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super(TrainingDataSet，self).__init__()
self.data_dict_X=X_train
self.data_dict_y=y_train
def__titem__(self，index):
t=self.data_dict_X[index，0:36]
t=torch.tensor(t).vie(6，6)
returnt，self.data_dict_y[index]
def__len__(self):
returnlen(self.data_dict_y)
cssTestDataSet(Dataset):
def__init__(self):
super(TestDataSet，self).__init__()
self.data_dict_X=X_validate
self.data_dict_y=y_validate
def__titem__(self，index):
t=self.data_dict_X[index，0:36]
t=torch.tensor(t).vie(6，6)
returnt，self.data_dict_y[index]
def__len__(self):
returnlen(self.data_dict_y)
defn_cssification():
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=0.001
lr=0.001
=VGGBaseSimpleS2().to(device)
print()
#loss
loss_func=nn.CrossEntropyLoss()
#optimizer
optimizer=torch.optim.Adam(.parameters()，lr=lr)
#optimizer=torch.optim.SGD(.parameters()，lr=lr，momentum=0.9，eight_decay=5e-4)
scheduler=torch.optim.lr_scheduler.StepLR(optimizer，step_size=5，gamma=0.9)
ifnotos.path.exists(“logCNN“):
os.mkdir(“logCNN“)
riter=tensorboardX.Summaryriter(“logCNN“)
forepochinran(epoch_num):
train_sum_loss=0
train_sum_rrect=0
train_sum_fp=0
train_sum_fn=0
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