[mlpack-git] master: Add RMVA class and function documentation. (39eefde)
gitdub at mlpack.org
gitdub at mlpack.org
Sun Jun 5 11:08:30 EDT 2016
Repository : https://github.com/mlpack/mlpack
On branch : master
Link : https://github.com/mlpack/mlpack/compare/1f562a1aba7ae55475afcc95659511c2b7f694e5...5b8fdce471328f722fcd8c0f22a6d995ce22c98b
>---------------------------------------------------------------
commit 39eefded8c6e4edd95c5b0c8110adf8d5aba24be
Author: Marcus Edel <marcus.edel at fu-berlin.de>
Date: Sat May 21 00:33:29 2016 +0200
Add RMVA class and function documentation.
>---------------------------------------------------------------
39eefded8c6e4edd95c5b0c8110adf8d5aba24be
src/mlpack/methods/rmva/rmva.hpp | 90 ++++++++++++++++++++++++++---------
src/mlpack/methods/rmva/rmva_impl.hpp | 7 ++-
2 files changed, 70 insertions(+), 27 deletions(-)
diff --git a/src/mlpack/methods/rmva/rmva.hpp b/src/mlpack/methods/rmva/rmva.hpp
index 6756465..7fb0510 100644
--- a/src/mlpack/methods/rmva/rmva.hpp
+++ b/src/mlpack/methods/rmva/rmva.hpp
@@ -2,7 +2,8 @@
* @file rmva.hpp
* @author Marcus Edel
*
- * Definition of the RNN class, which implements feed forward neural networks.
+ * Definition of the RecurrentNeuralAttention class, which implements the
+ * Recurrent Model for Visual Attention.
*/
#ifndef __MLPACK_METHODS_RMVA_RMVA_HPP
#define __MLPACK_METHODS_RMVA_RMVA_HPP
@@ -23,11 +24,32 @@ namespace mlpack {
namespace ann /** Artificial Neural Network. */ {
/**
- * An implementation of a standard feed forward network.
+ * This class implements the Recurrent Model for Visual Attention, using a
+ * variety of possible layer implementations.
*
- * @tparam LayerTypes Contains all layer modules used to construct the network.
- * @tparam OutputLayerType The outputlayer type used to evaluate the network.
- * @tparam PerformanceFunction Performance strategy used to claculate the error.
+ * For more information, see the following paper.
+ *
+ * @code
+ * @article{MnihHGK14,
+ * title={Recurrent Models of Visual Attention},
+ * author={Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu},
+ * journal={CoRR},
+ * volume={abs/1406.6247},
+ * year={2014}
+ * }
+ * @endcode
+ *
+ * @tparam LocatorType Type of locator network.
+ * @tparam LocationSensorType Type of location sensor network.
+ * @tparam GlimpseSensorType Type of glimpse sensor network.
+ * @tparam GlimpseType Type of glimpse network.
+ * @tparam StartType Type of start network.
+ * @tparam FeedbackType Type of feedback network.
+ * @tparam TransferType Type of transfer network.
+ * @tparam ClassifierType Type of classifier network.
+ * @tparam RewardPredictorType Type of reward predictor network.
+ * @tparam InitializationRuleType Rule used to initialize the weight matrix.
+ * @tparam MatType Matrix type (arma::mat or arma::sp_mat).
*/
template<
typename LocatorType,
@@ -60,11 +82,20 @@ class RecurrentNeuralAttention
MatType>;
/**
- * Construct the RNN object, which will construct a recurrent neural
- * network with the specified layers.
+ * Construct the RecurrentNeuralAttention object, which will construct the
+ * recurrent model for visual attentionh using the specified networks.
*
- * @param network The network modules used to construct the network.
- * @param outputLayer The outputlayer used to evaluate the network.
+ * @param locator The locator network.
+ * @param locationSensor The location sensor network.
+ * @param glimpseSensor The glimpse sensor network.
+ * @param glimpse The glimpse network.
+ * @param start The start network.
+ * @param feedback The feedback network.
+ * @param transfer The transfer network.
+ * @param classifier The classifier network.
+ * @param rewardPredictor The reward predictor network.
+ * @param nStep Number of steps for the back-propagate through time.
+ * @param initializeRule Rule used to initialize the weight matrix.
*/
template<typename TypeLocator,
typename TypeLocationSensor,
@@ -85,8 +116,20 @@ class RecurrentNeuralAttention
TypeClassifier&& classifier,
TypeRewardPredictor&& rewardPredictor,
const size_t nStep,
- InitializationRuleType initializeRule = InitializationRuleType());
-
+ InitializationRuleType initializeRule =
+ InitializationRuleType());
+ /**
+ * Train the network on the given input data using the given optimizer.
+ *
+ * This will use the existing model parameters as a starting point for the
+ * optimization. If this is not what you want, then you should access the
+ * parameters vector directly with Parameters() and modify it as desired.
+ *
+ * @tparam OptimizerType Type of optimizer to use to train the model.
+ * @param predictors Input training variables.
+ * @param responses Outputs results from input training variables.
+ * @param optimizer Instantiated optimizer used to train the model.
+ */
template<
template<typename> class OptimizerType = mlpack::optimization::RMSprop
>
@@ -105,8 +148,8 @@ class RecurrentNeuralAttention
void Predict(arma::mat& predictors, arma::mat& responses);
/**
- * Evaluate the feedforward network with the given parameters. This function
- * is usually called by the optimizer to train the model.
+ * Evaluate the network with the given parameters. This function is usually
+ * called by the optimizer to train the model.
*
* @param parameters Matrix model parameters.
* @param i Index of point to use for objective function evaluation.
@@ -118,8 +161,8 @@ class RecurrentNeuralAttention
const bool deterministic = true);
/**
- * Evaluate the gradient of the feedforward network with the given parameters,
- * and with respect to only one point in the dataset. This is useful for
+ * Evaluate the gradient of the network with the given parameters, and with
+ * respect to only one point in the dataset. This is useful for
* optimizers such as SGD, which require a separable objective function.
*
* @param parameters Matrix of the model parameters to be optimized.
@@ -174,7 +217,8 @@ class RecurrentNeuralAttention
ResetParameter(rewardPredictor);
ResetParameter(start);
- // Aample an initial starting actions by forwarding zeros through the locator.
+ // Sample an initial starting actions by forwarding zeros through the
+ // locator.
locatorInput.push_back(new arma::cube(arma::zeros<arma::cube>(inputSize, 1,
input.n_slices)));
@@ -378,7 +422,7 @@ class RecurrentNeuralAttention
template<typename T, typename P>
typename std::enable_if<
HasRecurrentParameterCheck<T, P&(T::*)()>::value, void>::type
- Save(const size_t layerNumber,
+ Save(const size_t /* layerNumber */,
boost::ptr_vector<MatType>& activations,
T& layer,
P& /* unused */)
@@ -626,7 +670,7 @@ class RecurrentNeuralAttention
template<size_t I = 1, typename DataType, typename... Tp>
typename std::enable_if<I == (sizeof...(Tp)), void>::type
- BackwardTail(const DataType& error, std::tuple<Tp...>& t)
+ BackwardTail(const DataType& /* error */, std::tuple<Tp...>& t)
{
std::get<sizeof...(Tp) - I>(t).Backward(
std::get<sizeof...(Tp) - I>(t).OutputParameter(),
@@ -740,15 +784,15 @@ class RecurrentNeuralAttention
// Nothing to do here
}
+ //! The locator network.
+ LocatorType locator;
+
//! The location sensor network.
LocationSensorType locationSensor;
//! The glimpse sensor network.
GlimpseSensorType glimpseSensor;
- //! The locator network.
- LocatorType locator;
-
//! The glimpse network.
GlimpseType glimpse;
@@ -905,8 +949,8 @@ class RecurrentNeuralAttention
arma::mat evaluationLocation;
}; // class RecurrentNeuralAttention
-}; // namespace ann
-}; // namespace mlpack
+} // namespace ann
+} // namespace mlpack
// Include implementation.
#include "rmva_impl.hpp"
diff --git a/src/mlpack/methods/rmva/rmva_impl.hpp b/src/mlpack/methods/rmva/rmva_impl.hpp
index 9efa39f..fb9fd7c 100644
--- a/src/mlpack/methods/rmva/rmva_impl.hpp
+++ b/src/mlpack/methods/rmva/rmva_impl.hpp
@@ -2,7 +2,7 @@
* @file rmva_impl.hpp
* @author Marcus Edel
*
- * Definition of the FFN class, which implements feed forward neural networks.
+ * Implementation of the Recurrent Model for Visual Attention.
*/
#ifndef __MLPACK_METHODS_RMVA_RMVA_IMPL_HPP
#define __MLPACK_METHODS_RMVA_RMVA_IMPL_HPP
@@ -13,7 +13,6 @@
namespace mlpack {
namespace ann /** Artificial Neural Network. */ {
-
template<
typename LocatorType,
typename LocationSensorType,
@@ -270,7 +269,7 @@ double RecurrentNeuralAttention<
location.clear();
feedbackActivationsInput.clear();
- // Aample an initial starting actions by forwarding zeros through the locator.
+ // Sample an initial starting actions by forwarding zeros through the locator.
locatorInput.push_back(new arma::cube(arma::zeros<arma::cube>(inputSize, 1,
input.n_slices)));
@@ -583,7 +582,7 @@ void RecurrentNeuralAttention<
UpdateGradients(std::get<0>(transfer).Delta(), locationSensor);
UpdateGradients(std::get<0>(transfer).Delta(), glimpseSensor);
- // feedback module
+ // Feedback module.
if (step != 0)
{
UpdateGradients(feedbackActivationsInput[step - 1],
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