for file in os.listdir(trainingCharsetsDir + '/' + str(trainingSet)):
charImg = cv.imread(
trainingCharsetsDir + '/' + str(trainingSet) + '/' + file,
cv.IMREAD_GRAYSCALE)
normalize_array(charImg, 255, 0)
# feedforward
#--------------------------- 1st ---------------------------------
inputLayerOutput = np.zeros(inputNeuronCount)
inputLayerOutput[0] = bias1
index = 1
for item in charImg.flat:
inputLayerOutput[index] = item
index += 1
layer1.input_signals = inputLayerOutput
#--------------------------- 1st ---------------------------------
#--------------------------- 2nd ---------------------------------
layer1Output[0] = bias1
index = 1
for item in layer1.calculateOutputSignals():
layer1Output[index] = item
index += 1
layer2.input_signals = layer1Output
#--------------------------- 2nd ---------------------------------
#------------------------- output --------------------------------
layer2Output = layer2.calculateOutputSignals()
#------------------------- output --------------------------------
