MNIST for C

This repository contains a header file for loading MNIST dataset in C language.

preparation

download the following data files
- train image : train-images.idx3-ubyte
- train label : train-labels.idx1-ubyte
- test image : t10k-images.idx3-ubyte
- test label : t10k-labels.idx1-ubyte
you can find these files inside /data directory in this repository
they are also available at http://yann.lecun.com/exdb/mnist/
download mnist.h
in mnist.h, set the appropriate data path to the following macros
- #define TRAIN_IMAGE (ex: "./data/train-images.idx3-ubyte")
- #define TRAIN_LABEL (ex: "./data/train-labels.idx1-ubyte")
- #define TEST_IMAGE (ex: "./data/t10k-images.idx3-ubyte")
- #define TEST_LABEL (ex: "./data/t10k-labels.idx1-ubyte")
add #include "mnist.h" in your C code

usage

after calling the following void function,
load_mnist();
you will be able to access the corresponding data through the following array:

train image : train_image[60000][784] (type: double, normalized, flattened)
train label : train_label[60000] (type: int)
test image : test_image[10000][784] (type: double, normalized, flattened)
test label : test_label[10000] (type: int)

functions

void load_mnist ( void )
load mnist data to respective array (as mentioned above)
void print_mnist_pixel ( double data_image[][], int num_data )
print pixel values of all mnist images to stdout
data_image[][]: train_image or test_image
num_data: NUM_TRAIN or NUM_TEST
void print_mnist_label ( double data_label[][], int num_data )
print label of all mnist data to stdout
data_label[][]: train_image or test_image
num_data: NUM_TRAIN or NUM_TEST
void save_mnist_pgm ( double data_image[][SIZE], int index )
save mnist image as pgm file
data_image[][]: train_image or test_image
index: index of data to save (0~59999 for train image, 0~9999 for test image)

example

#include "mnist.h"

int main(void)
{
	load_mnist();

	int i;
	for (i=0; i<784; i++) {
		printf("%1.1f ", test_image[0][i]);
		if ((i+1) % 28 == 0) putchar('\n');
	}
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.7 0.6 0.6 0.2 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.9 1.0 1.0 1.0 1.0 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.2 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.4 0.3 0.4 0.6 0.9 1.0 0.9 1.0 1.0 1.0 1.0 0.9 1.0 1.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.3 0.1 0.3 0.3 0.3 0.2 0.1 0.9 1.0 0.4 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 1.0 0.8 0.1 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.9 1.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 1.0 0.9 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 1.0 1.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 1.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8 1.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 1.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 1.0 0.9 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.9 1.0 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8 1.0 0.9 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.0 1.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.9 1.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 1.0 1.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.9 1.0 1.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 1.0 1.0 0.9 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 1.0 0.8 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
	// 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

	printf("label: %d\n", test_label[0]);
	// label: 7

	return 0;
}

to see an actual runnig code, compile and run example.c

takafumihoriuchi / mnist_for_c Goto Github PK

mnist_for_c's Introduction

MNIST for C

preparation

usage

functions

example

mnist_for_c's People

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mnist_for_c's Issues

System return void image from index 6

Loads only first image

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