|
| | GLayerSoftMax (size_t inputs, size_t outputs) |
| |
| | GLayerSoftMax (GDomNode *pNode) |
| |
| virtual | ~GLayerSoftMax () |
| |
| virtual void | activate () |
| | Applies the logistic activation function to the net vector to compute the activation vector, and also adjusts the weights so that the activations sum to 1. More...
|
| |
| virtual void | deactivateError () |
| | This method is a no-op, since cross-entropy training does not multiply by the derivative of the logistic function. More...
|
| |
| virtual const char * | type () |
| | Returns the type of this layer. More...
|
| |
| | GLayerClassic (size_t inputs, size_t outputs, GActivationFunction *pActivationFunction=NULL) |
| | General-purpose constructor. Takes ownership of pActivationFunction. If pActivationFunction is NULL, then GActivationTanH is used. More...
|
| |
| | GLayerClassic (GDomNode *pNode) |
| | Deserializing constructor. More...
|
| |
| | ~GLayerClassic () |
| |
| virtual double * | activation () |
| | Returns the activation values from the most recent call to feedForward(). More...
|
| |
| GActivationFunction * | activationFunction () |
| | Returns a pointer to the activation function used in this layer. More...
|
| |
| virtual void | backPropError (GNeuralNetLayer *pUpStreamLayer, size_t inputStart=0) |
| | Backpropagates the error from this layer into the upstream layer's error vector. (Assumes that the error in this layer has already been computed and deactivated. The error this computes is with respect to the output of the upstream layer.) More...
|
| |
| void | backPropErrorSingleOutput (size_t output, double *pUpStreamError) |
| | Backpropagates the error from a single output node to a hidden layer. (Assumes that the error in the output node has already been deactivated. The error this computes is with respect to the output of the upstream layer.) More...
|
| |
| double * | bias () |
| | Returns the bias vector of this layer. More...
|
| |
| const double * | bias () const |
| | Returns the bias vector of this layer. More...
|
| |
| double * | biasDelta () |
| | Returns a buffer used to store delta values for each bias in this layer. More...
|
| |
| virtual void | computeError (const double *pTarget) |
| | Computes the error terms associated with the output of this layer, given a target vector. (Note that this is the error of the output, not the error of the weights. To obtain the error term for the weights, deactivateError must be called.) More...
|
| |
| void | computeErrorSingleOutput (double target, size_t output) |
| | This is the same as computeError, except that it only computes the error of a single unit. More...
|
| |
| void | contractWeights (double factor, bool contractBiases) |
| | Contracts all the weights. (Assumes contractive error terms have already been set.) More...
|
| |
| virtual void | copyBiasToNet () |
| | Copies the bias vector into the net vector. More...
|
| |
| void | copySingleNeuronWeights (size_t source, size_t dest) |
| |
| virtual void | copyWeights (GNeuralNetLayer *pSource) |
| | Copy the weights from pSource to this layer. (Assumes pSource is the same type of layer.) More...
|
| |
| virtual size_t | countWeights () |
| | Returns the number of double-precision elements necessary to serialize the weights of this layer into a vector. More...
|
| |
| void | deactivateErrorSingleOutput (size_t output) |
| | Same as deactivateError, but only applies to a single unit in this layer. More...
|
| |
| virtual void | diminishWeights (double amount, bool regularizeBiases) |
| | Diminishes all the weights (that is, moves them in the direction toward 0) by the specified amount. More...
|
| |
| virtual void | dropConnect (GRand &rand, double probOfDrop) |
| | Randomly sets some of the weights to 0. (The dropped weights are restored when you call updateWeightsAndRestoreDroppedOnes.) More...
|
| |
| virtual void | dropOut (GRand &rand, double probOfDrop) |
| | Randomly sets the activation of some units to 0. More...
|
| |
| virtual double * | error () |
| | Returns a buffer used to store error terms for each unit in this layer. More...
|
| |
| void | feedForwardToOneOutput (const double *pIn, size_t output, bool inputBias) |
| | Feeds a vector forward through this layer to compute only the one specified output value. More...
|
| |
| void | feedForwardWithInputBias (const double *pIn) |
| | Feeds a vector forward through this layer. Uses the first value in pIn as an input bias. More...
|
| |
| virtual void | feedIn (const double *pIn, size_t inputStart, size_t inputCount) |
| | Feeds a portion of the inputs through the weights and updates the net. More...
|
| |
| void | getWeightsSingleNeuron (size_t outputNode, double *&weights) |
| | Gets the weights and bias of a single neuron. More...
|
| |
| virtual size_t | inputs () |
| | Returns the number of values expected to be fed as input into this layer. More...
|
| |
| virtual void | maxNorm (double max) |
| | Scales weights if necessary such that the manitude of the weights (not including the bias) feeding into each unit are <= max. More...
|
| |
| double * | net () |
| | Returns the net vector (that is, the values computed before the activation function was applied) from the most recent call to feedForward(). More...
|
| |
| virtual size_t | outputs () |
| | Returns the number of nodes or units in this layer. More...
|
| |
| virtual void | perturbWeights (GRand &rand, double deviation, size_t start=0, size_t count=INVALID_INDEX) |
| | Perturbs the weights that feed into the specifed units with Gaussian noise. start specifies the first unit whose incoming weights are perturbed. count specifies the maximum number of units whose incoming weights are perturbed. The default values for these parameters apply the perturbation to all units. More...
|
| |
| void | regularizeWeights (double factor, double power) |
| | Adjusts the value of each weight to, w = w - factor * pow(w, power). If power is 1, this is the same as calling scaleWeights. If power is 0, this is the same as calling diminishWeights. More...
|
| |
| virtual void | renormalizeInput (size_t input, double oldMin, double oldMax, double newMin=0.0, double newMax=1.0) |
| | Adjusts weights such that values in the new range will result in the same behavior that previously resulted from values in the old range. More...
|
| |
| virtual void | resetWeights (GRand &rand) |
| | Initialize the weights with small random values. More...
|
| |
| virtual void | resize (size_t inputs, size_t outputs, GRand *pRand=NULL, double deviation=0.03) |
| | Resizes this layer. If pRand is non-NULL, then it preserves existing weights when possible and initializes any others to small random values. More...
|
| |
| virtual void | scaleUnitIncomingWeights (size_t unit, double scalar) |
| | Scale weights that feed into the specified unit. More...
|
| |
| virtual void | scaleUnitOutgoingWeights (size_t input, double scalar) |
| | Scale weights that feed into this layer from the specified input. More...
|
| |
| virtual void | scaleWeights (double factor, bool scaleBiases) |
| | Multiplies all the weights in this layer by the specified factor. More...
|
| |
| virtual GDomNode * | serialize (GDom *pDoc) |
| | Marshall this layer into a DOM. More...
|
| |
| void | setWeightsSingleNeuron (size_t outputNode, const double *weights) |
| | Gets the weights and bias of a single neuron. More...
|
| |
| void | setWeightsToIdentity (size_t start=0, size_t count=(size_t)-1) |
| | Sets the weights of this layer to make it weakly approximate the identity function. start specifies the first unit whose incoming weights will be adjusted. count specifies the maximum number of units whose incoming weights are adjusted. More...
|
| |
| double * | slack () |
| | Returns a vector used to specify slack terms for each unit in this layer. More...
|
| |
| void | transformWeights (GMatrix &transform, const double *pOffset) |
| | Transforms the weights of this layer by the specified transformation matrix and offset vector. transform should be the pseudoinverse of the transform applied to the inputs. pOffset should be the negation of the offset added to the inputs after the transform, or the transformed offset that is added before the transform. More...
|
| |
| virtual double | unitIncomingWeightsL1Norm (size_t unit) |
| | Compute the L1 norm (sum of absolute values) of weights feeding into the specified unit. More...
|
| |
| virtual double | unitIncomingWeightsL2Norm (size_t unit) |
| | Compute the L2 norm (sum of squares) of weights feeding into the specified unit. More...
|
| |
| virtual double | unitOutgoingWeightsL1Norm (size_t input) |
| | Compute the L1 norm (sum of absolute values) of weights feeding into this layer from the specified input. More...
|
| |
| virtual double | unitOutgoingWeightsL2Norm (size_t input) |
| | Compute the L2 norm (sum of squares) of weights feeding into this layer from the specified input. More...
|
| |
| virtual void | updateBias (double learningRate, double momentum) |
| | Updates the bias of this layer by gradient descent. (Assumes the error has already been computed and deactivated.) More...
|
| |
| virtual void | updateWeights (const double *pUpStreamActivation, size_t inputStart, size_t inputCount, double learningRate, double momentum) |
| | Updates the weights that feed into this layer (not including the bias) by gradient descent. (Assumes the error has already been computed and deactivated.) More...
|
| |
| virtual void | updateWeightsAndRestoreDroppedOnes (const double *pUpStreamActivation, size_t inputStart, size_t inputCount, double learningRate, double momentum) |
| | This is a special weight update method for use with drop-connect. It updates the weights, and restores the weights that were previously dropped by a call to dropConnect. More...
|
| |
| void | updateWeightsSingleNeuron (size_t outputNode, const double *pUpStreamActivation, double learningRate, double momentum) |
| | Updates the weights and bias of a single neuron. (Assumes the error has already been computed and deactivated.) More...
|
| |
| virtual size_t | vectorToWeights (const double *pVector) |
| | Deserialize from a vector to the weights in this layer. Return the number of elements consumed. More...
|
| |
| GMatrix & | weights () |
| | Returns a reference to the weights matrix of this layer. More...
|
| |
| virtual size_t | weightsToVector (double *pOutVector) |
| | Serialize the weights in this layer into a vector. Return the number of elements written. More...
|
| |
| | GNeuralNetLayer () |
| |
| virtual | ~GNeuralNetLayer () |
| |
| void | feedForward (const double *pIn) |
| | Feeds in the bias and pIn, then computes the activation of this layer. More...
|
| |
| virtual void | feedIn (GNeuralNetLayer *pUpStreamLayer, size_t inputStart) |
| | Feeds the previous layer's activation into this layer. (Implementations for specialized hardware may override this method to avoid shuttling the previous layer's activation back to host memory.) More...
|
| |
| GMatrix * | feedThrough (const GMatrix &data) |
| | Feeds a matrix through this layer, one row at-a-time, and returns the resulting transformed matrix. More...
|
| |
| virtual void | updateWeights (GNeuralNetLayer *pUpStreamLayer, size_t inputStart, double learningRate, double momentum) |
| | Refines the weights by gradient descent. More...
|
| |
| virtual void | updateWeightsAndRestoreDroppedOnes (GNeuralNetLayer *pUpStreamLayer, size_t inputStart, double learningRate, double momentum) |
| | Refines the weights by gradient descent. More...
|
| |
| virtual bool | usesGPU () |
| | Returns true iff this layer does its computations in parallel on a GPU. More...
|
| |
Public Member Functions inherited from GClasses::GLayerClassic