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, false, false, true); // accumulate grad over batch
if (nIteration % m_nMiniBatch == 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);
}
//-----------------------------------------------------------------------------------------
// Simple Classification (using direct net surgery)
//-----------------------------------------------------------------------------------------
/// <summary>
/// The SimpleClassification sample is designed to show how to manually train the MNIST dataset using raw image data stored
/// in the \ProgramData\MyCaffe\test_data\images\mnist\training directory (previously loaded with the 'Export Images'
/// sample above).
/// </summary>
/// <remarks>
/// IMPORTANT: This sample is for demonstration, using the Simplest Classification method is the fastest recommended method that uses the Image Database.
///
/// This sample requires that you have already loaded the MNIST dataset into SQL (or SQLEXPRESS) using the MyCaffe
/// Test Application by selecting its 'Database | Load MNIST...' menu item.
/// </remarks>
/// <param name="sender">Specifies the event sender.</param>
/// <param name="e">Specifies the event argument.</param>
private void btnSimpleClassification_Click(object sender, EventArgs e)
{
Stopwatch sw = new Stopwatch();
int nBatchSize = 32;
SettingsCaffe settings = new SettingsCaffe();
settings.GpuIds = "0";
if (!Directory.Exists(m_strImageDirTesting) || !Directory.Exists(m_strImageDirTraining))
{
MessageBox.Show("You must first export the MNIST images by pressing the Export button!", "Export Needed", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
m_rgstrTrainingFiles = Directory.GetFiles(m_strImageDirTraining);
m_rgstrTrainingFiles = m_rgstrTrainingFiles.Where(p => p.Contains(".png")).ToArray();
m_rgstrTestingFiles = Directory.GetFiles(m_strImageDirTesting);
m_rgstrTestingFiles = m_rgstrTestingFiles.Where(p => p.Contains(".png")).ToArray();
string strSolver;
string strModel;
load_descriptors("mnist", out strSolver, out strModel); // Load the descriptors from their respective files (installed by MyCaffe Test Application install)
strModel = fixup_model(strModel, nBatchSize);
MyCaffeControl <float> mycaffe = new MyCaffeControl <float>(settings, m_log, m_evtCancel);
mycaffe.Load(Phase.TRAIN, // using the training phase.
strSolver, // solver descriptor, that specifies to use the SGD solver.
strModel, // simple LENET model descriptor.
null, null, null, false, null, false, false); // no weights are loaded and no image database is used.
// Perform your own training
Solver <float> solver = mycaffe.GetInternalSolver();
Net <float> net = mycaffe.GetInternalNet(Phase.TRAIN);
Blob <float> dataBlob = net.blob_by_name("data");
Blob <float> labelBlob = net.blob_by_name("label");
sw.Start();
int nIterations = 5000;
for (int i = 0; i < nIterations; i++)
{
// Load the data into the data and label blobs.
loadData(m_rgstrTrainingFiles, nBatchSize, dataBlob, labelBlob);
// Run the forward and backward passes.
double dfLoss;
net.Forward(out dfLoss);
net.ClearParamDiffs();
net.Backward();
// Apply the gradients calculated during Backward.
solver.ApplyUpdate(i);
// Output the loss.
if (sw.Elapsed.TotalMilliseconds > 1000)
{
m_log.Progress = (double)i / (double)nIterations;
m_log.WriteLine("Loss = " + dfLoss.ToString());
sw.Restart();
}
}
// Run testing using the MyCaffe control (who's internal Run net is already updated
// for it shares its weight memory with the training net.
net = mycaffe.GetInternalNet(Phase.TEST);
dataBlob = net.blob_by_name("data");
labelBlob = net.blob_by_name("label");
float fTotalAccuracy = 0;
nIterations = 100;
for (int i = 0; i < nIterations; i++)
{
// Load the data into the data and label blobs.
loadData(m_rgstrTestingFiles, nBatchSize, dataBlob, labelBlob);
// Run the forward pass.
double dfLoss;
BlobCollection <float> res = net.Forward(out dfLoss);
fTotalAccuracy += res[0].GetData(0);
// Output the training progress.
if (sw.Elapsed.TotalMilliseconds > 1000)
{
m_log.Progress = (double)i / (double)nIterations;
m_log.WriteLine("training...");
sw.Restart();
}
}
double dfAccuracy = (double)fTotalAccuracy / (double)nIterations;
m_log.WriteLine("Accuracy = " + dfAccuracy.ToString("P"));
MessageBox.Show("Average Accuracy = " + dfAccuracy.ToString("P"), "Traing/Test on MNIST Completed", MessageBoxButtons.OK, MessageBoxIcon.Information);
}
//-----------------------------------------------------------------------------------------
// Simple Classification (using direct net surgery)
//-----------------------------------------------------------------------------------------
/// <summary>
/// The SimpleClassification sample is designed to show how to manually train the MNIST dataset using raw image data stored
/// in the \ProgramData\MyCaffe\test_data\images\mnist\training directory (previously loaded with the 'Export Images'
/// sample above).
/// </summary>
/// <remarks>
/// This sample requires that you have already loaded the MNIST dataset into SQL (or SQLEXPRESS) using the MyCaffe
/// Test Application by selecting its 'Database | Load MNIST...' menu item.
/// </remarks>
/// <param name="sender">Specifies the event sender.</param>
/// <param name="e">Specifies the event argument.</param>
private void btnSimpleClassification_Click(object sender, EventArgs e)
{
Stopwatch sw = new Stopwatch();
int nBatchSize = 32;
SettingsCaffe settings = new SettingsCaffe();
settings.GpuIds = "0";
if (!Directory.Exists(m_strImageDirTraining) || !Directory.Exists(m_strImageDirTesting))
{
string strMsg = "You must first expand the MNIST dataset into the following directories:" + Environment.NewLine;
strMsg += "Training Images: '" + m_strImageDirTraining + "'" + Environment.NewLine;
strMsg += "Testing Images: '" + m_strImageDirTesting + "'" + Environment.NewLine + Environment.NewLine;
strMsg += "If you have Microsoft SQL or SQL Express installed, selecting the 'Export' button from the 'ImageClassification' project will export these images for you." + Environment.NewLine + Environment.NewLine;
strMsg += "If you DO NOT have Microsoft SQL or SQL Express, running the MyCaffe Test Application and selecting the 'Database | Load MNIST...' menu item with the 'Export to file only' check box checked, will export the images for you without SQL." + Environment.NewLine + Environment.NewLine;
strMsg += "To get the MNIST *.gz data files, please see http://yann.lecun.com/exdb/mnist/";
MessageBox.Show(strMsg, "Images Not Found", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
m_rgstrTrainingFiles = Directory.GetFiles(m_strImageDirTraining);
m_rgstrTrainingFiles = m_rgstrTrainingFiles.Where(p => p.Contains(".png")).ToArray();
m_rgstrTestingFiles = Directory.GetFiles(m_strImageDirTesting);
m_rgstrTestingFiles = m_rgstrTestingFiles.Where(p => p.Contains(".png")).ToArray();
string strSolver;
string strModel;
load_descriptors("mnist", out strSolver, out strModel); // Load the descriptors from their respective files (installed by MyCaffe Test Application install)
strModel = fixup_model(strModel, nBatchSize);
MyCaffeControl <float> mycaffe = new MyCaffeControl <float>(settings, m_log, m_evtCancel);
mycaffe.LoadLite(Phase.TRAIN, // using the training phase.
strSolver, // solver descriptor, that specifies to use the SGD solver.
strModel, // simple LENET model descriptor.
null); // no weights are loaded.
// Perform your own training
Solver <float> solver = mycaffe.GetInternalSolver();
Net <float> net = mycaffe.GetInternalNet(Phase.TRAIN);
Blob <float> dataBlob = net.blob_by_name("data");
Blob <float> labelBlob = net.blob_by_name("label");
sw.Start();
int nIterations = 5000;
for (int i = 0; i < nIterations; i++)
{
// Load the data into the data and label blobs.
loadData(m_rgstrTrainingFiles, nBatchSize, dataBlob, labelBlob);
// Run the forward and backward passes.
double dfLoss;
net.Forward(out dfLoss);
net.ClearParamDiffs();
net.Backward();
// Apply the gradients calculated during Backward.
solver.ApplyUpdate(i);
// Output the loss.
if (sw.Elapsed.TotalMilliseconds > 1000)
{
m_log.Progress = (double)i / (double)nIterations;
m_log.WriteLine("Loss = " + dfLoss.ToString());
sw.Restart();
}
}
// Run testing using the MyCaffe control (who's internal Run net is already updated
// for it shares its weight memory with the training net.
net = mycaffe.GetInternalNet(Phase.TEST);
dataBlob = net.blob_by_name("data");
labelBlob = net.blob_by_name("label");
float fTotalAccuracy = 0;
nIterations = 100;
for (int i = 0; i < nIterations; i++)
{
// Load the data into the data and label blobs.
loadData(m_rgstrTestingFiles, nBatchSize, dataBlob, labelBlob);
// Run the forward pass.
double dfLoss;
BlobCollection <float> res = net.Forward(out dfLoss);
fTotalAccuracy += res[0].GetData(0);
// Output the training progress.
if (sw.Elapsed.TotalMilliseconds > 1000)
{
m_log.Progress = (double)i / (double)nIterations;
m_log.WriteLine("training...");
sw.Restart();
}
}
double dfAccuracy = (double)fTotalAccuracy / (double)nIterations;
m_log.WriteLine("Accuracy = " + dfAccuracy.ToString("P"));
MessageBox.Show("Average Accuracy = " + dfAccuracy.ToString("P"), "Traing/Test on MNIST Completed", MessageBoxButtons.OK, MessageBoxIcon.Information);
// Save the trained weights for use later.
saveWeights(mycaffe, "my_weights");
Bitmap bmp = new Bitmap(m_rgstrTestingFiles[0]);
ResultCollection results = mycaffe.Run(bmp);
MyCaffeControl <float> mycaffe2 = mycaffe.Clone(0);
ResultCollection results2 = mycaffe2.Run(bmp);
// Release resources used.
mycaffe.Dispose();
mycaffe2.Dispose();
}
