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testCase neural-network

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s452693 2021-06-21 10:57:23 +02:00
parent 1f68e0d82d
commit 0290293ca0
1 changed files with 26 additions and 17 deletions
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43
src/neural_network.py
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@ -131,43 +131,52 @@ def testing(n, testingSamples, testingLabels):
testing(digitNetwork,dig_test_images,dig_test_labels) testing(digitNetwork,dig_test_images,dig_test_labels)
li = []
ourOwnDataset = []
record_cache = None record_cache = None
def testCase(inputWord): def testCase(inputWord):
len = len(inputWord) len = len(inputWord)
li = []
ourOwnDataset = []
word = "" word = ""
imgArray = imageio.imread(imageFileName, as_gray=True) for i in range(0,len-2):
for i in len-2: imgArray = imageio.imread(imageFileName, as_gray=True)
imgData = 255 - imgArray.reshape(784) imgData = 255 - imgArray.reshape(784)
imgData = (imgData/255 * 0.99) + 0.01 imgData = (imgData/255 * 0.99) + 0.01
word = word + recognizeLet(inputWord[i],imgData) #inputWord[i]
word = word + recognizeNum[inputWord[-2]] word = word + recognizeLet(letterNetwork ,imgData)
word = word + recognizeNum[inputWord[-1]] word = word + recognizeNum(digitNetwork, inputWord[-2])
word = word + recognizeNum(digitNetwork ,inputWord[-1])
assert record_cache.shape == ourOwnDataset[0].shape #assert record_cache.shape == ourOwnDataset[0].shape
labelInput = np.asfarray(li) #labelInput = np.asfarray(li)
#print(labelInput) #print(labelInput)
print('slowo: ', word) print('slowo: ', word)
pass pass
def recognizeLet(let,imgData):
def recognizeLet(n,imgData):
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'] 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']
record = np.append(label,imgData) #record = np.append(label,imgData)
outputs = n.query(imgData)
label = np.argmax(outputs) label = np.argmax(outputs)
return letters[int(label)] return letters[int(label)]
def recognizeNum(): def recognizeNum(n, imgData):
pass pass
record = np.append(label,imgData) #record = np.append(label,imgData)
outputs = n.query(imgData)
#print('Record: ',record) #print('Record: ',record)
ourOwnDataset.append(record) #ourOwnDataset.append(record)
if record_cache is None: #if record_cache is None:
record_cache = record # record_cache = record
#print(ood[0]) #print(ood[0])
li.append(label) #li.append(label)
label = np.argmax(outputs)
return str(label)
pass pass