[mlpack-git] master: Add implementation of the BiasLayer class. (9ce1ce1)

[gitdub at big.cc.gt.atl.ga.us]

Repository : https://github.com/mlpack/mlpack

On branch  : master
Link       : https://github.com/mlpack/mlpack/compare/904762495c039e345beba14c1142fd719b3bd50e...f94823c800ad6f7266995c700b1b630d5ffdcf40

>---------------------------------------------------------------

commit 9ce1ce1ff29cf06d7a311730f1e253220ab40b5f
Author: Marcus Edel <marcus.edel at fu-berlin.de>
Date:   Thu Jan 1 23:12:37 2015 +0100

    Add implementation of the BiasLayer class.


>---------------------------------------------------------------

9ce1ce1ff29cf06d7a311730f1e253220ab40b5f
 src/mlpack/methods/ann/layer/bias_layer.hpp | 128 ++++++++++++++++++++++++++++
 1 file changed, 128 insertions(+)

diff --git a/src/mlpack/methods/ann/layer/bias_layer.hpp b/src/mlpack/methods/ann/layer/bias_layer.hpp
new file mode 100644
index 0000000..dde95dd
--- /dev/null
+++ b/src/mlpack/methods/ann/layer/bias_layer.hpp
@@ -0,0 +1,128 @@
+/**
+ * @file bias_layer.hpp
+ * @author Marcus Edel
+ *
+ * Definition of the BiasLayer class, which implements a standard bias
+ * layer.
+ */
+#ifndef __MLPACK_METHOS_ANN_LAYER_BIAS_LAYER_HPP
+#define __MLPACK_METHOS_ANN_LAYER_BIAS_LAYER_HPP
+
+#include <mlpack/core.hpp>
+#include <mlpack/methods/ann/layer/layer_traits.hpp>
+#include <mlpack/methods/ann/activation_functions/identity_function.hpp>
+
+namespace mlpack {
+namespace ann /** Artificial Neural Network. */ {
+
+/**
+ * An implementation of a standard bias layer with a default value of one.
+ *
+ * @tparam ActivationFunction Activation function used for the bias layer
+ * (Default IdentityFunction).
+ * @tparam MatType Type of data (arma::mat or arma::sp_mat).
+ * @tparam VecType Type of data (arma::colvec, arma::mat or arma::sp_mat).
+ */
+template <
+    class ActivationFunction = IdentityFunction,
+    typename MatType = arma::mat,
+    typename VecType = arma::colvec
+>
+class BiasLayer
+
+{
+ public:
+  /**
+   * Create the BiasLayer object using the specified number of bias units.
+   *
+   * @param layerSize The number of neurons.
+   */
+  BiasLayer(const size_t layerSize) :
+      inputActivations(arma::ones<VecType>(layerSize)),
+      delta(arma::zeros<VecType>(layerSize)),
+      layerSize(layerSize)
+  {
+    // Nothing to do here.
+  }
+
+  /**
+   * Ordinary feed forward pass of a neural network, evaluating the function
+   * f(x) by propagating the activity forward through f.
+   *
+   * @param inputActivation Input data used for evaluating the specified
+   * activity function.
+   * @param outputActivation Data to store the resulting output activation.
+   */
+  void FeedForward(const VecType& inputActivation, VecType& outputActivation)
+  {
+    ActivationFunction::fn(inputActivation, outputActivation);
+  }
+
+  /**
+   * Ordinary feed backward pass of a neural network, calculating the function
+   * f(x) by propagating x backwards trough f. Using the results from the feed
+   * forward pass.
+   *
+   * @param inputActivation Input data used for calculating the function f(x).
+   * @param error The backpropagated error.
+   * @param delta The calculating delta using the partial derivative of the
+   * error with respect to a weight.
+   */
+  void FeedBackward(const VecType& inputActivation,
+                    const VecType& error,
+                    VecType& delta)
+  {
+    VecType derivative;
+    ActivationFunction::deriv(inputActivation, derivative);
+
+    delta = error % derivative;
+  }
+
+  //! Get the input activations.
+  const VecType& InputActivation() const { return inputActivations; }
+  //  //! Modify the input activations.
+  VecType& InputActivation() { return inputActivations; }
+
+  //! Get the detla.
+  VecType& Delta() const { return delta; }
+ //  //! Modify the delta.
+  VecType& Delta() { return delta; }
+
+  //! Get input size.
+  size_t InputSize() const { return layerSize; }
+  //  //! Modify the delta.
+  size_t& InputSize() { return layerSize; }
+
+  //! Get output size.
+  size_t OutputSize() const { return layerSize; }
+  //! Modify the output size.
+  size_t& OutputSize() { return layerSize; }
+
+ private:
+  //! Locally-stored input activation object.
+  VecType inputActivations;
+
+  //! Locally-stored delta object.
+  VecType delta;
+
+  //! Locally-stored number of neurons.
+  size_t layerSize;
+}; // class BiasLayer
+
+//! Layer traits for the bias layer.
+template<>
+class LayerTraits<BiasLayer<> >
+{
+ public:
+  /**
+   * If true, then the layer is binary.
+   */
+  static const bool IsBinary = false;
+  static const bool IsOutputLayer = false;
+  static const bool IsBiasLayer = true;
+};
+
+}; // namespace ann
+}; // namespace mlpack
+
+#endif