2021-06-20 16:56:42 +02:00
|
|
|
from emnist import extract_test_samples
|
|
|
|
from emnist import extract_training_samples
|
|
|
|
import numpy as np
|
|
|
|
import scipy.special
|
|
|
|
import glob
|
|
|
|
import imageio
|
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
""" pobranie obrazów cyfr i liter z biblioteki """
|
2021-06-20 16:56:42 +02:00
|
|
|
dig_train_images, dig_train_labels = extract_training_samples('digits')
|
|
|
|
dig_test_images, dig_test_labels = extract_test_samples('digits')
|
|
|
|
let_train_images, let_train_labels = extract_training_samples('letters')
|
|
|
|
let_test_images, let_test_labels = extract_test_samples('letters')
|
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
""" przekształcenie tablic """
|
2021-06-20 16:56:42 +02:00
|
|
|
dig_train_images = dig_train_images.reshape(len(dig_train_images),28*28)
|
|
|
|
dig_test_images = dig_test_images.reshape(len(dig_test_images),28*28)
|
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
let_train_images = let_train_images.reshape(len(let_train_images),28*28)
|
|
|
|
let_test_images = let_test_images.reshape(len(let_test_images),28*28)
|
2021-06-20 16:56:42 +02:00
|
|
|
|
|
|
|
|
|
|
|
class NeuralNetwork:
|
2021-06-21 01:43:32 +02:00
|
|
|
""" inicjalizacja sieci neuronowej """
|
2021-06-20 17:59:01 +02:00
|
|
|
def __init__(self, inputNodes, hiddenNodes, outputNodes, learningGrade, fileWeight, fileHidden):
|
2021-06-20 16:56:42 +02:00
|
|
|
self.inodes = inputNodes
|
|
|
|
self.hnodes = hiddenNodes
|
|
|
|
self.onodes = outputNodes
|
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
""" używane przy uczeniu sieci """
|
|
|
|
self.weights = (np.random.rand(self.hnodes, self.inodes) - 0.5)
|
|
|
|
self.hidden = (np.random.rand(self.onodes, self.hnodes) - 0.5)
|
|
|
|
""" używane przy pobieraniu danych o nauczonej sieci, z pliku """
|
|
|
|
# self.weights = np.load(fileWeight)
|
|
|
|
# self.hidden = np.load(fileHidden)
|
2021-06-20 16:56:42 +02:00
|
|
|
|
|
|
|
self.lr = learningGrade
|
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
""" funkcja aktywacji """
|
2021-06-20 16:56:42 +02:00
|
|
|
self.activationFunction = lambda x: scipy.special.expit(x)
|
|
|
|
|
|
|
|
pass
|
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
"""trening sieci neuronowej"""
|
2021-06-20 16:56:42 +02:00
|
|
|
def train(self, inputsList, targetsList):
|
2021-06-21 01:43:32 +02:00
|
|
|
""" konwersja list na tablice 2d """
|
2021-06-20 16:56:42 +02:00
|
|
|
inputs = np.array(inputsList,ndmin=2).T
|
|
|
|
targets = np.array(targetsList,ndmin=2).T
|
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
""" forward pass """
|
|
|
|
hiddenInputs = np.dot(self.weights, inputs) # input -> hidden layer
|
2021-06-20 16:56:42 +02:00
|
|
|
hiddenOutputs = self.activationFunction(hiddenInputs)
|
|
|
|
|
2021-06-20 22:27:23 +02:00
|
|
|
finalInputs = np.dot(self.hidden, hiddenOutputs)
|
2021-06-20 16:56:42 +02:00
|
|
|
finalOutputs = self.activationFunction(finalInputs)
|
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
""" backward pass """
|
2021-06-20 16:56:42 +02:00
|
|
|
outputErrors = targets - finalOutputs
|
|
|
|
x =self.weights.T
|
|
|
|
hiddenErrors = np.dot(self.hidden.T, outputErrors)
|
|
|
|
|
|
|
|
self.hidden += self.lr * np.dot((outputErrors * finalOutputs * (1.0 - finalOutputs)) , np.transpose(hiddenOutputs))
|
|
|
|
self.weights += self.lr * np.dot((hiddenErrors * hiddenOutputs * (1.0 - hiddenOutputs)) , np.transpose(inputs))
|
|
|
|
|
|
|
|
pass
|
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
""" zapisywanie wytrenowanej sieci do pliku """
|
2021-06-20 17:59:01 +02:00
|
|
|
def saveTraining(self, fileWeight, fileHidden):
|
|
|
|
np.save(fileWeight, self.weights)
|
|
|
|
np.save(fileHidden, self.hidden)
|
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
""" wykorzystanie sieci """
|
2021-06-20 16:56:42 +02:00
|
|
|
def query(self, inputsList):
|
2021-06-21 01:43:32 +02:00
|
|
|
""" konwersja listy na tablicę 2d """
|
2021-06-20 16:56:42 +02:00
|
|
|
inputs = np.array(inputsList, ndmin=2).T
|
|
|
|
|
|
|
|
hiddenInputs = np.dot(self.weights, inputs)
|
|
|
|
hiddenOutputs = self.activationFunction(hiddenInputs)
|
|
|
|
|
|
|
|
finalInputs = np.dot(self.hidden, hiddenOutputs)
|
|
|
|
finalOutputs = self.activationFunction(finalInputs)
|
|
|
|
|
|
|
|
return finalOutputs
|
|
|
|
|
2021-06-20 17:59:01 +02:00
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
""" tablice sieci neuronowych """
|
2021-06-20 17:59:01 +02:00
|
|
|
digitNetwork = NeuralNetwork(inputNodes=784, hiddenNodes=200, outputNodes=10, learningGrade=0.1, fileWeight="Dweights.npy", fileHidden="Dhidden.npy")
|
2021-06-21 01:43:32 +02:00
|
|
|
letterNetwork = NeuralNetwork(inputNodes=784, hiddenNodes=200, outputNodes=27, learningGrade=0.1, fileWeight="Lweights.npy", fileHidden="Lhidden.npy")
|
|
|
|
|
2021-06-20 16:56:42 +02:00
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
|
|
|
|
# trainNetwork(digitNetwork, "Dweights_test.npy", "Dhidden_test.npy", let_train_images, let_train_labels)
|
2021-06-20 22:27:23 +02:00
|
|
|
def trainNetwork(n, fWeight, fHidden, trainingSamples, trainingLabels):
|
2021-06-20 16:56:42 +02:00
|
|
|
epochs = 10
|
2021-06-21 01:43:32 +02:00
|
|
|
outputNodes = 27
|
2021-06-20 16:56:42 +02:00
|
|
|
for e in range(epochs):
|
|
|
|
m=0
|
|
|
|
print('Epoch', e+1)
|
|
|
|
|
2021-06-20 17:59:01 +02:00
|
|
|
for record in trainingSamples:
|
2021-06-21 01:43:32 +02:00
|
|
|
""" zmiana wartości przedziału z [0,255] na [0,1] """
|
2021-06-20 16:56:42 +02:00
|
|
|
inputs = (np.asfarray(record[0:])/255 * 0.99) + 0.01
|
|
|
|
|
|
|
|
targets = np.zeros(outputNodes) + 0.01
|
2021-06-20 22:27:23 +02:00
|
|
|
targets[trainingLabels[m]] = 0.99
|
2021-06-20 16:56:42 +02:00
|
|
|
n.train(inputs,targets)
|
|
|
|
|
|
|
|
m+=1
|
|
|
|
pass
|
|
|
|
pass
|
2021-06-20 17:59:01 +02:00
|
|
|
n.saveTraining(fileWeight=fWeight, fileHidden=fHidden)
|
2021-06-20 16:56:42 +02:00
|
|
|
|
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
def testing(n, testingSamples, testingLabels):
|
|
|
|
scorecard = []
|
|
|
|
k = 0
|
|
|
|
for record in testingSamples:
|
|
|
|
inputs = (np.asfarray(record[0:])/255 * 0.99) + 0.01
|
|
|
|
correctLabels = testingLabels[k]
|
|
|
|
|
|
|
|
outputs = n.query(inputs)
|
|
|
|
label = np.argmax(outputs)
|
|
|
|
|
|
|
|
if(label == correctLabels):
|
|
|
|
scorecard.append(1)
|
|
|
|
else:
|
|
|
|
scorecard.append(0)
|
|
|
|
k+=1
|
|
|
|
|
|
|
|
scorecardArray = np.asfarray(scorecard)
|
|
|
|
print('Performance', scorecardArray.sum() / scorecardArray.size)
|
|
|
|
|
|
|
|
testing(digitNetwork,dig_test_images,dig_test_labels)
|
2021-06-21 11:16:59 +02:00
|
|
|
testing(letterNetwork,let_test_images,let_test_labels)
|
2021-06-21 10:57:23 +02:00
|
|
|
|
|
|
|
li = []
|
|
|
|
ourOwnDataset = []
|
2021-06-21 02:49:08 +02:00
|
|
|
record_cache = None
|
|
|
|
def testCase(inputWord):
|
|
|
|
len = len(inputWord)
|
2021-06-21 10:57:23 +02:00
|
|
|
|
2021-06-21 02:49:08 +02:00
|
|
|
word = ""
|
2021-06-21 10:57:23 +02:00
|
|
|
for i in range(0,len-2):
|
|
|
|
imgArray = imageio.imread(imageFileName, as_gray=True)
|
2021-06-21 02:49:08 +02:00
|
|
|
imgData = 255 - imgArray.reshape(784)
|
|
|
|
imgData = (imgData/255 * 0.99) + 0.01
|
2021-06-21 10:57:23 +02:00
|
|
|
#inputWord[i]
|
|
|
|
word = word + recognizeLet(letterNetwork ,imgData)
|
2021-06-21 11:16:59 +02:00
|
|
|
|
|
|
|
i=len-2
|
|
|
|
for i in range(i,len):
|
|
|
|
imgArray = imageio.imread(imageFileName, as_gray=True)
|
|
|
|
imgData = 255 - imgArray.reshape(784)
|
|
|
|
imgData = (imgData/255 * 0.99) + 0.01
|
|
|
|
#inputWord[i]
|
|
|
|
word = word + recognizeNum(digitNetwork, imgData)
|
2021-06-21 02:49:08 +02:00
|
|
|
|
|
|
|
|
2021-06-21 10:57:23 +02:00
|
|
|
#assert record_cache.shape == ourOwnDataset[0].shape
|
|
|
|
#labelInput = np.asfarray(li)
|
2021-06-21 02:49:08 +02:00
|
|
|
#print(labelInput)
|
|
|
|
print('slowo: ', word)
|
|
|
|
pass
|
|
|
|
|
2021-06-21 10:57:23 +02:00
|
|
|
|
|
|
|
|
|
|
|
def recognizeLet(n,imgData):
|
2021-06-21 02:49:08 +02:00
|
|
|
letters=['','a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']
|
2021-06-21 10:57:23 +02:00
|
|
|
#record = np.append(label,imgData)
|
|
|
|
outputs = n.query(imgData)
|
2021-06-21 02:49:08 +02:00
|
|
|
label = np.argmax(outputs)
|
|
|
|
return letters[int(label)]
|
2021-06-21 01:43:32 +02:00
|
|
|
|
2021-06-21 10:57:23 +02:00
|
|
|
def recognizeNum(n, imgData):
|
2021-06-21 02:49:08 +02:00
|
|
|
pass
|
2021-06-21 01:43:32 +02:00
|
|
|
|
2021-06-21 10:57:23 +02:00
|
|
|
#record = np.append(label,imgData)
|
|
|
|
outputs = n.query(imgData)
|
2021-06-21 02:49:08 +02:00
|
|
|
#print('Record: ',record)
|
2021-06-21 10:57:23 +02:00
|
|
|
#ourOwnDataset.append(record)
|
|
|
|
#if record_cache is None:
|
|
|
|
# record_cache = record
|
2021-06-21 02:49:08 +02:00
|
|
|
#print(ood[0])
|
2021-06-21 10:57:23 +02:00
|
|
|
#li.append(label)
|
|
|
|
label = np.argmax(outputs)
|
|
|
|
return str(label)
|
2021-06-21 02:49:08 +02:00
|
|
|
pass
|
2021-06-21 01:43:32 +02:00
|
|
|
|
|
|
|
|
2021-06-21 02:49:08 +02:00
|
|
|
"""
|
2021-06-21 01:43:32 +02:00
|
|
|
li = []
|
2021-06-21 02:49:08 +02:00
|
|
|
#ourOwnDataset = np.asfarray(ood)
|
2021-06-21 01:43:32 +02:00
|
|
|
ourOwnDataset = []
|
|
|
|
|
|
|
|
record_cache = None
|
2021-06-21 02:49:08 +02:00
|
|
|
for imageFileName in glob.glob('cyfry/?.png'):
|
2021-06-21 01:43:32 +02:00
|
|
|
label = int(imageFileName[-5:-4])
|
|
|
|
print('loading...', imageFileName)
|
|
|
|
|
|
|
|
imgArray = imageio.imread(imageFileName, as_gray=True)
|
|
|
|
#print(' imgArray: ', imgArray)
|
|
|
|
imgData = 255 - imgArray.reshape(784)
|
|
|
|
#print('imgData1: ',imgData)
|
|
|
|
imgData = (imgData/255 * 0.99) + 0.01
|
|
|
|
#print('imgData2: ',imgData)
|
|
|
|
|
|
|
|
#print(np.min(imgData))
|
|
|
|
#print(np.max(imgData))
|
|
|
|
|
|
|
|
record = np.append(label,imgData)
|
|
|
|
#print('Record: ',record)
|
|
|
|
ourOwnDataset.append(record)
|
|
|
|
if record_cache is None:
|
|
|
|
record_cache = record
|
|
|
|
#print(ood[0])
|
|
|
|
li.append(label)
|
|
|
|
pass
|
|
|
|
|
|
|
|
assert record_cache.shape == ourOwnDataset[0].shape
|
|
|
|
labelInput = np.asfarray(li)
|
|
|
|
#print(labelInput)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
word = ""
|
2021-06-21 02:49:08 +02:00
|
|
|
for item in range(0,9):
|
2021-06-21 01:43:32 +02:00
|
|
|
correctLabels = labelInput[item]
|
2021-06-21 02:49:08 +02:00
|
|
|
outputs = n.query(ourOwnDataset[item][1:])
|
2021-06-21 01:43:32 +02:00
|
|
|
print(outputs)
|
|
|
|
|
|
|
|
label = np.argmax(outputs)
|
|
|
|
#print('Network says: ', label)
|
|
|
|
#labelString = np.array_str(label)
|
|
|
|
word = word + str(label)
|
2021-06-20 16:56:42 +02:00
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
print('slowo: ', word)
|
2021-06-21 02:49:08 +02:00
|
|
|
|
|
|
|
"""
|
|
|
|
|
2021-06-20 17:59:01 +02:00
|
|
|
|
2021-06-20 16:56:42 +02:00
|
|
|
|
|
|
|
|
2021-06-21 01:43:32 +02:00
|
|
|
##################################### URUCHOMIENIE TRENINGU
|
2021-06-21 11:16:59 +02:00
|
|
|
#trainNetwork(letterNetwork, "Lweights_test.npy", "Lhidden_test.npy", let_train_images, let_train_labels)
|
2021-06-21 01:43:32 +02:00
|
|
|
# trainNetwork(digitNetwork, "Dweights_test.npy", "Dhidden_test.npy", let_train_images, let_train_labels)
|