/// <summary>
/// Re-initialize the parameters of the layer.
/// </summary>
/// <param name="target">Specifies the weights to target (e.g. weights, bias or both).</param>
/// <returns>When handled, this method returns <i>true</i>, otherwise <i>false</i>.</returns>
public override bool ReInitializeParameters(WEIGHT_TARGET target)
{
base.ReInitializeParameters(target);
m_ipUa.ReInitializeParameters(target);
m_ipWa.ReInitializeParameters(target);
return(true);
}
/// <summary>
/// Re-initialize the parameters of the layer.
/// </summary>
/// <param name="target">Specifies the weights to target (e.g. weights, bias or both).</param>
/// <returns>When handled, this method returns <i>true</i>, otherwise <i>false</i>.</returns>
public override bool ReInitializeParameters(WEIGHT_TARGET target)
{
base.ReInitializeParameters(target);
if (target == WEIGHT_TARGET.BOTH || target == WEIGHT_TARGET.WEIGHTS)
{
for (int i = 0; i < 3; i++)
{
m_colBlobs[i].SetData(0);
}
}
return(true);
}
/// <summary>
/// Re-initialize the parameters of the layer.
/// </summary>
/// <param name="target">Specifies the weights to target (e.g. weights, bias or both).</param>
/// <returns>When handled, this method returns <i>true</i>, otherwise <i>false</i>.</returns>
public override bool ReInitializeParameters(WEIGHT_TARGET target)
{
base.ReInitializeParameters(target);
if (target == WEIGHT_TARGET.BOTH || target == WEIGHT_TARGET.BIAS)
{
FillerParameter fp = m_param.bias_param.filler;
if (fp == null)
{
fp = new FillerParameter("constant", 0.0);
}
Filler <T> filler = Filler <T> .Create(m_cuda, m_log, fp);
filler.Fill(m_colBlobs[0]);
}
return(true);
}
/// <summary>
/// Re-initialize the parameters of the layer.
/// </summary>
/// <param name="target">Specifies the weights to target (e.g. weights, bias or both).</param>
/// <returns>When handled, this method returns <i>true</i>, otherwise <i>false</i>.</returns>
public override bool ReInitializeParameters(WEIGHT_TARGET target)
{
base.ReInitializeParameters(target);
if (target == WEIGHT_TARGET.WEIGHTS || target == WEIGHT_TARGET.BOTH)
{
Filler <T> filler = Filler <T> .Create(m_cuda, m_log, m_param.convolution_param.weight_filler);
filler.Fill(m_colBlobs[0]);
}
if (m_param.convolution_param.bias_term && m_colBlobs.Count > 1 && (target == WEIGHT_TARGET.BOTH || target == WEIGHT_TARGET.BIAS))
{
Filler <T> fillerBias = Filler <T> .Create(m_cuda, m_log, m_param.convolution_param.bias_filler);
fillerBias.Fill(m_colBlobs[1]);
}
return(true);
}
/// <summary>
/// Re-initialize the parameters of the layer.
/// </summary>
/// <param name="target">Specifies the weights to target (e.g. weights, bias or both).</param>
/// <returns>When handled, this method returns <i>true</i>, otherwise <i>false</i>.</returns>
public override bool ReInitializeParameters(WEIGHT_TARGET target)
{
base.ReInitializeParameters(target);
if (target == WEIGHT_TARGET.BOTH || target == WEIGHT_TARGET.WEIGHTS)
{
Filler <T> weight_filler = Filler <T> .Create(m_cuda, m_log, m_param.inner_product_param.weight_filler);
weight_filler.Fill(m_colBlobs[0]);
}
if (m_param.inner_product_param.bias_term && m_colBlobs.Count > 1 && (target == WEIGHT_TARGET.BOTH || target == WEIGHT_TARGET.BIAS))
{
Filler <T> bias_filler = Filler <T> .Create(m_cuda, m_log, m_param.inner_product_param.bias_filler);
bias_filler.Fill(m_colBlobs[1]);
}
return(true);
}
/// <summary>
/// Re-initialize the parameters of the layer.
/// </summary>
/// <param name="target">Specifies the weights to target (e.g. weights, bias or both).</param>
/// <returns>When handled, this method returns <i>true</i>, otherwise <i>false</i>.</returns>
public override bool ReInitializeParameters(WEIGHT_TARGET target)
{
base.ReInitializeParameters(target);
if (target == WEIGHT_TARGET.BOTH || target == WEIGHT_TARGET.WEIGHTS)
{
Filler <T> weight_filler = Filler <T> .Create(m_cuda, m_log, m_param.lstm_simple_param.weight_filler);
weight_filler.Fill(m_colBlobs[0]);
weight_filler.Fill(m_colBlobs[1]);
}
if (target == WEIGHT_TARGET.BOTH || target == WEIGHT_TARGET.BIAS)
{
Filler <T> bias_filler = Filler <T> .Create(m_cuda, m_log, m_param.lstm_simple_param.bias_filler);
bias_filler.Fill(m_colBlobs[2]);
// Initialize the bias for the forget gate to 5.0 as described in the
// Clockwork RNN paper:
// [1] Koutnik, J., Greff, K., Gomez, F., Schmidhuber, J., 'A Clockwork RNN', 2014"
if (m_param.lstm_simple_param.enable_clockwork_forgetgate_bias)
{
double[] rgBias = convertD(m_colBlobs[2].mutable_cpu_data);
for (int i = m_nH; i < 2 * m_nH; i++)
{
rgBias[i] = 5.0;
}
m_colBlobs[2].mutable_cpu_data = convert(rgBias);
}
}
return(true);
}
