def parseCOMM(raw):
# TODO
enc = raw[0:1]
lang = raw[1:4]
desc = decode(enc, raw[4:])
i = raw[4:].index(terminator(enc)) + len(terminator(enc))
cont = decode(enc, raw[i:])
return {"raw": raw, "language": lang, "description": desc, "content": cont}
def codes_to_str(self, codes):
return decode(codes)
def code_to_char(self, code):
return decode([code])
dl.test_label_data[i] == np.amax(dl.test_label_data[i]))
max_index_prediction = np.where(
predictions[i] == np.amax(predictions[i]))
counter = 0
if (max_index_label[0][0] == max_index_prediction[0][0]):
if (accuracy_counter == 0):
counter = counter + 1
color = green_color
else:
color = red_color
cell = sheet.cell(column=1,
row=row_num,
value=const.decode(max_index_label[0][0]))
cell.fill = color
cell.border = thin_border
cell = sheet.cell(column=2,
row=row_num,
value=const.decode(max_index_prediction[0][0]))
cell.fill = color
cell.border = thin_border
for j in range(len(layer_model_prediction[i])):
cell = sheet.cell(column=3 + j,
row=row_num,
value=layer_model_prediction[i][j])
if (layer_model_prediction[i][j] > const.ACCTIVATION_BOUNDARY):
if (max_index_label[0][0] == max_index_prediction[0][0]):
color = green_color_strong
else:
def parseT000(raw):
# Text encoding $xx
# Information <text string according to encoding>
enc = raw[0]
dec = decode(enc, raw[1:])
return {"content": dec}
