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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/constant.hpp>
21 #include <mlpack/methods/ann/layer/dropout.hpp>
22 #include <mlpack/methods/ann/layer/elu.hpp>
23 #include <mlpack/methods/ann/layer/hard_tanh.hpp>
24 #include <mlpack/methods/ann/layer/join.hpp>
25 #include <mlpack/methods/ann/layer/leaky_relu.hpp>
26 #include <mlpack/methods/ann/layer/log_softmax.hpp>
27 #include <mlpack/methods/ann/layer/lookup.hpp>
28 #include <mlpack/methods/ann/layer/mean_squared_error.hpp>
29 #include <mlpack/methods/ann/layer/multiply_constant.hpp>
30 #include <mlpack/methods/ann/layer/negative_log_likelihood.hpp>
31 #include <mlpack/methods/ann/layer/max_pooling.hpp>
32 #include <mlpack/methods/ann/layer/mean_pooling.hpp>
33 #include <mlpack/methods/ann/layer/parametric_relu.hpp>
34 #include <mlpack/methods/ann/layer/reinforce_normal.hpp>
35 #include <mlpack/methods/ann/layer/select.hpp>
36 
37 // Convolution modules.
38 #include <mlpack/methods/ann/convolution_rules/border_modes.hpp>
39 #include <mlpack/methods/ann/convolution_rules/naive_convolution.hpp>
40 #include <mlpack/methods/ann/convolution_rules/fft_convolution.hpp>
41 
42 namespace mlpack {
43 namespace ann {
44 
45 template<typename InputDataType, typename OutputDataType> class AddMerge;
46 template<typename InputDataType, typename OutputDataType> class Concat;
47 template<typename InputDataType, typename OutputDataType> class DropConnect;
48 template<typename InputDataType, typename OutputDataType> class Glimpse;
49 template<typename InputDataType, typename OutputDataType> class Linear;
50 template<typename InputDataType, typename OutputDataType> class LinearNoBias;
51 template<typename InputDataType, typename OutputDataType> class LSTM;
52 template<typename InputDataType, typename OutputDataType> class Recurrent;
53 template<typename InputDataType, typename OutputDataType> class Sequential;
54 template<typename InputDataType, typename OutputDataType> class VRClassReward;
55 
56 template<
57  typename OutputLayerType,
58  typename InputDataType,
59  typename OutputDataType
60 >
61 class ConcatPerformance;
62 
63 template<
64  typename ForwardConvolutionRule,
65  typename BackwardConvolutionRule,
66  typename GradientConvolutionRule,
67  typename InputDataType,
68  typename OutputDataType
69 >
70 class Convolution;
71 
72 template<
73  typename InputDataType,
74  typename OutputDataType
75 >
76 class RecurrentAttention;
77 
78 using LayerTypes = boost::variant<
79  Add<arma::mat, arma::mat>*,
80  AddMerge<arma::mat, arma::mat>*,
81  BaseLayer<LogisticFunction, arma::mat, arma::mat>*,
82  BaseLayer<IdentityFunction, arma::mat, arma::mat>*,
83  BaseLayer<TanhFunction, arma::mat, arma::mat>*,
84  BaseLayer<RectifierFunction, arma::mat, arma::mat>*,
85  Concat<arma::mat, arma::mat>*,
86  ConcatPerformance<NegativeLogLikelihood<arma::mat, arma::mat>,
87  arma::mat, arma::mat>*,
88  Constant<arma::mat, arma::mat>*,
89  Convolution<NaiveConvolution<ValidConvolution>,
90  NaiveConvolution<FullConvolution>,
91  NaiveConvolution<ValidConvolution>, arma::mat, arma::mat>*,
92  DropConnect<arma::mat, arma::mat>*,
93  Dropout<arma::mat, arma::mat>*,
94  Glimpse<arma::mat, arma::mat>*,
95  HardTanH<arma::mat, arma::mat>*,
96  Join<arma::mat, arma::mat>*,
97  LeakyReLU<arma::mat, arma::mat>*,
98  Linear<arma::mat, arma::mat>*,
99  LinearNoBias<arma::mat, arma::mat>*,
100  LogSoftMax<arma::mat, arma::mat>*,
101  Lookup<arma::mat, arma::mat>*,
102  LSTM<arma::mat, arma::mat>*,
103  MaxPooling<arma::mat, arma::mat>*,
104  MeanPooling<arma::mat, arma::mat>*,
105  MeanSquaredError<arma::mat, arma::mat>*,
106  MultiplyConstant<arma::mat, arma::mat>*,
107  NegativeLogLikelihood<arma::mat, arma::mat>*,
108  PReLU<arma::mat, arma::mat>*,
109  Recurrent<arma::mat, arma::mat>*,
110  RecurrentAttention<arma::mat, arma::mat>*,
111  ReinforceNormal<arma::mat, arma::mat>*,
112  Select<arma::mat, arma::mat>*,
113  Sequential<arma::mat, arma::mat>*,
114  VRClassReward<arma::mat, arma::mat>*
115 >;
116 
117 } // namespace ann
118 } // namespace mlpack
119 
120 #endif
mlpack::ann::VRClassReward
Implementation of the variance reduced classification reinforcement layer.
Definition: layer_types.hpp:54
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:40
multiply_constant.hpp
mlpack
Linear algebra utility functions, generally performed on matrices or vectors.
Definition: binarize.hpp:18
mean_pooling.hpp
mlpack::ann::ReinforceNormal
Implementation of the reinforce normal layer.
Definition: reinforce_normal.hpp:29
mlpack::ann::Linear
Implementation of the Linear layer class.
Definition: layer_types.hpp:49
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:76
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:42
mlpack::ann::LSTM
An implementation of a lstm network layer.
Definition: layer_types.hpp:51
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:50
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 > *, 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 > *, DropConnect< arma::mat, arma::mat > *, Dropout< 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 > *, 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 > *, Select< arma::mat, arma::mat > *, Sequential< arma::mat, arma::mat > *, VRClassReward< arma::mat, arma::mat > * > LayerTypes
Definition: layer_types.hpp:115
mean_squared_error.hpp
mlpack::ann::NaiveConvolution
Computes the two-dimensional convolution.
Definition: naive_convolution.hpp:35
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:82
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
max_pooling.hpp
reinforce_normal.hpp
mlpack::ann::MultiplyConstant
Implementation of the multiply constant layer.
Definition: multiply_constant.hpp:29
parametric_relu.hpp
mlpack::ann::Recurrent
Implementation of the RecurrentLayer class.
Definition: layer_types.hpp:52
mlpack::ann::Sequential
Implementation of the Sequential class.
Definition: layer_types.hpp:53
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
leaky_relu.hpp
border_modes.hpp
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