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DeepBeliefNetwork.h
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33 
34 #ifndef __DEEPBELIEFNETWORK_H__
35 #define __DEEPBELIEFNETWORK_H__
36 
37 #include <shogun/lib/config.h>
38 #ifdef HAVE_EIGEN3
39 
40 #include <shogun/lib/common.h>
41 #include <shogun/base/SGObject.h>
42 #include <shogun/neuralnets/RBM.h>
43 #include <lib/SGMatrixList.h>
44 
45 namespace shogun
46 {
47 template <class T> class SGVector;
48 template <class T> class SGMatrix;
49 template <class T> class SGMatrixList;
50 template <class T> class CDenseFeatures;
51 template <class T> class CDynamicArray;
52 class CDynamicObjectArray;
53 class CNeuralNetwork;
54 class CNeuralLayer;
55 
92 {
93 public:
96 
102  CDeepBeliefNetwork(int32_t num_visible_units,
103  ERBMVisibleUnitType unit_type = RBMVUT_BINARY);
104 
105  virtual ~CDeepBeliefNetwork();
106 
112  virtual void add_hidden_layer(int32_t num_units);
113 
119  virtual void initialize(float64_t sigma = 0.01);
120 
125  virtual void set_batch_size(int32_t batch_size);
126 
132  virtual void pre_train(CDenseFeatures<float64_t>* features);
133 
140  virtual void pre_train(int32_t index, CDenseFeatures<float64_t>* features);
141 
148  virtual void train(CDenseFeatures<float64_t>* features);
149 
163  CDenseFeatures<float64_t>* features, int32_t i=-1);
164 
176  int32_t num_gibbs_steps=1, int32_t batch_size=1);
177 
179  virtual void reset_chain();
180 
194  CNeuralLayer* output_layer=NULL, float64_t sigma = 0.01);
195 
202  virtual SGMatrix<float64_t> get_weights(int32_t index,
204 
211  virtual SGVector<float64_t> get_biases(int32_t index,
213 
214  virtual const char* get_name() const { return "DeepBeliefNetwork"; }
215 
216 protected:
218  virtual void down_step(int32_t index, SGVector<float64_t> params,
220  bool sample_states = true);
221 
223  virtual void up_step(int32_t index, SGVector<float64_t> params,
225  bool sample_states = true);
226 
228  virtual void wake_sleep(SGMatrix<float64_t> data,
229  CRBM* top_rbm,
230  SGMatrixList<float64_t> sleep_states,
231  SGMatrixList<float64_t> wake_states,
232  SGMatrixList<float64_t> psleep_states,
233  SGMatrixList<float64_t> pwake_states,
234  SGVector<float64_t> gen_params,
235  SGVector<float64_t> rec_params,
236  SGVector<float64_t> gen_gradients,
237  SGVector<float64_t> rec_gradients);
238 
239 private:
240  void init();
241 
242 public:
247 
252 
257 
262 
267 
272 
277 
282 
287 
292 
297 
302 
306  int32_t cd_num_steps;
307 
312 
316  int32_t max_num_epochs;
317 
323 
326 
333 
343 
344 protected:
347 
349  int32_t m_num_layers;
350 
353 
356 
358  int32_t m_batch_size;
359 
362 
364  int32_t m_num_params;
365 
368 
373 
377 };
378 
379 }
380 #endif
381 #endif
A Restricted Boltzmann Machine.
Definition: RBM.h:123
virtual void wake_sleep(SGMatrix< float64_t > data, CRBM *top_rbm, SGMatrixList< float64_t > sleep_states, SGMatrixList< float64_t > wake_states, SGMatrixList< float64_t > psleep_states, SGMatrixList< float64_t > pwake_states, SGVector< float64_t > gen_params, SGVector< float64_t > rec_params, SGVector< float64_t > gen_gradients, SGVector< float64_t > rec_gradients)
virtual void train(CDenseFeatures< float64_t > *features)
virtual CDenseFeatures< float64_t > * transform(CDenseFeatures< float64_t > *features, int32_t i=-1)
SGMatrixList< float64_t > m_states
SGVector< int32_t > pt_gd_mini_batch_size
SGVector< int32_t > m_bias_index_offsets
virtual const char * get_name() const
CDynamicArray< int32_t > * m_layer_sizes
A generic multi-layer neural network.
virtual void pre_train(CDenseFeatures< float64_t > *features)
SGVector< float64_t > pt_gd_momentum
virtual void down_step(int32_t index, SGVector< float64_t > params, SGMatrix< float64_t > input, SGMatrix< float64_t > result, bool sample_states=true)
Base class for neural network layers.
Definition: NeuralLayer.h:87
SGVector< int32_t > pt_monitoring_interval
ERBMVisibleUnitType m_visible_units_type
SGVector< int32_t > m_weights_index_offsets
virtual void up_step(int32_t index, SGVector< float64_t > params, SGMatrix< float64_t > input, SGMatrix< float64_t > result, bool sample_states=true)
SGVector< float64_t > m_params
A Deep Belief Network.
Class SGObject is the base class of all shogun objects.
Definition: SGObject.h:112
SGVector< float64_t > pt_gd_learning_rate
virtual void add_hidden_layer(int32_t num_units)
double float64_t
Definition: common.h:50
virtual void set_batch_size(int32_t batch_size)
SGVector< float64_t > pt_gd_learning_rate_decay
SGVector< bool > pt_cd_persistent
SGVector< float64_t > pt_l1_coefficient
ERBMVisibleUnitType
Definition: RBM.h:55
virtual void initialize(float64_t sigma=0.01)
SGVector< int32_t > pt_monitoring_method
SGVector< float64_t > pt_l2_coefficient
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
virtual CNeuralNetwork * convert_to_neural_network(CNeuralLayer *output_layer=NULL, float64_t sigma=0.01)
virtual CDenseFeatures< float64_t > * sample(int32_t num_gibbs_steps=1, int32_t batch_size=1)
SGVector< bool > pt_cd_sample_visible
virtual SGMatrix< float64_t > get_weights(int32_t index, SGVector< float64_t > p=SGVector< float64_t >())
SGVector< int32_t > pt_max_num_epochs
virtual SGVector< float64_t > get_biases(int32_t index, SGVector< float64_t > p=SGVector< float64_t >())
SGVector< int32_t > pt_cd_num_steps

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