Package com.github.neuralnetworks.training.rbm

Examples of com.github.neuralnetworks.training.rbm.AparapiCDTrainer

162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
  cgb2.set(-0.4f, 0, 0);
  cgb2.set(0.2f, 1, 0);

  SimpleInputProvider inputProvider = new SimpleInputProvider(new float[][] { { 1, 0, 1 } }, null, 1, 1);

  AparapiCDTrainer firstTrainer = TrainerFactory.cdSigmoidTrainer(firstRBM, null, null, null, null, 1f, 0f, 0f, 0f, 1, true);
  firstTrainer.setLayerCalculator(NNFactory.rbmSigmoidSigmoid(firstRBM));

  RBM secondRBM = dbn.getLastNeuralNetwork();

  AparapiCDTrainer secondTrainer = TrainerFactory.cdSigmoidTrainer(secondRBM, null, null, null, null, 1f, 0f, 0f, 0f, 1, true);
  secondTrainer.setLayerCalculator(NNFactory.rbmSigmoidSigmoid(secondRBM));

  Map<NeuralNetwork, OneStepTrainer<?>> layerTrainers = new HashMap<>();
  layerTrainers.put(firstRBM, firstTrainer);
  layerTrainers.put(secondRBM, secondTrainer);
View Full Code Here
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
  cgb2.set(-0.4f, 0, 0);
  cgb2.set(0.2f, 1, 0);
 
  SimpleInputProvider inputProvider = new SimpleInputProvider(new float[][] { { 1, 0, 1 } }, null, 1, 1);

  AparapiCDTrainer firstTrainer = TrainerFactory.cdSigmoidTrainer(firstRBM, null, null, null, null, 0f, 0f, 0f, 0f, 0, true);
  firstTrainer.setLayerCalculator(NNFactory.rbmSigmoidSigmoid(firstRBM));

  AparapiCDTrainer secondTrainer = TrainerFactory.cdSigmoidTrainer(secondRBM, null, null, null, null, 1f, 0f, 0f, 0f, 1, true);
  secondTrainer.setLayerCalculator(NNFactory.rbmSigmoidSigmoid(secondRBM));

  Map<NeuralNetwork, OneStepTrainer<?>> layerTrainers = new HashMap<>();
  layerTrainers.put(firstRBM, firstTrainer);
  layerTrainers.put(secondRBM, secondTrainer);
 
View Full Code Here
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
  cgb2.set(-0.4f, 0, 0);
  cgb2.set(0.2f, 1, 0);

  SimpleInputProvider inputProvider = new SimpleInputProvider(new float[][] { { 1, 0, 1 } }, null);

  AparapiCDTrainer firstTrainer = TrainerFactory.cdSigmoidTrainer(firstRBM, null, null, null, null, 1f, 0f, 0f, 0f, 1, 1, 1, true);

  RBM secondRBM = dbn.getLastNeuralNetwork();

  AparapiCDTrainer secondTrainer = TrainerFactory.cdSigmoidTrainer(secondRBM, null, null, null, null, 1f, 0f, 0f, 0f, 1, 1, 1, true);

  Map<NeuralNetwork, OneStepTrainer<?>> layerTrainers = new HashMap<>();
  layerTrainers.put(firstRBM, firstTrainer);
  layerTrainers.put(secondRBM, secondTrainer);
View Full Code Here
288
289
290
291
292
293
294
295
296
297
298
299
300
  cgb2.set(-0.4f, 0, 0);
  cgb2.set(0.2f, 1, 0);

  SimpleInputProvider inputProvider = new SimpleInputProvider(new float[][] { { 1, 0, 1 } }, null);

  AparapiCDTrainer firstTrainer = TrainerFactory.cdSigmoidTrainer(firstRBM, null, null, null, null, 0f, 0f, 0f, 0f, 0, 1, 1, true);

  AparapiCDTrainer secondTrainer = TrainerFactory.cdSigmoidTrainer(secondRBM, null, null, null, null, 1f, 0f, 0f, 0f, 1, 1, 1, true);

  Map<NeuralNetwork, OneStepTrainer<?>> layerTrainers = new HashMap<>();
  layerTrainers.put(firstRBM, firstTrainer);
  layerTrainers.put(secondRBM, secondTrainer);
View Full Code Here
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
  TrainingInputProvider trainInputProvider = new SimpleInputProvider(new float[][] { { 1, 1, 1, 0, 0, 0 }, { 1, 0, 1, 0, 0, 0 }, { 1, 1, 0, 0, 0, 0 }, { 0, 1, 1, 0, 0, 0 }, { 0, 1, 1, 1, 0, 0 }, { 0, 0, 0, 1, 1, 1 }, { 0, 0, 1, 1, 1, 0 }, { 0, 0, 0, 1, 0, 1 }, { 0, 0, 0, 0, 1, 1 }, { 0, 0, 0, 1, 1, 0 } }, null);
  TrainingInputProvider testInputProvider = new SimpleInputProvider(new float[][] { { 1, 1, 1, 0, 0, 0 }, { 1, 0, 1, 0, 0, 0 }, { 1, 1, 0, 0, 0, 0 }, { 0, 1, 1, 0, 0, 0 }, { 0, 1, 1, 1, 0, 0 }, { 0, 0, 0, 1, 1, 1 }, { 0, 0, 1, 1, 1, 0 }, { 0, 0, 0, 1, 0, 1 }, { 0, 0, 0, 0, 1, 1 }, { 0, 0, 0, 1, 1, 0 } }, new float[][] { { 1, 0 }, { 1, 0 }, { 1, 0 }, { 1, 0 }, { 1, 0 }, { 0, 1 }, { 0, 1 }, { 0, 1 }, { 0, 1 }, { 0, 1 } });
  MultipleNeuronsOutputError error = new MultipleNeuronsOutputError();

  // Contrastive divergence training
  AparapiCDTrainer t = TrainerFactory.cdSigmoidTrainer(rbm, trainInputProvider, testInputProvider, error, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.02f, 0.5f, 0f, 0f, 1, 1, 100, false);

  // log data
  t.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), true, false));

  // training
  t.train();

  // testing
  t.test();

  assertEquals(0, t.getOutputError().getTotalNetworkError(), 0);
    }
View Full Code Here
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
  TrainingInputProvider trainInputProvider = new SimpleInputProvider(new float[][] { { 1, 1, 1, 0, 0, 0 }, { 1, 0, 1, 0, 0, 0 }, { 1, 1, 0, 0, 0, 0 }, { 0, 1, 1, 0, 0, 0 }, { 0, 1, 1, 1, 0, 0 }, { 0, 0, 0, 1, 1, 1 }, { 0, 0, 1, 1, 1, 0 }, { 0, 0, 0, 1, 0, 1 }, { 0, 0, 0, 0, 1, 1 }, { 0, 0, 0, 1, 1, 0 } }, null);
  TrainingInputProvider testInputProvider = new SimpleInputProvider(new float[][] { { 1, 1, 1, 0, 0, 0 }, { 1, 0, 1, 0, 0, 0 }, { 1, 1, 0, 0, 0, 0 }, { 0, 1, 1, 0, 0, 0 }, { 0, 1, 1, 1, 0, 0 }, { 0, 0, 0, 1, 1, 1 }, { 0, 0, 1, 1, 1, 0 }, { 0, 0, 0, 1, 0, 1 }, { 0, 0, 0, 0, 1, 1 }, { 0, 0, 0, 1, 1, 0 } }, new float[][] { { 1, 0 }, { 1, 0 }, { 1, 0 }, { 1, 0 }, { 1, 0 }, { 0, 1 }, { 0, 1 }, { 0, 1 }, { 0, 1 }, { 0, 1 } });
  MultipleNeuronsOutputError error = new MultipleNeuronsOutputError();

  // Persistent Contrastive divergence trainer
  AparapiCDTrainer t = TrainerFactory.cdSigmoidTrainer(rbm, trainInputProvider, testInputProvider, error, new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.02f, 0.5f, 0f, 0f, 1, 1, 100, true);

  // log data
  t.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), true, false));

  // training
  t.train();

  // testing
  t.test();
 
  assertEquals(0, t.getOutputError().getTotalNetworkError(), 0);
    }
View Full Code Here
249
250
251
252
253
254
255
256
257
258
259
260
261
  Matrix cgb2 = rbm.getHiddenBiasConnections().getWeights();
  cgb2.set(-0.4f, 0, 0);
  cgb2.set(0.2f, 1, 0);

  AparapiCDTrainer t = TrainerFactory.cdSigmoidTrainer(rbm, new SimpleInputProvider(new float[][] { { 1, 0, 1 } }, null), null, null, null, 1f, 0f, 0f, 0f, 1, 1, 1, true);

  t.train();

  assertEquals(0.52276707, cgb1.get(0, 0), 0.00001);
  assertEquals(- 0.54617375, cgb1.get(1, 0), 0.00001);
  assertEquals(0.51522285, cgb1.get(2, 0), 0.00001);
 
View Full Code Here
292
293
294
295
296
297
298
299
300
301
302
303
304
  Matrix cgb2 = rbm.getHiddenBiasConnections().getWeights();
  cgb2.set(-0.4f, 0, 0);
  cgb2.set(0.2f, 1, 0);

  AparapiCDTrainer t = TrainerFactory.cdSigmoidTrainer(rbm, new SimpleInputProvider(new float[][] { { 1, 0, 1 }, { 1, 1, 0 } }, null), null, null, null, 1f, 0f, 0f, 0f, 1, 1, 1, false);

  t.train();

  assertEquals(0.86090606, cgb1.get(0, 0), 0.00001);
  assertEquals(0.089616358, cgb1.get(1, 0), 0.00001);
  assertEquals(-0.11872697, cgb1.get(2, 0), 0.00001);
View Full Code Here
207
208
209
210
211
212
213
  RBMLayerCalculator lc = NNFactory.rbmSoftReluSoftRelu(rbm, trainingBatchSize);
  ConnectionCalculatorFullyConnected cc = (ConnectionCalculatorFullyConnected) lc.getNegPhaseHiddenToVisibleCC();
  cc.addPreTransferFunction(new BernoulliDistribution());

  return new AparapiCDTrainer(rbmProperties(rbm, lc, trainingSet, testingSet, error, rand, learningRate, momentum, l1weightDecay, l2weightDecay, gibbsSampling, trainingBatchSize, epochs, isPersistentCD));
    }
View Full Code Here
217
218
219
220
221
222
223
  RBMLayerCalculator lc = NNFactory.rbmSigmoidSigmoid(rbm, trainingBatchSize);
  ConnectionCalculatorFullyConnected cc = (ConnectionCalculatorFullyConnected) lc.getNegPhaseHiddenToVisibleCC();
  cc.addPreTransferFunction(new BernoulliDistribution());

  return new AparapiCDTrainer(rbmProperties(rbm, lc, trainingSet, testingSet, error, rand, learningRate, momentum, l1weightDecay, l2weightDecay, gibbsSampling, trainingBatchSize, epochs, isPersistentCD));
    }
View Full Code Here
TOP

Related Classes of com.github.neuralnetworks.training.rbm.AparapiCDTrainer

  • com.github.neuralnetworks.architecture.types.RBM
  • com.github.neuralnetworks.calculation.RBMLayerCalculator
  • com.github.neuralnetworks.samples.test.IrisTest
  • com.github.neuralnetworks.samples.test.MnistTest
  • com.github.neuralnetworks.test.DNNTest
  • com.github.neuralnetworks.test.RBMTest
  • com.github.neuralnetworks.training.TrainerFactory
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.