deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/embeddings/KerasEmbedding.java
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* * terms of the Apache License, Version 2.0 which is available at
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package org.deeplearning4j.nn.modelimport.keras.layers.embeddings;
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.api.layers.LayerConstraint;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.RNNFormat;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.EmbeddingSequenceLayer;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasConstraintUtils;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.nn.params.DefaultParamInitializer;
import org.deeplearning4j.nn.weights.IWeightInit;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import java.util.HashMap;
import java.util.Map;
import java.util.Set;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasInitilizationUtils.getWeightInitFromConfig;
import static org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils.getNOutFromConfig;
/**
* Imports an Embedding layer from Keras.
*
* @author dave@skymind.io
*/
@Slf4j
@Data
@EqualsAndHashCode(callSuper = false)
public class KerasEmbedding extends KerasLayer {
private final int NUM_TRAINABLE_PARAMS = 1;
private boolean zeroMasking;
private int inputDim;
private int inputLength;
private boolean inferInputLength;
/**
* Pass through constructor for unit tests
*
* @throws UnsupportedKerasConfigurationException Unsupported Keras config
*/
public KerasEmbedding() throws UnsupportedKerasConfigurationException {
}
/**
* 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 KerasEmbedding(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 KerasEmbedding(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
super(layerConfig, enforceTrainingConfig);
this.inputDim = getInputDimFromConfig(layerConfig);
this.inputLength = getInputLengthFromConfig(layerConfig);
this.inferInputLength = this.inputLength == 0;
if (this.inferInputLength)
this.inputLength = 1; // set dummy value, so shape inference works
this.zeroMasking = KerasLayerUtils.getZeroMaskingFromConfig(layerConfig, conf);
if (zeroMasking)
log.warn("Masking in keras and DL4J work differently. We do not completely support mask_zero flag " +
"on Embedding layers. Zero Masking for the Embedding layer only works with unidirectional LSTM for now."
+ " If you want to have this behaviour for your imported model " +
"in DL4J, apply masking as a pre-processing step to your input." +
"See https://deeplearning4j.konduit.ai/models/recurrent#masking-one-to-many-many-to-one-and-sequence-classification for more on this.");
IWeightInit init = getWeightInitFromConfig(layerConfig,
conf.getLAYER_FIELD_EMBEDDING_INIT(),
enforceTrainingConfig,
conf, kerasMajorVersion);
LayerConstraint embeddingConstraint = KerasConstraintUtils.getConstraintsFromConfig(
layerConfig, conf.getLAYER_FIELD_EMBEDDINGS_CONSTRAINT(), conf, kerasMajorVersion);
int nOutFromConfig = getNOutFromConfig(layerConfig, conf);
EmbeddingSequenceLayer.Builder builder = new EmbeddingSequenceLayer.Builder()
.name(this.layerName)
.nIn(inputDim)
.inputLength(inputLength)
.inferInputLength(inferInputLength)
.nOut(nOutFromConfig)
.dropOut(this.dropout).activation(Activation.IDENTITY)
.weightInit(init)
.biasInit(0.0)
.l1(this.weightL1Regularization)
.l2(this.weightL2Regularization)
.outputDataFormat(RNNFormat.NWC)
.hasBias(false);
if (embeddingConstraint != null)
builder.constrainWeights(embeddingConstraint);
this.layer = builder.build();
this.inputShape = new int[]{inputDim,1};
}
/**
* Get DL4J Embedding Sequence layer.
*
* @return Embedding Sequence layer
*/
public EmbeddingSequenceLayer getEmbeddingLayer() {
return (EmbeddingSequenceLayer) 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.getEmbeddingLayer().getOutputType(-1, preprocessor.getOutputType(inputType[0]));
}
return this.getEmbeddingLayer().getOutputType(-1, inputType[0]);
}
/**
* Returns number of trainable parameters in layer.
*
* @return number of trainable parameters (1)
*/
@Override
public int getNumParams() {
return NUM_TRAINABLE_PARAMS;
}
/**
* Set weights for layer.
*
* @param weights Embedding layer weights
*/
@Override
public void setWeights(Map<String, INDArray> weights) throws InvalidKerasConfigurationException {
this.weights = new HashMap<>();
// TODO: "embeddings" is incorrectly read as "s" for some applications
if (weights.containsKey("s")) {
INDArray kernel = weights.get("s");
weights.remove("s");
weights.put(conf.getLAYER_FIELD_EMBEDDING_WEIGHTS(), kernel);
}
if (!weights.containsKey(conf.getLAYER_FIELD_EMBEDDING_WEIGHTS()))
throw new InvalidKerasConfigurationException(
"Parameter " + conf.getLAYER_FIELD_EMBEDDING_WEIGHTS() + " does not exist in weights");
INDArray kernel = weights.get(conf.getLAYER_FIELD_EMBEDDING_WEIGHTS());
if (this.zeroMasking) {
kernel.putRow(0, Nd4j.zeros(kernel.columns()));
}
this.weights.put(DefaultParamInitializer.WEIGHT_KEY, kernel);
if (weights.size() > 2) {
Set<String> paramNames = weights.keySet();
paramNames.remove(conf.getLAYER_FIELD_EMBEDDING_WEIGHTS());
String unknownParamNames = paramNames.toString();
log.warn("Attempting to set weights for unknown parameters: "
+ unknownParamNames.substring(1, unknownParamNames.length() - 1));
}
}
/**
* Get Keras input length from Keras layer configuration. In Keras input_length, if present, denotes
* the number of indices to embed per mini-batch, i.e. input will be of shape (mb, input_length)
* and (mb, 1) else.
*
* @param layerConfig dictionary containing Keras layer configuration
* @return input length as int
*/
private int getInputLengthFromConfig(Map<String, Object> layerConfig) throws InvalidKerasConfigurationException {
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
if (!innerConfig.containsKey(conf.getLAYER_FIELD_INPUT_LENGTH()))
throw new InvalidKerasConfigurationException(
"Keras Embedding layer config missing " + conf.getLAYER_FIELD_INPUT_LENGTH() + " field");
if (innerConfig.get(conf.getLAYER_FIELD_INPUT_LENGTH()) == null) {
return 0;
} else {
return (int) innerConfig.get(conf.getLAYER_FIELD_INPUT_LENGTH());
}
}
/**
* Get Keras input dimension from Keras layer configuration.
*
* @param layerConfig dictionary containing Keras layer configuration
* @return input dim as int
*/
private int getInputDimFromConfig(Map<String, Object> layerConfig) throws InvalidKerasConfigurationException {
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
if (!innerConfig.containsKey(conf.getLAYER_FIELD_INPUT_DIM()))
throw new InvalidKerasConfigurationException(
"Keras Embedding layer config missing " + conf.getLAYER_FIELD_INPUT_DIM() + " field");
return (int) innerConfig.get(conf.getLAYER_FIELD_INPUT_DIM());
}
}