feat: ✨ Change Makefile to build IA separatly
This commit is contained in:
19
Makefile
19
Makefile
@ -9,9 +9,13 @@ RESOURCES_BUILD_DIR := $(BUILD_DIR)/resources
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MAIN_SRCS := $(shell find $(SRC_DIRS) -name *.cpp -or -name *.c -or -name *.s | grep -v main_solver.c)
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SOLVER_SRCS := ./src/main_solver.c ./src/utils/Solver/gridResolver.c
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# IA files
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IA_SRCS := ./src/network_main.c ./src/utils/AI/neural_utils.c ./src/utils/Application/ApplicationUtils.c ./src/utils/Image/ImageUtils.c
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IA_OBJS := $(IA_SRCS:%=$(BUILD_DIR)/%.o)
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MAIN_OBJS := $(MAIN_SRCS:%=$(BUILD_DIR)/%.o)
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SOLVER_OBJS := $(SOLVER_SRCS:%=$(BUILD_DIR)/%.o)
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DEPS := $(MAIN_OBJS:.o=.d) $(SOLVER_OBJS:.o=.d)
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DEPS := $(MAIN_OBJS:.o=.d) $(SOLVER_OBJS:.o=.d) $(IA_OBJS:.o=.d)
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INC_DIRS := $(shell find $(SRC_DIRS) -type d)
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INC_FLAGS := $(addprefix -I,$(INC_DIRS))
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@ -19,9 +23,9 @@ INC_FLAGS := $(addprefix -I,$(INC_DIRS))
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CPPFLAGS := $(INC_FLAGS) -MMD -MP `sdl2-config --cflags` -Wall -Wextra -Werror -g -pedantic -std=c99
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LDFLAGS := `sdl2-config --libs` -lSDL2_ttf -lSDL2_image -lm
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# Default rule: build both main and solver
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# Default rule: build all components
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.PHONY: all
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all: main solver
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all: main solver IA
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# Rule to copy resources
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.PHONY: copy_resources
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@ -43,6 +47,13 @@ solver: $(BUILD_DIR)/solver
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$(BUILD_DIR)/solver: $(SOLVER_OBJS)
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$(CC) $(SOLVER_OBJS) -o $@ $(LDFLAGS)
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# Build IA (network_main.c + neural_utils.c)
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.PHONY: IA
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IA: $(BUILD_DIR)/IA
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$(BUILD_DIR)/IA: $(IA_OBJS)
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$(CC) $(IA_OBJS) -o $@ $(LDFLAGS)
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# Build step for C source
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$(BUILD_DIR)/%.c.o: %.c
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mkdir -p $(dir $@)
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@ -62,4 +73,4 @@ prepare:
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make clean; bear -- make
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# Include the .d makefiles. The - at the front suppresses the errors of missing Makefiles.
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-include $(DEPS)
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-include $(DEPS)
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@ -2,11 +2,11 @@
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#include <stdlib.h>
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#include <err.h>
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#include <string.h>
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#include "../Application/ApplicationUtils.h"
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#include "../Image/ImageUtils.h"
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#include "utils/Application/ApplicationUtils.h"
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#include "utils/Image/ImageUtils.h"
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#include "neural_utils.h"
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void network_train(neural_network* network, char* training_data_dir, char* save_path, double batch_pourcent, size_t iteration, size_t warmup, size_t warmup_iteration, double learning_rate, size_t AdaFactor)
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void network_train(neural_network *network, char *training_data_dir, char *save_path, double batch_pourcent, size_t iteration, size_t warmup, size_t warmup_iteration, double learning_rate, size_t AdaFactor)
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{
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network->nb_input = 169;
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network->hidden_height = 200;
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@ -17,7 +17,7 @@ void network_train(neural_network* network, char* training_data_dir, char* save_
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size_t data_len;
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training_data* training_datas = load_dataset(training_data_dir, AdaFactor, &data_len);
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training_data *training_datas = load_dataset(training_data_dir, AdaFactor, &data_len);
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size_t batch_size = (size_t)(data_len * batch_pourcent);
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@ -30,7 +30,7 @@ void network_train(neural_network* network, char* training_data_dir, char* save_
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save_neural_network(network, save_path);
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}
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void network_retrain(char* network_path, char* training_data_dir, double batch_pourcent, size_t iteration, double learning_rate)
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void network_retrain(char *network_path, char *training_data_dir, double batch_pourcent, size_t iteration, double learning_rate)
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{
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neural_network network;
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@ -38,7 +38,7 @@ void network_retrain(char* network_path, char* training_data_dir, double batch_p
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size_t data_len;
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training_data* datas = load_dataset(training_data_dir, network.nb_output / 26, &data_len);
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training_data *datas = load_dataset(training_data_dir, network.nb_output / 26, &data_len);
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size_t batch_size = (size_t)(data_len * batch_pourcent);
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@ -47,7 +47,7 @@ void network_retrain(char* network_path, char* training_data_dir, double batch_p
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save_neural_network(&network, network_path);
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}
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void network_use(neural_network* network, double* inputs)
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void network_use(neural_network *network, double *inputs)
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{
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for (size_t i = 0; i < network->nb_input; i++)
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{
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@ -59,7 +59,7 @@ void network_use(neural_network* network, double* inputs)
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printf("Predicted character: %c\n", get_network_char_prediction(network, network->nb_output / 26));
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}
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void network_test(char* network_path, char* data_path)
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void network_test(char *network_path, char *data_path)
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{
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neural_network network;
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@ -68,33 +68,33 @@ void network_test(char* network_path, char* data_path)
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size_t AdaFactor = network.nb_output / 26;
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size_t nb_data;
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training_data* datas = load_dataset(data_path, AdaFactor, &nb_data);
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training_data *datas = load_dataset(data_path, AdaFactor, &nb_data);
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printf("Network total cost: %f\n", get_network_total_cost(&network, datas, nb_data));
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size_t suc = get_network_success_rate(&network, datas, nb_data, AdaFactor);
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printf("Network success rate: %li/%li: %.2f%%\n", suc, nb_data, (((double)suc / (double)nb_data) * 100.0));
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}
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void network_main(int argc, char* argv[])
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int main(int argc, char *argv[])
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{
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(void)argc;
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char* network_application_directory = path_get_directory(argv[0]);
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char *network_application_directory = path_get_directory(argv[0]);
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if (argc < 2)
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errx(EXIT_FAILURE, "missing arguments, usage: ./network <train, retrain, use, test or help>");
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char* action = argv[1];
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char *action = argv[1];
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neural_network network;
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if (strcmp(action, "train") == 0) //train network: ./network train <network.csv> <data directory> <batch pourcent> <iterations> <warmup> <warmup iterations> [learning_rate] [AdaFactor]
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if (strcmp(action, "train") == 0) // train network: ./network train <network.csv> <data directory> <batch pourcent> <iterations> <warmup> <warmup iterations> [learning_rate] [AdaFactor]
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{
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if (argc < 8)
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errx(EXIT_FAILURE, "missing arguments, usage: ./network train <network.csv> <data directory> <batch pourcent> <iterations> <warmup> <warmup iterations> [learning_rate] [AdaFactor]");
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char* network_path = combine_path(network_application_directory, argv[2]);
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char* data_dir = combine_path(network_application_directory, argv[3]);
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char *network_path = combine_path(network_application_directory, argv[2]);
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char *data_dir = combine_path(network_application_directory, argv[3]);
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double batch_pourcent = atof(argv[4]);
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if (batch_pourcent > 1)
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errx(EXIT_FAILURE, "invalid argument: <batch_pourcent> must be between 0 and 1");
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@ -112,11 +112,11 @@ void network_main(int argc, char* argv[])
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network_train(&network, data_dir, network_path, batch_pourcent, iterations, warmup, warmup_iterations, learning_rate, AdaFactor);
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}
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else if (strcmp(action, "retrain") == 0) //retrain network: ./network <network.csv> <data directory> <batch pourcent> <iterations>
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else if (strcmp(action, "retrain") == 0) // retrain network: ./network <network.csv> <data directory> <batch pourcent> <iterations>
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{
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if (argc < 4)
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errx(EXIT_FAILURE, "missing arguments, usage: ./network <network.csv> <data directory> <batch pourcent> <iterations> [learning_rate]");
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double batch_pourcent = atof(argv[3]);
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size_t iterations = (size_t)atoi(argv[4]);
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@ -126,14 +126,14 @@ void network_main(int argc, char* argv[])
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network_retrain(argv[1], argv[2], batch_pourcent, iterations, learning_rate);
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}
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else if (strcmp(action, "use") == 0) //use network: ./network use <network.csv> <image path>
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else if (strcmp(action, "use") == 0) // use network: ./network use <network.csv> <image path>
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{
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if (argc < 3)
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errx(EXIT_FAILURE, "missing arguments, usage: ./network use <network.csv> <image path>");
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char* network_path = combine_path(network_application_directory, argv[2]);
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char *network_path = combine_path(network_application_directory, argv[2]);
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char* input_path = combine_path(network_application_directory, argv[3]);
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char *input_path = combine_path(network_application_directory, argv[3]);
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neural_network network;
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@ -141,14 +141,14 @@ void network_main(int argc, char* argv[])
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network_use(&network, image_to_bool_array(input_path, NULL));
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}
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else if (strcmp(action, "test") == 0) //test network: ./network test <network.csv> <data directory>
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else if (strcmp(action, "test") == 0) // test network: ./network test <network.csv> <data directory>
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{
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if (argc < 3)
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errx(EXIT_FAILURE, "missing arguments, usage: ./network test <network.csv> <data directory>");
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char* network_path = combine_path(network_application_directory, argv[2]);
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char *network_path = combine_path(network_application_directory, argv[2]);
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char* data_path = combine_path(network_application_directory, argv[3]);
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char *data_path = combine_path(network_application_directory, argv[3]);
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network_test(network_path, data_path);
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}
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@ -160,6 +160,7 @@ void network_main(int argc, char* argv[])
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{
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errx(EXIT_FAILURE, "invalid arguments!");
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}
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return 1;
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/*printf("Succes rate: %i\n", (int)(get_network_success_rate(&network, training_datas, data_len, 1) * 100));
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@ -172,4 +173,4 @@ void network_main(int argc, char* argv[])
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printf("Character: a, predicted character: %c\n", get_network_char_prediction(&network));
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save_neural_network(&network, combine_path(network_application_directory, "../network.csv"));*/
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}
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}
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@ -1064,4 +1064,4 @@ void print_training_debug(neural_network* network, training_data* data, size_t d
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process_network(&network);
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printf("Character: a, predicted character: %c\n", get_network_char_prediction(&network));*/
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printf("Character: a, predicted character: %c\n", get_network_char_prediction(&network));*/
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@ -34,7 +34,7 @@ typedef struct
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double sigmoid(double x);
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double double_rand();
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double double_rand(double size);
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void init_neuron(neuron* n, size_t nb_connection);
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@ -74,4 +74,4 @@ void print_network_state(neural_network *network);
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void print_training_debug(neural_network* network, training_data* data, size_t data_len);
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#endif
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#endif
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