mlpack
layer_types.hpp
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1 
12 #ifndef MLPACK_METHODS_ANN_LAYER_LAYER_TYPES_HPP
13 #define MLPACK_METHODS_ANN_LAYER_LAYER_TYPES_HPP
14 
15 #include
16 
17 // Layer modules.
18 #include <mlpack/methods/ann/layer/add.hpp>
19 #include <mlpack/methods/ann/layer/alpha_dropout.hpp>
20 #include <mlpack/methods/ann/layer/base_layer.hpp>
21 #include <mlpack/methods/ann/layer/batch_norm.hpp>
22 #include <mlpack/methods/ann/layer/bilinear_interpolation.hpp>
23 #include <mlpack/methods/ann/layer/constant.hpp>
24 #include <mlpack/methods/ann/layer/concatenate.hpp>
25 #include <mlpack/methods/ann/layer/dropout.hpp>
26 #include <mlpack/methods/ann/layer/elu.hpp>
27 #include <mlpack/methods/ann/layer/hard_tanh.hpp>
28 #include <mlpack/methods/ann/layer/join.hpp>
29 #include <mlpack/methods/ann/layer/layer_norm.hpp>
30 #include <mlpack/methods/ann/layer/leaky_relu.hpp>
31 #include <mlpack/methods/ann/layer/c_relu.hpp>
32 #include <mlpack/methods/ann/layer/flexible_relu.hpp>
33 #include <mlpack/methods/ann/layer/linear_no_bias.hpp>
34 #include <mlpack/methods/ann/layer/linear3d.hpp>
35 #include <mlpack/methods/ann/layer/log_softmax.hpp>
36 #include <mlpack/methods/ann/layer/lookup.hpp>
37 #include <mlpack/methods/ann/layer/multihead_attention.hpp>
38 #include <mlpack/methods/ann/layer/multiply_constant.hpp>
39 #include <mlpack/methods/ann/layer/max_pooling.hpp>
40 #include <mlpack/methods/ann/layer/mean_pooling.hpp>
41 #include <mlpack/methods/ann/layer/noisylinear.hpp>
42 #include <mlpack/methods/ann/layer/adaptive_max_pooling.hpp>
43 #include <mlpack/methods/ann/layer/adaptive_mean_pooling.hpp>
44 #include <mlpack/methods/ann/layer/parametric_relu.hpp>
45 #include <mlpack/methods/ann/layer/positional_encoding.hpp>
46 #include <mlpack/methods/ann/layer/reinforce_normal.hpp>
47 #include <mlpack/methods/ann/layer/reparametrization.hpp>
48 #include <mlpack/methods/ann/layer/select.hpp>
49 #include <mlpack/methods/ann/layer/softmax.hpp>
50 #include <mlpack/methods/ann/layer/spatial_dropout.hpp>
51 #include <mlpack/methods/ann/layer/subview.hpp>
52 #include <mlpack/methods/ann/layer/virtual_batch_norm.hpp>
53 #include <mlpack/methods/ann/layer/hardshrink.hpp>
54 #include <mlpack/methods/ann/layer/celu.hpp>
55 #include <mlpack/methods/ann/layer/softshrink.hpp>
56 #include <mlpack/methods/ann/layer/radial_basis_function.hpp>
57 
58 // Convolution modules.
59 #include <mlpack/methods/ann/convolution_rules/border_modes.hpp>
60 #include <mlpack/methods/ann/convolution_rules/naive_convolution.hpp>
61 #include <mlpack/methods/ann/convolution_rules/fft_convolution.hpp>
62 
63 // Regularizers.
64 #include <mlpack/methods/ann/regularizer/no_regularizer.hpp>
65 
66 // Loss function modules.
67 #include <mlpack/methods/ann/loss_functions/negative_log_likelihood.hpp>
68 
69 namespace mlpack {
70 namespace ann {
71 
72 template<typename InputDataType, typename OutputDataType> class BatchNorm;
73 template<typename InputDataType, typename OutputDataType> class DropConnect;
74 template<typename InputDataType, typename OutputDataType> class Glimpse;
75 template<typename InputDataType, typename OutputDataType> class LayerNorm;
76 template<typename InputDataType, typename OutputDataType> class LSTM;
77 template<typename InputDataType, typename OutputDataType> class GRU;
78 template<typename InputDataType, typename OutputDataType> class FastLSTM;
79 template<typename InputDataType, typename OutputDataType> class VRClassReward;
80 template<typename InputDataType, typename OutputDataType> class Concatenate;
81 template<typename InputDataType, typename OutputDataType> class Padding;
82 
83 template<typename InputDataType,
84  typename OutputDataType,
85  typename RegularizerType>
86 class Linear;
87 
88 template<typename InputDataType,
89  typename OutputDataType,
90  typename Activation>
91 class RBF;
92 
93 template<typename InputDataType,
94  typename OutputDataType,
95  typename RegularizerType>
96 class LinearNoBias;
97 
98 template<typename InputDataType,
99  typename OutputDataType>
100 class NoisyLinear;
101 
102 template<typename InputDataType,
103  typename OutputDataType,
104  typename RegularizerType>
105 class Linear3D;
106 
107 template<typename InputDataType,
108  typename OutputDataType
109 >
110 class VirtualBatchNorm;
111 
112 template<typename InputDataType,
113  typename OutputDataType
114 >
115 class MiniBatchDiscrimination;
116 
117 template <typename InputDataType,
118  typename OutputDataType,
119  typename RegularizerType>
120 class MultiheadAttention;
121 
122 template<typename InputDataType,
123  typename OutputDataType
124 >
125 class Reparametrization;
126 
127 template<typename InputDataType,
128  typename OutputDataType,
129  typename... CustomLayers
130 >
131 class AddMerge;
132 
133 template<typename InputDataType,
134  typename OutputDataType,
135  bool residual,
136  typename... CustomLayers
137 >
138 class Sequential;
139 
140 template<typename InputDataType,
141  typename OutputDataType,
142  typename... CustomLayers
143 >
144 class Highway;
145 
146 template<typename InputDataType,
147  typename OutputDataType,
148  typename... CustomLayers
149 >
150 class Recurrent;
151 
152 template<typename InputDataType,
153  typename OutputDataType,
154  typename... CustomLayers
155 >
156 class Concat;
157 
158 template<
159  typename OutputLayerType,
160  typename InputDataType,
161  typename OutputDataType
162 >
163 class ConcatPerformance;
164 
165 template<
166  typename ForwardConvolutionRule,
167  typename BackwardConvolutionRule,
168  typename GradientConvolutionRule,
169  typename InputDataType,
170  typename OutputDataType
171 >
172 class Convolution;
173 
174 template<
175  typename ForwardConvolutionRule,
176  typename BackwardConvolutionRule,
177  typename GradientConvolutionRule,
178  typename InputDataType,
179  typename OutputDataType
180 >
181 class TransposedConvolution;
182 
183 template<
184  typename ForwardConvolutionRule,
185  typename BackwardConvolutionRule,
186  typename GradientConvolutionRule,
187  typename InputDataType,
188  typename OutputDataType
189 >
190 class AtrousConvolution;
191 
192 template<
193  typename InputDataType,
194  typename OutputDataType
195 >
196 class RecurrentAttention;
197 
198 template<typename InputDataType,
199  typename OutputDataType,
200  typename... CustomLayers
201 >
202 class MultiplyMerge;
203 
204 template <typename InputDataType,
205  typename OutputDataType,
206  typename... CustomLayers
207 >
208 class WeightNorm;
209 
210 template <typename InputDataType,
211  typename OutputDataType
212 >
213 class AdaptiveMaxPooling;
214 
215 template <typename InputDataType,
216  typename OutputDataType
217 >
218 class AdaptiveMeanPooling;
219 
220 using MoreTypes = boost::variant<
221  Linear3D*,
222  Glimpse*,
223  Highway*,
224  MultiheadAttention*,
225  Recurrent*,
226  RecurrentAttention*,
227  ReinforceNormal*,
228  Reparametrization*,
229  Select*,
230  Sequential*,
231  Sequential*,
232  Subview*,
233  VRClassReward*,
234  VirtualBatchNorm*,
235  RBF*,
236  BaseLayer*,
237  PositionalEncoding*
238 >;
239 
240 template <typename... CustomLayers>
241 using LayerTypes = boost::variant<
242  AdaptiveMaxPooling*,
243  AdaptiveMeanPooling*,
244  Add*,
245  AddMerge*,
246  AlphaDropout*,
247  AtrousConvolution,
248  NaiveConvolution,
249  NaiveConvolution,
250  arma::mat, arma::mat>*,
251  BaseLayer*,
252  BaseLayer*,
253  BaseLayer*,
254  BaseLayer*,
255  BaseLayer*,
256  BatchNorm*,
257  BilinearInterpolation*,
258  CELU*,
259  Concat*,
260  Concatenate*,
261  ConcatPerformance,
262  arma::mat, arma::mat>*,
263  Constant*,
264  Convolution,
265  NaiveConvolution,
266  NaiveConvolution, arma::mat, arma::mat>*,
267  CReLU*,
268  DropConnect*,
269  Dropout*,
270  ELU*,
271  FastLSTM*,
272  FlexibleReLU*,
273  GRU*,
274  HardTanH*,
275  Join*,
276  LayerNorm*,
277  LeakyReLU*,
278  Linear*,
279  LinearNoBias*,
280  LogSoftMax*,
281  Lookup*,
282  LSTM*,
283  MaxPooling*,
284  MeanPooling*,
285  MiniBatchDiscrimination*,
286  MultiplyConstant*,
287  MultiplyMerge*,
288  NegativeLogLikelihood*,
289  NoisyLinear*,
290  Padding*,
291  PReLU*,
292  Softmax*,
293  SpatialDropout*,
294  TransposedConvolution,
295  NaiveConvolution,
296  NaiveConvolution, arma::mat, arma::mat>*,
297  WeightNorm*,
298  MoreTypes,
299  CustomLayers*...
300 >;
301 
302 } // namespace ann
303 } // namespace mlpack
304 
305 #endif
radial_basis_function.hpp
alpha_dropout.hpp
adaptive_mean_pooling.hpp
mlpack::ann::VRClassReward
Implementation of the variance reduced classification reinforcement layer.
Definition: layer_types.hpp:79
mlpack::ann::Add
Implementation of the Add module class.
Definition: add.hpp:34
mlpack::ann::AdaptiveMaxPooling
Implementation of the AdaptiveMaxPooling layer.
Definition: adaptive_max_pooling.hpp:33
negative_log_likelihood.hpp
mlpack::ann::Concatenate
Implementation of the Concatenate module class.
Definition: concatenate.hpp:36
log_softmax.hpp
mlpack::ann::LogSoftMax
Implementation of the log softmax layer.
Definition: log_softmax.hpp:36
mlpack::ann::AddMerge
Implementation of the AddMerge module class.
Definition: add_merge.hpp:42
multiply_constant.hpp
mlpack
Linear algebra utility functions, generally performed on matrices or vectors.
Definition: add_to_cli11.hpp:21
mlpack::ann::Padding
Implementation of the Padding module class.
Definition: layer_types.hpp:81
mlpack::ann::VirtualBatchNorm
Declaration of the VirtualBatchNorm layer class.
Definition: layer_types.hpp:110
mlpack::ann::FlexibleReLU
The FlexibleReLU activation function, defined by.
Definition: flexible_relu.hpp:59
layer_norm.hpp
mean_pooling.hpp
mlpack::ann::TransposedConvolution
Implementation of the Transposed Convolution class.
Definition: layer_types.hpp:181
mlpack::ann::ReinforceNormal
Implementation of the reinforce normal layer.
Definition: reinforce_normal.hpp:34
mlpack::ann::LayerTypes
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
Definition: layer_types.hpp:300
virtual_batch_norm.hpp
mlpack::ann::Linear
Implementation of the Linear layer class.
Definition: layer_types.hpp:86
hard_tanh.hpp
mlpack::ann::LeakyReLU
The LeakyReLU activation function, defined by.
Definition: leaky_relu.hpp:44
mlpack::ann::RecurrentAttention
This class implements the Recurrent Model for Visual Attention, using a variety of possible layer imp...
Definition: layer_types.hpp:196
mlpack::ann::Convolution
Implementation of the Convolution class.
Definition: convolution.hpp:48
multihead_attention.hpp
mlpack::ann::PositionalEncoding
Positional Encoding injects some information about the relative or absolute position of the tokens in...
Definition: positional_encoding.hpp:37
mlpack::ann::MeanPooling
Implementation of the MeanPooling.
Definition: mean_pooling.hpp:33
base_layer.hpp
mlpack::ann::Reparametrization
Implementation of the Reparametrization layer class.
Definition: layer_types.hpp:125
mlpack::ann::Join
Implementation of the Join module class.
Definition: join.hpp:33
mlpack::ann::ConcatPerformance
Implementation of the concat performance class.
Definition: concat_performance.hpp:39
mlpack::ann::WeightNorm
Declaration of the WeightNorm layer class.
Definition: layer_types.hpp:208
mlpack::ann::HardTanH
The Hard Tanh activation function, defined by.
Definition: hard_tanh.hpp:49
mlpack::ann::Select
The select module selects the specified column from a given input matrix.
Definition: select.hpp:32
mlpack::ann::NegativeLogLikelihood
Implementation of the negative log likelihood layer.
Definition: negative_log_likelihood.hpp:35
mlpack::ann::Softmax
Implementation of the Softmax layer.
Definition: softmax.hpp:38
mlpack::ann::MultiheadAttention
Multihead Attention allows the model to jointly attend to information from different representation s...
Definition: layer_types.hpp:120
mlpack::ann::PReLU
The PReLU activation function, defined by (where alpha is trainable)
Definition: parametric_relu.hpp:45
mlpack::ann::AdaptiveMeanPooling
Implementation of the AdaptiveMeanPooling.
Definition: adaptive_mean_pooling.hpp:34
mlpack::ann::BaseLayer
Implementation of the base layer.
Definition: base_layer.hpp:65
mlpack::ann::Concat
Implementation of the Concat class.
Definition: concat.hpp:45
mlpack::ann::Highway
Implementation of the Highway layer.
Definition: highway.hpp:60
mlpack::ann::LSTM
Implementation of the LSTM module class.
Definition: layer_types.hpp:76
mlpack::ann::Linear3D
Implementation of the Linear3D layer class.
Definition: layer_types.hpp:105
flexible_relu.hpp
mlpack::ann::LayerNorm
Declaration of the Layer Normalization class.
Definition: layer_norm.hpp:65
mlpack::ann::Lookup
The Lookup class stores word embeddings and retrieves them using tokens.
Definition: lookup.hpp:41
mlpack::ann::NoisyLinear
Implementation of the NoisyLinear layer class.
Definition: layer_types.hpp:100
mlpack::ann::Subview
Implementation of the subview layer.
Definition: subview.hpp:34
mlpack::ann::MiniBatchDiscrimination
Implementation of the MiniBatchDiscrimination layer.
Definition: layer_types.hpp:115
mlpack::ann::MultiplyMerge
Implementation of the MultiplyMerge module class.
Definition: layer_types.hpp:202
fft_convolution.hpp
mlpack::ann::LinearNoBias
Implementation of the LinearNoBias class.
Definition: layer_types.hpp:96
positional_encoding.hpp
mlpack::ann::CReLU
A concatenated ReLU has two outputs, one ReLU and one negative ReLU, concatenated together...
Definition: c_relu.hpp:50
mlpack::ann::NaiveConvolution
Computes the two-dimensional convolution.
Definition: naive_convolution.hpp:35
mlpack::ann::GRU
An implementation of a gru network layer.
Definition: gru.hpp:58
mlpack::ann::Dropout
The dropout layer is a regularizer that randomly with probability 'ratio' sets input values to zero a...
Definition: dropout.hpp:53
no_regularizer.hpp
naive_convolution.hpp
mlpack::ann::Glimpse
The glimpse layer returns a retina-like representation (down-scaled cropped images) of increasing sca...
Definition: glimpse.hpp:88
mlpack::ann::DropConnect
The DropConnect layer is a regularizer that randomly with probability ratio sets the connection value...
Definition: dropconnect.hpp:63
batch_norm.hpp
max_pooling.hpp
c_relu.hpp
mlpack::ann::MoreTypes
boost::variant< Linear3D< arma::mat, arma::mat, NoRegularizer > *, Glimpse< arma::mat, arma::mat > *, Highway< arma::mat, arma::mat > *, MultiheadAttention< arma::mat, arma::mat, NoRegularizer > *, Recurrent< arma::mat, arma::mat > *, RecurrentAttention< arma::mat, arma::mat > *, ReinforceNormal< arma::mat, arma::mat > *, Reparametrization< arma::mat, arma::mat > *, Select< arma::mat, arma::mat > *, Sequential< arma::mat, arma::mat, false > *, Sequential< arma::mat, arma::mat, true > *, Subview< arma::mat, arma::mat > *, VRClassReward< arma::mat, arma::mat > *, VirtualBatchNorm< arma::mat, arma::mat > *, RBF< arma::mat, arma::mat, GaussianFunction > *, BaseLayer< GaussianFunction, arma::mat, arma::mat > *, PositionalEncoding< arma::mat, arma::mat > *> MoreTypes
Definition: layer_types.hpp:238
reinforce_normal.hpp
mlpack::ann::MultiplyConstant
Implementation of the multiply constant layer.
Definition: multiply_constant.hpp:34
spatial_dropout.hpp
mlpack::ann::AlphaDropout
The alpha - dropout layer is a regularizer that randomly with probability 'ratio' sets input values t...
Definition: alpha_dropout.hpp:50
mlpack::ann::CELU
The CELU activation function, defined by.
Definition: celu.hpp:60
mlpack::ann::BatchNorm
Declaration of the Batch Normalization layer class.
Definition: batch_norm.hpp:56
linear_no_bias.hpp
parametric_relu.hpp
mlpack::ann::Recurrent
Implementation of the RecurrentLayer class.
Definition: layer_types.hpp:150
mlpack::ann::Sequential
Implementation of the Sequential class.
Definition: layer_types.hpp:138
mlpack::ann::Constant
Implementation of the constant layer.
Definition: constant.hpp:34
mlpack::ann::MaxPooling
Implementation of the MaxPooling layer.
Definition: max_pooling.hpp:52
mlpack::ann::ELU
The ELU activation function, defined by.
Definition: elu.hpp:111
mlpack::ann::RBF
Implementation of the Radial Basis Function layer.
Definition: layer_types.hpp:91
adaptive_max_pooling.hpp
mlpack::ann::SpatialDropout
Implementation of the SpatialDropout layer.
Definition: spatial_dropout.hpp:48
mlpack::ann::BilinearInterpolation
Definition and Implementation of the Bilinear Interpolation Layer.
Definition: bilinear_interpolation.hpp:39
leaky_relu.hpp
bilinear_interpolation.hpp
mlpack::ann::FastLSTM
An implementation of a faster version of the Fast LSTM network layer.
Definition: fast_lstm.hpp:66
border_modes.hpp
mlpack::ann::AtrousConvolution
Implementation of the Atrous Convolution class.
Definition: atrous_convolution.hpp:52