From f599f7cf8b012f7edee6e498cf2e6b6ba57ffa83 Mon Sep 17 00:00:00 2001
From: akhandait <akhandait45@gmail.com>
Date: Sat, 7 Jul 2018 16:19:02 +0530
Subject: [PATCH] Overload Evaluate() function.

---
 src/mlpack/methods/ann/ffn.hpp      |  9 +++++++++
 src/mlpack/methods/ann/ffn_impl.hpp | 27 +++++++++++++++++++++++++++
 2 files changed, 36 insertions(+)

diff --git a/src/mlpack/methods/ann/ffn.hpp b/src/mlpack/methods/ann/ffn.hpp
index 7c826ae65f..8c9a8356e4 100644
--- a/src/mlpack/methods/ann/ffn.hpp
+++ b/src/mlpack/methods/ann/ffn.hpp
@@ -134,6 +134,15 @@ class FFN
    */
   void Predict(arma::mat predictors, arma::mat& results);
 
+  /**
+   * Evaluate the feedforward network with the given ppredictors and responses.
+   * This functions is usually used to monitor progress while training.
+   *
+   * @param predictors Input variables.
+   * @param responses Target outputs for input variables.
+   */
+  double Evaluate(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.
diff --git a/src/mlpack/methods/ann/ffn_impl.hpp b/src/mlpack/methods/ann/ffn_impl.hpp
index b180b9b0ec..03cfbae1c1 100644
--- a/src/mlpack/methods/ann/ffn_impl.hpp
+++ b/src/mlpack/methods/ann/ffn_impl.hpp
@@ -202,6 +202,33 @@ void FFN<OutputLayerType, InitializationRuleType, CustomLayers...>::Predict(
     results.col(i) = resultsTemp.col(0);
   }
 }
+template<typename OutputLayerType, typename InitializationRuleType,
+         typename... CustomLayers>
+double FFN<OutputLayerType, InitializationRuleType, CustomLayers...>::Evaluate(
+    arma::mat predictors, arma::mat responses)
+{
+  if (parameter.is_empty())
+    ResetParameters();
+
+  if (!deterministic)
+  {
+    deterministic = true;
+    ResetDeterministic();
+  }
+
+  Forward(std::move(predictors));
+
+  double res = outputLayer.Forward(
+      std::move(boost::apply_visitor(outputParameterVisitor, network.back())),
+      std::move(responses));
+
+  for (size_t i = 0; i < network.size(); ++i)
+  {
+    res += boost::apply_visitor(lossVisitor, network[i]);
+  }
+
+  return res;
+}
 
 template<typename OutputLayerType, typename InitializationRuleType,
          typename... CustomLayers>