSearchIterations;
protected Long seed;
protected OptimizationAlgorithm optimizationAlgo;
protected StepFunction stepFunction;
protected Boolean minimize;
protected GradientNormalization gradientNormalization;
protected Double gradientNormalizationThreshold;
protected ConvolutionMode convolutionMode;
protected List<LayerConstraint> constraints;
protected Boolean hasBiasConstraints;
protected Boolean hasWeightConstraints;
protected Boolean pretrain;
protected Boolean backprop;
protected BackpropType backpropType;
protected Integer tbpttFwdLength;
protected Integer tbpttBackLength;
protected WorkspaceMode trainingWorkspaceMode;
protected WorkspaceMode inferenceWorkspaceMode;
//Lombok builder. Note that the code below ADDS OR OVERRIDES the lombok implementation; the final builder class
// is the composite of the lombok parts and the parts defined here
//partial implementation to allow public no-arg constructor (lombok default is package private)
//Plus some implementations to match NeuralNetConfiguration builder methods
public static class Builder {
public Builder() {}
public Builder seed(int seed) {
this.seed = (long) seed;
return this;
}
public Builder seed(long seed) {
this.seed = seed;
return this;
}
public Builder iterations(int iterations) {
this.numIterations = iterations;
return this;
}
public Builder dropOut(double dropout){
return dropout(new Dropout(dropout));
}
public Builder activation(Activation activation) {
this.activationFn = activation.getActivationFunction();
return this;
}
public Builder updater(IUpdater updater) {
return iUpdater(updater);
}
@Deprecated
public Builder updater(Updater updater) {
return updater(updater.getIUpdaterWithDefaultConfig());
}
}
public NeuralNetConfiguration appliedNeuralNetConfiguration(NeuralNetConfiguration nnc) {
applyToNeuralNetConfiguration(nnc);
nnc = new NeuralNetConfiguration.Builder(nnc.clone()).build();
return nnc;
}
public void applyToNeuralNetConfiguration(NeuralNetConfiguration nnc) {
Layer l = nnc.getLayer();
Updater originalUpdater = null;
WeightInit origWeightInit = null;
if (l != null) {
if (dropout != null)
l.setIDropout(dropout);
}
if (l != null && l instanceof BaseLayer) {
BaseLayer bl = (BaseLayer) l;
origWeightInit = bl.getWeightInit();
if (activationFn != null)
bl.setActivationFn(activationFn);
if (weightInit != null)
bl.setWeightInit(weightInit);
if (biasInit != null)
bl.setBiasInit(biasInit);
if (dist != null)
bl.setDist(dist);
if (l1 != null)
bl.setL1(l1);
if (l2 != null)
bl.setL2(l2);
if (l1Bias != null)
bl.setL1Bias(l1Bias);
if (l2Bias != null)
bl.setL2Bias(l2Bias);
if (gradientNormalization != null)
bl.setGradientNormalization(gradientNormalization);
if (gradientNormalizationThreshold != null)
bl.setGradientNormalizationThreshold(gradientNormalizationThreshold);
if (iUpdater != null){
bl.setIUpdater(iUpdater);
}
if (biasUpdater != null){
bl.setBiasUpdater(biasUpdater);
}
if (weightNoise != null){
bl.setWeightNoise(weightNoise);
}
}
if (miniBatch != null)
nnc.setMiniBatch(miniBatch);
if (numIterations != null)
nnc.setNumIterations(numIterations);
if (maxNumLineSearchIterations != null)
nnc.setMaxNumLineSearchIterations(maxNumLineSearchIterations);
if (seed != null)
nnc.setSeed(seed);
if (optimizationAlgo != null)
nnc.setOptimizationAlgo(optimizationAlgo);
if (stepFunction != null)
nnc.setStepFunction(stepFunction);
if (minimize != null)
nnc.setMinimize(minimize);
if (convolutionMode != null && l instanceof ConvolutionLayer) {
((ConvolutionLayer) l).setConvolutionMode(convolutionMode);
}
if (convolutionMode != null && l instanceof SubsamplingLayer) {
((SubsamplingLayer) l).setConvolutionMode(convolutionMode);
}
//Check weight init. Remove dist if originally was DISTRIBUTION, and isn't now -> remove no longer needed distribution
if (l != null && l instanceof BaseLayer && origWeightInit == WeightInit.DISTRIBUTION && weightInit != null
&& weightInit != WeightInit.DISTRIBUTION) {
((BaseLayer) l).setDist(null);
}
//Perform validation. This also sets the defaults for updaters. For example, Updater.RMSProp -> set rmsDecay
if (l != null) {
LayerValidation.generalValidation(l.getLayerName(), l, dropout, l2, l2Bias, l1, l1Bias, dist, constraints, null, null);
}
//Also: update the LR, L1 and L2 maps, based on current config (which might be different to original config)
if (nnc.variables(false) != null) {
for (String s : nnc.variables(false)) {
nnc.setLayerParamLR(s);
}
}
}
public void applyToMultiLayerConfiguration(MultiLayerConfiguration conf) {
if (pretrain != null)
conf.setPretrain(pretrain);
if (backprop != null)
conf.setBackprop(backprop);
if (backpropType != null)
conf.setBackpropType(backpropType);
if (tbpttFwdLength != null)
conf.setTbpttFwdLength(tbpttFwdLength);
if (tbpttBackLength != null)
conf.setTbpttBackLength(tbpttBackLength);
}
public void applyToComputationGraphConfiguration(ComputationGraphConfiguration conf) {
if (pretrain != null)
conf.setPretrain(pretrain);
if (backprop != null)
conf.setBackprop(backprop);
if (backpropType != null)
conf.setBackpropType(backpropType);
if (tbpttFwdLength != null)
conf.setTbpttFwdLength(tbpttFwdLength);
if (tbpttBackLength != null)
conf.setTbpttBackLength(tbpttBackLength);
}
public NeuralNetConfiguration.Builder appliedNeuralNetConfigurationBuilder() {
NeuralNetConfiguration.Builder confBuilder = new NeuralNetConfiguration.Builder();
if (activationFn != null)
confBuilder.setActivationFn(activationFn);
if (weightInit != null)
confBuilder.setWeightInit(weightInit);
if (biasInit != null)
confBuilder.setBiasInit(biasInit);
if (dist != null)
confBuilder.setDist(dist);
if (l1 != null)
confBuilder.setL1(l1);
if (l2 != null)
confBuilder.setL2(l2);
if (l1Bias != null)
confBuilder.setL1Bias(l1Bias);
if (l2Bias != null)
confBuilder.setL2Bias(l2Bias);
if (dropout != null)
confBuilder.setIdropOut(dropout);
if (iUpdater != null)
confBuilder.updater(iUpdater);
if(biasUpdater != null)
confBuilder.biasUpdater(biasUpdater);
if (miniBatch != null)
confBuilder.setMiniBatch(miniBatch);
if (numIterations != null)
confBuilder.setNumIterations(numIterations);
if (maxNumLineSearchIterations != null)
confBuilder.setMaxNumLineSearchIterations(maxNumLineSearchIterations);
if (seed != null)
confBuilder.setSeed(seed);
if (optimizationAlgo != null)
confBuilder.setOptimizationAlgo(optimizationAlgo);
if (stepFunction != null)
confBuilder.setStepFunction(stepFunction);
if (minimize != null)
confBuilder.setMinimize(minimize);
if (gradientNormalization != null)
confBuilder.setGradientNormalization(gradientNormalization);
if (gradientNormalizationThreshold != null)
confBuilder.setGradientNormalizationThreshold(gradientNormalizationThreshold);
if (trainingWorkspaceMode != null)
confBuilder.trainingWorkspaceMode(trainingWorkspaceMode);
if (inferenceWorkspaceMode != null)
confBuilder.inferenceWorkspaceMode(inferenceWorkspaceMode);
return confBuilder;
}
public String toJson() {
try {
return NeuralNetConfiguration.mapper().writeValueAsString(this);
} catch (JsonProcessingException e) {
throw new RuntimeException(e);
}
}
public String toYaml() {
try {
return NeuralNetConfiguration.mapperYaml().writeValueAsString(this);
} catch (JsonProcessingException e) {
throw new RuntimeException(e);
}
}
public static FineTuneConfiguration fromJson(String json) {
try {
return NeuralNetConfiguration.mapper().readValue(json, FineTuneConfiguration.class);
} catch (IOException e) {
throw new RuntimeException(e);
}
}
public static FineTuneConfiguration fromYaml(String yaml) {
try {
return NeuralNetConfiguration.mapperYaml().readValue(yaml, FineTuneConfiguration.class);
} catch (IOException e) {
throw new RuntimeException(e);
}
}
}