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NeuralLogisticLayer.cpp
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1 /*
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31  * Written (W) 2014 Khaled Nasr
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33 
36 #include <shogun/lib/SGVector.h>
37 
38 using namespace shogun;
39 
41 {
42 }
43 
45 CNeuralLinearLayer(num_neurons)
46 {
47 }
48 
50  CDynamicObjectArray* layers)
51 {
52  CNeuralLinearLayer::compute_activations(parameters, layers);
53 
54  // apply logistic activation function
55  int32_t length = m_num_neurons*m_batch_size;
56  for (int32_t i=0; i<length; i++)
57  m_activations[i] = 1.0/(1.0+CMath::exp(-1.0*m_activations[i]));
58 }
59 
61  SGVector< float64_t > parameters)
62 {
64 
66  m_num_neurons, num_inputs, false);
67 
68  float64_t contraction_term = 0;
69  for (int32_t i=0; i<m_num_neurons; i++)
70  {
71  float64_t sum_j = 0;
72  for (int32_t j=0; j<num_inputs; j++)
73  sum_j += W(i,j)*W(i,j);
74 
75  for (int32_t k=0; k<m_batch_size; k++)
76  {
77  float64_t h_ = m_activations(i,k)*(1-m_activations(i,k));
78  contraction_term += h_*h_*sum_j;
79  }
80  }
81 
82  return (contraction_coefficient/m_batch_size) * contraction_term;
83 }
84 
86  SGVector< float64_t > parameters, SGVector< float64_t > gradients)
87 {
89 
91  m_num_neurons, num_inputs, false);
93  m_num_neurons, num_inputs, false);
94 
95  for (int32_t k = 0; k<m_batch_size; k++)
96  {
97  for (int32_t i=0; i<m_num_neurons; i++)
98  {
99  for (int32_t j=0; j<num_inputs; j++)
100  {
101  float64_t h = m_activations(i,k);
102  float64_t w = W(i,j);
103  float64_t h_ = w*h*(1-h);
104 
105  float64_t g = 2*w*(h-1)*h*(h*(2*w*h_-1)-w*h_+h*h);
106 
107  WG(i,j) += (contraction_coefficient/m_batch_size)*g;
108  }
109  }
110  }
111 }
112 
113 
115 {
117 
118  // multiply by the derivative of the logistic function
119  int32_t length = m_num_neurons*m_batch_size;
120  for (int32_t i=0; i<length; i++)
121  m_local_gradients[i] *= m_activations[i] * (1.0-m_activations[i]);
122 }

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