public AttentionDecoder(int sdim, int hdim, int dim, int depth)
{
Attention = new AttentionUnit(hdim);
this.hdim = hdim;
this.dim = dim;
this.depth = depth;
decoders.Add(new LSTMAttentionDecoderCell(sdim, hdim, dim));
for (int i = 1; i < depth; i++)
{
decoders.Add(new LSTMAttentionDecoderCell(0, hdim, hdim));
}
}
public AttentionDecoder(int batchSize, int hdim, int dim, int context, int depth, ArchTypeEnums archType, int deviceId, bool isDefaultDevice)
{
attentionLayer = new AttentionUnit(batchSize, hdim, context, archType, deviceId, isDefaultDevice);
this.hdim = hdim;
this.dim = dim;
this.depth = depth;
decoders.Add(new LSTMAttentionDecoderCell(batchSize, hdim, dim, archType, deviceId, isDefaultDevice));
for (int i = 1; i < depth; i++)
{
decoders.Add(new LSTMAttentionDecoderCell(batchSize, hdim, hdim, archType, deviceId, isDefaultDevice));
}
}
public AttentionDecoder(string name, int batchSize, int hdim, int dim, int context, int depth, int deviceId)
{
attentionLayer = new AttentionUnit($"{name}.AttnUnit", batchSize, hdim, context, deviceId);
this.hdim = hdim;
this.dim = dim;
this.depth = depth;
m_name = name;
decoders.Add(new LSTMAttentionDecoderCell($"{name}.LSTMAttn_0", batchSize, hdim, dim, context, deviceId));
for (int i = 1; i < depth; i++)
{
decoders.Add(new LSTMAttentionDecoderCell($"{name}.LSTMAttn_{i}", batchSize, hdim, hdim, context, deviceId));
}
}
public AttentionDecoder(string name, int hiddenDim, int embeddingDim, int contextDim, int depth, int deviceId)
{
m_attentionLayer = new AttentionUnit($"{name}.AttnUnit", hiddenDim, contextDim, deviceId);
m_name = name;
m_hdim = hiddenDim;
m_dim = embeddingDim;
m_context = contextDim;
m_depth = depth;
m_deviceId = deviceId;
m_decoders.Add(new LSTMAttentionDecoderCell($"{name}.LSTMAttn_0", hiddenDim, embeddingDim, contextDim, deviceId));
for (int i = 1; i < depth; i++)
{
m_decoders.Add(new LSTMAttentionDecoderCell($"{name}.LSTMAttn_{i}", hiddenDim, hiddenDim, contextDim, deviceId));
}
}
public AttentionDecoder(string name, int hiddenDim, int embeddingDim, int contextDim, float dropoutRatio, int depth, int deviceId, bool enableCoverageModel, bool isTrainable)
{
m_name = name;
m_hdim = hiddenDim;
m_embDim = embeddingDim;
m_context = contextDim;
m_depth = depth;
m_deviceId = deviceId;
// m_outputDim = outputDim;
m_dropoutRatio = dropoutRatio;
m_enableCoverageModel = enableCoverageModel;
m_isTrainable = isTrainable;
m_attentionLayer = new AttentionUnit($"{name}.AttnUnit", hiddenDim, contextDim, deviceId, enableCoverageModel, isTrainable: isTrainable);
m_decoders.Add(new LSTMAttentionDecoderCell($"{name}.LSTMAttn_0", hiddenDim, embeddingDim, contextDim, deviceId, isTrainable));
for (int i = 1; i < depth; i++)
{
m_decoders.Add(new LSTMAttentionDecoderCell($"{name}.LSTMAttn_{i}", hiddenDim, hiddenDim, contextDim, deviceId, isTrainable));
}
// m_decoderFFLayer = new FeedForwardLayer($"{name}.FeedForward", hiddenDim, outputDim, 0.0f, deviceId: deviceId, isTrainable: isTrainable);
}
public INeuralUnit CloneToDeviceAt(int deviceId)
{
AttentionUnit a = new AttentionUnit(m_name, m_hiddenDim, m_contextDim, deviceId, m_enableCoverageModel, m_isTrainable);
return(a);
}
public INeuralUnit CloneToDeviceAt(int deviceId)
{
AttentionUnit a = new AttentionUnit(m_name, m_hiddenDim, m_contextDim, deviceId);
return(a);
}