public SequenceLabel(string modelFilePath, ProcessorTypeEnums processorType, int[] deviceIds, float dropoutRatio = 0.0f, int maxSntSize = 128)
: base(deviceIds, processorType, modelFilePath)
{
this.m_dropoutRatio = dropoutRatio;
this.m_modelMetaData = this.LoadModel(this.CreateTrainableParameters) as SeqLabelModelMetaData;
this.m_maxSntSize = maxSntSize;
}
private bool CreateTrainableParameters(IModelMetaData mmd)
{
Logger.WriteLine($"Creating encoders and decoders...");
SeqLabelModelMetaData modelMetaData = mmd as SeqLabelModelMetaData;
RoundArray <int> raDeviceIds = new RoundArray <int>(DeviceIds);
if (modelMetaData.EncoderType == EncoderTypeEnums.BiLSTM)
{
m_encoder = new MultiProcessorNetworkWrapper <IEncoder>(
new BiEncoder("BiLSTMEncoder", modelMetaData.HiddenDim, modelMetaData.EmbeddingDim, modelMetaData.EncoderLayerDepth, raDeviceIds.GetNextItem(), isTrainable: true), DeviceIds);
m_decoderFFLayer = new MultiProcessorNetworkWrapper <FeedForwardLayer>(new FeedForwardLayer("FeedForward", modelMetaData.HiddenDim * 2, modelMetaData.Vocab.TargetWordSize, dropoutRatio: 0.0f, deviceId: raDeviceIds.GetNextItem(), isTrainable: true), DeviceIds);
}
else
{
m_encoder = new MultiProcessorNetworkWrapper <IEncoder>(
new TransformerEncoder("TransformerEncoder", modelMetaData.MultiHeadNum, modelMetaData.HiddenDim, modelMetaData.EmbeddingDim, modelMetaData.EncoderLayerDepth, m_dropoutRatio, raDeviceIds.GetNextItem(), isTrainable: true), DeviceIds);
m_decoderFFLayer = new MultiProcessorNetworkWrapper <FeedForwardLayer>(new FeedForwardLayer("FeedForward", modelMetaData.HiddenDim, modelMetaData.Vocab.TargetWordSize, dropoutRatio: 0.0f, deviceId: raDeviceIds.GetNextItem(), isTrainable: true), DeviceIds);
}
m_srcEmbedding = new MultiProcessorNetworkWrapper <IWeightTensor>(new WeightTensor(new long[2] {
modelMetaData.Vocab.SourceWordSize, modelMetaData.EmbeddingDim
}, raDeviceIds.GetNextItem(), normal: true, name: "SrcEmbeddings", isTrainable: true), DeviceIds);
// m_crfDecoder = new CRFDecoder(modelMetaData.Vocab.TargetWordSize);
return(true);
}
public SequenceLabel(int hiddenDim, int embeddingDim, int encoderLayerDepth, int multiHeadNum, EncoderTypeEnums encoderType,
float dropoutRatio, Vocab vocab, int[] deviceIds, ProcessorTypeEnums processorType, string modelFilePath) :
base(deviceIds, processorType, modelFilePath)
{
m_modelMetaData = new SeqLabelModelMetaData(hiddenDim, embeddingDim, encoderLayerDepth, multiHeadNum, encoderType, vocab);
m_dropoutRatio = dropoutRatio;
//Initializng weights in encoders and decoders
CreateTrainableParameters(m_modelMetaData);
}
public SequenceLabel(string modelFilePath, ProcessorTypeEnums processorType, int[] deviceIds, float dropoutRatio = 0.0f)
: base(deviceIds, processorType, modelFilePath)
{
m_dropoutRatio = dropoutRatio;
m_modelMetaData = LoadModel(CreateTrainableParameters) as SeqLabelModelMetaData;
}
