testCase neural-network
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@ -131,43 +131,52 @@ def testing(n, testingSamples, testingLabels):
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testing(digitNetwork,dig_test_images,dig_test_labels)
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li = []
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ourOwnDataset = []
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record_cache = None
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def testCase(inputWord):
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len = len(inputWord)
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li = []
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ourOwnDataset = []
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word = ""
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for i in range(0,len-2):
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imgArray = imageio.imread(imageFileName, as_gray=True)
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for i in len-2:
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imgData = 255 - imgArray.reshape(784)
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imgData = (imgData/255 * 0.99) + 0.01
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word = word + recognizeLet(inputWord[i],imgData)
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word = word + recognizeNum[inputWord[-2]]
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word = word + recognizeNum[inputWord[-1]]
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#inputWord[i]
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word = word + recognizeLet(letterNetwork ,imgData)
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word = word + recognizeNum(digitNetwork, inputWord[-2])
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word = word + recognizeNum(digitNetwork ,inputWord[-1])
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assert record_cache.shape == ourOwnDataset[0].shape
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labelInput = np.asfarray(li)
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#assert record_cache.shape == ourOwnDataset[0].shape
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#labelInput = np.asfarray(li)
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#print(labelInput)
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print('slowo: ', word)
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pass
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def recognizeLet(let,imgData):
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def recognizeLet(n,imgData):
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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']
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record = np.append(label,imgData)
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#record = np.append(label,imgData)
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outputs = n.query(imgData)
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label = np.argmax(outputs)
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return letters[int(label)]
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def recognizeNum():
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def recognizeNum(n, imgData):
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pass
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record = np.append(label,imgData)
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#record = np.append(label,imgData)
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outputs = n.query(imgData)
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#print('Record: ',record)
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ourOwnDataset.append(record)
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if record_cache is None:
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record_cache = record
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#ourOwnDataset.append(record)
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#if record_cache is None:
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# record_cache = record
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#print(ood[0])
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li.append(label)
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#li.append(label)
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label = np.argmax(outputs)
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return str(label)
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pass
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