network_init.hpp
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1 
13 #ifndef MLPACK_METHODS_ANN_INIT_RULES_NETWORK_INIT_HPP
14 #define MLPACK_METHODS_ANN_INIT_RULES_NETWORK_INIT_HPP
15 
16 #include <mlpack/prereqs.hpp>
17 
18 #include "../visitor/reset_visitor.hpp"
19 #include "../visitor/weight_size_visitor.hpp"
20 #include "../visitor/weight_set_visitor.hpp"
21 #include "init_rules_traits.hpp"
22 
24 
25 namespace mlpack {
26 namespace ann {
27 
32 template<typename InitializationRuleType, typename... CustomLayers>
34 {
35  public:
42  const InitializationRuleType& initializeRule = InitializationRuleType()) :
43  initializeRule(initializeRule)
44  {
45  // Nothing to do here.
46  }
47 
56  template <typename eT>
57  void Initialize(const std::vector<LayerTypes<CustomLayers...> >& network,
58  arma::Mat<eT>& parameter, size_t parameterOffset = 0)
59  {
60  // Determine the number of parameter/weights of the given network.
61  if (parameter.is_empty())
62  {
63  size_t weights = 0;
64  for (size_t i = 0; i < network.size(); ++i)
65  weights += boost::apply_visitor(weightSizeVisitor, network[i]);
66  parameter.set_size(weights, 1);
67  }
68 
69  // Initialize the network layer by layer or the complete network.
71  {
72  for (size_t i = 0, offset = parameterOffset; i < network.size(); ++i)
73  {
74  // Initialize the layer with the specified parameter/weight
75  // initialization rule.
76  const size_t weight = boost::apply_visitor(weightSizeVisitor,
77  network[i]);
78  arma::Mat<eT> tmp = arma::mat(parameter.memptr() + offset,
79  weight, 1, false, false);
80  initializeRule.Initialize(tmp, tmp.n_elem, 1);
81 
82  // Increase the parameter/weight offset for the next layer.
83  offset += weight;
84  }
85  }
86  else
87  {
88  initializeRule.Initialize(parameter, parameter.n_elem, 1);
89  }
90 
91  // Note: We can't merge the for loop into the for loop above because
92  // WeightSetVisitor also sets the parameter/weights of the inner modules.
93  // Inner Modules are held by the parent module e.g. the concat module can
94  // hold various other modules.
95  for (size_t i = 0, offset = parameterOffset; i < network.size(); ++i)
96  {
97  offset += boost::apply_visitor(WeightSetVisitor(parameter, offset),
98  network[i]);
99 
100  boost::apply_visitor(resetVisitor, network[i]);
101  }
102  }
103 
104  private:
107  InitializationRuleType initializeRule;
108 
110  ResetVisitor resetVisitor;
111 
113  WeightSizeVisitor weightSizeVisitor;
114 }; // class NetworkInitialization
115 
116 } // namespace ann
117 } // namespace mlpack
118 
119 #endif
Linear algebra utility functions, generally performed on matrices or vectors.
The core includes that mlpack expects; standard C++ includes and Armadillo.
WeightSizeVisitor returns the number of weights of the given module.
This is a template class that can provide information about various initialization methods...
WeightSetVisitor update the module parameters given the parameters set.
ResetVisitor executes the Reset() function.
NetworkInitialization(const InitializationRuleType &initializeRule=InitializationRuleType())
Use the given initialization rule to initialize the specified network.
boost::variant< AdaptiveMaxPooling< arma::mat, arma::mat > *, AdaptiveMeanPooling< arma::mat, arma::mat > *, Add< arma::mat, arma::mat > *, AddMerge< arma::mat, arma::mat > *, AlphaDropout< arma::mat, arma::mat > *, AtrousConvolution< NaiveConvolution< ValidConvolution >, NaiveConvolution< FullConvolution >, NaiveConvolution< ValidConvolution >, arma::mat, arma::mat > *, BaseLayer< LogisticFunction, arma::mat, arma::mat > *, BaseLayer< IdentityFunction, arma::mat, arma::mat > *, BaseLayer< TanhFunction, arma::mat, arma::mat > *, BaseLayer< SoftplusFunction, arma::mat, arma::mat > *, BaseLayer< RectifierFunction, arma::mat, arma::mat > *, BatchNorm< arma::mat, arma::mat > *, BilinearInterpolation< arma::mat, arma::mat > *, CELU< arma::mat, arma::mat > *, Concat< arma::mat, arma::mat > *, Concatenate< arma::mat, arma::mat > *, ConcatPerformance< NegativeLogLikelihood< arma::mat, arma::mat >, arma::mat, arma::mat > *, Constant< arma::mat, arma::mat > *, Convolution< NaiveConvolution< ValidConvolution >, NaiveConvolution< FullConvolution >, NaiveConvolution< ValidConvolution >, arma::mat, arma::mat > *, CReLU< arma::mat, arma::mat > *, DropConnect< arma::mat, arma::mat > *, Dropout< arma::mat, arma::mat > *, ELU< arma::mat, arma::mat > *, FastLSTM< arma::mat, arma::mat > *, GRU< arma::mat, arma::mat > *, HardTanH< arma::mat, arma::mat > *, Join< arma::mat, arma::mat > *, LayerNorm< arma::mat, arma::mat > *, LeakyReLU< arma::mat, arma::mat > *, Linear< arma::mat, arma::mat, NoRegularizer > *, LinearNoBias< arma::mat, arma::mat, NoRegularizer > *, LogSoftMax< arma::mat, arma::mat > *, Lookup< arma::mat, arma::mat > *, LSTM< arma::mat, arma::mat > *, MaxPooling< arma::mat, arma::mat > *, MeanPooling< arma::mat, arma::mat > *, MiniBatchDiscrimination< arma::mat, arma::mat > *, MultiplyConstant< arma::mat, arma::mat > *, MultiplyMerge< arma::mat, arma::mat > *, NegativeLogLikelihood< arma::mat, arma::mat > *, NoisyLinear< arma::mat, arma::mat > *, Padding< arma::mat, arma::mat > *, PReLU< arma::mat, arma::mat > *, Sequential< arma::mat, arma::mat, false > *, Sequential< arma::mat, arma::mat, true > *, Softmax< arma::mat, arma::mat > *, TransposedConvolution< NaiveConvolution< ValidConvolution >, NaiveConvolution< ValidConvolution >, NaiveConvolution< ValidConvolution >, arma::mat, arma::mat > *, WeightNorm< arma::mat, arma::mat > *, MoreTypes, CustomLayers *... > LayerTypes
This class is used to initialize the network with the given initialization rule.
void Initialize(const std::vector< LayerTypes< CustomLayers... > > &network, arma::Mat< eT > &parameter, size_t parameterOffset=0)
Initialize the specified network and store the results in the given parameter.