deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasDense.java
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* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
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* * information regarding copyright ownership.
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package org.deeplearning4j.nn.modelimport.keras.layers.core;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasActivationUtils;
import org.deeplearning4j.nn.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasInitilizationUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.nn.params.DefaultParamInitializer;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.api.ndarray.INDArray;
import java.util.HashMap;
import java.util.Map;
/**
* Imports a Dense layer from Keras.
*
* @author dave@skymind.io
*/
@Slf4j
@Data
@EqualsAndHashCode(callSuper = false)
public class KerasDense extends KerasLayer {
/* Keras layer parameter names. */
private int numTrainableParams = 2;
private boolean hasBias;
/**
* Pass-through constructor from KerasLayer
*
* @param kerasVersion major keras version
* @throws UnsupportedKerasConfigurationException Unsupported Keras config
*/
public KerasDense(Integer kerasVersion) throws UnsupportedKerasConfigurationException {
super(kerasVersion);
}
/**
* Constructor from parsed Keras layer configuration dictionary.
*
* @param layerConfig dictionary containing Keras layer configuration
* @throws InvalidKerasConfigurationException Invalid Keras config
* @throws UnsupportedKerasConfigurationException Unsupported Keras config
*/
public KerasDense(Map<String, Object> layerConfig)
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
this(layerConfig, true);
}
/**
* Constructor from parsed Keras layer configuration dictionary.
*
* @param layerConfig dictionary containing Keras layer configuration
* @param enforceTrainingConfig whether to enforce training-related configuration options
* @throws InvalidKerasConfigurationException Invalid Keras config
* @throws UnsupportedKerasConfigurationException Unsupported Keras config
*/
public KerasDense(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
super(layerConfig, enforceTrainingConfig);
hasBias = KerasLayerUtils.getHasBiasFromConfig(layerConfig, conf);
numTrainableParams = hasBias ? 2 : 1;
LayerConstraint biasConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_B_CONSTRAINT(), conf, kerasMajorVersion);
LayerConstraint weightConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_W_CONSTRAINT(), conf, kerasMajorVersion);
IWeightInit init = KerasInitilizationUtils.getWeightInitFromConfig(layerConfig, conf.getLAYER_FIELD_INIT(),
enforceTrainingConfig, conf, kerasMajorVersion);
DenseLayer.Builder builder = new DenseLayer.Builder().name(this.layerName)
.nOut(KerasLayerUtils.getNOutFromConfig(layerConfig, conf))
.dropOut(this.dropout).activation(KerasActivationUtils.getIActivationFromConfig(layerConfig, conf))
.weightInit(init)
.biasInit(0.0)
.l1(this.weightL1Regularization).l2(this.weightL2Regularization)
.hasBias(hasBias);
if (biasConstraint != null)
builder.constrainBias(biasConstraint);
if (weightConstraint != null)
builder.constrainWeights(weightConstraint);
this.layer = builder.build();
}
/**
* Get DL4J DenseLayer.
*
* @return DenseLayer
*/
public DenseLayer getDenseLayer() {
return (DenseLayer) this.layer;
}
/**
* Get layer output type.
*
* @param inputType Array of InputTypes
* @return output type as InputType
* @throws InvalidKerasConfigurationException Invalid Keras config
*/
@Override
public InputType getOutputType(InputType... inputType) throws InvalidKerasConfigurationException {
/* Check whether layer requires a preprocessor for this InputType. */
InputPreProcessor preprocessor = getInputPreprocessor(inputType[0]);
if (preprocessor != null) {
return this.getDenseLayer().getOutputType(-1, preprocessor.getOutputType(inputType[0]));
}
return this.getDenseLayer().getOutputType(-1, inputType[0]);
}
/**
* Returns number of trainable parameters in layer.
*
* @return number of trainable parameters (2)
*/
@Override
public int getNumParams() {
return numTrainableParams;
}
/**
* Set weights for layer.
*
* @param weights Dense layer weights
*/
@Override
public void setWeights(Map<String, INDArray> weights) throws InvalidKerasConfigurationException {
this.weights = new HashMap<>();
if (weights.containsKey(conf.getKERAS_PARAM_NAME_W()))
this.weights.put(DefaultParamInitializer.WEIGHT_KEY, weights.get(conf.getKERAS_PARAM_NAME_W()));
else
throw new InvalidKerasConfigurationException(
"Parameter " + conf.getKERAS_PARAM_NAME_W() + " does not exist in weights");
if (hasBias) {
if (weights.containsKey(conf.getKERAS_PARAM_NAME_B()))
this.weights.put(DefaultParamInitializer.BIAS_KEY, weights.get(conf.getKERAS_PARAM_NAME_B()));
else
throw new InvalidKerasConfigurationException(
"Parameter " + conf.getKERAS_PARAM_NAME_B() + " does not exist in weights");
}
KerasLayerUtils.removeDefaultWeights(weights, conf);
}
}