Refactor conv connection to support 3rd order tensors.
This commit is contained in:
Marcus Edel
@@ -1,33 +1,35 @@
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/**
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* @file cnn_conv_connection.hpp
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* @author Shangtong Zhang
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* @author Marcus Edel
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*
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* Implementation of the convolutional connection
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* between input layer and output layer.
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* Implementation of the convolutional connection between input layer and output
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* layer.
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*/
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#ifndef __MLPACK_METHODS_ANN_CONNECTIONS_CONV_CONNECTION_HPP
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#define __MLPACK_METHODS_ANN_CONNECTIONS_CONV_CONNECTION_HPP
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#include <mlpack/core.hpp>
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#include <mlpack/methods/ann/init_rules/nguyen_widrow_init.hpp>
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#include <mlpack/methods/ann/convolution/valid_convolution.hpp>
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#include <mlpack/methods/ann/convolution/full_convolution.hpp>
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#include <mlpack/methods/ann/connections/connection_traits.hpp>
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#include <mlpack/methods/ann/init_rules/random_init.hpp>
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#include <mlpack/methods/ann/optimizer/steepest_descent.hpp>
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#include <mlpack/methods/ann/convolution_rules/border_modes.hpp>
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#include <mlpack/methods/ann/convolution_rules/naive_convolution.hpp>
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namespace mlpack{
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namespace ann /** Artificial Neural Network. */{
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/**
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* Implementation of the convolutional connection class.
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* The convolutional connection performs convolution between input layer
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* and output layer.
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* Convolution is applied to every neuron in input layer.
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* The kernel used for convolution is stored in @weights.
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* Implementation of the convolutional connection class. The convolutional
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* connection performs convolution between input layer and output layer.
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* Convolution is applied to every neuron in input layer. The kernel used for
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* convolution is stored in @weights.
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*
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* Users can design their own convolution rule (ForwardConvolutionRule)
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* to perform forward process. But once user-defined forward convolution is used,
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* users have to design special BackwardConvolutionRule and GradientConvolutionRule
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* to perform backward process and calculate gradient corresponding to
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* ForwardConvolutionRule, aimed to guarantee the correctness of error flow.
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* to perform forward process. But once user-defined forward convolution is
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* used, users have to design special BackwardConvolutionRule and
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* GradientConvolutionRule to perform backward process and calculate gradient
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* corresponding to ForwardConvolutionRule, aimed to guarantee the correctness
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* of error flow.
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*
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* @tparam InputLayerType Type of the connected input layer.
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* @tparam OutputLayerType Type of the connected output layer.
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@@ -36,123 +38,165 @@ namespace ann /** Artificial Neural Network. */{
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* @tparam ForwardConvolutionRule Convolution to perform forward process.
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* @tparam BackwardConvolutionRule Convolution to perform backward process.
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* @tparam GradientConvolutionRule Convolution to calculate gradient.
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* @tparam MatType Type of data (arma::mat or arma::sp_mat).
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* @tparam DataType Type of data (arma::mat, arma::sp_mat or arma::cube).
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*/
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template<
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typename InputLayerType,
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typename OutputLayerType,
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typename OptimizerType,
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class WeightInitRule = NguyenWidrowInitialization<>,
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typename ForwardConvolutionRule = ValidConvolution,
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typename BackwardConvolutionRule = ValidConvolution,
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typename GradientConvolutionRule = RotatedKernelFullConvolution,
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typename MatType = arma::mat
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typename OptimizerType = SteepestDescent<>,
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class WeightInitRule = RandomInitialization,
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typename ForwardConvolutionRule = NaiveConvolution<ValidConvolution>,
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typename BackwardConvolutionRule = NaiveConvolution<FullConvolution>,
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typename GradientConvolutionRule = NaiveConvolution<ValidConvolution>,
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typename DataType = arma::cube
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>
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class ConvConnection
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{
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public:
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/**
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* Create the ConvConnection object using the specified input layer, output
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* layer, optimizer and weight initialize rule.
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* layer, optimizer and weight initialization rule.
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*
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* @param InputLayerType The input layer which is connected with the output
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* layer.
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* @param OutputLayerType The output layer which is connected with the input
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* layer.
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* @param OptimizerType The optimizer used to update the weights matrix.
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* @param weightsRows The number of rows of convolutional kernel.
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* @param weightsCols The number of cols of convolutional kernel.
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* @param WeightInitRule The weights initialize rule used to initialize the
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* weights matrix.
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* @param filterRows The number of rows of the convolutional kernel.
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* @param filterCols The number of cols of the convolutional kernel.
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* @param OptimizerType The optimizer used to update the weight matrix.
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* @param WeightInitRule The weights initialization rule used to initialize
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* the weights matrix.
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*/
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ConvConnection(InputLayerType& inputLayer,
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OutputLayerType& outputLayer,
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const size_t filterRows,
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const size_t filterCols,
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OptimizerType& optimizer,
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size_t weightsRows,
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size_t weightsCols,
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WeightInitRule weightInitRule = WeightInitRule()) :
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inputLayer(inputLayer), outputLayer(outputLayer), optimizer(optimizer)
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inputLayer(inputLayer),
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outputLayer(outputLayer),
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optimizer(&optimizer),
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ownsOptimizer(false)
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{
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weightInitRule.Initialize(weights, weightsRows, weightsCols);
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gradient = arma::zeros<MatType>(weightsRows, weightsCols);
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weightInitRule.Initialize(weights, filterRows, filterCols,
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outputLayer.LayerSlices());
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}
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/**
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* Ordinary feed forward pass of a neural network,
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* Apply convolution to every neuron in input layer and
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* put the output in the output layer.
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* Create the ConvConnection object using the specified input layer, output
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* layer, optimizer and weight initialization rule.
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*
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* @param InputLayerType The input layer which is connected with the output
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* layer.
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* @param OutputLayerType The output layer which is connected with the input
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* layer.
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* @param filterRows The number of rows of the convolutional kernel.
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* @param filterCols The number of cols of the convolutional kernel.
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* @param WeightInitRule The weights initialization rule used to initialize
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* the weights matrix.
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*/
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void FeedForward(const MatType& input)
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ConvConnection(InputLayerType& inputLayer,
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OutputLayerType& outputLayer,
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const size_t filterRows,
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const size_t filterCols,
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WeightInitRule weightInitRule = WeightInitRule()) :
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inputLayer(inputLayer),
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outputLayer(outputLayer),
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optimizer(new OptimizerType()),
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ownsOptimizer(true)
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{
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MatType output(outputLayer.InputActivation().n_rows,
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outputLayer.InputActivation().n_cols);
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ForwardConvolutionRule::conv(input, weights, output);
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weightInitRule.Initialize(weights, filterRows, filterCols,
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outputLayer.LayerSlices());
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}
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/**
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* Delete the conv connection object and its optimizer.
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*/
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~ConvConnection()
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{
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if (ownsOptimizer)
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delete optimizer;
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}
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/**
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* Ordinary feed forward pass of a neural network. Apply convolution to every
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* neuron in input layer and put the output in the output layer.
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*/
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template<typename InputType>
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void FeedForward(const InputType& input)
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{
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DataType output;
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ForwardConvolutionRule::Convolution(input, weights, output);
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outputLayer.InputActivation() += output;
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}
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/**
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* Ordinary feed backward pass of a neural network.
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* Pass the error from output layer to input layer and
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* calculate the delta of kernel weights.
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* Ordinary feed backward pass of a neural network. Pass the error from output
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* layer to input layer and calculate the delta of kernel weights.
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*
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* @param error The backpropagated error.
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*/
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void FeedBackward(const MatType& error)
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void FeedBackward(const DataType& error)
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{
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BackwardConvolutionRule::conv(inputLayer.InputActivation(), error, gradient);
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GradientConvolutionRule::conv(weights, error, delta);
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inputLayer.Delta() += delta;
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BackwardConvolutionRule::conv(weights, error, delta);
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}
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/*
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* Calculate the gradient using the output delta and the input activation.
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*
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* @param gradient The calculated gradient.
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*/
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void Gradient(DataType& gradient)
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{
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GradientConvolutionRule::Convolution(inputLayer.InputActivation(),
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outputLayer.Delta(), gradient);
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}
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//! Get the convolution kernel.
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MatType& Weights() const { return weights; }
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DataType& Weights() const { return weights; }
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//! Modify the convolution kernel.
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MatType& Weights() { return weights; }
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DataType& Weights() { return weights; }
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//! Get the input layer.
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InputLayerType& InputLayer() const { return inputLayer; }
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//! Modify the input layer.
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InputLayerType& InputLayer() { return inputLayer; }
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//! Get the output layer.
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OutputLayerType& OutputLayer() const { return outputLayer; }
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//! Modify the output layer.
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OutputLayerType& OutputLayer() { return outputLayer; }
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//! Get the optimzer.
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OptimizerType& Optimzer() const { return optimizer; }
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OptimizerType& Optimzer() const { return *optimizer; }
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//! Modify the optimzer.
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OptimizerType& Optimzer() { return optimizer; }
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OptimizerType& Optimzer() { return *optimizer; }
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//! Get the passed error in backward propagation.
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MatType& Delta() const { return delta; }
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DataType& Delta() const { return delta; }
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//! Modify the passed error in backward propagation.
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MatType& Delta() { return delta; }
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//! Get the gradient of kernel.
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MatType& Gradient() const { return gradient; }
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//! Modify the gradient of kernel.
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MatType& Gradient() { return gradient; }
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DataType& Delta() { return delta; }
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private:
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//! Locally-stored kernel weights.
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MatType weights;
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DataType weights;
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//! Locally-stored inputlayer.
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InputLayerType& inputLayer;
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//! Locally-stored outputlayer.
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OutputLayerType& outputLayer;
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//! Locally-stored optimizer.
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OptimizerType& optimizer;
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OptimizerType* optimizer;
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//! Parameter that indicates if the class owns a optimizer object.
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bool ownsOptimizer;
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//! Locally-stored passed error in backward propagation.
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MatType delta;
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//! Locally-stored gradient of kernel weights.
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MatType gradient;
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DataType delta;
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};// class ConvConnection
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}; // namespace ann
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}; // namespace mlpack
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