diff --git a/abl/models/lenet5.py b/abl/models/lenet5.py
deleted file mode 100644
index 1016d2c..0000000
--- a/abl/models/lenet5.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# coding: utf-8
-#================================================================#
-#   Copyright (C) 2021 Freecss All rights reserved.
-#   
-#   File Name     ：lenet5.py
-#   Author        ：freecss
-#   Email         ：karlfreecss@gmail.com
-#   Created Date  ：2021/03/03
-#   Description   ：
-#
-#================================================================#
-
-import sys
-sys.path.append("..")
-
-import torchvision
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.autograd import Variable
-import torchvision.transforms as transforms
-import numpy as np
-
-from models.basic_model import BasicModel
-import utils.plog as plog
-
-class LeNet5(nn.Module):
-    def __init__(self, num_classes=10, image_size=(28, 28)):
-        super().__init__()
-        self.conv1 = nn.Conv2d(1, 6, 3, padding=1)
-        self.conv2 = nn.Conv2d(6, 16, 3)
-        self.conv3 = nn.Conv2d(16, 16, 3)
-
-        feature_map_size = ((np.array(image_size) // 2 - 2) // 2 - 2)
-        num_features = 16 * feature_map_size[0] * feature_map_size[1]
-
-        self.fc1 = nn.Linear(num_features, 120)
-        self.fc2 = nn.Linear(120, 84)
-        self.fc3 = nn.Linear(84, num_classes)
-
-    def forward(self, x):
-        '''前向传播函数'''
-        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
-        x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))
-        x = F.relu(self.conv3(x))
-        x = x.view(-1, self.num_flat_features(x))
-        #print(x.size())
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        x = self.fc3(x)
-        return x
-
-    def num_flat_features(self, x):
-        size = x.size()[1:] 
-        num_features = 1
-        for s in size:
-            num_features *= s
-        return num_features 
-
-
-class SymbolNet(nn.Module):
-    def __init__(self, num_classes=14):
-        super(SymbolNet, self).__init__()
-        self.conv1 = nn.Conv2d(1, 32, 3, stride = 1, padding = 1)
-        self.conv2 = nn.Conv2d(32, 64, 3, stride = 1, padding = 1)
-        self.dropout1 = nn.Dropout2d(0.25)
-        self.dropout2 = nn.Dropout2d(0.5)
-        self.fc1 = nn.Linear(30976, 128)
-        self.fc2 = nn.Linear(128, num_classes)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = F.relu(x)
-        x = self.conv2(x)
-        x = F.max_pool2d(x, 2)
-        x = self.dropout1(x)
-        x = torch.flatten(x, 1)
-        x = self.fc1(x)
-        x = F.relu(x)
-        x = self.dropout2(x)
-        x = self.fc2(x)
-        return x