public void Reset(IWeightFactory weightFactory, int batchSize)
{
foreach (LSTMAttentionDecoderCell item in m_decoders)
{
item.Reset(weightFactory, batchSize);
}
}
public void Reset(IWeightFactory weightFactory, int batchSize)
{
foreach (var item in m_decoders)
{
item.Reset(weightFactory, batchSize);
}
}
public ComputeGraphTensor(IWeightFactory weightFactory, int deviceId, bool needBack = true)
{
weightTensorFactory = weightFactory as WeightTensorFactory;
needs_backprop = needBack;
this.deviceId = deviceId;
}
public ComputeGraph(IWeightFactory weightFactory, bool needBack = true)
{
weightMatrixFactory = weightFactory as WeightMatrixFactory;
this.needs_backprop = needBack;
}
public void Reset(IWeightFactory weightFactory)
{
foreach (var item in decoders)
{
item.Reset(weightFactory);
}
}
public void SetBatchSize(IWeightFactory weightFactory, int batchSize)
{
foreach (var item in encoders)
{
item.SetBatchSize(weightFactory, batchSize);
}
}
public void Reset(IWeightFactory weightFactory, int batchSize)
{
foreach (LSTMCell item in encoders)
{
item.Reset(weightFactory, batchSize);
}
}
public void SetBatchSize(IWeightFactory weightFactory, int batchSize)
{
attentionLayer.SetBatchSize(batchSize);
foreach (var item in decoders)
{
item.SetBatchSize(weightFactory, batchSize);
}
}
public void Reset(IWeightFactory weightFactory)
{
foreach (var item in forwardEncoders)
{
item.Reset(weightFactory);
}
foreach (var item in backwardEncoders)
{
item.Reset(weightFactory);
}
}
public void Reset(IWeightFactory weightFactory, int batchSize)
{
foreach (var item in m_forwardEncoders)
{
item.Reset(weightFactory, batchSize);
}
foreach (var item in m_backwardEncoders)
{
item.Reset(weightFactory, batchSize);
}
}
public ComputeGraphTensor(IWeightFactory weightFactory, int deviceId, bool needBack = true, bool visNetwork = false, ConcurrentList <Action> backprop = null)
{
m_backprop = backprop != null ? backprop : new ConcurrentList <Action>();
m_weightTensorFactory = weightFactory as WeightTensorFactory;
m_needsBackprop = needBack;
m_deviceId = deviceId;
m_visNeuralNetwork = visNetwork;
if (m_visNeuralNetwork)
{
// Initialize parameters for neural network visualization
m_opsViz = new Microsoft.Msagl.Drawing.Graph();
m_setEdges = new HashSet <string>();
}
}
public void Reset(IWeightFactory weightFactory, int batchSize)
{
if (this.m_hidden != null)
{
this.m_hidden.Dispose();
this.m_hidden = null;
}
if (this.m_cell != null)
{
this.m_cell.Dispose();
this.m_cell = null;
}
this.m_hidden = weightFactory.CreateWeightTensor(batchSize, this.m_hdim, this.m_deviceId, true, $"{this.m_name}.{nameof(this.m_hidden)}", true);
this.m_cell = weightFactory.CreateWeightTensor(batchSize, this.m_hdim, this.m_deviceId, true, $"{this.m_name}.{nameof(this.m_cell)}", true);
}
public void Reset(IWeightFactory weightFactory, int batchSize)
{
if (m_hidden != null)
{
m_hidden.Dispose();
m_hidden = null;
}
if (m_cell != null)
{
m_cell.Dispose();
m_cell = null;
}
m_hidden = weightFactory.CreateWeightTensor(batchSize, m_hdim, m_deviceId, true, name: $"{m_name}.{nameof(m_hidden)}", isTrainable: true);
m_cell = weightFactory.CreateWeightTensor(batchSize, m_hdim, m_deviceId, true, name: $"{m_name}.{nameof(m_cell)}", isTrainable: true);
}
public ComputeGraphTensor(IWeightFactory weightFactory, int deviceId, bool needBack = true, ConcurrentList <Action> backprop = null, bool isSubGraph = false)
{
m_backprop = backprop != null ? backprop : new ConcurrentList <Action>();
m_weightTensorFactory = weightFactory as WeightTensorFactory;
m_needsBackprop = needBack;
m_deviceId = deviceId;
//m_visNeuralNetwork = visNetwork;
m_isSubGraph = isSubGraph;
//m_name2SubGraph = new Dictionary<string, Subgraph>();
//if (m_visNeuralNetwork)
//{
// // Initialize parameters for neural network visualization
// m_opsViz = new Microsoft.Msagl.Drawing.Graph();
// m_setEdges = new HashSet<string>();
//}
m_tensorsBindToCurrentGraph = new List <IWeightTensor>();
}
public void Reset(IWeightFactory weightFactory, int batchSize)
{
this.Hidden = weightFactory.CreateWeightTensor(batchSize, this.m_hiddenDim, this.m_deviceId, true, $"{this.m_name}.{nameof(this.Hidden)}", true);
this.Cell = weightFactory.CreateWeightTensor(batchSize, this.m_hiddenDim, this.m_deviceId, true, $"{this.m_name}.{nameof(this.Cell)}", true);
}
private void Reset(IWeightFactory weightFactory, Encoder encoder, Encoder reversEncoder, AttentionDecoder decoder)
{
encoder.Reset(weightFactory);
reversEncoder.Reset(weightFactory);
decoder.Reset(weightFactory);
}
public void Reset(IWeightFactory weightFactory)
{
Hidden = weightFactory.CreateWeights(m_batchSize, m_hdim, m_deviceId, true, name: $"{m_name}.{nameof(Hidden)}", isTrainable: true);
Cell = weightFactory.CreateWeights(m_batchSize, m_hdim, m_deviceId, true, name: $"{m_name}.{nameof(Cell)}", isTrainable: true);
}
public void Reset(IWeightFactory weightFactory, int batchSize)
{
}
public void Reset(IWeightFactory weightFactory, int batchSize)
{
Hidden = weightFactory.CreateWeightTensor(batchSize, m_hiddenDim, m_deviceId, true, name: $"{m_name}.{nameof(Hidden)}", isTrainable: true);
Cell = weightFactory.CreateWeightTensor(batchSize, m_hiddenDim, m_deviceId, true, name: $"{m_name}.{nameof(Cell)}", isTrainable: true);
}
public void SetBatchSize(IWeightFactory weightFactory, int batchSize)
{
this.batchSize = batchSize;
Reset(weightFactory);
}
public void Reset(IWeightFactory weightFactory)
{
ht = weightFactory.CreateWeights(batchSize, hdim, deviceId, true);
ct = weightFactory.CreateWeights(batchSize, hdim, deviceId, true);
}
public ComputeGraphMKL(IWeightFactory weightFactory, bool needBack = true)
: base(weightFactory, needBack)
{
}
public void Reset(IWeightFactory weightFactory)
{
}
