public void Train(int nIteration, TRAIN_STEP step)
{
m_mycaffe.Log.Enable = false;
// Run data/clip groups > 1 in non batch mode.
if (m_nRecurrentSequenceLength != 1 && m_rgData != null && m_rgData.Count > 1 && m_rgClip != null)
{
prepareBlob(m_blobActionOneHot1, m_blobActionOneHot);
prepareBlob(m_blobDiscountedR1, m_blobDiscountedR);
prepareBlob(m_blobPolicyGradient1, m_blobPolicyGradient);
for (int i = 0; i < m_rgData.Count; i++)
{
copyBlob(i, m_blobActionOneHot1, m_blobActionOneHot);
copyBlob(i, m_blobDiscountedR1, m_blobDiscountedR);
copyBlob(i, m_blobPolicyGradient1, m_blobPolicyGradient);
List <Datum> rgData1 = new List <Datum>()
{
m_rgData[i]
};
List <Datum> rgClip1 = new List <Datum>()
{
m_rgClip[i]
};
m_memData.AddDatumVector(rgData1, rgClip1, 1, true, true);
m_solver.Step(1, step, true, false, true, true);
}
m_blobActionOneHot.ReshapeLike(m_blobActionOneHot1);
m_blobDiscountedR.ReshapeLike(m_blobDiscountedR1);
m_blobPolicyGradient.ReshapeLike(m_blobPolicyGradient1);
m_rgData = null;
m_rgClip = null;
}
else
{
m_solver.Step(1, step, true, false, true, true);
}
m_colAccumulatedGradients.Accumulate(m_mycaffe.Cuda, m_net.learnable_parameters, true);
if (nIteration % m_nMiniBatch == 0 || step == TRAIN_STEP.BACKWARD || step == TRAIN_STEP.BOTH)
{
m_net.learnable_parameters.CopyFrom(m_colAccumulatedGradients, true);
m_colAccumulatedGradients.SetDiff(0);
m_solver.ApplyUpdate(nIteration);
m_net.ClearParamDiffs();
}
m_mycaffe.Log.Enable = true;
}
public void Train(int nIteration)
{
m_mycaffe.Log.Enable = false;
m_solver.Step(1, TRAIN_STEP.NONE, true, false, true);
m_colAccumulatedGradients.Accumulate(m_mycaffe.Cuda, m_net.learnable_parameters, true);
if (nIteration % m_nMiniBatch == 0)
{
m_net.learnable_parameters.CopyFrom(m_colAccumulatedGradients, true);
m_colAccumulatedGradients.SetDiff(0);
m_solver.ApplyUpdate(nIteration);
m_net.ClearParamDiffs();
}
m_mycaffe.Log.Enable = true;
}
/// <summary>
/// Train the model at the current iteration.
/// </summary>
/// <param name="nIteration">Specifies the current iteration.</param>
/// <param name="rgSamples">Contains the samples to train the model with along with the priorities associated with the samples.</param>
/// <param name="nActionCount">Specifies the number of actions in the action set.</param>
public void Train(int nIteration, MemoryCollection rgSamples, int nActionCount)
{
m_rgSamples = rgSamples;
if (m_nActionCount != nActionCount)
{
throw new Exception("The logit output of '" + m_nActionCount.ToString() + "' does not match the action count of '" + nActionCount.ToString() + "'!");
}
// Get next_q_values
m_mycaffe.Log.Enable = false;
setNextStateData(m_netTarget, rgSamples);
m_netTarget.ForwardFromTo(0, m_netTarget.layers.Count - 2);
setCurrentStateData(m_netOutput, rgSamples);
m_memLoss.OnGetLoss += m_memLoss_ComputeTdLoss;
if (m_nMiniBatch == 1)
{
m_solver.Step(1);
}
else
{
m_solver.Step(1, TRAIN_STEP.NONE, true, m_bUseAcceleratedTraining, true, true);
m_colAccumulatedGradients.Accumulate(m_mycaffe.Cuda, m_netOutput.learnable_parameters, true);
if (nIteration % m_nMiniBatch == 0)
{
m_netOutput.learnable_parameters.CopyFrom(m_colAccumulatedGradients, true);
m_colAccumulatedGradients.SetDiff(0);
m_dfLearningRate = m_solver.ApplyUpdate(nIteration);
m_netOutput.ClearParamDiffs();
}
}
m_memLoss.OnGetLoss -= m_memLoss_ComputeTdLoss;
m_mycaffe.Log.Enable = true;
resetNoise(m_netOutput);
resetNoise(m_netTarget);
}
