mirror of
https://github.com/augustin64/projet-tipe
synced 2025-01-23 15:16:26 +01:00
Rename mnist folder to dense
This commit is contained in:
parent
2a88621c34
commit
04087c3de4
38
Makefile
38
Makefile
@ -1,19 +1,19 @@
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BUILDDIR := ./build
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SRCDIR := ./src
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CACHE_DIR := ./.cache
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NVCC := nvcc
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NVCC := nvcc-no
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CUDA_INCLUDE := /opt/cuda/include # Default installation path for ArchLinux, may be different
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NVCC_INSTALLED := $(shell command -v $(NVCC) 2> /dev/null)
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MNIST_SRCDIR := $(SRCDIR)/mnist
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DENSE_SRCDIR := $(SRCDIR)/dense
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CNN_SRCDIR := $(SRCDIR)/cnn
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MNIST_SRC := $(wildcard $(MNIST_SRCDIR)/*.c)
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MNIST_SRC := $(wildcard $(DENSE_SRCDIR)/*.c)
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CNN_SRC := $(wildcard $(CNN_SRCDIR)/*.c)
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CNN_SRC_CUDA := $(wildcard $(CNN_SRCDIR)/*.cu)
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MNIST_OBJ = $(filter-out $(BUILDDIR)/mnist_main.o $(BUILDDIR)/mnist_utils.o $(BUILDDIR)/mnist_preview.o, $(MNIST_SRC:$(MNIST_SRCDIR)/%.c=$(BUILDDIR)/mnist_%.o))
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MNIST_OBJ = $(filter-out $(BUILDDIR)/dense_main.o $(BUILDDIR)/dense_utils.o $(BUILDDIR)/dense_preview.o, $(MNIST_SRC:$(DENSE_SRCDIR)/%.c=$(BUILDDIR)/dense_%.o))
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CNN_OBJ = $(filter-out $(BUILDDIR)/cnn_main.o $(BUILDDIR)/cnn_preview.o $(BUILDDIR)/cnn_export.o, $(CNN_SRC:$(CNN_SRCDIR)/%.c=$(BUILDDIR)/cnn_%.o))
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CNN_OBJ_CUDA = $(CNN_SRC:$(CNN_SRCDIR)/%.cu=$(BUILDDIR)/cnn_%.o)
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@ -40,30 +40,23 @@ NVCCFLAGS = -g
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# -fsanitize=address -lasan
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#! WARNING: test/cnn-neuron_io fails with this option enabled
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all: mnist cnn;
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all: dense cnn;
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#
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# Build mnist
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# Build dense
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#
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# Executables
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mnist: $(BUILDDIR)/mnist-main $(BUILDDIR)/mnist-utils $(BUILDDIR)/mnist-preview;
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dense: $(BUILDDIR)/dense-main $(BUILDDIR)/dense-utils $(BUILDDIR)/dense-preview;
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$(BUILDDIR)/mnist-main: $(MNIST_SRCDIR)/main.c $(BUILDDIR)/mnist.o $(BUILDDIR)/mnist_neuron_io.o $(BUILDDIR)/mnist_neural_network.o
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$(CC) $(MNIST_SRCDIR)/main.c $(BUILDDIR)/mnist.o $(BUILDDIR)/mnist_neuron_io.o $(BUILDDIR)/mnist_neural_network.o -o $(BUILDDIR)/mnist-main $(CFLAGS) $(LD_CFLAGS)
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$(BUILDDIR)/mnist-utils: $(MNIST_SRCDIR)/utils.c $(BUILDDIR)/mnist_neural_network.o $(BUILDDIR)/mnist_neuron_io.o $(BUILDDIR)/mnist.o
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$(BUILDDIR)/dense-main: $(DENSE_SRCDIR)/main.c $(BUILDDIR)/mnist.o $(BUILDDIR)/dense_neuron_io.o $(BUILDDIR)/dense_neural_network.o
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$(CC) $^ -o $@ $(CFLAGS) $(LD_CFLAGS)
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$(BUILDDIR)/mnist-preview: $(MNIST_SRCDIR)/preview.c $(BUILDDIR)/mnist.o
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$(BUILDDIR)/dense-utils: $(DENSE_SRCDIR)/utils.c $(BUILDDIR)/dense_neural_network.o $(BUILDDIR)/dense_neuron_io.o $(BUILDDIR)/mnist.o
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$(CC) $^ -o $@ $(CFLAGS) $(LD_CFLAGS)
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# .o files
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$(BUILDDIR)/mnist.o: $(MNIST_SRCDIR)/mnist.c $(MNIST_SRCDIR)/include/mnist.h
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$(CC) -c $< -o $@ $(CFLAGS)
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$(BUILDDIR)/dense-preview: $(DENSE_SRCDIR)/preview.c $(BUILDDIR)/mnist.o
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$(CC) $^ -o $@ $(CFLAGS) $(LD_CFLAGS)
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$(BUILDDIR)/mnist.cuda.o: $(MNIST_SRCDIR)/mnist.c $(MNIST_SRCDIR)/include/mnist.h
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$(CC) -c $< -o $@ $(CFLAGS) -DUSE_CUDA -lcuda -I$(CUDA_INCLUDE)
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$(BUILDDIR)/mnist_%.o: $(MNIST_SRCDIR)/%.c $(MNIST_SRCDIR)/include/%.h
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$(BUILDDIR)/dense_%.o: $(DENSE_SRCDIR)/%.c $(DENSE_SRCDIR)/include/%.h
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$(CC) -c $< -o $@ $(CFLAGS)
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@ -171,8 +164,7 @@ prepare-tests:
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$(BUILDDIR)/test-cnn_%: $(TEST_SRCDIR)/cnn_%.c $(CNN_OBJ) $(BUILDDIR)/colors.o $(BUILDDIR)/mnist.o $(BUILDDIR)/utils.o $(BUILDDIR)/memory_management.o
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$(CC) $^ -o $@ $(CFLAGS) $(LD_CFLAGS)
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# mnist.o est déjà inclus en tant que mnist_mnist.o
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$(BUILDDIR)/test-mnist_%: $(TEST_SRCDIR)/mnist_%.c $(MNIST_OBJ) $(BUILDDIR)/colors.o
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$(BUILDDIR)/test-dense_%: $(TEST_SRCDIR)/dense_%.c $(MNIST_OBJ) $(BUILDDIR)/colors.o $(BUILDDIR)/mnist.o
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$(CC) $^ -o $@ $(CFLAGS) $(LD_CFLAGS)
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$(BUILDDIR)/test-memory_management: $(TEST_SRCDIR)/memory_management.c $(BUILDDIR)/colors.o $(BUILDDIR)/utils.o $(BUILDDIR)/test_memory_management.o
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@ -208,9 +200,9 @@ endif
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webserver: $(CACHE_DIR)/mnist-reseau-fully-connected.bin $(CACHE_DIR)/mnist-reseau-cnn.bin
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FLASK_APP="src/webserver/app.py" flask run
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$(CACHE_DIR)/mnist-reseau-fully-connected.bin: $(BUILDDIR)/mnist-main
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$(CACHE_DIR)/mnist-reseau-fully-connected.bin: $(BUILDDIR)/dense-main
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@mkdir -p $(CACHE_DIR)
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$(BUILDDIR)/mnist-main train \
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$(BUILDDIR)/dense-main train \
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--images "data/mnist/train-images-idx3-ubyte" \
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--labels "data/mnist/train-labels-idx1-ubyte" \
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--out "$(CACHE_DIR)/mnist-reseau-fully-connected.bin"
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@ -4,7 +4,7 @@
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#include <string.h>
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#include "../include/memory_management.h"
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#include "../mnist/include/mnist.h"
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#include "../include/mnist.h"
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#include "include/neuron_io.h"
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#include "include/struct.h"
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#include "include/jpeg.h"
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@ -8,7 +8,7 @@
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#include <omp.h>
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#include "../include/memory_management.h"
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#include "../mnist/include/mnist.h"
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#include "../include/mnist.h"
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#include "include/initialisation.h"
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#include "include/test_network.h"
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#include "include/neuron_io.h"
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@ -107,6 +107,7 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
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float loss;
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float batch_loss; // May be redundant with loss, but gives more informations
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float accuracy;
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float batch_accuracy;
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float current_accuracy;
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@ -257,6 +258,7 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
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for (int j=0; j < batches_epoques; j++) {
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batch_loss = 0.;
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batch_accuracy = 0.;
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#ifdef USE_MULTITHREADING
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if (j == batches_epoques-1) {
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nb_remaining_images = nb_images_total_remaining;
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@ -293,6 +295,7 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
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accuracy += train_parameters[k]->accuracy / (float) nb_images_total;
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loss += train_parameters[k]->loss/nb_images_total;
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batch_loss += train_parameters[k]->loss/BATCHES;
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batch_accuracy += train_parameters[k]->accuracy / (float) BATCHES; // C'est faux pour le dernier batch mais on ne l'affiche pas pour lui (enfin très rapidement)
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}
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}
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@ -304,7 +307,7 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
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}
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}
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current_accuracy = accuracy * nb_images_total/((j+1)*BATCHES);
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printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: " YELLOW "%0.2f%%" RESET, nb_threads, i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100);
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printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: " YELLOW "%0.2f%%" RESET " \tBatch Accuracy: " YELLOW "%0.2f%%" RESET, nb_threads, i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100, batch_accuracy*100);
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fflush(stdout);
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#else
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(void)nb_images_total_remaining; // Juste pour enlever un warning
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@ -320,13 +323,14 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
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accuracy += train_params->accuracy / (float) nb_images_total;
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current_accuracy = accuracy * nb_images_total/((j+1)*BATCHES);
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batch_accuracy += train_params->accuracy / (float)BATCHES;
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loss += train_params->loss/nb_images_total;
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batch_loss += train_params->loss/BATCHES;
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update_weights(network, network);
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update_bias(network, network);
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printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: " YELLOW "%0.4f%%" RESET, i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100);
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printf("\rÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: " YELLOW "%0.4f%%" RESET "\tBatch Accuracy: " YELLOW "%0.2f%%" RESET, i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100, batch_accuracy*100);
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fflush(stdout);
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#endif
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}
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@ -4,12 +4,29 @@
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#include <string.h>
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#include <math.h>
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#include <time.h>
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#include <stdbool.h>
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#include "neuron.h"
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#ifndef DEF_NEURAL_NETWORK_H
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#define DEF_NEURAL_NETWORK_H
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#define LEARNING_RATE 0.1
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// Retourne un nombre aléatoire entre 0 et 1
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#define RAND_DOUBLE() ((double)rand())/((double)RAND_MAX)
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//Coefficient leaking ReLU
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#define COEFF_LEAKY_RELU 0.2
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#define MAX_RESEAU 100000
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#define PRINT_POIDS false
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#define PRINT_BIAIS false
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// Mettre à 1 pour désactiver
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#define DROPOUT 0.7
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#define ENTRY_DROPOUT 0.85
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bool drop(float prob);
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/*
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* Fonction max pour les floats
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@ -41,7 +58,7 @@ void deletion_of_network(Network* network);
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* les données on été insérées dans la première couche. Le résultat
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* de la propagation se trouve dans la dernière couche
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*/
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void forward_propagation(Network* network);
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void forward_propagation(Network* network, bool is_training);
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/*
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* Renvoie la liste des sorties voulues à partir du nombre voulu
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@ -7,7 +7,7 @@
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#include <sys/sysinfo.h>
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#include "include/main.h"
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#include "include/mnist.h"
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#include "../include/mnist.h"
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#include "../include/colors.h"
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#include "include/neuron_io.h"
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#include "include/neural_network.h"
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@ -47,7 +47,7 @@ void print_image(unsigned int width, unsigned int height, int** image, float* pr
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int indice_max(float* tab, int n) {
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int indice = -1;
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float maxi = FLT_MIN;
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float maxi = -FLT_MAX;
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for (int i=0; i < n; i++) {
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if (tab[i] > maxi) {
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@ -87,7 +87,11 @@ void help(char* call) {
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void write_image_in_network(int** image, Network* network, int height, int width) {
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for (int i=0; i < height; i++) {
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for (int j=0; j < width; j++) {
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network->layers[0]->neurons[i*height+j]->z = (float)image[i][j] / 255.0f;
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if (!drop(ENTRY_DROPOUT)) {
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network->layers[0]->neurons[i*height+j]->z = (float)image[i][j] / 255.0f;
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} else {
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network->layers[0]->neurons[i*height+j]->z = 0;
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}
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}
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}
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}
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@ -113,7 +117,7 @@ void* train_thread(void* parameters) {
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for (int i=start; i < start+nb_images; i++) {
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write_image_in_network(images[shuffle[i]], network, height, width);
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desired_output = desired_output_creation(network, labels[shuffle[i]]);
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forward_propagation(network);
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forward_propagation(network, true);
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backward_propagation(network, desired_output);
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for (int k=0; k < nb_neurons_last_layer; k++) {
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@ -138,7 +142,7 @@ void train(int epochs, int layers, int neurons, char* recovery, char* image_file
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//int* repartition = malloc(sizeof(int)*layers);
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int nb_neurons_last_layer = 10;
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int repartition[3] = {neurons, 42, nb_neurons_last_layer};
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int repartition[2] = {neurons, nb_neurons_last_layer};
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float accuracy;
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float current_accuracy;
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@ -239,6 +243,8 @@ void train(int epochs, int layers, int neurons, char* recovery, char* image_file
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write_network(out, network);
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if (delta != NULL)
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write_delta_network(delta, delta_network);
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test(out, "data/mnist/t10k-images-idx3-ubyte", "data/mnist/t10k-labels-idx1-ubyte", false);
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}
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write_network(out, network);
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if (delta != NULL) {
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@ -293,7 +299,7 @@ float** recognize(char* modele, char* entree) {
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results[i] = (float*)malloc(sizeof(float)*last_layer->nb_neurons);
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write_image_in_network(images[i], network, height, width);
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forward_propagation(network);
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forward_propagation(network, false);
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for (int j=0; j < last_layer->nb_neurons; j++) {
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results[i][j] = last_layer->neurons[j]->z;
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@ -388,7 +394,7 @@ int main(int argc, char* argv[]) {
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}
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if (! strcmp(argv[1], "train")) {
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int epochs = EPOCHS;
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int layers = 3;
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int layers = 2;
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int neurons = 784;
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int nb_images = -1;
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int start = 0;
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@ -7,18 +7,9 @@
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#include <time.h>
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#include "include/neuron.h"
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#include "include/neural_network.h"
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// Définit le taux d'apprentissage du réseau neuronal, donc la rapidité d'adaptation du modèle (compris entre 0 et 1)
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// Cette valeur peut évoluer au fur et à mesure des époques (linéaire c'est mieux)
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#define LEARNING_RATE 0.1
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// Retourne un nombre aléatoire entre 0 et 1
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#define RAND_DOUBLE() ((double)rand())/((double)RAND_MAX)
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//Coefficient leaking ReLU
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#define COEFF_LEAKY_RELU 0.2
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#define MAX_RESEAU 100000
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#define PRINT_POIDS false
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#define PRINT_BIAIS false
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#ifndef __CUDACC__
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// The functions and macros below are already defined when using NVCC
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@ -30,6 +21,10 @@ float max(float a, float b){
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#endif
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bool drop(float prob) {
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return (rand() % 100) > 100*prob;
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}
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float sigmoid(float x){
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return 1/(1 + exp(-x));
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}
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@ -104,12 +99,12 @@ void deletion_of_network(Network* network) {
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void forward_propagation(Network* network) {
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void forward_propagation(Network* network, bool is_training) {
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Layer* layer; // Couche actuelle
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Layer* pre_layer; // Couche précédente
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Neuron* neuron;
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float sum;
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float max_z;
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float max_z = INT_MIN;
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for (int i=1; i < network->nb_layers; i++) { // La première couche contient déjà des valeurs
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sum = 0;
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@ -126,7 +121,18 @@ void forward_propagation(Network* network) {
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}
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if (i < network->nb_layers-1) {
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neuron->z = leaky_ReLU(neuron->z);
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if (!is_training) {
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if (j == 0) {
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neuron->z = ENTRY_DROPOUT*leaky_ReLU(neuron->z);
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} else {
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neuron->z = DROPOUT*leaky_ReLU(neuron->z);
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}
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} else if (!drop(DROPOUT)) {
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neuron->z = leaky_ReLU(neuron->z);
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} else {
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neuron->z = 0.;
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}
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} else { // Softmax seulement pour la dernière couche
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max_z = max(max_z, neuron->z);
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}
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@ -190,12 +196,16 @@ void backward_propagation(Network* network, int* desired_output) {
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changes += (neuron->weights[k]*neuron->last_back_weights[k])/neurons_nb;
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}
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changes = changes*leaky_ReLU_derivative(neuron->z);
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neuron->back_bias += changes;
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neuron->last_back_bias = changes;
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if (neuron->z != 0) {
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neuron->back_bias += changes;
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neuron->last_back_bias = changes;
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}
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for (int l=0; l < network->layers[i]->nb_neurons; l++){
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neuron2 = network->layers[i]->neurons[l];
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neuron2->back_weights[j] += neuron2->weights[j]*changes;
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neuron2->last_back_weights[j] = neuron2->weights[j]*changes;
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if (neuron->z != 0) {
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neuron2->back_weights[j] += neuron2->weights[j]*changes;
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neuron2->last_back_weights[j] = neuron2->weights[j]*changes;
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}
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}
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}
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}
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@ -3,7 +3,7 @@
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#include <stdint.h>
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#include <inttypes.h>
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#include "include/mnist.h"
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#include "../include/mnist.h"
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void print_image(unsigned int width, unsigned int height, int** image) {
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@ -5,7 +5,7 @@
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#include "include/neural_network.h"
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#include "include/neuron_io.h"
|
||||
#include "include/mnist.h"
|
||||
#include "../include/mnist.h"
|
||||
|
||||
/*
|
||||
Contient un ensemble de fonctions utiles pour le débogage
|
||||
@ -48,7 +48,7 @@ def recognize_mnist(image):
|
||||
|
||||
try:
|
||||
output = subprocess.check_output([
|
||||
'build/mnist-main',
|
||||
'build/dense-main',
|
||||
'recognize',
|
||||
'--modele', '.cache/mnist-reseau-fully-connected.bin',
|
||||
'--in', '.cache/image-idx3-ubyte',
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
#include <stdio.h>
|
||||
#include <inttypes.h>
|
||||
|
||||
#include "../src/mnist/include/mnist.h"
|
||||
#include "../src/include/mnist.h"
|
||||
#include "../src/include/colors.h"
|
||||
|
||||
|
||||
@ -3,8 +3,8 @@
|
||||
#include <stdint.h>
|
||||
#include <inttypes.h>
|
||||
|
||||
#include "../src/mnist/include/neural_network.h"
|
||||
#include "../src/mnist/include/neuron_io.h"
|
||||
#include "../src/dense/include/neural_network.h"
|
||||
#include "../src/dense/include/neuron_io.h"
|
||||
#include "../src/include/colors.h"
|
||||
|
||||
int main() {
|
||||
@ -4,8 +4,8 @@
|
||||
#include <stdint.h>
|
||||
#include <inttypes.h>
|
||||
|
||||
#include "../src/mnist/include/neuron_io.h"
|
||||
#include "../src/mnist/include/neural_network.h"
|
||||
#include "../src/dense/include/neuron_io.h"
|
||||
#include "../src/dense/include/neural_network.h"
|
||||
#include "../src/include/colors.h"
|
||||
|
||||
|
||||
@ -3,21 +3,21 @@
|
||||
set -e
|
||||
|
||||
OUT="build"
|
||||
make $OUT/mnist-utils
|
||||
make $OUT/dense-utils
|
||||
|
||||
echo "Compte des labels"
|
||||
"$OUT/mnist-utils" count-labels -l data/mnist/t10k-labels-idx1-ubyte
|
||||
"$OUT/dense-utils" count-labels -l data/mnist/t10k-labels-idx1-ubyte
|
||||
echo -e "\033[32mOK\033[0m"
|
||||
|
||||
echo "Création du réseau"
|
||||
mkdir -p .test-cache
|
||||
"$OUT/mnist-utils" creer-reseau -n 3 -o .test-cache/reseau.bin
|
||||
"$OUT/dense-utils" creer-reseau -n 3 -o .test-cache/reseau.bin
|
||||
echo -e "\033[32mOK\033[0m"
|
||||
|
||||
echo "Affichage poids"
|
||||
"$OUT/mnist-utils" print-poids -r .test-cache/reseau.bin > /dev/null
|
||||
"$OUT/dense-utils" print-poids -r .test-cache/reseau.bin > /dev/null
|
||||
echo -e "\033[32mOK\033[0m"
|
||||
|
||||
echo "Affichage biais"
|
||||
"$OUT/mnist-utils" print-biais -r .test-cache/reseau.bin > /dev/null
|
||||
"$OUT/dense-utils" print-biais -r .test-cache/reseau.bin > /dev/null
|
||||
echo -e "\033[32mOK\033[0m"
|
||||
Loading…
Reference in New Issue
Block a user