public Brain(MyCaffeControl <T> mycaffe, PropertySet properties, CryptoRandom random, Phase phase)
{
m_mycaffe = mycaffe;
m_net = mycaffe.GetInternalNet(phase);
m_solver = mycaffe.GetInternalSolver();
m_properties = properties;
m_random = random;
m_memData = m_net.FindLayer(LayerParameter.LayerType.MEMORYDATA, null) as MemoryDataLayer <T>;
m_memLoss = m_net.FindLayer(LayerParameter.LayerType.MEMORY_LOSS, null) as MemoryLossLayer <T>;
m_softmax = m_net.FindLayer(LayerParameter.LayerType.SOFTMAX, null) as SoftmaxLayer <T>;
if (m_memData == null)
{
throw new Exception("Could not find the MemoryData Layer!");
}
if (m_memLoss == null)
{
throw new Exception("Could not find the MemoryLoss Layer!");
}
m_memData.OnDataPack += memData_OnDataPack;
m_memLoss.OnGetLoss += memLoss_OnGetLoss;
m_blobDiscountedR = new Blob <T>(mycaffe.Cuda, mycaffe.Log);
m_blobPolicyGradient = new Blob <T>(mycaffe.Cuda, mycaffe.Log);
m_blobActionOneHot = new Blob <T>(mycaffe.Cuda, mycaffe.Log);
m_blobDiscountedR1 = new Blob <T>(mycaffe.Cuda, mycaffe.Log);
m_blobPolicyGradient1 = new Blob <T>(mycaffe.Cuda, mycaffe.Log);
m_blobActionOneHot1 = new Blob <T>(mycaffe.Cuda, mycaffe.Log);
m_blobLoss = new Blob <T>(mycaffe.Cuda, mycaffe.Log);
m_blobAprobLogit = new Blob <T>(mycaffe.Cuda, mycaffe.Log);
if (m_softmax != null)
{
LayerParameter p = new LayerParameter(LayerParameter.LayerType.SOFTMAXCROSSENTROPY_LOSS);
p.loss_weight.Add(1);
p.loss_weight.Add(0);
p.loss_param.normalization = LossParameter.NormalizationMode.NONE;
m_softmaxCe = new SoftmaxCrossEntropyLossLayer <T>(mycaffe.Cuda, mycaffe.Log, p);
}
m_colAccumulatedGradients = m_net.learnable_parameters.Clone();
m_colAccumulatedGradients.SetDiff(0);
int nMiniBatch = mycaffe.CurrentProject.GetBatchSize(phase);
if (nMiniBatch != 0)
{
m_nMiniBatch = nMiniBatch;
}
m_nMiniBatch = m_properties.GetPropertyAsInt("MiniBatch", m_nMiniBatch);
}
