/// <summary> /// Get the hidden layer features from a visible layer /// --------- /// Assuming the RBM has been trained (so that weights for the network have been learned), /// run the network on a set of hidden units, to get a sample of the visible units. /// /// Parameters /// --------- /// data: A matrix where each row consists of the states of the hidden units. /// /// Returns /// --------- /// visible_states: A matrix where each row consists of the visible units activated from the hidden /// units in the data matrix passed in. /// </summary> public double[][] GetHiddenLayer(double[][] dataArray) { var num_examples = dataArray.Length; // Create a matrix, where each row is to be the hidden units (plus a bias unit) // sampled from a training example. var hidden_states = RealMatrix.Ones(num_examples, m_numHiddenElements + 1); var data = new RealMatrix(dataArray); // Insert bias units of 1 into the first column of data. data = data.InsertCol(1); // np.insert(data, 0, 1, axis = 1) // Calculate the activations of the hidden units. var hiddenActivations = data * m_weights; // Calculate the probabilities of turning the hidden units on. //激活函数是指的如何把“激活的神经元的特征”通过函数把特征保留并映射出来,这是神经网络能解决非线性问题关键 //激活函数,就是在神经网络的神经元上运行的函数,负责将神经元的输入映射到输出端。 var hiddenProbs = ActivationFunctions.Logistic(hiddenActivations); // Turn the hidden units on with their specified probabilities. //以指定的概率打开隐藏的单元 hidden_states = hiddenProbs > Distributions.UniformRandromMatrix(num_examples, m_numHiddenElements + 1); // Ignore the bias units. hidden_states = hidden_states.RemoveFirstCol(); return(hidden_states); }
/// <summary> /// Day dream - Reconstruct a randrom matrix (An interesting way of seeing strong features the machine has learnt). /// /// Randomly initialize the visible units once, and start running alternating Gibbs sampling steps /// (where each step consists of updating all the hidden units, and then updating all of the visible units), /// taking a sample of the visible units at each step. /// /// Note that we only initialize the network "once", so these samples are correlated. /// --------- /// samples: A matrix, where each row is a sample of the visible units produced while the network was daydreaming。 /// </summary> /// <param name="numberOfSamples">How many images/dreams</param> /// <returns>Array of Reconstructed dreams</returns> public double[][] DayDream(int numberOfSamples) { //Create a matrix, where each row is to be a sample of of the visible units //(with an extra bias unit), initialized to all ones. var data = RealMatrix.Ones(numberOfSamples, m_numVisibleElements + 1); //Take the first sample from a uniform distribution. data.Update(0, 1, Distributions.UniformRandromMatrixBool(1, m_numVisibleElements), 1); //Start the alternating Gibbs sampling. //Note that we keep the hidden units binary states, but leave the //visible units as real probabilities. //See section 3 of Hinton's "A Practical Guide to Training Restricted Boltzmann Machines" for more on why. for (int i = 0; i < numberOfSamples; i++) { var visible = data.Submatrix(i, 0, 1).ToVector(); //Calculate the activations of the hidden units. var hidden_activations = (visible * m_weights).ToVector(); //Calculate the probabilities of turning the hidden units on. var hidden_probs = ActivationFunctions.Logistic(hidden_activations); //Turn the hidden units on with their specified probabilities. var hidden_states = hidden_probs > RVector.Random(m_numHiddenElements + 1); //Always fix the bias unit to 1. hidden_states[0] = 1; //Recalculate the probabilities that the visible units are on. var visible_activations = (hidden_states * m_weights.Transpose).ToVector(); var visible_probs = ActivationFunctions.Logistic(visible_activations); var visible_states = visible_probs > RVector.Random(m_numVisibleElements + 1); data.Update(visible_states, 0, false, i, 0); } return(data.Submatrix(0, 1).ToArray()); }