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NeuralNetwork.h
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33 
34 #ifndef __NEURALNETWORK_H__
35 #define __NEURALNETWORK_H__
36 
37 #include <shogun/lib/common.h>
38 #include <shogun/machine/Machine.h>
39 #include <shogun/lib/SGVector.h>
40 #include <shogun/lib/SGMatrix.h>
41 
42 namespace shogun
43 {
44 template<class T> class CDenseFeatures;
45 class CDynamicObjectArray;
46 class CNeuralLayer;
47 
50 {
53 };
54 
110 class CNeuralNetwork : public CMachine
111 {
112 friend class CDeepBeliefNetwork;
113 
114 public:
116  CNeuralNetwork();
117 
125 
132  virtual void set_layers(CDynamicObjectArray* layers);
133 
137  virtual void connect(int32_t i, int32_t j);
138 
142  virtual void quick_connect();
143 
145  virtual void disconnect(int32_t i, int32_t j);
146 
148  virtual void disconnect_all();
149 
155  virtual void initialize(float64_t sigma = 0.01f);
156 
157  virtual ~CNeuralNetwork();
158 
160  virtual CBinaryLabels* apply_binary(CFeatures* data);
165 
177 
182  virtual void set_labels(CLabels* lab);
183 
189 
191  virtual EProblemType get_machine_problem_type() const;
192 
208  virtual float64_t check_gradients(float64_t approx_epsilon=1.0e-3,
209  float64_t s = 1.0e-9);
210 
216 
219 
222 
224  int32_t get_num_inputs() { return m_num_inputs; }
225 
227  int32_t get_num_outputs();
228 
231 
232  virtual const char* get_name() const { return "NeuralNetwork";}
233 
234 protected:
236  virtual bool train_machine(CFeatures* data=NULL);
237 
239  virtual bool train_gradient_descent(SGMatrix<float64_t> inputs,
240  SGMatrix<float64_t> targets);
241 
243  virtual bool train_lbfgs(SGMatrix<float64_t> inputs,
244  SGMatrix<float64_t> targets);
245 
255  virtual SGMatrix<float64_t> forward_propagate(CFeatures* data, int32_t j=-1);
256 
267  virtual SGMatrix<float64_t> forward_propagate(SGMatrix<float64_t> inputs, int32_t j=-1);
268 
277  virtual void set_batch_size(int32_t batch_size);
278 
293  SGMatrix<float64_t> targets, SGVector<float64_t> gradients);
294 
305  SGMatrix<float64_t> targets);
306 
314 
315  virtual bool is_label_valid(CLabels *lab) const;
316 
318  CNeuralLayer* get_layer(int32_t i);
319 
324 
330 
331 private:
332  void init();
333 
335  static float64_t lbfgs_evaluate(void *userdata,
336  const float64_t *W,
337  float64_t *grad,
338  const int32_t n,
339  const float64_t step);
340 
342  static int lbfgs_progress(void *instance,
343  const float64_t *x,
344  const float64_t *g,
345  const float64_t fx,
346  const float64_t xnorm,
347  const float64_t gnorm,
348  const float64_t step,
349  int n,
350  int k,
351  int ls
352  );
353 
355  template<class T>
356  SGVector<T> get_section(SGVector<T> v, int32_t i);
357 public:
360 
363 
366 
376 
386 
393 
401 
406  int32_t max_num_epochs;
407 
413 
416 
423 
433 
445 protected:
447  int32_t m_num_inputs;
448 
450  int32_t m_num_layers;
451 
454 
459 
462 
465 
470 
476 
480  int32_t m_batch_size;
481 
486 
487 private:
491  const SGMatrix<float64_t>* m_lbfgs_temp_inputs;
492  const SGMatrix<float64_t>* m_lbfgs_temp_targets;
493 };
494 
495 }
496 #endif
EMachineType
Definition: Machine.h:33
SGVector< int32_t > m_index_offsets
virtual CBinaryLabels * apply_binary(CFeatures *data)
Real Labels are real-valued labels.
virtual const char * get_name() const
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
SGVector< float64_t > get_parameters()
virtual bool train_machine(CFeatures *data=NULL)
SGVector< float64_t > m_params
A generic multi-layer neural network.
SGMatrix< bool > m_adj_matrix
SGMatrix< float64_t > features_to_matrix(CFeatures *features)
virtual void disconnect(int32_t i, int32_t j)
Base class for neural network layers.
Definition: NeuralLayer.h:87
virtual bool train_gradient_descent(SGMatrix< float64_t > inputs, SGMatrix< float64_t > targets)
virtual void quick_connect()
virtual float64_t compute_error(SGMatrix< float64_t > inputs, SGMatrix< float64_t > targets)
A generic learning machine interface.
Definition: Machine.h:143
float64_t gd_learning_rate_decay
SGVector< bool > m_param_regularizable
virtual CMulticlassLabels * apply_multiclass(CFeatures *data)
Multiclass Labels for multi-class classification.
ENNOptimizationMethod optimization_method
A Deep Belief Network.
EProblemType
Definition: Machine.h:110
CDynamicObjectArray * m_layers
float64_t gd_error_damping_coeff
virtual void connect(int32_t i, int32_t j)
virtual void set_batch_size(int32_t batch_size)
virtual void disconnect_all()
double float64_t
Definition: common.h:50
virtual CRegressionLabels * apply_regression(CFeatures *data)
Dynamic array class for CSGObject pointers that creates an array that can be used like a list or an a...
CDynamicObjectArray * get_layers()
virtual float64_t check_gradients(float64_t approx_epsilon=1.0e-3, float64_t s=1.0e-9)
CNeuralLayer * get_layer(int32_t i)
virtual bool is_label_valid(CLabels *lab) const
virtual CDenseFeatures< float64_t > * transform(CDenseFeatures< float64_t > *data)
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
virtual void set_labels(CLabels *lab)
virtual bool train_lbfgs(SGMatrix< float64_t > inputs, SGMatrix< float64_t > targets)
virtual EMachineType get_classifier_type()
The class Features is the base class of all feature objects.
Definition: Features.h:68
SGMatrix< float64_t > labels_to_matrix(CLabels *labs)
virtual SGMatrix< float64_t > forward_propagate(CFeatures *data, int32_t j=-1)
virtual EProblemType get_machine_problem_type() const
Binary Labels for binary classification.
Definition: BinaryLabels.h:37
virtual void initialize(float64_t sigma=0.01f)
virtual void set_layers(CDynamicObjectArray *layers)
SGVector< float64_t > * get_layer_parameters(int32_t i)
ENNOptimizationMethod
Definition: NeuralNetwork.h:49
virtual float64_t compute_gradients(SGMatrix< float64_t > inputs, SGMatrix< float64_t > targets, SGVector< float64_t > gradients)

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