def __loadMNISTdatabase(self):
if not self.__mnistTrainingLoaded:
self.UIelements.writeToLog(
'Loading MNIST training database to memory...')
self.UIelements.updateProgressBar(1)
mnistDatabase = mnist.database()
trainingImages, trainingLabels = mnistDatabase.getData(
range(60000), 'training')
self.trainer.setTrainingSet(trainingImages, trainingLabels)
validationImages, validationLabels = mnistDatabase.getData(
range(10000), 'test')
self.trainer.setValidationSet(validationImages, validationLabels)
self.__mnistTrainingLoaded = True
self.UIelements.writeToLog('done.\n\n')
self.UIelements.updateProgressBar(100)
time.sleep(0.2)
self.trainingProgressBar['value'] = 0
else:
self.UIelements.writeToLog(
'MNIST training database already loaded into memory.\n\n')
def __evaluateNetwork(self):
if self.trainer.getNetwork().getStructure() == []:
self.__writeToLog(
'ERROR: No network to evaluate, intialise or load from file.\n'
)
return
self.__writeToLog('Verifying network structure...')
if self.verboseLog.get():
self.__writeToLog(
'\nNetwork structure: ' +
str(self.trainer.getNetwork().getStructure())[1:-1] + '\n')
if self.trainer.getNetwork().getStructure(
)[0] != 784 or self.trainer.getNetwork().getStructure()[-1] != 10:
self.__writeToLog(
'ERROR: Network must have input size of 784 and output size of 10.\n'
)
return
if self.verboseLog.get():
self.__writeToLog('Verifying network structure...')
self.__writeToLog('done.\n\n')
if not self.__mnistTestData:
self.__writeToLog('Loading MNIST testing database to memory...')
self.__mnistTestData = mnist.database(True)
self.__writeToLog('done.\n\n')
else:
self.__writeToLog(
'MNIST testing database already loaded into memory.\n\n')
self.__confusionMatrix = np.zeros((10, 10))
self.__writeToLog(
'Evaluating neural network against MNIST testing database...')
network = self.trainer.getNetwork()
for i in range(10000):
if i % 100 == 0:
self.__updateEvaluateProgressBar(i / 100)
if i % 300 == 0:
self.__drawConfusionMatrix(False)
testImage, actualLabel = self.__mnistTestData.getData(i, 'test')
network.setNeuronActivation(0, range(784), testImage.reshape(784))
network.evaluate()
networkPrediction = np.argmax(
network.getNeuronActivation(-1, range(10)))
self.__confusionMatrix[actualLabel][networkPrediction] += 1
totalCorrectPredictions = 0
for i in range(10):
totalCorrectPredictions += self.__confusionMatrix[i][i]
networkAccuracy = totalCorrectPredictions / 10000
self.__writeToLog('done.\n\n')
self.__updateEvaluateProgressBar(0)
self.performanceLabelContent.set(
'Overall network accuracy: {:.2%}'.format(networkAccuracy))
self.__drawConfusionMatrix()
def loadMNIST(self):
self.mnistData = mnist.database()
self.__mnistLoaded = True
# -*- coding: utf-8 -*-
import tkinter as tk
from tkinter import filedialog
from tkinter import messagebox
import math
import mnist
import neuralnetwork as nn
from struct import unpack
import numpy as np
mnistTestData = mnist.database(loadTestOnly=True)
class testingGUI:
def __init__(self, master):
self.master = master
master.title('MNIST Neural Network Test Interface')
# drawing canvas area
pxSize = 15
self.pixelSize = pxSize
self.drawingCanvas = tk.Canvas(width=pxSize * 28,
height=pxSize * 28,
bg='#000044')
self.drawingCanvas.grid(row=0, column=0)
self.drawingCanvas.bind('<B1-Motion>', self.__paintOnCanvas)
self.drawingCanvas.bind('<Button-1>', self.__paintOnCanvas)
self.__addDrawingBackground()
self.processDrawingButton = tk.Button(text='Process Digit',