├── .idea ├── .name ├── Deep Learning_Nodejs.iml ├── encodings.xml ├── jsLibraryMappings.xml ├── misc.xml ├── modules.xml ├── scopes │ └── scope_settings.xml ├── vcs.xml └── workspace.xml ├── README.md ├── examples ├── cdbn.js ├── crbm.js ├── dbn.js ├── logistic_regression.js ├── mlp.js └── rbm.js ├── lib ├── CDBN.js ├── CRBM.js ├── DBN.js ├── HiddenLayer.js ├── LogisticRegression.js ├── MLP.js ├── RBM.js ├── dnn.js ├── math.js └── utils.js └── package.json /.idea/.name: -------------------------------------------------------------------------------- 1 | Deep Learning_Nodejs -------------------------------------------------------------------------------- /.idea/Deep Learning_Nodejs.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | -------------------------------------------------------------------------------- /.idea/encodings.xml: 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| 478 | 479 | 480 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # dnn 2 | 3 | Simple deep learning library for node.js. 4 | 5 | Includes Logistic-Regression, MLP, RBM, DBN, CRBM, CDBN. (Deep Neural Network) 6 | 7 | RBM is using contrastive-divergence for its training algorithm. 8 | 9 | ## Installation 10 | ``` 11 | $ npm install dnn 12 | ``` 13 | 14 | ## Features 15 | 16 | * Logistic Regression 17 | * MLP (Multi-Layer Perceptron) 18 | * RBM (Restricted Boltzmann Machine) 19 | * DBN (Deep Belief Network) 20 | * CRBM (Restricted Boltzmann Machine with continuous-valued inputs) 21 | * CDBN (Deep Belief Network with continuous-valued inputs) 22 | 23 | ## Logistic Regression 24 | ```javascript 25 | var dnn = require('dnn'); 26 | var x = [[1,1,1,0,0,0], 27 | [1,0,1,0,0,0], 28 | [1,1,1,0,0,0], 29 | [0,0,1,1,1,0], 30 | [0,0,1,1,0,0], 31 | [0,0,1,1,1,0]]; 32 | var y = [[1, 0], 33 | [1, 0], 34 | [1, 0], 35 | [0, 1], 36 | [0, 1], 37 | [0, 1]]; 38 | 39 | var lrClassifier = new dnn.LogisticRegression({ 40 | 'input' : x, 41 | 'label' : y, 42 | 'n_in' : 6, 43 | 'n_out' : 2 44 | }); 45 | 46 | lrClassifier.set('log level',1); // 0 : nothing, 1 : info, 2 : warning. 47 | 48 | var training_epochs = 800, lr = 0.01; 49 | 50 | lrClassifier.train({ 51 | 'lr' : lr, 52 | 'epochs' : training_epochs 53 | }); 54 | 55 | x = [[1, 1, 0, 0, 0, 0], 56 | [0, 0, 0, 1, 1, 0], 57 | [1, 1, 1, 1, 1, 0]]; 58 | 59 | console.log("Result : ",lrClassifier.predict(x)); 60 | ``` 61 | 62 | ## MLP (Multi-Layer Perceptron) 63 | ```javascript 64 | var dnn = require('dnn'); 65 | var x = [[0.4, 0.5, 0.5, 0., 0., 0.], 66 | [0.5, 0.3, 0.5, 0., 0., 0.], 67 | [0.4, 0.5, 0.5, 0., 0., 0.], 68 | [0., 0., 0.5, 0.3, 0.5, 0.], 69 | [0., 0., 0.5, 0.4, 0.5, 0.], 70 | [0., 0., 0.5, 0.5, 0.5, 0.]]; 71 | var y = [[1, 0], 72 | [1, 0], 73 | [1, 0], 74 | [0, 1], 75 | [0, 1], 76 | [0, 1]]; 77 | 78 | var mlp = new dnn.MLP({ 79 | 'input' : x, 80 | 'label' : y, 81 | 'n_ins' : 6, 82 | 'n_outs' : 2, 83 | 'hidden_layer_sizes' : [4,4,5] 84 | }); 85 | 86 | mlp.set('log level',1); // 0 : nothing, 1 : info, 2 : warning. 87 | 88 | mlp.train({ 89 | 'lr' : 0.6, 90 | 'epochs' : 20000 91 | }); 92 | 93 | a = [[0.5, 0.5, 0., 0., 0., 0.], 94 | [0., 0., 0., 0.5, 0.5, 0.], 95 | [0.5, 0.5, 0.5, 0.5, 0.5, 0.]]; 96 | 97 | console.log(mlp.predict(a)); 98 | ``` 99 | 100 | ## RBM (Restricted Boltzmann Machine) 101 | ```javascript 102 | var dnn = require('dnn'); 103 | var data = [[1,1,1,0,0,0], 104 | [1,0,1,0,0,0], 105 | [1,1,1,0,0,0], 106 | [0,0,1,1,1,0], 107 | [0,0,1,1,0,0], 108 | [0,0,1,1,1,0]]; 109 | 110 | var rbm = new dnn.RBM({ 111 | input : data, 112 | n_visible : 6, 113 | n_hidden : 2 114 | }); 115 | 116 | rbm.set('log level',1); // 0 : nothing, 1 : info, 2 : warning. 117 | 118 | var trainingEpochs = 500; 119 | 120 | rbm.train({ 121 | lr : 0.6, 122 | k : 1, // CD-k. 123 | epochs : trainingEpochs 124 | }); 125 | 126 | var v = [[1, 1, 0, 0, 0, 0], 127 | [0, 0, 0, 1, 1, 0]]; 128 | 129 | console.log(rbm.reconstruct(v)); 130 | console.log(rbm.sampleHgivenV(v)[0]); // get hidden layer probabilities from visible unit. 131 | ``` 132 | 133 | ## DBN (Deep Belief Network) 134 | ```javascript 135 | var dnn = require('dnn'); 136 | var x = [[1,1,1,0,0,0], 137 | [1,0,1,0,0,0], 138 | [1,1,1,0,0,0], 139 | [0,0,1,1,1,0], 140 | [0,0,1,1,0,0], 141 | [0,0,1,1,1,0]]; 142 | var y = [[1, 0], 143 | [1, 0], 144 | [1, 0], 145 | [0, 1], 146 | [0, 1], 147 | [0, 1]]; 148 | 149 | var pretrain_lr = 0.6, pretrain_epochs = 900, k = 1, finetune_lr = 0.6, finetune_epochs = 500; 150 | 151 | var dbn = new dnn.DBN({ 152 | 'input' : x, 153 | 'label' : y, 154 | 'n_ins' : 6, 155 | 'n_outs' : 2, 156 | 'hidden_layer_sizes' : [10,12,11,8,6,4] 157 | }); 158 | 159 | dbn.set('log level',1); // 0 : nothing, 1 : info, 2 : warning. 160 | 161 | // Pre-Training using using RBM 162 | dbn.pretrain({ 163 | 'lr' : pretrain_lr, 164 | 'k' : k, // RBM CD-k. 165 | 'epochs' : pretrain_epochs 166 | }); 167 | 168 | // Fine-Tuning dbn using mlp backpropagation. 169 | dbn.finetune({ 170 | 'lr' : finetune_lr, 171 | 'epochs' : finetune_epochs 172 | }); 173 | 174 | /* 175 | for(var i =0;i<6;i++) { 176 | console.log(i+1,"th layer W : ",dbn.sigmoidLayers[i].W); 177 | } 178 | */ 179 | 180 | x = [[1, 1, 0, 0, 0, 0], 181 | [0, 0, 0, 1, 1, 0], 182 | [1, 1, 1, 1, 1, 0]]; 183 | 184 | console.log(dbn.predict(x)); 185 | ``` 186 | 187 | ## CRBM (Restricted Boltzmann Machine with continuous-valued inputs) 188 | ```javascript 189 | var dnn = require('dnn'); 190 | var data = [[0.4, 0.5, 0.5, 0., 0., 0.7], 191 | [0.5, 0.3, 0.5, 0., 1, 0.6], 192 | [0.4, 0.5, 0.5, 0., 1, 0.9], 193 | [0., 0., 0., 0.3, 0.5, 0.], 194 | [0., 0., 0., 0.4, 0.5, 0.], 195 | [0., 0., 0., 0.5, 0.5, 0.]]; 196 | 197 | var crbm = new dnn.CRBM({ 198 | input : data, 199 | n_visible : 6, 200 | n_hidden : 5 201 | }); 202 | 203 | crbm.set('log level',1); // 0 : nothing, 1 : info, 2 : warning. 204 | 205 | crbm.train({ 206 | lr : 0.6, 207 | k : 1, // CD-k. 208 | epochs : 1500 209 | }); 210 | 211 | var v = [[0.5, 0.5, 0., 0., 0., 0.], 212 | [0., 0., 0., 0.5, 0.5, 0.]]; 213 | 214 | console.log(crbm.reconstruct(v)); 215 | console.log(crbm.sampleHgivenV(v)[0]); // get hidden layer probabilities from visible unit. 216 | ``` 217 | 218 | ## CDBN (Deep Belief Network with continuous-valued inputs) 219 | ```javascript 220 | var dnn = require('dnn') 221 | 222 | var x = [[0.4, 0.5, 0.5, 0., 0., 0.], 223 | [0.5, 0.3, 0.5, 0., 0., 0.], 224 | [0.4, 0.5, 0.5, 0., 0., 0.], 225 | [0., 0., 0.5, 0.3, 0.5, 0.], 226 | [0., 0., 0.5, 0.4, 0.5, 0.], 227 | [0., 0., 0.5, 0.5, 0.5, 0.]]; 228 | 229 | var y = [[1, 0], 230 | [1, 0], 231 | [1, 0], 232 | [0, 1], 233 | [0, 1], 234 | [0, 1]]; 235 | 236 | var cdbn = new dnn.CDBN({ 237 | 'input' : x, 238 | 'label' : y, 239 | 'n_ins' : 6, 240 | 'n_outs' : 2, 241 | 'hidden_layer_sizes' : [10,12,11,8,6,4] 242 | }); 243 | 244 | cdbn.set('log level',1); // 0 : nothing, 1 : info, 2 : warning. 245 | 246 | var pretrain_lr = 0.8, pretrain_epochs = 1600, k= 1, finetune_lr = 0.84, finetune_epochs = 10000; 247 | 248 | // Pre-Training using using RBM, CRBM. 249 | cdbn.pretrain({ 250 | 'lr' : pretrain_lr, 251 | 'k' : k, // RBM CD-k. 252 | 'epochs' : pretrain_epochs 253 | }); 254 | 255 | // Fine-Tuning dbn using mlp backpropagation. 256 | cdbn.finetune({ 257 | 'lr' : finetune_lr, 258 | 'epochs' : finetune_epochs 259 | }); 260 | 261 | /* 262 | for(var i =0;i<6;i++) { 263 | console.log(i+1,"th layer W : ",cdbn.sigmoidLayers[i].W); 264 | } 265 | */ 266 | 267 | a = [[0.5, 0.5, 0., 0., 0., 0.], 268 | [0., 0., 0., 0.5, 0.5, 0.], 269 | [0.1,0.2,0.4,0.4,0.3,0.6]]; 270 | 271 | console.log(cdbn.predict(a)); 272 | ``` 273 | 274 | ##License 275 | 276 | (The MIT License) 277 | 278 | Copyright (c) 2014 Joon-Ku Kang <junku901@gmail.com> 279 | 280 | Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the 'Software'), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: 281 | 282 | The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 283 | 284 | THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 285 | -------------------------------------------------------------------------------- /examples/cdbn.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Created by joonkukang on 2014. 1. 15.. 3 | */ 4 | var dnn = require('../lib/dnn') 5 | 6 | var x = [[0.4, 0.5, 0.5, 0., 0., 0.], 7 | [0.5, 0.3, 0.5, 0., 0., 0.], 8 | [0.4, 0.5, 0.5, 0., 0., 0.], 9 | [0., 0., 0.5, 0.3, 0.5, 0.], 10 | [0., 0., 0.5, 0.4, 0.5, 0.], 11 | [0., 0., 0.5, 0.5, 0.5, 0.]]; 12 | 13 | var y = [[1, 0], 14 | [1, 0], 15 | [1, 0], 16 | [0, 1], 17 | [0, 1], 18 | [0, 1]]; 19 | 20 | var cdbn = new dnn.CDBN({ 21 | 'input' : x, 22 | 'label' : y, 23 | 'n_ins' : 6, 24 | 'n_outs' : 2, 25 | 'hidden_layer_sizes' : [10,12,11,8,6,4] 26 | }); 27 | 28 | for(var i =0;i<6;i++) { 29 | console.log("ith layer W : ",cdbn.sigmoidLayers[i].W); 30 | } 31 | 32 | cdbn.set('log level',1); 33 | var pretrain_lr = 0.8, pretrain_epochs = 2000, k= 1, finetune_lr = 0.84, finetune_epochs = 10000; 34 | 35 | // Pre-Training using using RBM, CRBM. 36 | cdbn.pretrain({ 37 | 'lr' : pretrain_lr, 38 | 'k' : k, 39 | 'epochs' : pretrain_epochs 40 | }); 41 | 42 | // Fine-Tuning dbn using mlp backpropagation. 43 | cdbn.finetune({ 44 | 'lr' : finetune_lr, 45 | 'epochs' : finetune_epochs 46 | }); 47 | 48 | a = [[0.5, 0.5, 0., 0., 0., 0.], 49 | [0., 0., 0., 0.5, 0.5, 0.], 50 | [0.1,0.2,0.4,0.4,0.3,0.6]]; 51 | 52 | console.log(cdbn.predict(a)) -------------------------------------------------------------------------------- /examples/crbm.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Created by joonkukang on 2014. 1. 15.. 3 | */ 4 | var dnn = require('../lib/dnn'); 5 | var data = [[0.4, 0.5, 0.5, 0., 0., 0.7], 6 | [0.5, 0.3, 0.5, 0., 1, 0.6], 7 | [0.4, 0.5, 0.5, 0., 1, 0.9], 8 | [0., 0., 0., 0.3, 0.5, 0.], 9 | [0., 0., 0., 0.4, 0.5, 0.], 10 | [0., 0., 0., 0.5, 0.5, 0.]]; 11 | 12 | var crbm = new dnn.CRBM({ 13 | input : data, 14 | n_visible : 6, 15 | n_hidden : 5 16 | }); 17 | 18 | crbm.set('log level',1); 19 | 20 | crbm.train({ 21 | lr : 0.6, 22 | k : 1, 23 | epochs : 1500 24 | }); 25 | 26 | 27 | 28 | var v = [[0.5, 0.5, 0., 0., 0., 0.], 29 | [0., 0., 0., 0.5, 0.5, 0.]]; 30 | 31 | console.log(crbm.reconstruct(v)); 32 | console.log(crbm.sampleHgivenV(v)[0]); 33 | -------------------------------------------------------------------------------- /examples/dbn.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Created by joonkukang on 2014. 1. 15.. 3 | */ 4 | var dnn = require('../lib/dnn'); 5 | var x = [[1,1,1,0,0,0], 6 | [1,0,1,0,0,0], 7 | [1,1,1,0,0,0], 8 | [0,0,1,1,1,0], 9 | [0,0,1,1,0,0], 10 | [0,0,1,1,1,0]]; 11 | var y = [[1, 0], 12 | [1, 0], 13 | [1, 0], 14 | [0, 1], 15 | [0, 1], 16 | [0, 1]]; 17 | 18 | var pretrain_lr = 0.6, pretrain_epochs = 900, k = 1, finetune_lr = 0.6, finetune_epochs = 500; 19 | 20 | var dbn = new dnn.DBN({ 21 | 'input' : x, 22 | 'label' : y, 23 | 'n_ins' : 6, 24 | 'n_outs' : 2, 25 | 'hidden_layer_sizes' : [10,12,11,8,6,4] 26 | }); 27 | 28 | dbn.set('log level',1); 29 | 30 | // Pre-Training using using RBM 31 | dbn.pretrain({ 32 | 'lr' : pretrain_lr, 33 | 'k' : k, 34 | 'epochs' : pretrain_epochs 35 | }); 36 | 37 | // Fine-Tuning dbn using mlp backpropagation. 38 | dbn.finetune({ 39 | 'lr' : finetune_lr, 40 | 'epochs' : finetune_epochs 41 | }); 42 | 43 | x = [[1, 1, 0, 0, 0, 0], 44 | [0, 0, 0, 1, 1, 0], 45 | [1, 1, 1, 1, 1, 0]]; 46 | 47 | console.log(dbn.predict(x)); -------------------------------------------------------------------------------- /examples/logistic_regression.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Created by joonkukang on 2014. 1. 15.. 3 | */ 4 | var dnn = require('../lib/dnn'); 5 | var x = [[1,1,1,0,0,0], 6 | [1,0,1,0,0,0], 7 | [1,1,1,0,0,0], 8 | [0,0,1,1,1,0], 9 | [0,0,1,1,0,0], 10 | [0,0,1,1,1,0]]; 11 | var y = [[1, 0], 12 | [1, 0], 13 | [1, 0], 14 | [0, 1], 15 | [0, 1], 16 | [0, 1]]; 17 | 18 | var classifier = new dnn.LogisticRegression({ 19 | 'input' : x, 20 | 'label' : y, 21 | 'n_in' : 6, 22 | 'n_out' : 2 23 | }); 24 | 25 | classifier.set('log level',1); 26 | 27 | var training_epochs = 800, lr = 0.01; 28 | 29 | classifier.train({ 30 | 'lr' : lr, 31 | 'epochs' : training_epochs 32 | }); 33 | 34 | x = [[1, 1, 0, 0, 0, 0], 35 | [0, 0, 0, 1, 1, 0], 36 | [1, 1, 1, 1, 1, 0]]; 37 | 38 | console.log("Result : ",classifier.predict(x)); -------------------------------------------------------------------------------- /examples/mlp.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Created by joonkukang on 2014. 1. 15.. 3 | */ 4 | var dnn = require('../lib/dnn'); 5 | var x = [[0.4, 0.5, 0.5, 0., 0., 0.], 6 | [0.5, 0.3, 0.5, 0., 0., 0.], 7 | [0.4, 0.5, 0.5, 0., 0., 0.], 8 | [0., 0., 0.5, 0.3, 0.5, 0.], 9 | [0., 0., 0.5, 0.4, 0.5, 0.], 10 | [0., 0., 0.5, 0.5, 0.5, 0.]]; 11 | var y = [[1, 0], 12 | [1, 0], 13 | [1, 0], 14 | [0, 1], 15 | [0, 1], 16 | [0, 1]]; 17 | 18 | var mlp = new dnn.MLP({ 19 | 'input' : x, 20 | 'label' : y, 21 | 'n_ins' : 6, 22 | 'n_outs' : 2, 23 | 'hidden_layer_sizes' : [4,4,5] 24 | }); 25 | 26 | mlp.set('log level',1); 27 | 28 | mlp.train({ 29 | 'lr' : 0.6, 30 | 'epochs' : 20000 31 | }); 32 | 33 | a = [[0.5, 0.5, 0., 0., 0., 0.], 34 | [0., 0., 0., 0.5, 0.5, 0.], 35 | [0.5, 0.5, 0.5, 0.5, 0.5, 0.]]; 36 | 37 | console.log(mlp.predict(a)); -------------------------------------------------------------------------------- /examples/rbm.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Created by joonkukang on 2014. 1. 15.. 3 | */ 4 | var dnn = require('../lib/dnn'); 5 | var data = [[1,1,1,0,0,0], 6 | [1,0,1,0,0,0], 7 | [1,1,1,0,0,0], 8 | [0,0,1,1,1,0], 9 | [0,0,1,1,0,0], 10 | [0,0,1,1,1,0]]; 11 | 12 | var rbm = new dnn.RBM({ 13 | input : data, 14 | n_visible : 6, 15 | n_hidden : 2 16 | }); 17 | 18 | rbm.set('log level',1); 19 | var trainingEpochs = 500; 20 | 21 | rbm.train({ 22 | lr : 0.6, 23 | k : 1, 24 | epochs : trainingEpochs 25 | }); 26 | 27 | 28 | 29 | var v = [[1, 1, 0, 0, 0, 0], 30 | [0, 0, 0, 1, 1, 0]]; 31 | 32 | console.log(rbm.reconstruct(v)); 33 | console.log(rbm.sampleHgivenV(v)[0]); -------------------------------------------------------------------------------- /lib/CDBN.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Created by joonkukang on 2014. 1. 13.. 3 | */ 4 | var math = require('./utils').math; 5 | LogisticRegression = require('./LogisticRegression'); 6 | HiddenLayer = require('./HiddenLayer'); 7 | RBM = require('./RBM'); 8 | CRBM = require('./CRBM'); 9 | DBN = require('./DBN'); 10 | 11 | 12 | CDBN = module.exports = function (settings) { 13 | var self = this; 14 | self.x = settings['input']; 15 | self.y = settings['label']; 16 | self.sigmoidLayers = []; 17 | self.rbmLayers = []; 18 | self.nLayers = settings['hidden_layer_sizes'].length; 19 | self.hiddenLayerSizes = settings['hidden_layer_sizes']; 20 | self.nIns = settings['n_ins']; 21 | self.nOuts = settings['n_outs']; 22 | 23 | self.settings = { 24 | 'log level' : 1 // 0 : nothing, 1 : info, 2: warn 25 | }; 26 | // Constructing Deep Neural Network 27 | var i; 28 | for(i=0 ; i 0) { 87 | console.log("DBN RBM",i,"th Layer Final Cross Entropy: ",rbm.getReconstructionCrossEntropy()); 88 | console.log("DBN RBM",i,"th Layer Pre-Training Completed."); 89 | } 90 | 91 | // Synchronization between RBM and sigmoid Layer 92 | self.sigmoidLayers[i].W = rbm.W; 93 | self.sigmoidLayers[i].b = rbm.hbias; 94 | } 95 | if(self.settings['log level'] > 0) 96 | console.log("DBN Pre-Training Completed.") 97 | }; 98 | 99 | DBN.prototype.finetune = function (settings) { 100 | var self = this; 101 | var lr = 0.2, epochs = 1000; 102 | if(typeof settings['lr'] !== 'undefined') 103 | lr = settings['lr']; 104 | if(typeof settings['epochs'] !== 'undefined') 105 | epochs = settings['epochs']; 106 | 107 | //Fine-Tuning Using MLP (Back Propagation) 108 | var i; 109 | var pretrainedWArray = [], pretrainedBArray = []; // HiddenLayer W,b values already pretrained by RBM. 110 | for(i=0; i 0) { 37 | var progress = (1.*i/epochs)*100; 38 | if(progress > currentProgress) { 39 | console.log("LogisticRegression",progress.toFixed(0),"% Completed."); 40 | currentProgress++; 41 | } 42 | } 43 | } 44 | if(self.settings['log level'] > 0) 45 | console.log("LogisticRegression Final Cross Entropy : ",self.getReconstructionCrossEntropy()); 46 | }; 47 | 48 | LogisticRegression.prototype.getReconstructionCrossEntropy = function () { 49 | var self = this; 50 | var probYgivenX = math.softmaxMat(math.addMatVec(math.mulMat(self.x,self.W),self.b)); 51 | var a = math.mulMatElementWise(self.y, math.activateMat(probYgivenX,Math.log)); 52 | var b = math.mulMatElementWise(math.mulMatScalar(math.addMatScalar(self.y,-1),-1), 53 | math.activateMat(math.mulMatScalar(math.addMatScalar(probYgivenX,-1),-1),Math.log)); 54 | var crossEntropy = -math.meanVec(math.sumMatAxis(math.addMat(a,b),1)); 55 | return crossEntropy; 56 | }; 57 | 58 | LogisticRegression.prototype.predict = function (x) { 59 | var self = this; 60 | return math.softmaxMat(math.addMatVec(math.mulMat(x,self.W),self.b)); 61 | }; 62 | 63 | LogisticRegression.prototype.set = function(property,value) { 64 | var self = this; 65 | self.settings[property] = value; 66 | } -------------------------------------------------------------------------------- /lib/MLP.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Created by joonkukang on 2014. 1. 14.. 3 | */ 4 | var math = require('./utils').math; 5 | 6 | MLP = module.exports = function (settings) { 7 | var self = this; 8 | self.x = settings['input']; 9 | self.y = settings['label']; 10 | self.sigmoidLayers = []; 11 | self.nLayers = settings['hidden_layer_sizes'].length; 12 | self.settings = { 13 | 'log level' : 1 // 0 : nothing, 1 : info, 2: warn 14 | }; 15 | var i; 16 | for(i=0 ; i