action="store_true")
args = parser.parse_args()
load_data = LoadData()
slide_sequences, slide_labels = load_data.get_RNN_data()
network = Network()
if args.train:
network.train_and_save(slide_sequences,
slide_labels,
"CORRECT",
should_save_auc=True)
else:
network.load_results("CORRECT")
if args.conf:
print('DISPLAYING CONFUSION MATRICES:\n')
conf = network.get_confusion_matrices()
for slide_id in range(NUM_SLIDES):
print('Slide ' + str(slide_id + 1) + ':\n', conf[slide_id])
if args.acc:
print('DISPLAYING ACCURACIES:\n')
acc = network.get_accuracies()
for slide_id in range(NUM_SLIDES):
print('Slide ' + str(slide_id + 1) + ':', acc[slide_id], '\n')
print('Average accuracy of slide 2 to 21:', acc[1:].mean())
if args.auc:
section=None)
print('slide_sequences[0][0]: ', slide_sequences[0][0])
print('discard_last_dup_sequences[0][0]: ',
discard_last_dup_sequences[0][0])
print('slide_sequences[9][9]: ', slide_sequences[9][9])
print('discard_last_dup_sequences[9][9]: ',
discard_last_dup_sequences[9][9])
slide_sequences = discard_last_dup_sequences
if args.train:
network.train_and_save(slide_sequences,
slide_selections,
"SELECTION",
should_save_auc=False)
else:
network.load_results("SELECTION")
if args.conf:
print('DISPLAYING CONFUSION MATRICES:\n')
conf = network.get_confusion_matrices()
for slide_id in range(NUM_SLIDES):
print('Slide ' + str(slide_id + 1) + ':\n', conf[slide_id])
if args.acc:
print('DISPLAYING ACCURACIES:\n')
acc = network.get_accuracies()
for slide_id in range(NUM_SLIDES):
print('Slide ' + str(slide_id + 1) + ':', acc[slide_id], '\n')
print('Average accuracy of slide 2 to 21:', acc[1:].mean())
if args.auc:
