告密者的下场(2/4)
= X_train
= y_train
def __getitem__(self, index):
t = [index, 0:36]
t = (t).view(6, 6)
return t, [index]
def __len__(self):
return len()
class TestDataSet(Dataset):
def __init__(self):
super(TestDataSet, self).__init__()
= X_validate
= y_validate
def __getitem__(self, index):
t = [index, 0:36]
t = (t).view(6, 6)
return t, [index]
def __len__(self):
return len()
def cnn_classification():
batch_size = 256
trainDataLoader = DataLoader(TrainingDataSet(), batch_size=batch_size, shuffle=False)
testDataLoader = DataLoader(TestDataSet(), batch_size=batch_size, shuffle=False)
epoch_num = 200
#lr =
lr =
net = VGGBaseSimpleS2().to(device)
print(net)
# loss
loss_func = ()
# optimizer
optimizer = ((), lr=lr)
# optimizer = ((), lr=lr, momentum=, weight_decay=5e-4)
scheduler = .StepLR(optimizer, step_size=5, gamma=)
if not (“logCNN“):
(“logCNN“)
writer = (“logCNN“)
for epoch in range(epoch_num):
train_sum_loss = 0
train_sum_correct = 0
train_sum_fp = 0
train_sum_fn = 0
train_sum_tp = 0
train_sum_tn = 0
for i, data in enumerate(trainDataLoader):
()
inputs, labels = data
inputs = (1).to()
labels = ()
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