diff --git a/src/mlpack/methods/ann/connections/full_connection.hpp b/src/mlpack/methods/ann/connections/full_connection.hpp index dcccb9bb66..953665beee 100644 --- a/src/mlpack/methods/ann/connections/full_connection.hpp +++ b/src/mlpack/methods/ann/connections/full_connection.hpp @@ -29,7 +29,7 @@ template< typename InputLayerType, typename OutputLayerType, typename OptimizerType, - class WeightInitRule = NguyenWidrowInitialization<>, + class WeightInitRule = NguyenWidrowInitialization, typename MatType = arma::mat, typename VecType = arma::colvec > diff --git a/src/mlpack/methods/ann/connections/fullself_connection.hpp b/src/mlpack/methods/ann/connections/fullself_connection.hpp index 470be4ee11..1ad50b9961 100644 --- a/src/mlpack/methods/ann/connections/fullself_connection.hpp +++ b/src/mlpack/methods/ann/connections/fullself_connection.hpp @@ -31,7 +31,7 @@ template< typename InputLayerType, typename OutputLayerType, typename OptimizerType, - class WeightInitRule = NguyenWidrowInitialization<>, + class WeightInitRule = NguyenWidrowInitialization, typename MatType = arma::mat, typename VecType = arma::colvec > diff --git a/src/mlpack/methods/ann/connections/self_connection.hpp b/src/mlpack/methods/ann/connections/self_connection.hpp index 3b67a8a376..8dbe5e006e 100644 --- a/src/mlpack/methods/ann/connections/self_connection.hpp +++ b/src/mlpack/methods/ann/connections/self_connection.hpp @@ -31,7 +31,7 @@ template< typename InputLayerType, typename OutputLayerType, typename OptimizerType, - class WeightInitRule = NguyenWidrowInitialization<>, + class WeightInitRule = NguyenWidrowInitialization, typename MatType = arma::mat, typename VecType = arma::colvec > diff --git a/src/mlpack/methods/ann/layer/lstm_layer.hpp b/src/mlpack/methods/ann/layer/lstm_layer.hpp index 921657c752..7ec9300d16 100644 --- a/src/mlpack/methods/ann/layer/lstm_layer.hpp +++ b/src/mlpack/methods/ann/layer/lstm_layer.hpp @@ -36,7 +36,7 @@ template < class GateActivationFunction = LogisticFunction, class StateActivationFunction = TanhFunction, class OutputActivationFunction = TanhFunction, - class WeightInitRule = NguyenWidrowInitialization<>, + class WeightInitRule = NguyenWidrowInitialization, typename OptimizerType = SteepestDescent<>, typename MatType = arma::mat, typename VecType = arma::colvec diff --git a/src/mlpack/tests/feedforward_network_test.cpp b/src/mlpack/tests/feedforward_network_test.cpp index 5f67fbc01d..a4ce5e2147 100644 --- a/src/mlpack/tests/feedforward_network_test.cpp +++ b/src/mlpack/tests/feedforward_network_test.cpp @@ -159,12 +159,12 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkTest) arma::mat testLabels = dataset.submat(dataset.n_rows - 3, 0, dataset.n_rows - 1, dataset.n_cols - 1); - RandomInitialization<> randInitA(1, 2); + RandomInitialization randInitA(1, 2); // Vanilla neural net with logistic activation function. // Because 92 percent of the patients are not hyperthyroid a the neural // network mst be significant better than 92%. - BuildVanillaNetwork, + BuildVanillaNetwork, BinaryClassificationLayer<>, @@ -174,7 +174,7 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkTest) dataset.load("mnist_first250_training_4s_and_9s.arm"); - RandomInitialization<> randInitB(-0.5, 0.5); + RandomInitialization randInitB(-0.5, 0.5); // Normalize each point since these are images. for (size_t i = 0; i < dataset.n_cols; ++i) @@ -184,7 +184,7 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkTest) labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1); // Vanilla neural net with logistic activation function. - BuildVanillaNetwork, + BuildVanillaNetwork, BinaryClassificationLayer<>, @@ -192,7 +192,7 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkTest) (dataset, labels, dataset, labels, 100, 100, 0.6, 10, randInitB); // Vanilla neural net with tanh activation function. - BuildVanillaNetwork, + BuildVanillaNetwork, BinaryClassificationLayer<>, @@ -208,7 +208,7 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkConvergenceTest) arma::mat input; arma::mat labels; - RandomInitialization<> randInit(0.5, 1); + RandomInitialization randInit(0.5, 1); // Test on a non-linearly separable dataset (XOR). input << 0 << 1 << 1 << 0 << arma::endr @@ -216,7 +216,7 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkConvergenceTest) labels << 0 << 0 << 1 << 1; // Vanilla neural net with logistic activation function. - BuildVanillaNetwork, + BuildVanillaNetwork, BinaryClassificationLayer<>, @@ -224,7 +224,7 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkConvergenceTest) (input, labels, input, labels, 4, 0, 0, 0.01, randInit); // Vanilla neural net with tanh activation function. - BuildVanillaNetwork, + BuildVanillaNetwork, BinaryClassificationLayer<>, @@ -237,7 +237,7 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkConvergenceTest) labels << 0 << 0 << 1 << 0; // vanilla neural net with sigmoid activation function. - BuildVanillaNetwork, + BuildVanillaNetwork, BinaryClassificationLayer<>, @@ -245,7 +245,7 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkConvergenceTest) (input, labels, input, labels, 4, 0, 0, 0.01, randInit); // Vanilla neural net with tanh activation function. - BuildVanillaNetwork, + BuildVanillaNetwork, BinaryClassificationLayer<>, @@ -358,7 +358,7 @@ BOOST_AUTO_TEST_CASE(NetworkDecreasingErrorTest) arma::mat dataset; dataset.load("mnist_first250_training_4s_and_9s.arm"); - RandomInitialization<> randInitB(-0.5, 0.5); + RandomInitialization randInitB(-0.5, 0.5); // Normalize each point since these are images. for (size_t i = 0; i < dataset.n_cols; ++i) @@ -368,7 +368,7 @@ BOOST_AUTO_TEST_CASE(NetworkDecreasingErrorTest) labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1) += 1; // Vanilla neural net with logistic activation function. - BuildNetworkOptimzer, + BuildNetworkOptimzer, BinaryClassificationLayer<>, diff --git a/src/mlpack/tests/init_rules_test.cpp b/src/mlpack/tests/init_rules_test.cpp index b9864526d7..8c89d7b1de 100644 --- a/src/mlpack/tests/init_rules_test.cpp +++ b/src/mlpack/tests/init_rules_test.cpp @@ -25,7 +25,7 @@ BOOST_AUTO_TEST_SUITE(InitRulesTest); BOOST_AUTO_TEST_CASE(ConstantInitTest) { arma::mat weights; - RandomInitialization<> constantInit(1, 1); + RandomInitialization constantInit(1, 1); constantInit.Initialize(weights, 100, 100); bool b = arma::all(arma::vectorise(weights) == 1); @@ -36,7 +36,7 @@ BOOST_AUTO_TEST_CASE(ConstantInitTest) BOOST_AUTO_TEST_CASE(OrthogonalInitTest) { arma::mat weights; - OrthogonalInitialization<> orthogonalInit; + OrthogonalInitialization orthogonalInit; orthogonalInit.Initialize(weights, 100, 200); arma::mat orthogonalWeights = arma::eye(100, 100); @@ -60,7 +60,7 @@ BOOST_AUTO_TEST_CASE(OrthogonalInitGainTest) arma::mat weights; const double gain = 2; - OrthogonalInitialization<> orthogonalInit(gain); + OrthogonalInitialization orthogonalInit(gain); orthogonalInit.Initialize(weights, 100, 200); arma::mat orthogonalWeights = arma::eye(100, 100); diff --git a/src/mlpack/tests/recurrent_network_test.cpp b/src/mlpack/tests/recurrent_network_test.cpp index dcfa338085..e29face51b 100644 --- a/src/mlpack/tests/recurrent_network_test.cpp +++ b/src/mlpack/tests/recurrent_network_test.cpp @@ -119,7 +119,7 @@ BOOST_AUTO_TEST_CASE(SequenceClassificationTest) SteepestDescent< > conOptimizer3(hiddenLayer0.InputSize(), hiddenLayer1.OutputSize(), 1, 0); - NguyenWidrowInitialization<> randInit; + NguyenWidrowInitialization randInit; FullConnection< decltype(inputLayer), @@ -316,7 +316,7 @@ BOOST_AUTO_TEST_CASE(FeedForwardRecurrentNetworkTest) arma::mat input; arma::mat labels; - RandomInitialization<> randInit(1, 1); + RandomInitialization randInit(1, 1); // Test on a non-linearly separable dataset (XOR). input << 0 << 1 << 1 << 0 << arma::endr @@ -324,7 +324,7 @@ BOOST_AUTO_TEST_CASE(FeedForwardRecurrentNetworkTest) labels << 0 << 0 << 1 << 1; // Vanilla neural net with logistic activation function. - CompareVanillaNetworks, + CompareVanillaNetworks, BinaryClassificationLayer<>, @@ -332,7 +332,7 @@ BOOST_AUTO_TEST_CASE(FeedForwardRecurrentNetworkTest) (input, labels, input, labels, 10, 10, randInit); // Vanilla neural net with identity activation function. - CompareVanillaNetworks, + CompareVanillaNetworks, BinaryClassificationLayer<>, @@ -340,7 +340,7 @@ BOOST_AUTO_TEST_CASE(FeedForwardRecurrentNetworkTest) (input, labels, input, labels, 1, 1, randInit); // Vanilla neural net with rectifier activation function. - CompareVanillaNetworks, + CompareVanillaNetworks, BinaryClassificationLayer<>, @@ -348,7 +348,7 @@ BOOST_AUTO_TEST_CASE(FeedForwardRecurrentNetworkTest) (input, labels, input, labels, 10, 10, randInit); // Vanilla neural net with softsign activation function. - CompareVanillaNetworks, + CompareVanillaNetworks, BinaryClassificationLayer<>, @@ -356,7 +356,7 @@ BOOST_AUTO_TEST_CASE(FeedForwardRecurrentNetworkTest) (input, labels, input, labels, 10, 10, randInit); // Vanilla neural net with tanh activation function. - CompareVanillaNetworks, + CompareVanillaNetworks, BinaryClassificationLayer<>, @@ -585,7 +585,7 @@ void ReberGrammarTestNetwork(HiddenLayerType& hiddenLayer0, SteepestDescent< > conOptimizer3(hiddenLayer0.OutputSize(), hiddenLayer1.InputSize(), 0.1); - NguyenWidrowInitialization<> randInit; + NguyenWidrowInitialization randInit; FullConnection< decltype(inputLayer), @@ -820,7 +820,7 @@ void DistractedSequenceRecallTestNetwork(HiddenLayerType& hiddenLayer0) SteepestDescent< > conOptimizer3(hiddenLayer0.OutputSize(), hiddenLayer1.InputSize(), 0.1); - NguyenWidrowInitialization<> randInit; + NguyenWidrowInitialization randInit; FullConnection< decltype(inputLayer),