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 <boost/variant.hpp>
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/log_softmax.hpp>
34 #include <mlpack/methods/ann/layer/lookup.hpp>
35 #include <mlpack/methods/ann/layer/multiply_constant.hpp>
36 #include <mlpack/methods/ann/layer/max_pooling.hpp>
37 #include <mlpack/methods/ann/layer/mean_pooling.hpp>
38 #include <mlpack/methods/ann/layer/parametric_relu.hpp>
39 #include <mlpack/methods/ann/layer/reinforce_normal.hpp>
40 #include <mlpack/methods/ann/layer/reparametrization.hpp>
41 #include <mlpack/methods/ann/layer/select.hpp>
42 #include <mlpack/methods/ann/layer/subview.hpp>
43 
44 // Convolution modules.
45 #include <mlpack/methods/ann/convolution_rules/border_modes.hpp>
46 #include <mlpack/methods/ann/convolution_rules/naive_convolution.hpp>
47 #include <mlpack/methods/ann/convolution_rules/fft_convolution.hpp>
48 
49 // Loss function modules.
50 #include <mlpack/methods/ann/loss_functions/negative_log_likelihood.hpp>
51 
52 namespace mlpack {
53 namespace ann {
54 
55 template<typename InputDataType, typename OutputDataType> class BatchNorm;
56 template<typename InputDataType, typename OutputDataType> class DropConnect;
57 template<typename InputDataType, typename OutputDataType> class Glimpse;
58 template<typename InputDataType, typename OutputDataType> class LayerNorm;
59 template<typename InputDataType, typename OutputDataType> class Linear;
60 template<typename InputDataType, typename OutputDataType> class LinearNoBias;
61 template<typename InputDataType, typename OutputDataType> class LSTM;
62 template<typename InputDataType, typename OutputDataType> class GRU;
63 template<typename InputDataType, typename OutputDataType> class FastLSTM;
64 template<typename InputDataType, typename OutputDataType> class VRClassReward;
65 template<typename InputDataType, typename OutputDataType> class Concatenate;
66 
67 template<typename InputDataType,
68  typename OutputDataType
69 >
70 class Reparametrization;
71 
72 template<typename InputDataType,
73  typename OutputDataType,
74  typename... CustomLayers
75 >
76 class AddMerge;
77 
78 template<typename InputDataType,
79  typename OutputDataType,
80  bool residual,
81  typename... CustomLayers
82 >
83 class Sequential;
84 
85 template<typename InputDataType,
86  typename OutputDataType,
87  typename... CustomLayers
88 >
89 class Recurrent;
90 
91 template<typename InputDataType,
92  typename OutputDataType,
93  typename... CustomLayers
94 >
95 class Concat;
96 
97 template<
98  typename OutputLayerType,
99  typename InputDataType,
100  typename OutputDataType
101 >
102 class ConcatPerformance;
103 
104 template<
105  typename ForwardConvolutionRule,
106  typename BackwardConvolutionRule,
107  typename GradientConvolutionRule,
108  typename InputDataType,
109  typename OutputDataType
110 >
111 class Convolution;
112 
113 template<
114  typename ForwardConvolutionRule,
115  typename BackwardConvolutionRule,
116  typename GradientConvolutionRule,
117  typename InputDataType,
118  typename OutputDataType
119 >
120 class TransposedConvolution;
121 
122 template<
123  typename ForwardConvolutionRule,
124  typename BackwardConvolutionRule,
125  typename GradientConvolutionRule,
126  typename InputDataType,
127  typename OutputDataType
128 >
129 class AtrousConvolution;
130 
131 template<
132  typename InputDataType,
133  typename OutputDataType
134 >
135 class RecurrentAttention;
136 
137 template<typename InputDataType,
138  typename OutputDataType,
139  typename... CustomLayers
140 >
141 class MultiplyMerge;
142 
143 template <typename... CustomLayers>
144 using LayerTypes = boost::variant<
145  Add<arma::mat, arma::mat>*,
146  AddMerge<arma::mat, arma::mat>*,
147  AtrousConvolution<NaiveConvolution<ValidConvolution>,
148  NaiveConvolution<FullConvolution>,
149  NaiveConvolution<ValidConvolution>,
150  arma::mat, arma::mat>*,
151  BaseLayer<LogisticFunction, arma::mat, arma::mat>*,
152  BaseLayer<IdentityFunction, arma::mat, arma::mat>*,
153  BaseLayer<TanhFunction, arma::mat, arma::mat>*,
154  BaseLayer<RectifierFunction, arma::mat, arma::mat>*,
155  BaseLayer<SoftplusFunction, arma::mat, arma::mat>*,
156  BatchNorm<arma::mat, arma::mat>*,
157  BilinearInterpolation<arma::mat, arma::mat>*,
158  Concat<arma::mat, arma::mat>*,
159  Concatenate<arma::mat, arma::mat>*,
160  ConcatPerformance<NegativeLogLikelihood<arma::mat, arma::mat>,
161  arma::mat, arma::mat>*,
162  Constant<arma::mat, arma::mat>*,
163  Convolution<NaiveConvolution<ValidConvolution>,
164  NaiveConvolution<FullConvolution>,
165  NaiveConvolution<ValidConvolution>, arma::mat, arma::mat>*,
166  TransposedConvolution<NaiveConvolution<ValidConvolution>,
167  NaiveConvolution<FullConvolution>,
168  NaiveConvolution<ValidConvolution>, arma::mat, arma::mat>*,
169  DropConnect<arma::mat, arma::mat>*,
170  Dropout<arma::mat, arma::mat>*,
171  AlphaDropout<arma::mat, arma::mat>*,
172  ELU<arma::mat, arma::mat>*,
173  FlexibleReLU<arma::mat, arma::mat>*,
174  Glimpse<arma::mat, arma::mat>*,
175  HardTanH<arma::mat, arma::mat>*,
176  Join<arma::mat, arma::mat>*,
177  LayerNorm<arma::mat, arma::mat>*,
178  LeakyReLU<arma::mat, arma::mat>*,
179  CReLU<arma::mat, arma::mat>*,
180  Linear<arma::mat, arma::mat>*,
181  LinearNoBias<arma::mat, arma::mat>*,
182  LogSoftMax<arma::mat, arma::mat>*,
183  Lookup<arma::mat, arma::mat>*,
184  LSTM<arma::mat, arma::mat>*,
185  GRU<arma::mat, arma::mat>*,
186  FastLSTM<arma::mat, arma::mat>*,
187  MaxPooling<arma::mat, arma::mat>*,
188  MeanPooling<arma::mat, arma::mat>*,
189  MultiplyConstant<arma::mat, arma::mat>*,
190  MultiplyMerge<arma::mat, arma::mat>*,
191  NegativeLogLikelihood<arma::mat, arma::mat>*,
192  PReLU<arma::mat, arma::mat>*,
193  Recurrent<arma::mat, arma::mat>*,
194  RecurrentAttention<arma::mat, arma::mat>*,
195  ReinforceNormal<arma::mat, arma::mat>*,
196  Reparametrization<arma::mat, arma::mat>*,
197  Select<arma::mat, arma::mat>*,
198  Sequential<arma::mat, arma::mat, false>*,
199  Sequential<arma::mat, arma::mat, true>*,
200  Subview<arma::mat, arma::mat>*,
201  VRClassReward<arma::mat, arma::mat>*,
202  CustomLayers*...
203 >;
204 
205 } // namespace ann
206 } // namespace mlpack
207 
208 #endif
alpha_dropout.hpp
mlpack::ann::VRClassReward
Implementation of the variance reduced classification reinforcement layer.
Definition: layer_types.hpp:64
mlpack::ann::Add
Implementation of the Add module class.
Definition: add.hpp:34
negative_log_likelihood.hpp
mlpack::ann::Concatenate
Implementation of the Concatenate module class.
Definition: concatenate.hpp:36
log_softmax.hpp
mlpack::ann::LayerTypes
boost::variant< Add< arma::mat, arma::mat > *, AddMerge< 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< RectifierFunction, arma::mat, arma::mat > *, BaseLayer< SoftplusFunction, arma::mat, arma::mat > *, BatchNorm< arma::mat, arma::mat > *, BilinearInterpolation< 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 > *, TransposedConvolution< NaiveConvolution< ValidConvolution >, NaiveConvolution< FullConvolution >, NaiveConvolution< ValidConvolution >, arma::mat, arma::mat > *, DropConnect< arma::mat, arma::mat > *, Dropout< arma::mat, arma::mat > *, AlphaDropout< arma::mat, arma::mat > *, ELU< arma::mat, arma::mat > *, FlexibleReLU< arma::mat, arma::mat > *, Glimpse< arma::mat, arma::mat > *, HardTanH< arma::mat, arma::mat > *, Join< arma::mat, arma::mat > *, LayerNorm< arma::mat, arma::mat > *, LeakyReLU< arma::mat, arma::mat > *, CReLU< arma::mat, arma::mat > *, Linear< arma::mat, arma::mat > *, LinearNoBias< arma::mat, arma::mat > *, LogSoftMax< arma::mat, arma::mat > *, Lookup< arma::mat, arma::mat > *, LSTM< arma::mat, arma::mat > *, GRU< arma::mat, arma::mat > *, FastLSTM< arma::mat, arma::mat > *, MaxPooling< arma::mat, arma::mat > *, MeanPooling< arma::mat, arma::mat > *, MultiplyConstant< arma::mat, arma::mat > *, MultiplyMerge< arma::mat, arma::mat > *, NegativeLogLikelihood< arma::mat, arma::mat > *, PReLU< arma::mat, arma::mat > *, 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 > *, CustomLayers *... > LayerTypes
Definition: layer_types.hpp:203
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
.hpp
Definition: add_to_po.hpp:21
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:120
mlpack::ann::ReinforceNormal
Implementation of the reinforce normal layer.
Definition: reinforce_normal.hpp:34
mlpack::ann::Linear
Implementation of the Linear layer class.
Definition: layer_types.hpp:59
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:135
mlpack::ann::Convolution
Implementation of the Convolution class.
Definition: convolution.hpp:46
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:70
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::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::PReLU
The PReLU activation function, defined by (where alpha is trainable)
Definition: parametric_relu.hpp:45
mlpack::ann::BaseLayer
Implementation of the base layer.
Definition: base_layer.hpp:49
mlpack::ann::Concat
Implementation of the Concat class.
Definition: concat.hpp:45
mlpack::ann::LSTM
Implementation of the LSTM module class.
Definition: layer_types.hpp:61
flexible_relu.hpp
mlpack::ann::LayerNorm
Declaration of the Layer Normalization class.
Definition: layer_norm.hpp:65
mlpack::ann::Lookup
Implementation of the Lookup class.
Definition: lookup.hpp:35
mlpack::ann::Subview
Implementation of the subview layer.
Definition: subview.hpp:34
mlpack::ann::MultiplyMerge
Implementation of the MultiplyMerge module class.
Definition: layer_types.hpp:141
fft_convolution.hpp
mlpack::ann::LinearNoBias
Implementation of the LinearNoBias class.
Definition: layer_types.hpp:60
mlpack::ann::CReLU
A concatenated ReLU has two outputs, one ReLU and one negative ReLU, concatenated together...
Definition: c_relu.hpp:49
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:57
mlpack::ann::Dropout
The dropout layer is a regularizer that randomly with probability &#39;ratio&#39; sets input values to zero a...
Definition: dropout.hpp:52
naive_convolution.hpp
mlpack::ann::Glimpse
The glimpse layer returns a retina-like representation (down-scaled cropped images) of increasing sca...
Definition: glimpse.hpp:87
mlpack::ann::DropConnect
The DropConnect layer is a regularizer that randomly with probability ratio sets the connection value...
Definition: dropconnect.hpp:62
batch_norm.hpp
max_pooling.hpp
c_relu.hpp
reinforce_normal.hpp
mlpack::ann::MultiplyConstant
Implementation of the multiply constant layer.
Definition: multiply_constant.hpp:34
mlpack::ann::AlphaDropout
The alpha - dropout layer is a regularizer that randomly with probability &#39;ratio&#39; sets input values t...
Definition: alpha_dropout.hpp:50
mlpack::ann::BatchNorm
Declaration of the Batch Normalization layer class.
Definition: batch_norm.hpp:56
parametric_relu.hpp
mlpack::ann::Recurrent
Implementation of the RecurrentLayer class.
Definition: layer_types.hpp:89
mlpack::ann::Sequential
Implementation of the Sequential class.
Definition: layer_types.hpp:83
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:109
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:61
border_modes.hpp
mlpack::ann::AtrousConvolution
Implementation of the Atrous Convolution class.
Definition: atrous_convolution.hpp:50