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/base_layer.hpp>
20 #include <mlpack/methods/ann/layer/batch_norm.hpp>
21 #include <mlpack/methods/ann/layer/bilinear_interpolation.hpp>
22 #include <mlpack/methods/ann/layer/constant.hpp>
23 #include <mlpack/methods/ann/layer/cross_entropy_error.hpp>
24 #include <mlpack/methods/ann/layer/dropout.hpp>
25 #include <mlpack/methods/ann/layer/elu.hpp>
26 #include <mlpack/methods/ann/layer/hard_tanh.hpp>
27 #include <mlpack/methods/ann/layer/join.hpp>
28 #include <mlpack/methods/ann/layer/leaky_relu.hpp>
29 #include <mlpack/methods/ann/layer/log_softmax.hpp>
30 #include <mlpack/methods/ann/layer/lookup.hpp>
31 #include <mlpack/methods/ann/layer/mean_squared_error.hpp>
32 #include <mlpack/methods/ann/layer/multiply_constant.hpp>
33 #include <mlpack/methods/ann/layer/negative_log_likelihood.hpp>
34 #include <mlpack/methods/ann/layer/max_pooling.hpp>
35 #include <mlpack/methods/ann/layer/mean_pooling.hpp>
36 #include <mlpack/methods/ann/layer/parametric_relu.hpp>
37 #include <mlpack/methods/ann/layer/reinforce_normal.hpp>
38 #include <mlpack/methods/ann/layer/sigmoid_cross_entropy_error.hpp>
39 #include <mlpack/methods/ann/layer/select.hpp>
40 
41 // Convolution modules.
42 #include <mlpack/methods/ann/convolution_rules/border_modes.hpp>
43 #include <mlpack/methods/ann/convolution_rules/naive_convolution.hpp>
44 #include <mlpack/methods/ann/convolution_rules/fft_convolution.hpp>
45 
46 namespace mlpack {
47 namespace ann {
48 
49 
50 template<typename InputDataType, typename OutputDataType> class BatchNorm;
51 template<typename InputDataType, typename OutputDataType> class DropConnect;
52 template<typename InputDataType, typename OutputDataType> class Glimpse;
53 template<typename InputDataType, typename OutputDataType> class Linear;
54 template<typename InputDataType, typename OutputDataType> class LinearNoBias;
55 template<typename InputDataType, typename OutputDataType> class LSTM;
56 template<typename InputDataType, typename OutputDataType> class GRU;
57 template<typename InputDataType, typename OutputDataType> class FastLSTM;
58 template<typename InputDataType, typename OutputDataType> class VRClassReward;
59 
60 template<typename InputDataType,
61  typename OutputDataType,
62  typename... CustomLayers
63 >
64 class AddMerge;
65 
66 template<typename InputDataType,
67  typename OutputDataType,
68  typename... CustomLayers
69 >
70 class Sequential;
71 
72 template<typename InputDataType,
73  typename OutputDataType,
74  typename... CustomLayers
75 >
76 class Recurrent;
77 
78 template<typename InputDataType,
79  typename OutputDataType,
80  typename... CustomLayers
81 >
82 class Concat;
83 
84 template<
85  typename OutputLayerType,
86  typename InputDataType,
87  typename OutputDataType
88 >
89 class ConcatPerformance;
90 
91 template<
92  typename ForwardConvolutionRule,
93  typename BackwardConvolutionRule,
94  typename GradientConvolutionRule,
95  typename InputDataType,
96  typename OutputDataType
97 >
98 class Convolution;
99 
100 template<
101  typename InputDataType,
102  typename OutputDataType
103 >
104 class RecurrentAttention;
105 
106 template <typename... CustomLayers>
107 using LayerTypes = boost::variant<
108  Add<arma::mat, arma::mat>*,
109  AddMerge<arma::mat, arma::mat>*,
110  BaseLayer<LogisticFunction, arma::mat, arma::mat>*,
111  BaseLayer<IdentityFunction, arma::mat, arma::mat>*,
112  BaseLayer<TanhFunction, arma::mat, arma::mat>*,
113  BaseLayer<RectifierFunction, arma::mat, arma::mat>*,
114  BatchNorm<arma::mat, arma::mat>*,
115  BilinearInterpolation<arma::mat, arma::mat>*,
116  Concat<arma::mat, arma::mat>*,
117  ConcatPerformance<NegativeLogLikelihood<arma::mat, arma::mat>,
118  arma::mat, arma::mat>*,
119  Constant<arma::mat, arma::mat>*,
120  Convolution<NaiveConvolution<ValidConvolution>,
121  NaiveConvolution<FullConvolution>,
122  NaiveConvolution<ValidConvolution>, arma::mat, arma::mat>*,
123  CrossEntropyError<arma::mat, arma::mat>*,
124  DropConnect<arma::mat, arma::mat>*,
125  Dropout<arma::mat, arma::mat>*,
126  ELU<arma::mat, arma::mat>*,
127  Glimpse<arma::mat, arma::mat>*,
128  HardTanH<arma::mat, arma::mat>*,
129  Join<arma::mat, arma::mat>*,
130  LeakyReLU<arma::mat, arma::mat>*,
131  Linear<arma::mat, arma::mat>*,
132  LinearNoBias<arma::mat, arma::mat>*,
133  LogSoftMax<arma::mat, arma::mat>*,
134  Lookup<arma::mat, arma::mat>*,
135  LSTM<arma::mat, arma::mat>*,
136  GRU<arma::mat, arma::mat>*,
137  FastLSTM<arma::mat, arma::mat>*,
138  MaxPooling<arma::mat, arma::mat>*,
139  MeanPooling<arma::mat, arma::mat>*,
140  MeanSquaredError<arma::mat, arma::mat>*,
141  MultiplyConstant<arma::mat, arma::mat>*,
142  NegativeLogLikelihood<arma::mat, arma::mat>*,
143  PReLU<arma::mat, arma::mat>*,
144  Recurrent<arma::mat, arma::mat>*,
145  RecurrentAttention<arma::mat, arma::mat>*,
146  ReinforceNormal<arma::mat, arma::mat>*,
147  SigmoidCrossEntropyError<arma::mat, arma::mat>*,
148  Select<arma::mat, arma::mat>*,
149  Sequential<arma::mat, arma::mat>*,
150  VRClassReward<arma::mat, arma::mat>*,
151  CustomLayers*...
152 >;
153 
154 } // namespace ann
155 } // namespace mlpack
156 
157 #endif
mlpack::ann::VRClassReward
Implementation of the variance reduced classification reinforcement layer.
Definition: layer_types.hpp:58
mlpack::ann::Add
Implementation of the Add module class.
Definition: add.hpp:34
negative_log_likelihood.hpp
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
.hpp
Definition: add_to_po.hpp:21
mean_pooling.hpp
mlpack::ann::ReinforceNormal
Implementation of the reinforce normal layer.
Definition: reinforce_normal.hpp:34
cross_entropy_error.hpp
mlpack::ann::Linear
Implementation of the Linear layer class.
Definition: layer_types.hpp:53
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:104
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::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:47
mlpack::ann::Concat
Implementation of the Concat class.
Definition: concat.hpp:44
mlpack::ann::LSTM
An implementation of a lstm network layer.
Definition: layer_types.hpp:55
mlpack::ann::Lookup
Implementation of the Lookup class.
Definition: lookup.hpp:35
fft_convolution.hpp
mlpack::ann::LinearNoBias
Implementation of the LinearNoBias class.
Definition: layer_types.hpp:54
sigmoid_cross_entropy_error.hpp
mean_squared_error.hpp
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 ratio sets input values to zero and...
Definition: dropout.hpp:54
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
mlpack::ann::MeanSquaredError
The mean squared error performance function measures the network&#39;s performance according to the mean ...
Definition: mean_squared_error.hpp:34
batch_norm.hpp
max_pooling.hpp
reinforce_normal.hpp
mlpack::ann::MultiplyConstant
Implementation of the multiply constant layer.
Definition: multiply_constant.hpp:34
mlpack::ann::CrossEntropyError
The cross-entropy performance function measures the network&#39;s performance according to the cross-entr...
Definition: cross_entropy_error.hpp:34
mlpack::ann::BatchNorm
Declaration of the Batch Normalization layer class.
Definition: batch_norm.hpp:57
parametric_relu.hpp
mlpack::ann::Recurrent
Implementation of the RecurrentLayer class.
Definition: layer_types.hpp:76
mlpack::ann::LayerTypes
boost::variant< Add< arma::mat, arma::mat > *, AddMerge< 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 > *, BatchNorm< arma::mat, arma::mat > *, BilinearInterpolation< arma::mat, arma::mat > *, Concat< 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 > *, CrossEntropyError< arma::mat, arma::mat > *, DropConnect< arma::mat, arma::mat > *, Dropout< arma::mat, arma::mat > *, ELU< arma::mat, arma::mat > *, Glimpse< arma::mat, arma::mat > *, HardTanH< arma::mat, arma::mat > *, Join< arma::mat, arma::mat > *, LeakyReLU< 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 > *, MeanSquaredError< arma::mat, arma::mat > *, MultiplyConstant< 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 > *, SigmoidCrossEntropyError< arma::mat, arma::mat > *, Select< arma::mat, arma::mat > *, Sequential< arma::mat, arma::mat > *, VRClassReward< arma::mat, arma::mat > *, CustomLayers *... > LayerTypes
Definition: layer_types.hpp:152
mlpack::ann::Sequential
Implementation of the Sequential class.
Definition: layer_types.hpp:70
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:61
mlpack::ann::SigmoidCrossEntropyError
The SigmoidCrossEntropyError performance function measures the network&#39;s performance according to the...
Definition: sigmoid_cross_entropy_error.hpp:51
mlpack::ann::BilinearInterpolation
Definition and Implementation of the Bilinear Interpolation Layer.
Definition: bilinear_interpolation.hpp:38
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