告密者的下场(3/4)
inputs, labels = (device), (device)
outputs = net(inputs)
loss = loss_func(outputs, labels)
()
()
()
_, pred = (, dim=1)
acc = ().cpu().sum()
one = (labels)
zero = (labels)
tn = ((labels == zero) * (pred == zero)).sum()
tp = ((labels == one) * (pred == one)).sum()
fp = ((labels == zero) * (pred == one)).sum()
fn = ((labels == one) * (pred == zero)).sum()
train_sum_fn += ()
train_sum_fp += ()
train_sum_tn += ()
train_sum_tp += ()
train_sum_loss += ()
train_sum_correct += ()
train_loss = train_sum_loss * / len(trainDataLoader)
train_correct = train_sum_correct * / len(trainDataLoader) / batch_size
train_precision = train_sum_tp * / (train_sum_fp + train_sum_tp)
train_recall = train_sum_tp * / (train_sum_fn + train_sum_tp)
(“train loss“, train_loss, global_step=epoch)
(“train correct“,
train_correct, global_step=epoch)
(“train precision“,
train_precision, global_step=epoch)
(“train recall“, train_recall, global_step=epoch)
if not (“models_aug_CNN“):
(“models_aug_CNN“)
((), “models_aug_CNN/{}.pth“.format(epoch + 1))
()
sum_loss = 0
sum_correct = 0
test_sum_fp = 0
test_sum_fn = 0
test_sum_tp = 0
test_sum_tn = 0
for i, data in enumerate(testDataLoader):
()
inputs, labels = data
inputs = (1).to()
labels = ()
inputs, labels = (device), (device)
outputs = net(inputs)
loss = loss_func(outputs, labels)
_, pred = (, dim=1)
acc = ().cpu().sum()
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