testCase neural-network

src/neural_network.py

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