mlpack 3.4.2
gru.hpp
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1
26#ifndef MLPACK_METHODS_ANN_LAYER_GRU_HPP
27#define MLPACK_METHODS_ANN_LAYER_GRU_HPP
28
29#include <list>
30#include <limits>
31
32#include <mlpack/prereqs.hpp>
33
34#include "../visitor/delta_visitor.hpp"
35#include "../visitor/output_parameter_visitor.hpp"
36
37#include "layer_types.hpp"
38#include "add_merge.hpp"
39#include "sequential.hpp"
40
41namespace mlpack {
42namespace ann {
43
54template <
55 typename InputDataType = arma::mat,
56 typename OutputDataType = arma::mat
57>
58class GRU
59{
60 public:
62 GRU();
63
71 GRU(const size_t inSize,
72 const size_t outSize,
73 const size_t rho = std::numeric_limits<size_t>::max());
74
82 template<typename eT>
83 void Forward(const arma::Mat<eT>& input, arma::Mat<eT>& output);
84
94 template<typename eT>
95 void Backward(const arma::Mat<eT>& /* input */,
96 const arma::Mat<eT>& gy,
97 arma::Mat<eT>& g);
98
99 /*
100 * Calculate the gradient using the output delta and the input activation.
101 *
102 * @param input The input parameter used for calculating the gradient.
103 * @param error The calculated error.
104 * @param gradient The calculated gradient.
105 */
106 template<typename eT>
107 void Gradient(const arma::Mat<eT>& input,
108 const arma::Mat<eT>& /* error */,
109 arma::Mat<eT>& /* gradient */);
110
111 /*
112 * Resets the cell to accept a new input. This breaks the BPTT chain starts a
113 * new one.
114 *
115 * @param size The current maximum number of steps through time.
116 */
117 void ResetCell(const size_t size);
118
120 bool Deterministic() const { return deterministic; }
122 bool& Deterministic() { return deterministic; }
123
125 size_t Rho() const { return rho; }
127 size_t& Rho() { return rho; }
128
130 OutputDataType const& Parameters() const { return weights; }
132 OutputDataType& Parameters() { return weights; }
133
135 OutputDataType const& OutputParameter() const { return outputParameter; }
137 OutputDataType& OutputParameter() { return outputParameter; }
138
140 OutputDataType const& Delta() const { return delta; }
142 OutputDataType& Delta() { return delta; }
143
145 OutputDataType const& Gradient() const { return gradient; }
147 OutputDataType& Gradient() { return gradient; }
148
150 std::vector<LayerTypes<> >& Model() { return network; }
151
153 size_t InSize() const { return inSize; }
154
156 size_t OutSize() const { return outSize; }
157
161 template<typename Archive>
162 void serialize(Archive& ar, const unsigned int /* version */);
163
164 private:
166 size_t inSize;
167
169 size_t outSize;
170
172 size_t rho;
173
175 size_t batchSize;
176
178 OutputDataType weights;
179
181 LayerTypes<> input2GateModule;
182
184 LayerTypes<> output2GateModule;
185
187 LayerTypes<> outputHidden2GateModule;
188
190 LayerTypes<> inputGateModule;
191
193 LayerTypes<> hiddenStateModule;
194
196 LayerTypes<> forgetGateModule;
197
199 OutputParameterVisitor outputParameterVisitor;
200
202 DeltaVisitor deltaVisitor;
203
205 DeleteVisitor deleteVisitor;
206
208 std::vector<LayerTypes<> > network;
209
211 size_t forwardStep;
212
214 size_t backwardStep;
215
217 size_t gradientStep;
218
220 std::list<arma::mat> outParameter;
221
223 arma::mat allZeros;
224
226 std::list<arma::mat>::iterator prevOutput;
227
229 std::list<arma::mat>::iterator backIterator;
230
232 std::list<arma::mat>::iterator gradIterator;
233
235 arma::mat prevError;
236
238 bool deterministic;
239
241 OutputDataType delta;
242
244 OutputDataType gradient;
245
247 OutputDataType outputParameter;
248}; // class GRU
249
250} // namespace ann
251} // namespace mlpack
252
253// Include implementation.
254#include "gru_impl.hpp"
255
256#endif
DeleteVisitor executes the destructor of the instantiated object.
DeltaVisitor exposes the delta parameter of the given module.
An implementation of a gru network layer.
Definition: gru.hpp:59
OutputDataType const & Delta() const
Get the delta.
Definition: gru.hpp:140
size_t & Rho()
Modify the maximum number of steps to backpropagate through time (BPTT).
Definition: gru.hpp:127
OutputDataType const & Parameters() const
Get the parameters.
Definition: gru.hpp:130
size_t OutSize() const
Get the number of output units.
Definition: gru.hpp:156
GRU()
Create the GRU object.
std::vector< LayerTypes<> > & Model()
Get the model modules.
Definition: gru.hpp:150
size_t Rho() const
Get the maximum number of steps to backpropagate through time (BPTT).
Definition: gru.hpp:125
void Forward(const arma::Mat< eT > &input, arma::Mat< eT > &output)
Ordinary feed forward pass of a neural network, evaluating the function f(x) by propagating the activ...
OutputDataType const & OutputParameter() const
Get the output parameter.
Definition: gru.hpp:135
GRU(const size_t inSize, const size_t outSize, const size_t rho=std::numeric_limits< size_t >::max())
Create the GRU layer object using the specified parameters.
bool & Deterministic()
Modify the value of the deterministic parameter.
Definition: gru.hpp:122
bool Deterministic() const
The value of the deterministic parameter.
Definition: gru.hpp:120
OutputDataType const & Gradient() const
Get the gradient.
Definition: gru.hpp:145
void ResetCell(const size_t size)
OutputDataType & Gradient()
Modify the gradient.
Definition: gru.hpp:147
void Backward(const arma::Mat< eT > &, const arma::Mat< eT > &gy, arma::Mat< eT > &g)
Ordinary feed backward pass of a neural network, calculating the function f(x) by propagating x backw...
OutputDataType & OutputParameter()
Modify the output parameter.
Definition: gru.hpp:137
size_t InSize() const
Get the number of input units.
Definition: gru.hpp:153
void Gradient(const arma::Mat< eT > &input, const arma::Mat< eT > &, arma::Mat< eT > &)
void serialize(Archive &ar, const unsigned int)
Serialize the layer.
OutputDataType & Parameters()
Modify the parameters.
Definition: gru.hpp:132
OutputDataType & Delta()
Modify the delta.
Definition: gru.hpp:142
OutputParameterVisitor exposes the output parameter of the given module.
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 > *, FlexibleReLU< 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 > *, Softmax< arma::mat, arma::mat > *, SpatialDropout< arma::mat, arma::mat > *, TransposedConvolution< NaiveConvolution< ValidConvolution >, NaiveConvolution< ValidConvolution >, NaiveConvolution< ValidConvolution >, arma::mat, arma::mat > *, WeightNorm< arma::mat, arma::mat > *, MoreTypes, CustomLayers *... > LayerTypes
Linear algebra utility functions, generally performed on matrices or vectors.
Definition: cv.hpp:1
The core includes that mlpack expects; standard C++ includes and Armadillo.