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DeepAutoencoder.h
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33 
34 #ifndef __DEEPAUTOENCODER_H__
35 #define __DEEPAUTOENCODER_H__
36 
37 #include <shogun/lib/common.h>
39 
40 namespace shogun
41 {
42 template <class T> class CDenseFeatures;
43 
88 {
89 public:
92 
100  CDeepAutoencoder(CDynamicObjectArray* layers, float64_t sigma = 0.01);
101 
102  virtual ~CDeepAutoencoder() {}
103 
124  virtual void pre_train(CFeatures* data);
125 
135 
145 
157  CNeuralLayer* output_layer=NULL, float64_t sigma = 0.01);
158 
170  virtual void set_contraction_coefficient(float64_t coeff);
171 
172  virtual const char* get_name() const { return "DeepAutoencoder"; }
173 
174 protected:
182 
183 private:
184  void init();
185 
187  template<class T>
188  SGVector<T> get_section(SGVector<T> v, int32_t i);
189 
190 public:
195 
200 
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251 
256 
257 protected:
261 };
262 }
263 #endif
virtual void set_contraction_coefficient(float64_t coeff)
SGVector< int32_t > pt_max_num_epochs
Represents a single layer neural autoencoder.
Definition: Autoencoder.h:86
virtual CDenseFeatures< float64_t > * transform(CDenseFeatures< float64_t > *data)
SGVector< float64_t > pt_contraction_coefficient
A generic multi-layer neural network.
Base class for neural network layers.
Definition: NeuralLayer.h:87
SGVector< float64_t > pt_gd_error_damping_coeff
SGVector< float64_t > pt_epsilon
Represents a muti-layer autoencoder.
virtual CDenseFeatures< float64_t > * reconstruct(CDenseFeatures< float64_t > *data)
shogun vector
virtual void pre_train(CFeatures *data)
double float64_t
Definition: common.h:50
SGVector< float64_t > pt_l1_coefficient
Dynamic array class for CSGObject pointers that creates an array that can be used like a list or an a...
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
virtual float64_t compute_error(SGMatrix< float64_t > targets)
virtual const char * get_name() const
The class Features is the base class of all feature objects.
Definition: Features.h:68
virtual CNeuralNetwork * convert_to_neural_network(CNeuralLayer *output_layer=NULL, float64_t sigma=0.01)
SGVector< int32_t > pt_gd_mini_batch_size
SGVector< float64_t > pt_noise_parameter
SGVector< int32_t > pt_noise_type
SGVector< float64_t > pt_l2_coefficient
SGVector< int32_t > pt_optimization_method
SGVector< float64_t > pt_gd_momentum
SGVector< float64_t > pt_gd_learning_rate
SGVector< float64_t > pt_gd_learning_rate_decay

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