org.deeplearning4j.nn.conf.serde.BaseNetConfigDeserializer Maven / Gradle / Ivy
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* * information regarding copyright ownership.
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package org.deeplearning4j.nn.conf.serde;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.layers.BaseLayer;
import org.deeplearning4j.nn.conf.layers.BaseOutputLayer;
import org.deeplearning4j.nn.conf.layers.Layer;
import org.deeplearning4j.nn.weights.*;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.activations.impl.*;
import org.nd4j.linalg.learning.config.*;
import org.nd4j.linalg.learning.regularization.L1Regularization;
import org.nd4j.linalg.learning.regularization.Regularization;
import org.nd4j.linalg.learning.regularization.WeightDecay;
import org.nd4j.linalg.lossfunctions.ILossFunction;
import org.nd4j.linalg.lossfunctions.impl.*;
import org.nd4j.shade.jackson.core.JsonParser;
import org.nd4j.shade.jackson.core.JsonProcessingException;
import org.nd4j.shade.jackson.databind.DeserializationContext;
import org.nd4j.shade.jackson.databind.JsonDeserializer;
import org.nd4j.shade.jackson.databind.JsonMappingException;
import org.nd4j.shade.jackson.databind.deser.ResolvableDeserializer;
import org.nd4j.shade.jackson.databind.deser.std.StdDeserializer;
import org.nd4j.shade.jackson.databind.node.ObjectNode;
import java.io.IOException;
import java.lang.reflect.InvocationTargetException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.Map;
@Slf4j
public abstract class BaseNetConfigDeserializer extends StdDeserializer implements ResolvableDeserializer {
protected final JsonDeserializer defaultDeserializer;
public BaseNetConfigDeserializer(JsonDeserializer defaultDeserializer, Class deserializedType) {
super(deserializedType);
this.defaultDeserializer = defaultDeserializer;
}
@Override
public abstract T deserialize(JsonParser jp, DeserializationContext ctxt)
throws IOException, JsonProcessingException;
protected boolean requiresIUpdaterFromLegacy(Layer[] layers){
for(Layer l : layers){
if(l instanceof BaseLayer){
BaseLayer bl = (BaseLayer)l;
if(bl.getIUpdater() == null && bl.initializer().numParams(bl) > 0){
return true;
}
}
}
return false;
}
protected boolean requiresDropoutFromLegacy(Layer[] layers){
for(Layer l : layers){
if(l.getIDropout() != null){
return false;
}
}
return true;
}
protected boolean requiresRegularizationFromLegacy(Layer[] layers){
for(Layer l : layers){
if(l instanceof BaseLayer && ((BaseLayer)l).getRegularization() == null){
return true;
}
}
return false;
}
protected boolean requiresWeightInitFromLegacy(Layer[] layers){
for(Layer l : layers){
if(l instanceof BaseLayer && ((BaseLayer)l).getWeightInitFn() == null){
return true;
}
}
return false;
}
protected boolean requiresActivationFromLegacy(Layer[] layers){
for(Layer l : layers){
if(l instanceof BaseLayer && ((BaseLayer)l).getActivationFn() == null){
return true;
}
}
return false;
}
protected boolean requiresLegacyLossHandling(Layer[] layers){
for(Layer l : layers){
if(l instanceof BaseOutputLayer && ((BaseOutputLayer)l).getLossFn() == null){
return true;
}
}
return false;
}
protected void handleUpdaterBackwardCompatibility(BaseLayer layer, ObjectNode on){
if(on != null && on.has("updater")){
String updaterName = on.get("updater").asText();
if(updaterName != null){
Updater u = Updater.valueOf(updaterName);
IUpdater iu = u.getIUpdaterWithDefaultConfig();
double lr = on.get("learningRate").asDouble();
double eps;
if(on.has("epsilon")){
eps = on.get("epsilon").asDouble();
} else {
eps = Double.NaN;
}
double rho = on.get("rho").asDouble();
switch (u){
case SGD:
((Sgd)iu).setLearningRate(lr);
break;
case ADAM:
if(Double.isNaN(eps)){
eps = Adam.DEFAULT_ADAM_EPSILON;
}
((Adam)iu).setLearningRate(lr);
((Adam)iu).setBeta1(on.get("adamMeanDecay").asDouble());
((Adam)iu).setBeta2(on.get("adamVarDecay").asDouble());
((Adam)iu).setEpsilon(eps);
break;
case ADAMAX:
if(Double.isNaN(eps)){
eps = AdaMax.DEFAULT_ADAMAX_EPSILON;
}
((AdaMax)iu).setLearningRate(lr);
((AdaMax)iu).setBeta1(on.get("adamMeanDecay").asDouble());
((AdaMax)iu).setBeta2(on.get("adamVarDecay").asDouble());
((AdaMax)iu).setEpsilon(eps);
break;
case ADADELTA:
if(Double.isNaN(eps)){
eps = AdaDelta.DEFAULT_ADADELTA_EPSILON;
}
((AdaDelta)iu).setRho(rho);
((AdaDelta)iu).setEpsilon(eps);
break;
case NESTEROVS:
((Nesterovs)iu).setLearningRate(lr);
((Nesterovs)iu).setMomentum(on.get("momentum").asDouble());
break;
case NADAM:
if(Double.isNaN(eps)){
eps = Nadam.DEFAULT_NADAM_EPSILON;
}
((Nadam)iu).setLearningRate(lr);
((Nadam)iu).setBeta1(on.get("adamMeanDecay").asDouble());
((Nadam)iu).setBeta2(on.get("adamVarDecay").asDouble());
((Nadam)iu).setEpsilon(eps);
break;
case ADAGRAD:
if(Double.isNaN(eps)){
eps = AdaGrad.DEFAULT_ADAGRAD_EPSILON;
}
((AdaGrad)iu).setLearningRate(lr);
((AdaGrad)iu).setEpsilon(eps);
break;
case RMSPROP:
if(Double.isNaN(eps)){
eps = RmsProp.DEFAULT_RMSPROP_EPSILON;
}
((RmsProp)iu).setLearningRate(lr);
((RmsProp)iu).setEpsilon(eps);
((RmsProp)iu).setRmsDecay(on.get("rmsDecay").asDouble());
break;
default:
//No op
break;
}
layer.setIUpdater(iu);
}
}
}
protected void handleL1L2BackwardCompatibility(BaseLayer baseLayer, ObjectNode on){
if(on != null && (on.has("l1") || on.has("l2"))){
//Legacy format JSON
baseLayer.setRegularization(new ArrayList());
baseLayer.setRegularizationBias(new ArrayList());
if(on.has("l1")){
double l1 = on.get("l1").doubleValue();
if(l1 > 0.0){
baseLayer.getRegularization().add(new L1Regularization(l1));
}
}
if(on.has("l2")){
double l2 = on.get("l2").doubleValue();
if(l2 > 0.0){
//Default to non-LR based WeightDecay, to match behaviour in 1.0.0-beta3
baseLayer.getRegularization().add(new WeightDecay(l2, false));
}
}
if(on.has("l1Bias")){
double l1Bias = on.get("l1Bias").doubleValue();
if(l1Bias > 0.0){
baseLayer.getRegularizationBias().add(new L1Regularization(l1Bias));
}
}
if(on.has("l2Bias")){
double l2Bias = on.get("l2Bias").doubleValue();
if(l2Bias > 0.0){
//Default to non-LR based WeightDecay, to match behaviour in 1.0.0-beta3
baseLayer.getRegularizationBias().add(new WeightDecay(l2Bias, false));
}
}
}
}
protected void handleWeightInitBackwardCompatibility(BaseLayer baseLayer, ObjectNode on){
if(on != null && on.has("weightInit") ){
//Legacy format JSON
if(on.has("weightInit")){
String wi = on.get("weightInit").asText();
try{
WeightInit w = WeightInit.valueOf(wi);
Distribution d = null;
if(w == WeightInit.DISTRIBUTION && on.has("dist")){
String dist = on.get("dist").toString();
d = NeuralNetConfiguration.mapper().readValue(dist, Distribution.class);
}
IWeightInit iwi = w.getWeightInitFunction(d);
baseLayer.setWeightInitFn(iwi);
} catch (Throwable t){
log.warn("Failed to infer weight initialization from legacy JSON format",t);
}
}
}
}
//Changed after 0.7.1 from "activationFunction" : "softmax" to "activationFn" :