PLearn 0.1
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00001 // -*- C++ -*- 00002 // mNNet.h 00003 // 00004 // Copyright (C) 2007 Yoshua Bengio 00005 // 00006 // Redistribution and use in source and binary forms, with or without 00007 // modification, are permitted provided that the following conditions are met: 00008 // 00009 // 1. Redistributions of source code must retain the above copyright 00010 // notice, this list of conditions and the following disclaimer. 00011 // 00012 // 2. Redistributions in binary form must reproduce the above copyright 00013 // notice, this list of conditions and the following disclaimer in the 00014 // documentation and/or other materials provided with the distribution. 00015 // 00016 // 3. The name of the authors may not be used to endorse or promote 00017 // products derived from this software without specific prior written 00018 // permission. 00019 // 00020 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR 00021 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES 00022 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN 00023 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 00024 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED 00025 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 00026 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 00027 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 00028 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 00029 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 00030 // 00031 // This file is part of the PLearn library. For more information on the PLearn 00032 // library, go to the PLearn Web site at www.plearn.org 00033 00034 // Authors: Yoshua Bengio, PAM 00035 00039 #ifndef mNNet_INC 00040 #define mNNet_INC 00041 00042 #include <plearn_learners/generic/PLearner.h> 00043 00044 namespace PLearn { 00045 00049 class mNNet : public PLearner 00050 { 00051 typedef PLearner inherited; 00052 00053 public: 00054 //##### Public Build Options ############################################ 00055 00057 int noutputs; 00058 00060 TVec<int> hidden_layer_sizes; 00061 00063 real init_lrate; 00064 00066 real lrate_decay; 00067 00069 int minibatch_size; 00070 00072 string output_type; 00073 00075 real output_layer_L1_penalty_factor; 00076 00077 public: 00078 //##### Public Not Build Options ######################################## 00079 00080 public: 00081 //##### Public Member Functions ######################################### 00082 00083 mNNet(); 00084 00085 00086 //##### PLearner Member Functions ####################################### 00087 00090 virtual int outputsize() const; 00091 00095 virtual void forget(); 00096 00100 virtual void train(); 00101 00103 virtual void computeOutput(const Vec& input, Vec& output) const; 00104 virtual void computeOutputs(const Mat& input, Mat& output) const; 00105 virtual void computeOutputsAndCosts(const Mat& input, const Mat& target, 00106 Mat& output, Mat& costs) const; 00107 00109 virtual void computeCostsFromOutputs(const Vec& input, const Vec& output, 00110 const Vec& target, Vec& costs) const; 00111 00114 virtual TVec<std::string> getTestCostNames() const; 00115 00118 virtual TVec<std::string> getTrainCostNames() const; 00119 00120 // *** SUBCLASS WRITING: *** 00121 // While in general not necessary, in case of particular needs 00122 // (efficiency concerns for ex) you may also want to overload 00123 // some of the following methods: computeOutputAndCosts(), 00124 // computeCostsOnly(), test(), nTestCosts(), nTrainCosts(), 00125 // resetInternalState(), isStatefulLearner() 00126 00127 //##### PLearn::Object Protocol ######################################### 00128 00129 // Declares other standard object methods. 00130 // ### If your class is not instantiatable (it has pure virtual methods) 00131 // ### you should replace this by PLEARN_DECLARE_ABSTRACT_OBJECT_METHODS 00132 PLEARN_DECLARE_OBJECT(mNNet); 00133 00134 // Simply calls inherited::build() then build_() 00135 virtual void build(); 00136 00138 // (PLEASE IMPLEMENT IN .cc) 00139 virtual void makeDeepCopyFromShallowCopy(CopiesMap& copies); 00140 00141 protected: 00142 //##### Protected Options ############################################### 00143 00144 // ### Declare protected option fields (such as learned parameters) here 00145 00147 int n_layers; 00148 00150 TVec<int> layer_sizes; 00151 00153 Vec all_params; 00155 TVec<Mat> biases; 00156 TVec<Mat> weights; 00160 TVec<Mat> layer_params; 00161 00163 Vec all_params_gradient; 00164 TVec<Mat> layer_params_gradient; 00165 00167 Mat neuron_gradients; // one row per example of a minibatch, has 00168 // concatenation of layer 0, layer 1, ... gradients. 00169 TVec<Mat> neuron_gradients_per_layer; // pointing into neuron_gradients 00170 // (one row per example of a minibatch) 00171 00173 mutable Mat neuron_extended_outputs; 00174 mutable TVec<Mat> neuron_extended_outputs_per_layer; // with 1's in the 00175 // first pseudo-neuron, for doing biases 00176 mutable TVec<Mat> neuron_outputs_per_layer; 00177 00178 Mat targets; // one target row per example in a minibatch 00179 Vec example_weights; // one element per example in a minibatch 00180 Mat train_costs; // one row per example in a minibatch 00181 00183 real cumulative_training_time; 00184 00185 protected: 00186 //##### Protected Member Functions ###################################### 00187 00189 static void declareOptions(OptionList& ol); 00190 00192 virtual void onlineStep(int t, const Mat& targets, Mat& train_costs, Vec example_weights); 00193 00196 virtual void fpropNet(int n_examples) const; 00197 00200 virtual void fbpropLoss(const Mat& output, const Mat& target, const Vec& example_weights, Mat& train_costs) const; 00201 00202 virtual void bpropUpdateNet(const int t); 00203 virtual void bpropNet(const int t); 00204 00205 void l1regularizeOutputs(); 00206 00207 private: 00208 //##### Private Member Functions ######################################## 00209 00211 // (PLEASE IMPLEMENT IN .cc) 00212 void build_(); 00213 00214 private: 00215 //##### Private Data Members ############################################ 00216 00217 }; 00218 00219 // Declares a few other classes and functions related to this class 00220 DECLARE_OBJECT_PTR(mNNet); 00221 00222 } // end of namespace PLearn 00223 00224 #endif 00225 00226 00227 /* 00228 Local Variables: 00229 mode:c++ 00230 c-basic-offset:4 00231 c-file-style:"stroustrup" 00232 c-file-offsets:((innamespace . 0)(inline-open . 0)) 00233 indent-tabs-mode:nil 00234 fill-column:79 00235 End: 00236 */ 00237 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :