Merge branch 'master' into test-fixes

src/mlpack/methods/ann/ffn.hpp

@@ -191,6 +191,7 @@ class FFN
   /**
    * Evaluate the feedforward network with the given parameters.
    * This function is usually called by the optimizer to train the model.
+   * This just calls the overload of EvaluateWithGradient() with batchSize = 1.
    *
    * @param parameters Matrix model parameters.
    * @param gradient Matrix to output gradient into.
@@ -201,8 +202,7 @@ class FFN
    /**
    * Evaluate the feedforward network with the given parameters, but using only
    * a number of data points. This is useful for optimizers such as SGD, which
-   * require a separable objective function.  This just calls the overload of
-   * Evaluate() with deterministic = true.
+   * require a separable objective function.
    *
    * @param parameters Matrix model parameters.
    * @param begin Index of the starting point to use for objective function

src/mlpack/methods/ann/rnn.hpp

@@ -183,8 +183,7 @@ class RNN
 
   /**
    * Evaluate the recurrent neural network with the given parameters. This
-   * function is usually called by the optimizer to train the model.  This just
-   * calls the other overload of Evaluate() with deterministic = true.
+   * function is usually called by the optimizer to train the model.
    *
    * @param parameters Matrix model parameters.
    * @param begin Index of the starting point to use for objective function

src/mlpack/methods/ann/rnn_impl.hpp

@@ -304,13 +304,10 @@ EvaluateWithGradient(const arma::mat& /* parameters */,
       responseSeq = seqNum;
     }
 
-    if (!deterministic)
+    for (size_t l = 0; l < network.size(); ++l)
     {
-      for (size_t l = 0; l < network.size(); ++l)
-      {
-        boost::apply_visitor(SaveOutputParameterVisitor(
-            std::move(moduleOutputParameter)), network[l]);
-      }
+      boost::apply_visitor(SaveOutputParameterVisitor(
+          std::move(moduleOutputParameter)), network[l]);
     }
 
     performance += outputLayer.Forward(std::move(boost::apply_visitor(