Minor style fixes.
This commit is contained in:
marcus
@@ -1,6 +1,7 @@
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/**
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* @file feedforward_network_test.cpp
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* @author Marcus Edel
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* @author Palash Ahuja
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*
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* Tests the feed forward network.
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*/
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@@ -288,7 +289,8 @@ BOOST_AUTO_TEST_CASE(DropoutNetworkTest)
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}
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/**
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* Train and evaluate a DropConnect network(with a baselayer) with the specified structure.
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* Train and evaluate a DropConnect network(with a baselayer) with the
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* specified structure.
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*/
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template<
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typename PerformanceFunction,
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@@ -304,66 +306,71 @@ void BuildDropConnectNetwork(MatType& trainData,
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const size_t maxEpochs,
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const double classificationErrorThreshold)
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{
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/*
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* Construct a feed forward network with trainData.n_rows input nodes,
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* hiddenLayerSize hidden nodes and trainLabels.n_rows output nodes. The
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* network struct that looks like:
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*
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* Input Hidden DropConnect Output
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* Layer Layer Layer Layer
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* +-----+ +-----+ +-----+ +-----+
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* | | | | | | | |
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* | +------>| +------>| +------>| |
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* | | +>| | | | | |
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* +-----+ | +--+--+ +-----+ +-----+
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* |
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* Bias |
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* Layer |
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* +-----+ |
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* | | |
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* | +-----+
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* | |
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* +-----+
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*
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*
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*/
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LinearLayer<> inputLayer(trainData.n_rows, hiddenLayerSize);
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BiasLayer<> biasLayer(hiddenLayerSize);
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BaseLayer<PerformanceFunction> hiddenLayer0;
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/*
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* Construct a feed forward network with trainData.n_rows input nodes,
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* hiddenLayerSize hidden nodes and trainLabels.n_rows output nodes. The
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* network struct that looks like:
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*
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* Input Hidden DropConnect Output
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* Layer Layer Layer Layer
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* +-----+ +-----+ +-----+ +-----+
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* | | | | | | | |
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* | +------>| +------>| +------>| |
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* | | +>| | | | | |
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* +-----+ | +--+--+ +-----+ +-----+
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* |
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* Bias |
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* Layer |
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* +-----+ |
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* | | |
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* | +-----+
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* | |
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* +-----+
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*
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*
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*/
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LinearLayer<> inputLayer(trainData.n_rows, hiddenLayerSize);
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BiasLayer<> biasLayer(hiddenLayerSize);
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BaseLayer<PerformanceFunction> hiddenLayer0;
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LinearLayer<> hiddenLayer1(hiddenLayerSize, trainLabels.n_rows);
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DropConnectLayer<decltype(hiddenLayer1)> dropConnectLayer0(hiddenLayer1);
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LinearLayer<> hiddenLayer1(hiddenLayerSize, trainLabels.n_rows);
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DropConnectLayer<decltype(hiddenLayer1)> dropConnectLayer0(hiddenLayer1);
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BaseLayer<PerformanceFunction> outputLayer;
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BaseLayer<PerformanceFunction> outputLayer;
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OutputLayerType classOutputLayer;
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OutputLayerType classOutputLayer;
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auto modules = std::tie(inputLayer, biasLayer, hiddenLayer0,
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dropConnectLayer0, outputLayer);
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auto modules = std::tie(inputLayer, biasLayer, hiddenLayer0,
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dropConnectLayer0, outputLayer);
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FFN<decltype(modules), decltype(classOutputLayer), RandomInitialization,
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PerformanceFunctionType> net(modules, classOutputLayer);
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RMSprop<decltype(net)> opt(net, 0.01, 0.88, 1e-8,
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maxEpochs * trainData.n_cols, 1e-18);
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net.Train(trainData, trainLabels, opt);
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MatType prediction;
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net.Predict(testData, prediction);
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FFN<decltype(modules), decltype(classOutputLayer), RandomInitialization,
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PerformanceFunctionType> net(modules, classOutputLayer);
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size_t error = 0;
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for (size_t i = 0; i < testData.n_cols; i++)
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{
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if (arma::sum(arma::sum(
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arma::abs(prediction.col(i) - testLabels.col(i)))) == 0)
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{
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error++;
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}
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}
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double classificationError = 1 - double(error) / testData.n_cols;
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BOOST_REQUIRE_LE(classificationError, classificationErrorThreshold);
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RMSprop<decltype(net)> opt(net, 0.01, 0.88, 1e-8,
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maxEpochs * trainData.n_cols, 1e-18);
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net.Train(trainData, trainLabels, opt);
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MatType prediction;
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net.Predict(testData, prediction);
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size_t error = 0;
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for (size_t i = 0; i < testData.n_cols; i++)
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{
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if (arma::sum(arma::sum(
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arma::abs(prediction.col(i) - testLabels.col(i)))) == 0)
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{
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error++;
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}
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}
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double classificationError = 1 - double(error) / testData.n_cols;
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BOOST_REQUIRE_LE(classificationError, classificationErrorThreshold);
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}
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/**
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* Train and evaluate a DropConnect network(with a linearlayer) with the specified structure.
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* Train and evaluate a DropConnect network(with a linearlayer) with the
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* specified structure.
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*/
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template<
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typename PerformanceFunction,
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@@ -379,60 +386,64 @@ void BuildDropConnectNetworkLinear(MatType& trainData,
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const size_t maxEpochs,
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const double classificationErrorThreshold)
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{
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/*
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* Construct a feed forward network with trainData.n_rows input nodes,
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* hiddenLayerSize hidden nodes and trainLabels.n_rows output nodes. The
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* network struct that looks like:
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*
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* Input Hidden DropConnect Output
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* Layer Layer Layer Layer
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* +-----+ +-----+ +-----+ +-----+
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* | | | | | | | |
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* | +------>| +------>| +------>| |
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* | | +>| | | | | |
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* +-----+ | +--+--+ +-----+ +-----+
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* |
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* Bias |
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* Layer |
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* +-----+ |
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* | | |
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* | +-----+
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* | |
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* +-----+
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*
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*
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*/
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LinearLayer<> inputLayer(trainData.n_rows, hiddenLayerSize);
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BiasLayer<> biasLayer(hiddenLayerSize);
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BaseLayer<PerformanceFunction> hiddenLayer0;
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const size_t number_of_rows = trainLabels.n_rows;
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DropConnectLayer<> dropConnectLayer0(hiddenLayerSize, number_of_rows);
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/*
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* Construct a feed forward network with trainData.n_rows input nodes,
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* hiddenLayerSize hidden nodes and trainLabels.n_rows output nodes. The
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* network struct that looks like:
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*
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* Input Hidden DropConnect Output
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* Layer Layer Layer Layer
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* +-----+ +-----+ +-----+ +-----+
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* | | | | | | | |
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* | +------>| +------>| +------>| |
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* | | +>| | | | | |
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* +-----+ | +--+--+ +-----+ +-----+
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* |
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* Bias |
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* Layer |
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* +-----+ |
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* | | |
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* | +-----+
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* | |
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* +-----+
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*
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*
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*/
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LinearLayer<> inputLayer(trainData.n_rows, hiddenLayerSize);
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BiasLayer<> biasLayer(hiddenLayerSize);
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BaseLayer<PerformanceFunction> hiddenLayer0;
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BaseLayer<PerformanceFunction> outputLayer;
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DropConnectLayer<> dropConnectLayer0(hiddenLayerSize, trainLabels.n_rows);
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OutputLayerType classOutputLayer;
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auto modules = std::tie(inputLayer, biasLayer, hiddenLayer0,
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dropConnectLayer0, outputLayer);
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BaseLayer<PerformanceFunction> outputLayer;
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FFN<decltype(modules), decltype(classOutputLayer), RandomInitialization,
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PerformanceFunctionType> net(modules, classOutputLayer);
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RMSprop<decltype(net)> opt(net, 0.01, 0.88, 1e-8,
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maxEpochs * trainData.n_cols, 1e-18);
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net.Train(trainData, trainLabels, opt);
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MatType prediction;
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net.Predict(testData, prediction);
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OutputLayerType classOutputLayer;
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auto modules = std::tie(inputLayer, biasLayer, hiddenLayer0,
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dropConnectLayer0, outputLayer);
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size_t error = 0;
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for (size_t i = 0; i < testData.n_cols; i++)
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{
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if (arma::sum(arma::sum(
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arma::abs(prediction.col(i) - testLabels.col(i)))) == 0)
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{
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error++;
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}
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}
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double classificationError = 1 - double(error) / testData.n_cols;
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BOOST_REQUIRE_LE(classificationError, classificationErrorThreshold);
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FFN<decltype(modules), decltype(classOutputLayer), RandomInitialization,
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PerformanceFunctionType> net(modules, classOutputLayer);
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RMSprop<decltype(net)> opt(net, 0.01, 0.88, 1e-8,
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maxEpochs * trainData.n_cols, 1e-18);
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net.Train(trainData, trainLabels, opt);
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MatType prediction;
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net.Predict(testData, prediction);
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size_t error = 0;
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for (size_t i = 0; i < testData.n_cols; i++)
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{
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if (arma::sum(arma::sum(
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arma::abs(prediction.col(i) - testLabels.col(i)))) == 0)
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{
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error++;
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}
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}
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double classificationError = 1 - double(error) / testData.n_cols;
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BOOST_REQUIRE_LE(classificationError, classificationErrorThreshold);
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}
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/**
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* Train the dropconnect network on a larger dataset.
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@@ -464,8 +475,8 @@ BOOST_AUTO_TEST_CASE(DropConnectNetworkTest)
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(trainData, trainLabels, testData, testLabels, 4, 100, 0.1);
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BuildDropConnectNetworkLinear<LogisticFunction,
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BinaryClassificationLayer,
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MeanSquaredErrorFunction>
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BinaryClassificationLayer,
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MeanSquaredErrorFunction>
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(trainData, trainLabels, testData, testLabels, 4, 100, 0.1);
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dataset.load("mnist_first250_training_4s_and_9s.arm");
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@@ -479,24 +490,15 @@ BOOST_AUTO_TEST_CASE(DropConnectNetworkTest)
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// Vanilla neural net with logistic activation function.
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BuildDropConnectNetwork<LogisticFunction,
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BinaryClassificationLayer,
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MeanSquaredErrorFunction>
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BinaryClassificationLayer,
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MeanSquaredErrorFunction>
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(dataset, labels, dataset, labels, 8, 30, 0.4);
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BuildDropConnectNetworkLinear<LogisticFunction,
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BinaryClassificationLayer,
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MeanSquaredErrorFunction>
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BinaryClassificationLayer,
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MeanSquaredErrorFunction>
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(dataset, labels, dataset, labels, 8, 30, 0.4);
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// Vanilla neural net with tanh activation function.
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BuildDropConnectNetwork<TanhFunction,
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BinaryClassificationLayer,
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MeanSquaredErrorFunction>
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(dataset, labels, dataset, labels, 8, 30, 0.4);
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BuildDropConnectNetworkLinear<TanhFunction,
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BinaryClassificationLayer,
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MeanSquaredErrorFunction>
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(dataset, labels, dataset, labels, 8, 30, 0.4);
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}
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BOOST_AUTO_TEST_SUITE_END();
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