Remove accidentally reintroduced file.

2022-08-15 13:08:32 -04:00
parent 9cb15fc800
commit 94d4384f20
1 changed files with 0 additions and 138 deletions
@@ -1,138 +0,0 @@
-/**
- * @file tests/feedforward_network_2_test.cpp
- * @author Marcus Edel
- * @author Palash Ahuja
- *
- * Tests the feed forward network.
- *
- * mlpack is free software; you may redistribute it and/or modify it under the
- * terms of the 3-clause BSD license.  You should have received a copy of the
- * 3-clause BSD license along with mlpack.  If not, see
- * http://www.opensource.org/licenses/BSD-3-Clause for more information.
- */
-
-#include <mlpack/core.hpp>
-
-#include <mlpack/methods/ann/ann.hpp>
-#include <mlpack/methods/kmeans/kmeans.hpp>
-
-#include "catch.hpp"
-#include "serialization.hpp"
-#include "custom_layer.hpp"
-
-using namespace mlpack;
-using namespace mlpack::ann;
-using namespace mlpack::kmeans;
-
-/**
- * Train and evaluate a model with the specified structure.
- */
-template<typename MatType = arma::mat, typename ModelType>
-void TestNetwork(ModelType& model,
-                 MatType& trainData,
-                 MatType& trainLabels,
-                 MatType& testData,
-                 MatType& testLabels,
-                 const size_t maxEpochs,
-                 const double classificationErrorThreshold)
-{
-  ens::RMSProp opt(0.01, 32, 0.88, 1e-8, maxEpochs * trainData.n_cols, -1);
-  model.Train(trainData, trainLabels, opt);
-
-  MatType predictionTemp;
-  model.Predict(testData, predictionTemp);
-  MatType prediction = arma::zeros<MatType>(1, predictionTemp.n_cols);
-
-  for (size_t i = 0; i < predictionTemp.n_cols; ++i)
-  {
-    prediction(i) = arma::as_scalar(arma::find(
-        arma::max(predictionTemp.col(i)) == predictionTemp.col(i), 1)) + 1;
-  }
-
-  size_t correct = arma::accu(prediction == testLabels);
-  double classificationError = 1 - double(correct) / testData.n_cols;
-  REQUIRE(classificationError <= classificationErrorThreshold);
-}
-
-/**
- * Train the RBF network on a larger dataset.
- */
-TEST_CASE("RBFNetworkTest", "[FeedForwardNetworkTest]")
-{
-  // Load the dataset.
-  arma::mat trainData;
-  if (!data::Load("thyroid_train.csv", trainData))
-    FAIL("Cannot open thyroid_train.csv");
-
-  arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
-  trainData.shed_row(trainData.n_rows - 1);
-
-  arma::mat trainLabels1 = arma::zeros(3, trainData.n_cols);
-  for (size_t i = 0; i < trainData.n_cols; i++)
-  {
-    trainLabels1.col(i).row((trainLabels(i) - 1)) = 1;
-  }
-
-  arma::mat testData;
-  if (!data::Load("thyroid_test.csv", testData))
-    FAIL("Cannot open thyroid_test.csv");
-
-  arma::mat testLabels = testData.row(testData.n_rows - 1);
-  testData.shed_row(testData.n_rows - 1);
-
-  /*
-   * Construct a feed forward network with trainData.n_rows input nodes,
-   * hiddenLayerSize hidden nodes and trainLabels.n_rows output nodes. The
-   * network structure looks like:
-   *
-   *  Input         RBF          Activation    Output
-   *  Layer         Layer         Layer        Layer
-   * +-----+       +-----+       +-----+       +-----+
-   * |     |       |     |       |     |       |     |
-   * |     +------>|     +------>|     +------>|     |
-   * |     |       |     |       |     |       |     |
-   * +-----+       +--+--+       +-----+       +-----+
-   */
-  arma::mat centroids;
-  KMeans<> kmeans;
-  kmeans.Cluster(trainData, 8, centroids);
-
-  FFN<MeanSquaredError> model;
-  model.Add<RBF>(8, centroids);
-  model.Add<Linear>(3);
-
-  // RBFN neural net with MeanSquaredError.
-  TestNetwork<>(model, trainData, trainLabels1, testData, testLabels, 10, 0.1);
-
-  arma::mat dataset;
-  dataset.load("mnist_first250_training_4s_and_9s.arm");
-
-  // Normalize each point since these are images.
-  for (size_t i = 0; i < dataset.n_cols; ++i)
-  {
-    dataset.col(i) /= norm(dataset.col(i), 2);
-  }
-
-  arma::mat labels = arma::zeros(1, dataset.n_cols);
-  labels.submat(0, labels.n_cols / 2, 0, labels.n_cols - 1).fill(1);
-
-  arma::mat labels1 = arma::zeros(2, dataset.n_cols);
-
-  for (size_t i = 0; i < dataset.n_cols; ++i)
-  {
-    labels1.col(i).row(labels(i)) = 1;
-  }
-  labels += 1;
-
-  arma::mat centroids1;
-  arma::Row<size_t> assignments;
-  KMeans<> kmeans1;
-  kmeans1.Cluster(dataset, 140, centroids1);
-
-  FFN<MeanSquaredError> model1;
-  model1.Add<RBF>(140, centroids1, 4.1);
-  model1.Add<Linear>(2);
-
-  // RBFN neural net with MeanSquaredError.
-  TestNetwork<>(model1, dataset, labels1, dataset, labels, 10, 0.1);
-}