C # में प्रतिबंधित बोल्ट्जमैन मशीन का कार्यान्वयन

 public void Train(IMultilayerNeuralNetwork network, IList<DataItem> data) 
 
       ,  .      ,       . 
 
 LearningAlgorithmConfig config = new LearningAlgorithmConfig() { BatchSize = 10, //  MaxEpoches = 1000, //      GibbsSamplingChainLength = 30, //   k  CD-k LearningRate = 0.01, //  CostFunctionRecalculationStep = 1, //     Momentum = 0.9, //  MinError = 0, //      MinErrorChange = 0, //      UseBiases = true //      }; 
 
       . 
 
  ILayer visibleLayer = network.Layers[0]; // RBM    ,     ILayer hiddenLayer = network.Layers[1]; //    if (!_config.UseBiases) //      { for (int i = 0; i < visibleLayer.Neurons.Length; i++) { visibleLayer.Neurons[i].Bias = 0d; } for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { hiddenLayer.Neurons[i].Bias = 0d; } } //init momentum //         double[,] momentumSpeedWeights = new double[visibleLayer.Neurons.Length, hiddenLayer.Neurons.Length]; double[] momentumSpeedVisibleBiases = new double[visibleLayer.Neurons.Length]; double[] momentumSpeedHiddenBiases = new double[hiddenLayer.Neurons.Length]; //init stop factors bool stopFlag = false; double lastError = Double.MaxValue; //    ,      double lastErrorChange = double.MaxValue; double learningRate = _config.LearningRate; int currentEpoche = 0; BatchEnumerator<DataItem<double>> batchEnumerator = new BatchEnumerator<DataItem<double>>(data, _config.BatchSize, true); 
 
 
  BatchEnumerator'    ,        . 
 
      : 
 
   do { DateTime dtStart = DateTime.Now; //start batch processing foreach (IList<DataItem<double>> batch in batchEnumerator) { //batch gradient //         ,     /    double[,] nablaWeights = new double[visibleLayer.Neurons.Length, hiddenLayer.Neurons.Length]; double[] nablaHiddenBiases = new double[hiddenLayer.Neurons.Length]; double[] nablaVisibleBiases = new double[visibleLayer.Neurons.Length]; #region iterate through batch //... #endregion #region compute mean of wights nabla, and update them //... #endregion } #region Logging and error calculation //... #endregion //   currentEpoche++; if (currentEpoche >= _config.MaxEpoches) { stopFlag = true; Logger.Instance.Log("Stop: currentEpoche:" + currentEpoche + " >= _config.MaxEpoches:" + _config.MaxEpoches); } else if (_config.MinError >= lastError) { stopFlag = true; Logger.Instance.Log("Stop: _config.MinError:" + _config.MinError + " >= lastError:" + lastError); } else if (_config.MinErrorChange >= lastErrorChange) { stopFlag = true; Logger.Instance.Log("Stop: _config.MinErrorChange:" + _config.MinErrorChange + " >= lastErrorChange:" + lastErrorChange); } } while (!stopFlag); 
 
 
    ,    Gibbs sampling,      . 
 
 #region iterate through batch //        foreach (DataItem<double> dataItem in batch) { //init visible layer states //     /    for (int i = 0; i < dataItem.Input.Length; i++) { visibleLayer.Neurons[i].LastState = dataItem.Input[i]; } #region Gibbs sampling for (int k = 0; k <= _config.GibbsSamplingChainLength; k++) { //calculate hidden states probabilities //           ,     hiddenLayer.Compute(); #region accumulate negative phase //      -   ,       if (k == _config.GibbsSamplingChainLength) { for (int i = 0; i < visibleLayer.Neurons.Length; i++) { for (int j = 0; j < hiddenLayer.Neurons.Length; j++) { //     nablaWeights[i, j] -= visibleLayer.Neurons[i].LastState * hiddenLayer.Neurons[j].LastState; } } if (_config.UseBiases) { for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { nablaHiddenBiases[i] -= hiddenLayer.Neurons[i].LastState; } for (int i = 0; i < visibleLayer.Neurons.Length; i++) { nablaVisibleBiases[i] -= visibleLayer.Neurons[i].LastState; } } break; } #endregion //sample hidden states //    ,     ,       ,        Gibbs sampling for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { hiddenLayer.Neurons[i].LastState = _r.NextDouble() <= hiddenLayer.Neurons[i].LastState ? 1d : 0d; } #region accumulate positive phase //   ,      if (k == 0) { for (int i = 0; i < visibleLayer.Neurons.Length; i++) { for (int j = 0; j < hiddenLayer.Neurons.Length; j++) { nablaWeights[i, j] += visibleLayer.Neurons[i].LastState* hiddenLayer.Neurons[j].LastState; } } if (_config.UseBiases) { for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { nablaHiddenBiases[i] += hiddenLayer.Neurons[i].LastState; } for (int i = 0; i < visibleLayer.Neurons.Length; i++) { nablaVisibleBiases[i] += visibleLayer.Neurons[i].LastState; } } } #endregion //calculate visible probs //    visibleLayer.Compute(); //  ,            ,     ,  .    ;           //todo: may be not do sampling, like in 3.2 of http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf //sample visible //for (int i = 0; i < visibleLayer.Neurons.Length; i++) //{ // visibleLayer.Neurons[i].LastState = _r.NextDouble() <= visibleLayer.Neurons[i].LastState ? 1d : 0d; //} } #endregion } 
 
 
         ,    .    :         (  )     .      . 
 
  
 
 #region compute mean of wights nabla, and update them for (int i = 0; i < visibleLayer.Neurons.Length; i++) { for (int j = 0; j < hiddenLayer.Neurons.Length; j++) { //      ,      momentumSpeedWeights[i, j] = _config.Momentum*momentumSpeedWeights[i, j] + nablaWeights[i, j]/batch.Count; //       visibleLayer.Neurons[i].Weights[j] += learningRate * momentumSpeedWeights[i, j]; hiddenLayer.Neurons[j].Weights[i] = visibleLayer.Neurons[i].Weights[j]; } } if (_config.UseBiases) { for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { momentumSpeedHiddenBiases[i] = _config.Momentum*momentumSpeedHiddenBiases[i] + nablaHiddenBiases[i]/batch.Count; hiddenLayer.Neurons[i].Bias += learningRate * momentumSpeedHiddenBiases[i]; } for (int i = 0; i < visibleLayer.Neurons.Length; i++) { momentumSpeedVisibleBiases[i] = _config.Momentum*momentumSpeedVisibleBiases[i] + nablaVisibleBiases[i]/batch.Count; visibleLayer.Neurons[i].Bias += learningRate * momentumSpeedVisibleBiases[i]; } } 
 
 
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 #region Logging and error calculation string msg = "Epoche #" + currentEpoche; #region calculate error if (currentEpoche % _config.CostFunctionRecalculationStep == 0) { #region calculating squared error with reconstruction IMetrics<double> sed = MetricsCreator.SquareEuclideanDistance(); double d = 0; foreach (DataItem<double> dataItem in data) { d += sed.Calculate(dataItem.Input, network.ComputeOutput(dataItem.Input)); } msg += "; SqDist is " + d; lastErrorChange = Math.Abs(lastError - d); lastError = d; #endregion } #endregion msg += "; Time: " + (DateTime.Now - dtStart).Duration().ToString(); Logger.Instance.Log(msg); 
 
 
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   RBM 
       .      ,      ,     ,      ( 260  29  29 ):      . 
 
     : 
 
 LearningAlgorithmConfig: 
 LearningRate = 0.01 
 BatchSize = 10 
 RegularizationFactor = 0 
 MaxEpoches = 1000 
 MinError = 0 
 MinErrorChange = 0 
 CostFunctionRecalculationStep = 1 
 ErrorFunction = 
 Momentum = 0.9 
 NeuronLocalGainLimit: not setted 
 GibbsSamplingChainLength = 30 
 UseBiases = True 
 
  ,     1000 . ..   10,       26  ,    26000 .     . 
 
    ,     (26  )     13128,     76. 
 
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         : 
  
 
    ,     (        RBM). 
 
   ,   -  ,    . ,  .      841 (29*29)    .   ,   841     29  29 ,        ,   .         ,       ,    ,    .   : 
 
  
 
               MNIST : http://deeplearning.net/tutorial/_images/filters_at_epoch_14.png .         ,        =) 
 
       RBM  ,      ,        . 
 
  
 https://class.coursera.org/neuralnets-2012-001/ http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf http://deeplearning.net/tutorial/rbm.html#contrastive-divergence-cd-k http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/DBNEquations http://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf     
 
 PS     ambikontur        ! 
 
 UPD : 
                
  
     
  

public void Train(IMultilayerNeuralNetwork network, IList<DataItem> data)

, . , .

LearningAlgorithmConfig config = new LearningAlgorithmConfig() { BatchSize = 10, // MaxEpoches = 1000, // GibbsSamplingChainLength = 30, // k CD-k LearningRate = 0.01, // CostFunctionRecalculationStep = 1, // Momentum = 0.9, // MinError = 0, // MinErrorChange = 0, // UseBiases = true // };

.

ILayer visibleLayer = network.Layers[0]; // RBM , ILayer hiddenLayer = network.Layers[1]; // if (!_config.UseBiases) // { for (int i = 0; i < visibleLayer.Neurons.Length; i++) { visibleLayer.Neurons[i].Bias = 0d; } for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { hiddenLayer.Neurons[i].Bias = 0d; } } //init momentum // double[,] momentumSpeedWeights = new double[visibleLayer.Neurons.Length, hiddenLayer.Neurons.Length]; double[] momentumSpeedVisibleBiases = new double[visibleLayer.Neurons.Length]; double[] momentumSpeedHiddenBiases = new double[hiddenLayer.Neurons.Length]; //init stop factors bool stopFlag = false; double lastError = Double.MaxValue; // , double lastErrorChange = double.MaxValue; double learningRate = _config.LearningRate; int currentEpoche = 0; BatchEnumerator<DataItem<double>> batchEnumerator = new BatchEnumerator<DataItem<double>>(data, _config.BatchSize, true);


BatchEnumerator' , .

:

do { DateTime dtStart = DateTime.Now; //start batch processing foreach (IList<DataItem<double>> batch in batchEnumerator) { //batch gradient // , / double[,] nablaWeights = new double[visibleLayer.Neurons.Length, hiddenLayer.Neurons.Length]; double[] nablaHiddenBiases = new double[hiddenLayer.Neurons.Length]; double[] nablaVisibleBiases = new double[visibleLayer.Neurons.Length]; #region iterate through batch //... #endregion #region compute mean of wights nabla, and update them //... #endregion } #region Logging and error calculation //... #endregion // currentEpoche++; if (currentEpoche >= _config.MaxEpoches) { stopFlag = true; Logger.Instance.Log("Stop: currentEpoche:" + currentEpoche + " >= _config.MaxEpoches:" + _config.MaxEpoches); } else if (_config.MinError >= lastError) { stopFlag = true; Logger.Instance.Log("Stop: _config.MinError:" + _config.MinError + " >= lastError:" + lastError); } else if (_config.MinErrorChange >= lastErrorChange) { stopFlag = true; Logger.Instance.Log("Stop: _config.MinErrorChange:" + _config.MinErrorChange + " >= lastErrorChange:" + lastErrorChange); } } while (!stopFlag);


, Gibbs sampling, .

#region iterate through batch // foreach (DataItem<double> dataItem in batch) { //init visible layer states // / for (int i = 0; i < dataItem.Input.Length; i++) { visibleLayer.Neurons[i].LastState = dataItem.Input[i]; } #region Gibbs sampling for (int k = 0; k <= _config.GibbsSamplingChainLength; k++) { //calculate hidden states probabilities // , hiddenLayer.Compute(); #region accumulate negative phase // - , if (k == _config.GibbsSamplingChainLength) { for (int i = 0; i < visibleLayer.Neurons.Length; i++) { for (int j = 0; j < hiddenLayer.Neurons.Length; j++) { // nablaWeights[i, j] -= visibleLayer.Neurons[i].LastState * hiddenLayer.Neurons[j].LastState; } } if (_config.UseBiases) { for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { nablaHiddenBiases[i] -= hiddenLayer.Neurons[i].LastState; } for (int i = 0; i < visibleLayer.Neurons.Length; i++) { nablaVisibleBiases[i] -= visibleLayer.Neurons[i].LastState; } } break; } #endregion //sample hidden states // , , , Gibbs sampling for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { hiddenLayer.Neurons[i].LastState = _r.NextDouble() <= hiddenLayer.Neurons[i].LastState ? 1d : 0d; } #region accumulate positive phase // , if (k == 0) { for (int i = 0; i < visibleLayer.Neurons.Length; i++) { for (int j = 0; j < hiddenLayer.Neurons.Length; j++) { nablaWeights[i, j] += visibleLayer.Neurons[i].LastState* hiddenLayer.Neurons[j].LastState; } } if (_config.UseBiases) { for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { nablaHiddenBiases[i] += hiddenLayer.Neurons[i].LastState; } for (int i = 0; i < visibleLayer.Neurons.Length; i++) { nablaVisibleBiases[i] += visibleLayer.Neurons[i].LastState; } } } #endregion //calculate visible probs // visibleLayer.Compute(); // , , , . ; //todo: may be not do sampling, like in 3.2 of http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf //sample visible //for (int i = 0; i < visibleLayer.Neurons.Length; i++) //{ // visibleLayer.Neurons[i].LastState = _r.NextDouble() <= visibleLayer.Neurons[i].LastState ? 1d : 0d; //} } #endregion }


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#region compute mean of wights nabla, and update them for (int i = 0; i < visibleLayer.Neurons.Length; i++) { for (int j = 0; j < hiddenLayer.Neurons.Length; j++) { // , momentumSpeedWeights[i, j] = _config.Momentum*momentumSpeedWeights[i, j] + nablaWeights[i, j]/batch.Count; // visibleLayer.Neurons[i].Weights[j] += learningRate * momentumSpeedWeights[i, j]; hiddenLayer.Neurons[j].Weights[i] = visibleLayer.Neurons[i].Weights[j]; } } if (_config.UseBiases) { for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { momentumSpeedHiddenBiases[i] = _config.Momentum*momentumSpeedHiddenBiases[i] + nablaHiddenBiases[i]/batch.Count; hiddenLayer.Neurons[i].Bias += learningRate * momentumSpeedHiddenBiases[i]; } for (int i = 0; i < visibleLayer.Neurons.Length; i++) { momentumSpeedVisibleBiases[i] = _config.Momentum*momentumSpeedVisibleBiases[i] + nablaVisibleBiases[i]/batch.Count; visibleLayer.Neurons[i].Bias += learningRate * momentumSpeedVisibleBiases[i]; } }


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#region Logging and error calculation string msg = "Epoche #" + currentEpoche; #region calculate error if (currentEpoche % _config.CostFunctionRecalculationStep == 0) { #region calculating squared error with reconstruction IMetrics<double> sed = MetricsCreator.SquareEuclideanDistance(); double d = 0; foreach (DataItem<double> dataItem in data) { d += sed.Calculate(dataItem.Input, network.ComputeOutput(dataItem.Input)); } msg += "; SqDist is " + d; lastErrorChange = Math.Abs(lastError - d); lastError = d; #endregion } #endregion msg += "; Time: " + (DateTime.Now - dtStart).Duration().ToString(); Logger.Instance.Log(msg);


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RBM
. , , , ( 260 29 29 ): .

:

LearningAlgorithmConfig:
LearningRate = 0.01
BatchSize = 10
RegularizationFactor = 0
MaxEpoches = 1000
MinError = 0
MinErrorChange = 0
CostFunctionRecalculationStep = 1
ErrorFunction =
Momentum = 0.9
NeuronLocalGainLimit: not setted
GibbsSamplingChainLength = 30
UseBiases = True

, 1000 . .. 10, 26 , 26000 . .

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, ( RBM).

, - , . , . 841 (29*29) . , 841 29 29 , , . , , , . :



MNIST : http://deeplearning.net/tutorial/_images/filters_at_epoch_14.png . , =)

RBM , , .


https://class.coursera.org/neuralnets-2012-001/ http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf http://deeplearning.net/tutorial/rbm.html#contrastive-divergence-cd-k http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/DBNEquations http://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf

PS ambikontur !

UPD :

 public void Train(IMultilayerNeuralNetwork network, IList<DataItem> data) 
 
       ,  .      ,       . 
 
 LearningAlgorithmConfig config = new LearningAlgorithmConfig() { BatchSize = 10, //  MaxEpoches = 1000, //      GibbsSamplingChainLength = 30, //   k  CD-k LearningRate = 0.01, //  CostFunctionRecalculationStep = 1, //     Momentum = 0.9, //  MinError = 0, //      MinErrorChange = 0, //      UseBiases = true //      }; 
 
       . 
 
  ILayer visibleLayer = network.Layers[0]; // RBM    ,     ILayer hiddenLayer = network.Layers[1]; //    if (!_config.UseBiases) //      { for (int i = 0; i < visibleLayer.Neurons.Length; i++) { visibleLayer.Neurons[i].Bias = 0d; } for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { hiddenLayer.Neurons[i].Bias = 0d; } } //init momentum //         double[,] momentumSpeedWeights = new double[visibleLayer.Neurons.Length, hiddenLayer.Neurons.Length]; double[] momentumSpeedVisibleBiases = new double[visibleLayer.Neurons.Length]; double[] momentumSpeedHiddenBiases = new double[hiddenLayer.Neurons.Length]; //init stop factors bool stopFlag = false; double lastError = Double.MaxValue; //    ,      double lastErrorChange = double.MaxValue; double learningRate = _config.LearningRate; int currentEpoche = 0; BatchEnumerator<DataItem<double>> batchEnumerator = new BatchEnumerator<DataItem<double>>(data, _config.BatchSize, true); 
 
 
  BatchEnumerator'    ,        . 
 
      : 
 
   do { DateTime dtStart = DateTime.Now; //start batch processing foreach (IList<DataItem<double>> batch in batchEnumerator) { //batch gradient //         ,     /    double[,] nablaWeights = new double[visibleLayer.Neurons.Length, hiddenLayer.Neurons.Length]; double[] nablaHiddenBiases = new double[hiddenLayer.Neurons.Length]; double[] nablaVisibleBiases = new double[visibleLayer.Neurons.Length]; #region iterate through batch //... #endregion #region compute mean of wights nabla, and update them //... #endregion } #region Logging and error calculation //... #endregion //   currentEpoche++; if (currentEpoche >= _config.MaxEpoches) { stopFlag = true; Logger.Instance.Log("Stop: currentEpoche:" + currentEpoche + " >= _config.MaxEpoches:" + _config.MaxEpoches); } else if (_config.MinError >= lastError) { stopFlag = true; Logger.Instance.Log("Stop: _config.MinError:" + _config.MinError + " >= lastError:" + lastError); } else if (_config.MinErrorChange >= lastErrorChange) { stopFlag = true; Logger.Instance.Log("Stop: _config.MinErrorChange:" + _config.MinErrorChange + " >= lastErrorChange:" + lastErrorChange); } } while (!stopFlag); 
 
 
    ,    Gibbs sampling,      . 
 
 #region iterate through batch //        foreach (DataItem<double> dataItem in batch) { //init visible layer states //     /    for (int i = 0; i < dataItem.Input.Length; i++) { visibleLayer.Neurons[i].LastState = dataItem.Input[i]; } #region Gibbs sampling for (int k = 0; k <= _config.GibbsSamplingChainLength; k++) { //calculate hidden states probabilities //           ,     hiddenLayer.Compute(); #region accumulate negative phase //      -   ,       if (k == _config.GibbsSamplingChainLength) { for (int i = 0; i < visibleLayer.Neurons.Length; i++) { for (int j = 0; j < hiddenLayer.Neurons.Length; j++) { //     nablaWeights[i, j] -= visibleLayer.Neurons[i].LastState * hiddenLayer.Neurons[j].LastState; } } if (_config.UseBiases) { for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { nablaHiddenBiases[i] -= hiddenLayer.Neurons[i].LastState; } for (int i = 0; i < visibleLayer.Neurons.Length; i++) { nablaVisibleBiases[i] -= visibleLayer.Neurons[i].LastState; } } break; } #endregion //sample hidden states //    ,     ,       ,        Gibbs sampling for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { hiddenLayer.Neurons[i].LastState = _r.NextDouble() <= hiddenLayer.Neurons[i].LastState ? 1d : 0d; } #region accumulate positive phase //   ,      if (k == 0) { for (int i = 0; i < visibleLayer.Neurons.Length; i++) { for (int j = 0; j < hiddenLayer.Neurons.Length; j++) { nablaWeights[i, j] += visibleLayer.Neurons[i].LastState* hiddenLayer.Neurons[j].LastState; } } if (_config.UseBiases) { for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { nablaHiddenBiases[i] += hiddenLayer.Neurons[i].LastState; } for (int i = 0; i < visibleLayer.Neurons.Length; i++) { nablaVisibleBiases[i] += visibleLayer.Neurons[i].LastState; } } } #endregion //calculate visible probs //    visibleLayer.Compute(); //  ,            ,     ,  .    ;           //todo: may be not do sampling, like in 3.2 of http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf //sample visible //for (int i = 0; i < visibleLayer.Neurons.Length; i++) //{ // visibleLayer.Neurons[i].LastState = _r.NextDouble() <= visibleLayer.Neurons[i].LastState ? 1d : 0d; //} } #endregion } 
 
 
         ,    .    :         (  )     .      . 
 
  
 
 #region compute mean of wights nabla, and update them for (int i = 0; i < visibleLayer.Neurons.Length; i++) { for (int j = 0; j < hiddenLayer.Neurons.Length; j++) { //      ,      momentumSpeedWeights[i, j] = _config.Momentum*momentumSpeedWeights[i, j] + nablaWeights[i, j]/batch.Count; //       visibleLayer.Neurons[i].Weights[j] += learningRate * momentumSpeedWeights[i, j]; hiddenLayer.Neurons[j].Weights[i] = visibleLayer.Neurons[i].Weights[j]; } } if (_config.UseBiases) { for (int i = 0; i < hiddenLayer.Neurons.Length; i++) { momentumSpeedHiddenBiases[i] = _config.Momentum*momentumSpeedHiddenBiases[i] + nablaHiddenBiases[i]/batch.Count; hiddenLayer.Neurons[i].Bias += learningRate * momentumSpeedHiddenBiases[i]; } for (int i = 0; i < visibleLayer.Neurons.Length; i++) { momentumSpeedVisibleBiases[i] = _config.Momentum*momentumSpeedVisibleBiases[i] + nablaVisibleBiases[i]/batch.Count; visibleLayer.Neurons[i].Bias += learningRate * momentumSpeedVisibleBiases[i]; } } 
 
 
   -  .     ,   ,  /       ,     ,      ,  .   :        . ,       ,   ,        .       . 
 
 #region Logging and error calculation string msg = "Epoche #" + currentEpoche; #region calculate error if (currentEpoche % _config.CostFunctionRecalculationStep == 0) { #region calculating squared error with reconstruction IMetrics<double> sed = MetricsCreator.SquareEuclideanDistance(); double d = 0; foreach (DataItem<double> dataItem in data) { d += sed.Calculate(dataItem.Input, network.ComputeOutput(dataItem.Input)); } msg += "; SqDist is " + d; lastErrorChange = Math.Abs(lastError - d); lastError = d; #endregion } #endregion msg += "; Time: " + (DateTime.Now - dtStart).Duration().ToString(); Logger.Instance.Log(msg); 
 
 
     . 
 
   RBM 
       .      ,      ,     ,      ( 260  29  29 ):      . 
 
     : 
 
 LearningAlgorithmConfig: 
 LearningRate = 0.01 
 BatchSize = 10 
 RegularizationFactor = 0 
 MaxEpoches = 1000 
 MinError = 0 
 MinErrorChange = 0 
 CostFunctionRecalculationStep = 1 
 ErrorFunction = 
 Momentum = 0.9 
 NeuronLocalGainLimit: not setted 
 GibbsSamplingChainLength = 30 
 UseBiases = True 
 
  ,     1000 . ..   10,       26  ,    26000 .     . 
 
    ,     (26  )     13128,     76. 
 
       ( ,  ): 
  
 
         : 
  
 
    ,     (        RBM). 
 
   ,   -  ,    . ,  .      841 (29*29)    .   ,   841     29  29 ,        ,   .         ,       ,    ,    .   : 
 
  
 
               MNIST : http://deeplearning.net/tutorial/_images/filters_at_epoch_14.png .         ,        =) 
 
       RBM  ,      ,        . 
 
  
 https://class.coursera.org/neuralnets-2012-001/ http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf http://deeplearning.net/tutorial/rbm.html#contrastive-divergence-cd-k http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/DBNEquations http://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf     
 
 PS     ambikontur        ! 
 
 UPD :