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https://github.com/davidalbertonogueira/MLP.git
synced 2025-12-17 12:24:40 +03:00
add cmake build
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11
src/MLP.cpp
11
src/MLP.cpp
@@ -10,8 +10,10 @@
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#include <fstream>
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#include <vector>
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#include <algorithm>
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#include "easylogging++.h"
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#ifdef MLP_EASYLOGGING
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#include "easylogging++.h"
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#endif
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//desired call sintax : MLP({64*64,20,4}, {"sigmoid", "linear"},
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@@ -206,7 +208,9 @@ void MLP::Train(const std::vector<TrainingSample> &training_sample_set_with_bias
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temp_training << predicted_output[i];
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}
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temp_training << "]";
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#ifdef MLP_EASYLOGGING
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LOG(INFO) << temp_training.str();
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#endif
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}
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for (int j = 0; j < predicted_output.size(); j++) {
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@@ -221,13 +225,15 @@ void MLP::Train(const std::vector<TrainingSample> &training_sample_set_with_bias
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learning_rate);
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}
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#ifdef MLP_EASYLOGGING
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if (output_log && ((i % (max_iterations / 10)) == 0))
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LOG(INFO) << "Iteration " << i << " cost function f(error): "
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<< current_iteration_cost_function;
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#endif
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if (current_iteration_cost_function < min_error_cost)
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break;
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}
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#ifdef MLP_EASYLOGGING
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LOG(INFO) << "Iteration " << i << " cost function f(error): "
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<< current_iteration_cost_function;
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@@ -235,6 +241,7 @@ void MLP::Train(const std::vector<TrainingSample> &training_sample_set_with_bias
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LOG(INFO) << "******* TRAINING ENDED *******";
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LOG(INFO) << "******* " << i << " iters *******";
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LOG(INFO) << "******************************";
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#endif
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//{
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// int layer_i = -1;
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// int node_i = -1;
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