Add standardized comment about MatType.

src/mlpack/methods/ann/layer/add.hpp

@@ -23,7 +23,8 @@ namespace ann /** Artificial Neural Network. */ {
  * Implementation of the Add layer. The Add module applies a bias term to the
  * incoming data.
  *
- * @tparam MatType Matrix type used as inputs, outputs, and weights.
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType>
 class AddType : public Layer<MatType>

src/mlpack/methods/ann/layer/alpha_dropout.hpp

@@ -40,10 +40,8 @@ namespace ann /** Artificial Neural Network. */ {
  * }
  * @endcode
  *
- * @tparam MatType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class AlphaDropoutType : public Layer<MatType>

src/mlpack/methods/ann/layer/concatenate.hpp

@@ -27,10 +27,8 @@ namespace ann /** Artificial Neural Network. */ {
  *
  * After this layer is applied, the shape of the data will be a vector.
  *
- * @tparam MatType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class ConcatenateType : public Layer<MatType>

src/mlpack/methods/ann/layer/convolution.hpp

@@ -62,10 +62,8 @@ namespace ann /** Artificial Neural Network. */ {
  * @tparam ForwardConvolutionRule Convolution to perform forward process.
  * @tparam BackwardConvolutionRule Convolution to perform backward process.
  * @tparam GradientConvolutionRule Convolution to calculate gradient.
- * @tparam MatType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template <
     typename ForwardConvolutionRule = NaiveConvolution<ValidConvolution>,

src/mlpack/methods/ann/layer/dropconnect.hpp

@@ -43,11 +43,8 @@ namespace ann /** Artificial Neural Network. */ {
  * }
  * @endcode
  *
- * @tparam MatType The type of the layer's inputs. The layer automatically
- *     cast inputs to this type (Default: arma::mat).
- * @tparam MatType The type of the computation which also causes the output
- *     to also be in this type. The type also allows the computation and weight
- *     type to differ from the input type (Default: arma::mat).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class DropConnectType : public Layer<MatType>

src/mlpack/methods/ann/layer/dropout.hpp

@@ -41,11 +41,8 @@ namespace ann /** Artificial Neural Network. */ {
  * }
  * @endcode
  *
- * @tparam MatType The type of the layer's inputs. The layer automatically
- *     cast inputs to this type (Default: arma::mat).
- * @tparam MatType The type of the computation which also causes the output
- *     to also be in this type. The type also allows the computation and weight
- *     type to differ from the input type (Default: arma::mat).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class DropoutType : public Layer<MatType>

src/mlpack/methods/ann/layer/layer.hpp

@@ -45,12 +45,8 @@ namespace ann {
  * Forward(), Backward() and Gradient().  The weights of the layers are tracked
  * in layer.Parameters().
  *
- * @tparam MatType The type of the layer's inputs. Layers automatically cast
- *     inputs to this type (default: arma::mat).
- * @tparam MatType The type of the layer's computation which also causes the
- *     computations and output to also be in this type. The type also allows the
- *     computation and weight type to differ from the input type
- *     (default: arma::mat).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class Layer

src/mlpack/methods/ann/layer/leaky_relu.hpp

@@ -34,11 +34,8 @@ namespace ann /** Artificial Neural Network. */ {
  * \right.
  * @f}
  *
- * @tparam MatType The type of the layer's inputs. The layer automatically
- *     cast inputs to this type (Default: arma::mat).
- * @tparam MatType The type of the computation which also causes the output
- *     to also be in this type. The type also allows the computation and weight
- *     type to differ from the input type (Default: arma::mat).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class LeakyReLUType : public Layer<MatType>

src/mlpack/methods/ann/layer/linear.hpp

@@ -30,11 +30,8 @@ namespace ann /** Artificial Neural Network. */ {
  * must be either a vector or matrix. If the input is a matrix, then each column
  * is assumed to be an input sample of given batch.
  *
- * @tparam MatType The type of the layer's inputs. The layer automatically
- *    cast inputs to this type (Default: arma::mat).
- * @tparam MatType The type of the computation which also causes the output
- *    to also be in this type. The type also allows the computation and weight
- *    type to differ from the input type (Default: arma::mat).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  * @tparam RegularizerType Type of the regularizer to be used (Default no
  *    regularizer).
  */

src/mlpack/methods/ann/layer/linear3d.hpp

@@ -29,12 +29,10 @@ namespace ann /** Artificial Neural Network. */ {
  * Shape of input : (inSize * nPoints, batchSize)
  * Shape of output : (outSize * nPoints, batchSize)
  *
- * @tparam MatType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
-template <
+template<
     typename MatType = arma::mat,
     typename RegularizerType = NoRegularizer
 >

src/mlpack/methods/ann/layer/linear_no_bias.hpp

@@ -25,11 +25,8 @@ namespace ann /** Artificial Neural Network. */ {
  * Implementation of the LinearNoBias class. The LinearNoBias class represents a
  * single layer of a neural network.
  *
- * @tparam MatType The type of the layer's inputs. The layer automatically
- *    cast inputs to this type (Default: arma::mat).
- * @tparam MatType The type of the computation which also causes the output
- *    to also be in this type. The type also allows the computation and weight
- *    type to differ from the input type (Default: arma::mat).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  * @tparam RegularizerType Type of the regularizer to be used (Default no
  *    regularizer).
  */

src/mlpack/methods/ann/layer/log_softmax.hpp

@@ -26,11 +26,8 @@ namespace ann /** Artificial Neural Network. */ {
  * (NegativeLogLikelihoodLayer), which expects that the input contains
  * log-probabilities for each class.
  *
- * @tparam MatType The type of the layer's inputs. The layer automatically
- *    cast inputs to this type (Default: arma::mat).
- * @tparam MatType The type of the computation which also causes the output
- *    to also be in this type. The type also allows the computation and weight
- *    type to differ from the input type (Default: arma::mat).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template <typename MatType = arma::mat>
 class LogSoftMaxType : public Layer<MatType>

src/mlpack/methods/ann/layer/lstm.hpp

@@ -52,10 +52,8 @@ namespace ann /** Artificial Neural Network. */ {
  * \see FastLSTM for a faster LSTM version which combines the calculation of the
  * input, forget, output gates and hidden state in a single step.
  *
- * @tparam MatType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class LSTMType : public RecurrentLayer<MatType>

src/mlpack/methods/ann/layer/max_pooling.hpp

@@ -52,10 +52,8 @@ class MaxPoolingRule
 /**
  * Implementation of the MaxPooling layer.
  *
- * @tparam MatType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class MaxPoolingType : public Layer<MatType>

src/mlpack/methods/ann/layer/multi_layer.hpp

@@ -25,6 +25,9 @@ namespace ann {
  * It's likely not very useful to use this layer directly; instead, this layer
  * is meant as a base class for use by other layers that must store and use
  * multiple layers.
+ *
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType>
 class MultiLayer : public Layer<MatType>

src/mlpack/methods/ann/layer/noisylinear.hpp

@@ -24,11 +24,8 @@ namespace ann /** Artificial Neural Network. */ {
  * Implementation of the NoisyLinear layer class. It represents a single
  * layer of a neural network, with parametric noise added to its weights.
  *
- * @tparam MatType The type of the layer's inputs. The layer automatically
- *    cast inputs to this type (Default: arma::mat).
- * @tparam MatType The type of the computation which also causes the output
- *    to also be in this type. The type also allows the computation and weight
- *    type to differ from the input type (Default: arma::mat).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class NoisyLinearType : public Layer<MatType>

src/mlpack/methods/ann/layer/padding.hpp

@@ -19,13 +19,11 @@ namespace mlpack {
 namespace ann /** Artificial Neural Network. */ {
 
 /**
- * Implementation of the Padding module class. The Padding module applies a bias term
- * to the incoming data.
+ * Implementation of the Padding module class. The Padding module applies
+ * (zero-valued) padding on the input data.
  *
- * @tparam MatType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class PaddingType : public Layer<MatType>

src/mlpack/methods/ann/layer/radial_basis_function.hpp

@@ -21,7 +21,6 @@
 namespace mlpack {
 namespace ann /** Artificial Neural Network. */ {
 
-
 /**
  * Implementation of the Radial Basis Function layer. The RBFType class when use
  * with a non-linear activation function acts as a Radial Basis Function which
@@ -38,10 +37,8 @@ namespace ann /** Artificial Neural Network. */ {
  * }
  * @endcode
  *
- * @tparam MatType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  * @tparam Activation Type of the activation function (mlpack::ann::Gaussian).
  */
 

src/mlpack/methods/ann/layer/recurrent_layer.hpp

@@ -15,9 +15,13 @@
 namespace mlpack {
 namespace ann {
 
-template<
-    typename MatType = arma::mat
->
+/**
+ * TODO: comment
+ *
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
+ */
+template<typename MatType = arma::mat>
 class RecurrentLayer : public Layer<MatType>
 {
  public:

src/mlpack/methods/ann/loss_functions/binary_cross_entropy_loss.hpp

@@ -18,13 +18,11 @@ namespace mlpack {
 namespace ann /** Artificial Neural Network. */ {
 
 /**
- * The binary-cross-entropy performance function measures the
- * Binary Cross Entropy between the target and the output.
+ * The binary-cross-entropy performance function measures the Binary Cross
+ * Entropy between the target and the output.
  *
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class BCELossType

src/mlpack/methods/ann/loss_functions/cosine_embedding_loss.hpp

@@ -26,11 +26,9 @@ namespace ann /** Artificial Neural Network. */ {
  * f(x) = 1 - cos(x1, x2) , for y = 1
  * f(x) = max(0, cos(x1, x2) - margin) , for y = -1
  * @f}
- * 
- * @tparam MatType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ *
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class CosineEmbeddingLossType

src/mlpack/methods/ann/loss_functions/dice_loss.hpp

@@ -38,10 +38,8 @@ namespace ann /** Artificial Neural Network. */ {
  * }
  * @endcode
  *
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class DiceLossType

src/mlpack/methods/ann/loss_functions/earth_mover_distance.hpp

@@ -21,10 +21,8 @@ namespace ann /** Artificial Neural Network. */ {
  * The earth mover distance function measures the network's performance
  * according to the Kantorovich-Rubinstein duality approximation.
  *
- * @tparam MatType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class EarthMoverDistanceType

src/mlpack/methods/ann/loss_functions/empty_loss.hpp

@@ -23,10 +23,8 @@ namespace ann /** Artificial Neural Network. */ {
  * The empty loss does nothing, letting the user calculate the loss outside
  * the model.
  *
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class EmptyLossType

src/mlpack/methods/ann/loss_functions/hinge_embedding_loss.hpp

@@ -24,10 +24,8 @@ namespace ann /** Artificial Neural Network. */ {
  * The Hinge Embedding loss function is often used to compute the loss
  * between y_true and y_pred.
  *
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class HingeEmbeddingLossType

src/mlpack/methods/ann/loss_functions/hinge_loss.hpp

@@ -25,10 +25,8 @@ namespace ann /** Artificial Neural Network. */ {
  * The hinge loss \f$l(y_true, y_pred)\f$ is defined as
  * \f$l(y_true, y_pred) = max(0, 1 - y_true*y_pred)\f$.
  *
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class HingeLossType

src/mlpack/methods/ann/loss_functions/huber_loss.hpp

@@ -24,10 +24,8 @@ namespace ann /** Artificial Neural Network. */ {
  * and linear for large values, with equal values and slopes of the different
  * sections at the two points where \f$ |y - f(x)| = delta \f$.
  *
- * @tparam MatType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class HuberLossType

src/mlpack/methods/ann/loss_functions/kl_divergence.hpp

@@ -33,10 +33,8 @@ namespace ann /** Artificial Neural Network. */ {
  * }
  * @endcode
  *
- * @tparam MatType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class KLDivergenceType

src/mlpack/methods/ann/loss_functions/l1_loss.hpp

@@ -21,10 +21,8 @@ namespace ann /** Artificial Neural Network. */ {
  * The L1 loss is a loss function that measures the mean absolute error (MAE) 
  * between each element in the input x and target y 
  *
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class L1LossType

src/mlpack/methods/ann/loss_functions/log_cosh_loss.hpp

@@ -23,10 +23,8 @@ namespace ann /** Artificial Neural Network. */ {
  * variational auto encoder. This function is the log of hyperbolic
  * cosine of difference between true values and predicted values.
  *
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class LogCoshLossType

src/mlpack/methods/ann/loss_functions/margin_ranking_loss.hpp

@@ -22,11 +22,9 @@ namespace ann /** Artificial Neural Network. */ {
  * values of 1 or -1. If the label is 1 then the first input should be ranked
  * higher than the second input at a distance larger than a margin, and vice-
  * versa if the label is -1.
- * 
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ *
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class MarginRankingLossType

src/mlpack/methods/ann/loss_functions/mean_absolute_percentage_error.hpp

@@ -37,10 +37,8 @@ namespace ann /** Artificial Neural Network. */ {
  * }
  * @endcode
  *
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class MeanAbsolutePercentageErrorType

src/mlpack/methods/ann/loss_functions/mean_bias_error.hpp

@@ -18,13 +18,11 @@ namespace mlpack {
 namespace ann /** Artificial Neural Network. */ {
 
 /**
- * The mean bias error performance function measures the network's
- * performance according to the mean of errors.
+ * The mean bias error performance function measures the network's performance
+ * according to the mean of errors.
  *
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class MeanBiasErrorType

src/mlpack/methods/ann/loss_functions/mean_squared_error.hpp

@@ -21,11 +21,8 @@ namespace ann /** Artificial Neural Network. */ {
  * The mean squared error performance function measures the network's
  * performance according to the mean of squared errors.
  *
- * @tparam ActivationFunction Activation function used for the embedding layer.
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class MeanSquaredErrorType

src/mlpack/methods/ann/loss_functions/mean_squared_logarithmic_error.hpp

@@ -18,13 +18,11 @@ namespace mlpack {
 namespace ann /** Artificial Neural Network. */ {
 
 /**
- * The mean squared logarithmic error performance function measures the network's
- * performance according to the mean of squared logarithmic errors.
+ * The mean squared logarithmic error performance function measures the
+ * network's performance according to the mean of squared logarithmic errors.
  *
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class MeanSquaredLogarithmicErrorType

src/mlpack/methods/ann/loss_functions/multilabel_softmargin_loss.hpp

@@ -22,10 +22,14 @@ namespace mlpack {
 namespace ann /** Artificial Neural Network. */ {
 
 /**
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * The Multi-label Soft Margin Loss function.
+ *
+ * It is a criterion that optimizes a multi-label one-versus-all loss based on
+ * max-entropy, between input x and target y of size (N, C) where N is the
+ * batch size and C is the number of classes.
+ *
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class MultiLabelSoftMarginLossType

src/mlpack/methods/ann/loss_functions/negative_log_likelihood.hpp

@@ -20,13 +20,11 @@ namespace ann /** Artificial Neural Network. */ {
 /**
  * Implementation of the negative log likelihood layer. The negative log
  * likelihood layer expectes that the input contains log-probabilities for each
- * class. The layer also expects a class index, in the range between 1 and the
+ * class. The layer also expects a class index in the range [0, numClasses - 1]
  * number of classes, as target when calling the Forward function.
  *
- * @tparam MatType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class NegativeLogLikelihoodType

src/mlpack/methods/ann/loss_functions/poisson_nll_loss.hpp

@@ -20,14 +20,11 @@ namespace ann /** Artificial Neural Network. */ {
 
 /**
  * Implementation of the Poisson negative log likelihood loss. This loss
- * function expects input for each class. It also expects a class index,
- * in the range between 1 and the number of classes, as target when calling
- * the Forward function.
+ * function expects input for each class. It also expects a class index, in the
+ * range [0, numClasses - 1], as target when calling the Forward function.
  *
- * @tparam MatType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class PoissonNLLLossType

src/mlpack/methods/ann/loss_functions/reconstruction_loss.hpp

@@ -23,10 +23,8 @@ namespace ann /** Artificial Neural Network. */ {
  * performance equal to the negative log probability of the target with
  * the input distribution.
  *
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  * @tparam DistType The type of distribution parametrized by the input.
  */
 template<

src/mlpack/methods/ann/loss_functions/sigmoid_cross_entropy_error.hpp

@@ -40,10 +40,8 @@ namespace ann /** Artificial Neural Network. */ {
  * }
  * @endcode
  *
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class SigmoidCrossEntropyErrorType

src/mlpack/methods/ann/loss_functions/soft_margin_loss.hpp

@@ -22,10 +22,14 @@ namespace mlpack {
 namespace ann /** Artificial Neural Network. */ {
 
 /**
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * The Soft Margin Loss function.
+ *
+ * It is a criterion that optimizes a two-class classification logistic loss,
+ * between input x and target y, both having the same shape, with the target
+ * containing only the values 1 or -1.
+ *
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class SoftMarginLossType

src/mlpack/methods/ann/loss_functions/triplet_margin_loss.hpp

@@ -24,22 +24,21 @@ namespace ann /** Artificial Neural Network. */ {
  * of the positive (truthy) and negative (falsy) inputs.
  * The distance between two samples A and B is defined as square of L2 norm
  * of A-B.
- * 
+ *
  * For more information, refer the following paper.
  *
  * @code
  * @article{Schroff2015,
  *   author  = {Florian Schroff, Dmitry Kalenichenko, James Philbin},
- *   title   = {FaceNet: A Unified Embedding for Face Recognition and Clustering},
+ *   title   = {FaceNet: A Unified Embedding for Face Recognition and
+ *              Clustering},
  *   year    = {2015},
  *   url     = {https://arxiv.org/abs/1503.03832},
  * }
  * @endcode
  *
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam OutputDataType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class TripletMarginLossType

src/mlpack/methods/ann/loss_functions/vr_class_reward.hpp

@@ -21,13 +21,11 @@ namespace ann /** Artificial Neural Network. */ {
 /**
  * Implementation of the variance reduced classification reinforcement layer.
  * This layer is meant to be used in combination with the reinforce normal layer
- * (ReinforceNormalLayer), which expects that an reward:
- * (1 for success, 0 otherwise).
+ * (ReinforceNormalLayer), which expects that the reward is 1 for success, and 0
+ * otherwise.
  *
- * @tparam InputDataType Type of the input data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
- * @tparam MatType Type of the output data (arma::colvec, arma::mat,
- *         arma::sp_mat or arma::cube).
+ * @tparam MatType Matrix representation to accept as input and use for
+ *    computation.
  */
 template<typename MatType = arma::mat>
 class VRClassRewardType