/** * @file logic_gates.c * @brief Example: Train a neural network to compute all 16 two-input logic functions. * @author Oscar Sotomayor (Titux) * @date 2026 * * This example creates a 3-16 neural network and trains it to learn all 16 possible * two-input logic functions. It demonstrates network construction, training, evaluation, * and model serialization. * Expected output: * * ```sh * ~/NeuroTIC/examples$ time bash test.sh logic_gates * Compiling project in location: examples * Project name: logic_gates * Platform: CPU * * Attemps: 474 * * ========================================================================================================================= * | A | B | NULL | NOR | EXA | NOTB | EXB | NOTA | XOR | NAND | AND | XNOR | A | IMPA | B | IMPB | OR | ALL | * |---|---|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------| * | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | * | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | * | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | * | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | * ========================================================================================================================= * * ========================================================================================================================= * | A | B | NULL | NOR | EXA | NOTB | EXB | NOTA | XOR | NAND | AND | XNOR | A | IMPA | B | IMPB | OR | ALL | * |---|---|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------| * | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | * | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | * | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | * | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | * ========================================================================================================================= * * * real 0m0.599s * user 0m0.448s * sys 0m0.155s * * * ``` * * @code{.c} */ #include #include "ntcomplete.h" int main( void ){ // Network structure: 2 inputs, 2 layers (one hidden layer with 3 neurons and output layer with 16 neurons) CREATE_NET_FEEDFORWARD( network , 2 , ((uint16_t []){3,16}) ); // Set activation functions to sigmoid for all neurons: Following activation functions distribution had shown the best efficiency equilibrium between training attemps, convergenece and computational work, in BOOLEAN vs SIGMOID tests. network.nn[0][0].fn= NTACT_BOOLEAN; //<- First hiiden neuron. for( uint16_t j= 1 ; j < network.neurons[0] ; j++ ) network.nn[0][j].fn= NTACT_SIGMOID; for( uint16_t j= 0 ; j < network.neurons[1] ; j++ ) network.nn[1][j].fn= NTACT_BOOLEAN; // Initialize weights randomly randnet( &network ); // Prepare training data for all 16 two-input logic functions traindata_t data={ .learning_rate= 0.1, .tolerance= 0.0, .max_attempts= 10000000, .samples= 4 }; // Allocate training data newtraindata( &data , &network ); // Define input-output pairs for all logic functions for( uint64_t i= 0 ; i < data.samples ; i++ ){ for( input_t j= 0 ; j < network.inputs ; j++ ) data.in[i][j]= ( i >> j ) & 1; data.results[i][0]= 0; // NULL 0000 data.results[i][1]= !( data.in[i][0] || data.in[i][1] ); // NOR 1000 data.results[i][2]= data.in[i][0] && !data.in[i][1]; // EXA 0100 data.results[i][3]= !data.in[i][1]; // NOTB 1100 data.results[i][4]= !data.in[i][0] && data.in[i][1]; // EXB 0010 data.results[i][5]= !data.in[i][0]; // NOTA 1010 data.results[i][6]= data.in[i][0] != data.in[i][1]; // XOR 0110 data.results[i][7]= !( data.in[i][0] && data.in[i][1] ); // NAND 1110 data.results[i][8]= data.in[i][0] && data.in[i][1]; // AND 0001 data.results[i][9]= data.in[i][0] == data.in[i][1]; // XNOR 1001 data.results[i][10]= data.in[i][0]; // A 0101 data.results[i][11]= data.in[i][0] || !data.in[i][1]; // IMPB 1101 data.results[i][12]= data.in[i][1]; // B 0011 data.results[i][13]= !data.in[i][0] || data.in[i][1]; // IMPA 1011 data.results[i][14]= data.in[i][0] || data.in[i][1]; // OR 0111 data.results[i][15]= 1; // ALL 1111 } // Train the network using backpropagation printf( "\nAttemps: %li" , backpropagation( &network , &data ) ); // Evaluate and display results printf( "\n\n=========================================================================================================================" ); printf( "\n| A | B | NULL | NOR | EXA | NOTB | EXB | NOTA | XOR | NAND | AND | XNOR | A | IMPA | B | IMPB | OR | ALL |" ); printf( "\n|---|---|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|" ); for( sample_t i= 0 ; i < data.samples ; i++ ){ for( input_t j= 0 ; j < network.inputs ; j++ ) network.in[j]= &data.in[i][j]; feedforward( &network ); printf( "\n| %.0f | %.0f |" , data.in[i][0] , data.in[i][1] ); for( uint16_t j= 0 ; j < network.neurons[network.layers - 1] ; j++ ) printf( " %.0f |" , *network.out[j] ); } printf( "\n========================================================================================================================="); // Save and reload the trained network savenet( &network , "logic_gates" ); net_s network_copy= loadnet( "logic_gates" ); // Display results from the loaded network to verify persistence printf( "\n\n=========================================================================================================================" ); printf( "\n| A | B | NULL | NOR | EXA | NOTB | EXB | NOTA | XOR | NAND | AND | XNOR | A | IMPA | B | IMPB | OR | ALL |" ); printf( "\n|---|---|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|------|" ); for( sample_t i= 0 ; i < data.samples ; i++ ){ for( input_t j= 0 ; j < network_copy.inputs ; j++ ) network_copy.in[j]= &data.in[i][j]; feedforward( &network_copy ); printf( "\n| %.0f | %.0f |" , data.in[i][0] , data.in[i][1] ); for( uint16_t j= 0 ; j < network_copy.neurons[network_copy.layers - 1] ; j++ ) printf( " %.0f |" , *network_copy.out[j] ); } printf( "\n=========================================================================================================================\n\n"); // Clean up remove( "logic_gates.ntic" ); return 0; } /** @endcode */