#ifndef _FFNet_h_ #define _FFNet_h_ /* FFNet.h * * Copyright (C) 1997-2008 David Weenink * * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or (at * your option) any later version. * * This program is distributed in the hope that it will be useful, but * WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU * General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* djmw 19970515 djmw 20020712 GPL header. djmw 20031025 Added FFNet_getMinimum. djmw 20040420 FFNet_create and Pattern&Categories: To FFNet changes. djmw 20040422 FFNet_extractWeights added. djmw 20040505 FFNet_getNodeNumberFromUnitNumber added. djmw 20071024 Latest modification. djmw 20080121 float -> double */ #ifndef _Data_h_ #include "Data.h" #endif #ifndef _Categories_h_ #include "Categories.h" #endif #ifndef _Minimizers_h_ #include "Minimizers.h" #endif #ifndef _TableOfReal_h_ #include "TableOfReal.h" #endif #include "FFNet_def.h" #define FFNet_methods Data_methods oo_CLASS_CREATE (FFNet, Data); /* Parameters: * long nLayers : the #layers in the net (exclusive the inputs) * nUnitsInLayer : array[0..nLayers] the #units in each layer * nUnitsInLayer[0] : #inputs * nUnitsInLayer[nLayers] :#outputs * nWeights : the total #weights in the net (inclusive bias) * double *w : array[1..nWeights] with the connection strengths * *activity : array[1..nNodes] with activities * outputLabels : labels belonging to the outputs * BOOKKEEPING: * long nNodes : total #nodes: bias modelled as unit with constant activity) * *isbias : array[1..nNodes] set 1 if node is bias else 0 * *nodeFirst : array[1..nNodes] first node connected to this unit * *nodeLast: : array[1..nNodes] last node connected to this unit * *wFirst : array[1..nNodes] first index in *w for this unit * *wLast : array[1..nNodes] last (inclusive the bias) * LEARNING: * int *wSelected : array[1..nWeights] weights selected for minimization * double *deriv : array[1..nNodes] derivative of nonlinearity at node * *error : array[1..nNodes] the error at node * *dw : array[1..nWeights] total derivative for weights * *dwi : array[1..nWeights] derivative per pattern * long dimension : dimension of minimizer space (<= my nWeights) * long nPatterns : the #patterns to be learned * double **inputPattern: matrix[1..nPatterns][1..nInputs] * double **targetActivation: matrix[1..nPatterns][1..nOutputs] * double accumulatedCost : accumulated costs of testing/training with patterns * * A network consists of nLayers layers. Layer numbering is from 0...nLayers. * Layer 0 is the input layer, the highest numbered layer is the output layer * (nLayers <= 4) * Each layer consists of a number of units. The biases of all the units in a layer * are modelled with connections to an extra unit in the lower layer (with constant * activity 1.0). Nodes refers to 'units' + 'bias units'. * The variable 'nNodes' is the total number of nodes (inclusive bias nodes). * E.g. the topology (2,3,4), i.e., 2 inputs, 3 units in the first layer * and 4 units in the second layer (outputs) is modelled * with (2+1)+ (3+1)+ (4) = 11 nodes. * The numbering of the weights is as follows (indices 1..nWeights): * E.g., topology (I,H,O) (I inputs, H hidden units and O output units) * There are a total of H* (I+1) + O* (H+1) weights in this net. * w[1] - w[I] : I (1)->H (1), I (2)->H (1) ... I (I)->H (1) * w[I+1] : bias->H (1) * w[ (I+1)+1] - w[2 (I+1)-1] : I (1)->H (2), I (2)->H (2) ... I (I)->H (2) * w[2 (I+1)] : bias->H (2) * ... * w[ (H-1) (I+1)+1] - w[H (I+1)-1] : I (1)->H (H), I (2)->H (H) ... I (I)->H (H) * w[H (I+1)] : bias->H (H) * M = H (I+1) * w[M+1] - w[M+H] : H (1)->O (1), H (2)->O (1) ... H (H)->O (1) * w[M+H+1] : bias->O (1) * w[M+ (H+1)+1] - w (M+2 (H+1)-1) : H (1)->O (2), H (2)->O (2) ... H (H)->O (2) * w[M+2 (H+1)] : bias->O (1) * ... * w[M+ (O-1)* (H+1)+1] - w[M+O (H+1)-1] : H (1)->O (O), H (2)->O (O) ... H (H)->O (O) * w[m+o (h+1)-1] : bias->O (O) * * Internals: * * A number of auxiliary arrays for efficient calculations have been setup. * For a node k we need to know: * 1. isbias[1..nNodes] : usage: if (isbias[k]) ... * true if node k is a bias node. There are nLayers bias nodes * 2. nodeFirst[1..nNodes] : usage is j=nodeFirst[k]; * j is the first node that is connected to k . * 3. nodeLast[1..nNodes] : usage is j=nodeLast[k] * j is the last node that is connected to k (bias included). * For the calculation of the errors, during learning, in unit k we need to * know which weights from the preceeding layer connect to it. * 4. wFirst[1..nNodes] : usage j=wFirst[k] * w[j] is first weight to node k. * 5. wLast[1..nNodes] : usage j=wLast[k] * w[j] is last weight to node k. */ /* FFNet::copy * copy everything except minimizer, patterns and inputs. */ int FFNet_init (FFNet me, long numberOfInputs, long nodesInLayer1, long nodesInLayer2, long numberOfOutputs, int outputsAreLinear ); FFNet FFNet_create (long numberOfInputs, long numberInLayer1, long numberInLayer2, long numberOfOutputs, int outputsAreLinear ); void FFNet_createNameFromTopology (FFNet me, MelderString *name); /* Create names as -, --, --- for 1, 2 or 3 layer networks. */ #define FFNet_COST_MSE 1 void FFNet_setCostFunction (FFNet me, int type); #define FFNet_NONLIN_SIGMOID 1 void FFNet_setNonLinearity (FFNet me, int type); int FFNet_setOutputCategories (FFNet me, Categories thee); void FFNet_reset (FFNet me, double wrange); /* reset the neural net: * initialize all (selected) weights and biases with random numbers from the * interval (-wrange, wrange). * forget links with minimizer. */ void FFNet_propagateToLayer (FFNet me, const double input[], double activity[], long layer); /* propagate the input through the net to layer and calculate the activities */ void FFNet_propagate (FFNet me, const double input[], double output[]); /* step (1) feed forward input from "input layer" to "output layer" * if output != NULL the output activity is copied into output. * postcondition: my activities defined */ double FFNet_computeError (FFNet me, const double target[]); /* step (2) calculate error on output nodes w.r.t. desired output */ /* step (3) backpropagate this error to previous nodes */ /* precondition: step (1) */ void FFNet_computeDerivative (FFNet me); /* step (4) compute derivative in my dwi */ /* Precondition: step (3) */ long FFNet_getWinningUnit (FFNet me, int labeling); /* labeling = 1 : winner-takes-all */ /* labeling = 2 : stochastic */ void FFNet_selectAllWeights (FFNet me); void FFNet_selectBiasesInLayer (FFNet me, long layer); long FFNet_dimensionOfSearchSpace (FFNet me); /* count the selected weights */ long FFNet_getNumberOfWeights (FFNet me); /* return my nWeights */ void FFNet_weightConnectsUnits (FFNet me, long index, long *fromUnit, long *toUnit, long *layer); /* * w[index] connects unit fromUnit in "layer-1" with unit toUnit in "layer". * fromUnit returns 0 then w[index] is bias. */ long FFNet_getNodeNumberFromUnitNumber (FFNet me, long unit, long layer); void FFNet_nodeToUnitInLayer (FFNet me, long node, long *unit, long *layer); /* translate node index to unit "unit" in layer "layer" */ long FFNet_getNumberOfLayers (FFNet me); long FFNet_getNumberOfUnits (FFNet me); long FFNet_getNumberOfHiddenLayers (FFNet me); long FFNet_getNumberOfUnitsInLayer (FFNet me, int layer); double FFNet_getMinimum (FFNet me); void FFNet_drawTopology (FFNet me, Graphics g); void FFNet_drawActivation (FFNet me, Graphics g); void FFNet_drawWeightsToLayer (FFNet me, Graphics g, int toLayer, int scaling, int garnish); /* Deprecated: the strengths of the weights that connect to the nodes in later 'layer' */ /* are drawn with boxes. The area of each box corresponds to the strength. */ /* Black boxes have negative strength? */ void FFNet_drawCostHistory (FFNet me, Graphics g, long from_iteration, long to_iteration, double from_cost, double to_cost, int garnish); /* draw cost vs epochs */ Collection FFNet_createIrisExample (long numberOfHidden1, long numberOfHidden2); TableOfReal FFNet_extractWeights (FFNet me, long layer); void FFNet_drawWeights (FFNet me, Graphics g, long layer, int garnish); FFNet FFNet_and_TabelOfReal_to_FFNet (FFNet me, TableOfReal him, long layer); #endif /* _FFNet_h_ */