Deep Neural Network for MNIST Handwriting Recognition  1.0
Deep Neural Network for MNIST Handwriting Recognition
Functions
mnist-stats.c File Reference

Utitlies for displaying details of processing the MNIST data set in the terminal screen. More...

#include <string.h>
#include "screen.h"
#include "mnist-utils.h"
#include "mnist-stats.h"

Functions

void displayImageFrame (int row, int col)
 Outputs a 28x28 text frame at a defined screen position. More...
 
void displayImage (MNIST_Image *img, int lbl, int cls, int row, int col)
 Outputs a 28x28 MNIST image as charachters ("."s and "X"s) More...
 
void displayTrainingProgress (int imgCount, int errCount)
 Outputs progress to the console while processing MNIST training images. More...
 
void displayTestingProgress (int imgCount, int errCount)
 Outputs progress to the console while processing MNIST testing images. More...
 

Detailed Description

Utitlies for displaying details of processing the MNIST data set in the terminal screen.

Author
Matt Lind
Date
July 2015

Function Documentation

void displayImage ( MNIST_Image img,
int  lbl,
int  cls,
int  row,
int  col 
)

Outputs a 28x28 MNIST image as charachters ("."s and "X"s)

Parameters
imgPointer to a MNIST image
lblTarget classification
clsActual classification
rowRow on screen (x-coordinate) where to display the image
colColumn on screen (y-coordinate) where to display the image
void displayImageFrame ( int  row,
int  col 
)

Outputs a 28x28 text frame at a defined screen position.

Parameters
rowRow of terminal screen
colColumn of terminal screen
void displayTestingProgress ( int  imgCount,
int  errCount 
)

Outputs progress to the console while processing MNIST testing images.

Parameters
imgCountNumber of images already read from the MNIST file
errCountNumber of errors (images incorrectly classified)
void displayTrainingProgress ( int  imgCount,
int  errCount 
)

Outputs progress to the console while processing MNIST training images.

Parameters
imgCountNumber of images already read from the MNIST file
errCountNumber of errors (images incorrectly classified)