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NeuralSoftmaxLayer.cpp
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1 /*
2  * Copyright (c) 2014, Shogun Toolbox Foundation
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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  // to avoid exponentiating large numbers, the maximum activation is
55  // subtracted from all the activations and the computations are done in the
56  // log domain
57 
59 
60  for (int32_t j=0; j<m_batch_size; j++)
61  {
62  float64_t sum = 0;
63  for (int32_t i=0; i<m_num_neurons; i++)
64  {
65  sum += CMath::exp(m_activations[i+j*m_num_neurons]-max);
66  }
67  float64_t normalizer = CMath::log(sum);
68  for (int32_t k=0; k<m_num_neurons; k++)
69  {
71  CMath::exp(m_activations[k+j*m_num_neurons]-max-normalizer);
72  }
73  }
74 }
75 
77 {
78  if (targets.num_rows == 0)
79  SG_ERROR("Cannot be used as a hidden layer\n");
80 
81  int32_t len = m_num_neurons*m_batch_size;
82  for (int32_t i=0; i< len; i++)
83  {
84  m_local_gradients[i] = (m_activations[i]-targets[i])/m_batch_size;
85  }
86 }
87 
89 {
90  int32_t len = m_num_neurons*m_batch_size;
91  float64_t sum = 0;
92  for (int32_t i=0; i< len; i++)
93  {
94  // to prevent taking the log of a zero
95  if (m_activations[i]==0)
96  sum += targets[i]*CMath::log(1e-50);
97  else
98  sum += targets[i]*CMath::log(m_activations[i]);
99  }
100  return -1*sum/m_batch_size;
101 }

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