更新代码

X1.py

@@ -18,6 +18,253 @@
 SetParaFunc为给基本操作函数赋参数值的函数；ParaString 为同类不同图元的参数，ObjX, 
 ObjY为对象在仿真输出屏幕中的位置（示例中未给出，由图形界面中的位置产生）。
 '''
+class ModelObj: # 网络对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        self.ObjID = ObjID  # 图元号
+        self.ObjType = ObjType  # 图元类别
+        self.ObjLable = ObjLable  # 对象标签
+        self.ParaString = ParaString  # 参数字符串
+        self.ObjX = ObjX  # 对象位置x坐标
+        self.ObjY = ObjY  # 对象位置y坐标
+
+class Data_Class(ModelObj):  # 数据集网络对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.LoadData = self.load_data  # 基本操作函数
+        self.SetDataPara = self.set_data_para  # 参数设置函数
+
+    def load_data(self, DataPara):
+        pass  # X3具体实现
+
+    def set_data_para(self):
+        pass  # X3具体实现
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.LoadData,
+                  self.SetDataPara, self.ParaString, self.ObjX, self.ObjY]
+        return result
+# if __name__ == '__main__':
+#     DataSet = Data_Class("DataSet1", 1, "数据集1", ".", 120, 330)
+#     print(DataSet)
+class Conv_Class(ModelObj):  # 卷积对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.ConvProc = self.conv_proc  # 基本操作函数
+        self.SetConvPara = self.setconv_para  # 参数设置函数
+
+    def conv_proc(self, image, ConvPara):
+        pass  # X3具体实现
+
+    def setconv_para(self):
+        pass  # X3具体实现
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.ConvProc, self.SetConvPara, self.ParaString, self.ObjX,
+                  self.ObjY]
+        return result
+
+
+class Pool_Class(ModelObj):  # 池化对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.MaxPoolProc = self.pool_proc  # 基本操作函数
+        self.SetPollPara = self.setpool_para  # 参数设置函数
+
+    def pool_proc(self, image, PoolPara):
+        pass  # X3具体实现
+
+    def setpool_para(self):
+        pass  # X3具体实现
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.MaxPoolProc, self.SetPollPara, self.ParaString, self.ObjX,
+                  self.ObjY]
+        return result
+class FullConn_Class(ModelObj):  # 全连接对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.FullConnProc = self.fullconn_proc  # 基本操作函数
+        self.SetFullConnPara = self.setfullconn_para  # 参数设置函数
+
+    def fullconn_proc(self, inputdata, FullConnPara):
+        pass  # X3具体实现
+
+    def setfullconn_para(self, data):
+        pass  # X3具体实现
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.FullConnProc, self.SetFullConnPara, self.ParaString, self.ObjX,
+                  self.ObjY]
+        return result
+
+class Nonline_Class(ModelObj):  # 非线性对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.NonlinearProc = self.nonlinear_proc  # 基本操作函数
+        self.SetNonLPara = self.setnonl_para  # 参数设置函数
+
+    def nonlinear_proc(self, inputdata, NonLPara):
+        pass  # X3具体实现
+
+    def setnonl_para(self):
+        pass  # X3具体实现
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.NonlinearProc, self.SetNonLPara, self.ParaString, self.ObjX,
+                  self.ObjY]
+        return result
+
+class Classifier_Class(ModelObj):  # 分类对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.ClassifierProc = self.classifier_proc  # 基本操作函数
+        self.SetClassifyPara = self.setclassify_para  # 参数设置函数
+
+    def classifier_proc(self, inputdata, ClassifyPara):
+        pass  # X3具体实现
+
+    def setclassify_para(self):
+        pass  # X3具体实现
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.ClassifierProc, self.SetClassifyPara, self.ParaString, self.ObjX,
+                  self.ObjY]
+        return result
+
+class Error_Class(ModelObj):  # 误差计算对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.ErrorProc = self.error_proc  # 基本操作函数
+        self.SetErrorPara = self.seterror_para  # 参数设置函数
+
+    def error_proc(self, input, label, ErrorPara):
+        pass  # X3具体实现
+
+    def seterror_para(self):
+        pass  # X3具体实现
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.ErrorProc, self.SetErrorPara, self.ParaString, self.ObjX,
+                  self.ObjY]
+        return result
+
+class AjConv_Class(ModelObj):  # 卷积调整对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.AjConvProc = self.ajconv_proc  # 基本操作函数
+        self.SetAjConvPara = self.setajconv_para  # 参数设置函数
+
+    def ajconv_proc(self, input, AjConvPara):
+        pass  # X3具体实现
+
+    def setajconv_para(self):
+        pass  # X3具体实现
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.AjConvProc, self.SetAjConvPara, self.ParaString, self.ObjX,
+                  self.ObjY]
+        return result
+
+class AjFullconn_Class(ModelObj):  # 全连接调整对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.AjFullconnProc = self.ajfullconn_proc  # 基本操作函数
+        self.SetAjFCPara = self.setajfc_para  # 参数设置函数
+
+    def ajfullconn_proc(self):
+        pass  # X3具体实现
+
+    def setajfc_para(self, AjFCPara):
+        print(AjFCPara)  # X3具体实现
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.AjFullconnProc, self.SetAjFCPara, self.ParaString, self.ObjX,
+                  self.ObjY]
+        return result
+# AjFullconn = AjFullconn_Class("AjFullconn", 9,
+#                               "全连接调整1",  [], 510, 120)
+# AjFullconn.SetAjFCPara('rrr')
+DataSet = Data_Class("DataSet1", 1, "数据集1", [], 120, 330).output()
+Conv = Conv_Class("Conv1", 2, "卷积1", [], 250, 330).output()
+Pool = Pool_Class("Pool1", 3, "最大池化1", [], 380, 330).output()
+FullConn = FullConn_Class("FullConn1", 4, "全连接1", [], 510, 330).output()
+Nonline = Nonline_Class("Nonline1", 5, "非线性函数1", [], 640, 330).output()
+Classifier = Classifier_Class("Classifier1", 6, "分类1", [], 780, 330).output()
+Error = Error_Class("Error1", 7, "误差计算1", [], 710, 124).output()
+AjConv = AjConv_Class("AjConv1", 8, "卷积调整1", [], 250, 70).output()
+AjFullconn = AjFullconn_Class("AjFullconn1", 9,
+                              "全连接调整1",  [], 510, 120).output()
+AllModelObj = [DataSet, Conv, Pool, FullConn, Nonline, Classifier, Error, AjConv, AjFullconn]
+# AjFullconn.SetAjFCPara(7)
+# AjFullconn.SetAjFCPara(7)
+# print(AllModelObj)
+# 定义网络连接对象类
+class ModelConn:
+    def __init__(self, ConnObjID, ConnType, NobjS, NobjE):
+        self.ConnObjID = ConnObjID  # 连接线编号
+        self.ConnType = ConnType  # 连接线类别
+        self.NobjS = NobjS  # 源图元对象
+        self.NobjE = NobjE  # 目标图元对象
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ConnObjID, self.ConnType, self.NobjS, self.NobjE]
+        return result
+# if __name__ == '__main__':
+#     ···AllModelObj方法继承
+#     # 创建连接对象实例
+#     Line1 = ModelConn(1, 1, DataSet.ObjID, Conv.ObjID)
+#     Line2 = ModelConn(2, 1, Conv.ObjID, Pool.ObjID)
+#     Line3 = ModelConn(3, 1, Pool.ObjID, FullConn.ObjID)
+#     Line4 = ModelConn(4, 1, FullConn.ObjID, Nonline.ObjID)
+#     Line5 = ModelConn(5, 1, Nonline.ObjID, Classifier.ObjID)
+#     Line6 = ModelConn(6, 1, Classifier.ObjID, Error.ObjID)
+#     Line7 = ModelConn(7, 2, Error.ObjID, AjFullconn.ObjID)
+#     Line8 = ModelConn(8, 2, Error.ObjID, AjConv.ObjID)
+#     Line9 = ModelConn(9, 2, AjFullconn.ObjID, FullConn.ObjID)
+#     Line10 = ModelConn(10, 2, AjConv.ObjID, Conv.ObjID)
+#
+#     # 网络连接对象总表
+#     AllModelConn = [[Line1], [Line2], [Line3], [Line4],
+#                     [Line5], [Line6], [Line7], [Line8],
+#                     [Line9], [Line10]]
+#     print(AllModelConn)
+# X16.1赋值
+
+AllModelObj = [
+    ['DataSet1', 1, '数据集1', 'LoadData',
+        'SetDataPara', [], 120, 330],
+    ['Conv1', 2, '卷积1', 'ConvProc',
+        'SetConvPara', [], 250, 330],
+    ['Pool1', 3, '最大池化1', 'MaxPoolProc',
+        'SetPollPara', [], 380, 330],
+    ['FullConn1',4,'全连接1','FullConnProc',
+        'SetFullConnPara', [], 510,330],
+    ['Nonline1',5,'非线性函数1', 'NonlinearProc',
+        'SetNonLPara', [], 640, 330],
+    ['Classifier1',6,'分类1','ClassifierProc',
+         'SetClassifyPara',[],780,330],
+    ['Error1', 7, '误差计算1', 'ErrorProc',
+      'SetErrorPara', [], 710, 124],
+    ['AjConv1', 8, '卷积调整1', 'AjConvProc',
+         'SetAjConvPara', [], 250, 70],
+    ['AjFullconn1',9,'全连接调整1','AjFullconnProc',
+         'SetAjFCPara',[],510,120]]
+AllModelConn = [
+[1, 1, 'DataSet1', 'Conv1'], [2, 1, 'Conv1', 'Pool1'],
+[3, 1, 'Pool1', 'FullConn1'], [4, 1, 'FullConn1', 'Nonline1'],
+[5, 1, 'Nonline1', 'Classifier1'], [6, 1, 'Classifier1', 'Error1'],
+[7, 2, 'Error1', 'AjFullconn1'], [8, 2, 'Error1', 'AjConv1'],
+[9, 2, 'AjFullconn1', 'FullConn1'], [10, 2, 'AjConv1', 'Conv1']]
 
 # 定义一个函数，根据给定的参数生成一个AllModelObj对象
 def create_almodelobj():
@@ -43,6 +290,7 @@ def create_almodelobj():
     all_model.append(obj)
   return all_model
 
+
 # if __name__ == '__main__':
 #     all_model  = create_almodelobj()
 #     print(all_model)
@@ -68,6 +316,7 @@ def create_allmodelconn():
     for i in range(len(conn_obj_id)) # 循环遍历连接线编号的长度
   ]
   return all_conn
+# print(create_allmodelconn())
 
 # if __name__ == '__main__':
 #     all_conn  = create_allmodelconn()

X2_new.py

@@ -0,0 +1,136 @@
+import tkinter as tk
+from PIL import Image, ImageTk
+from X1 import *
+global Viewcanvas # 定义画布
+global Root # 主窗口
+global AllModelObj #网络对象
+
+'''
+【编程16.5】编制程序：依据AllModelObj和AllModelConn数据结构产生如图16.2的输出界面。
+【目的及编程说明】读者通过编程16.5可理解卷积神经网络模型构建的输出界面。数据结构及初始值参见【编程16.1】。
+'''
+
+
+def create_instance():
+    global AllModelObj
+    global DataSet, Conv, Pool, FullConn, Nonline, Classifier, Error, AjConv, AjFullconn
+    DataSet = Data_Class("DataSet1", 1, "数据集1", ".", 120, 330)
+    Conv = Conv_Class("Conv1", 2, "卷积1", ".", 250, 330)
+    Pool = Pool_Class("Pool1", 3, "最大池化1", ".", 380, 330)
+    FullConn = FullConn_Class("FullConn1", 4, "全连接1", ".", 510, 330)
+    Nonline = Nonline_Class("Nonline1", 5, "非线性函数1", ".", 640, 330)
+    Classifier = Classifier_Class("Classifier1", 6, "分类1", ".", 780, 330)
+    Error = Error_Class("Error1", 7, "误差计算1", ".", 710, 124)
+    AjConv = AjConv_Class("AjConv1", 8, "卷积调整1", ".", 250, 70)
+    AjFullconn = AjFullconn_Class("AjFullconn1", 9, "全连接调整1", ".", 510, 120)
+    AllModelObj = [DataSet, Conv, Pool, FullConn, Nonline, Classifier, Error, AjConv, AjFullconn]
+
+def connect_class():
+    global AllModelConn
+    # 创建连接对象实例
+    Line1 = ModelConn(1, 1, DataSet.ObjID, Conv.ObjID).output()
+    Line2 = ModelConn(2, 1, Conv.ObjID, Pool.ObjID).output()
+    Line3 = ModelConn(3, 1, Pool.ObjID, FullConn.ObjID).output()
+    Line4 = ModelConn(4, 1, FullConn.ObjID, Nonline.ObjID).output()
+    Line5 = ModelConn(5, 1, Nonline.ObjID, Classifier.ObjID).output()
+    Line6 = ModelConn(6, 1, Classifier.ObjID, Error.ObjID).output()
+    Line7 = ModelConn(7, 2, Error.ObjID, AjFullconn.ObjID).output()
+    Line8 = ModelConn(8, 2, Error.ObjID, AjConv.ObjID).output()
+    Line9 = ModelConn(9, 2, AjFullconn.ObjID, FullConn.ObjID).output()
+    Line10 = ModelConn(10, 2, AjConv.ObjID, Conv.ObjID).output()
+    # 网络连接对象总表
+    AllModelConn = [Line1, Line2, Line3, Line4,
+                    Line5, Line6, Line7, Line8,
+                    Line9, Line10]
+
+def element(path):
+    img = Image.open(path)          # 加载图元对应的图片文件
+    img = img.resize((60, 50))    # 使用resize方法调整图片
+    img = ImageTk.PhotoImage(img) # 把Image对象转换成PhotoImage对象
+    Root.img = img                # 保存图片的引用，防止被垃圾回收
+    return img
+
+def window():
+    global Root
+    global Viewcanvas
+    Root = tk.Tk()  # 创建一个主窗口
+    # 设置窗口的大小为1200*750
+    window_width = 900  # 窗口的宽度
+    window_height = 550  # 窗口的高度
+    Root.title("神经网络可视化")
+    Root.geometry("900x550")  # 设置窗口的大小和位置
+    # 创建一个画布，用于绘制矩形框，设置画布的大小和背景色
+    Viewcanvas = tk.Canvas(Root, width=window_width, height=window_height, bg="white")
+    # 将画布添加到主窗口中
+    Viewcanvas.pack()
+    # 绘制矩形框，使用不同的颜色和线宽，指定矩形框的左上角和右下角坐标，填充色，边框色和边框宽度
+    Viewcanvas.create_rectangle(5, 5, 895, 545, fill=None, outline="lightblue", width=2)
+
+def connecting_lines(obj_x, obj_y, text, text_record,image):
+    Viewcanvas.create_image(obj_x, obj_y, image=image) # 创建图元对象
+    Viewcanvas.create_text(obj_x + text_record[0], obj_y + text_record[1], text=text, font=("黑体", 14)) # 创建图元对象的标签
+
+def conn_lines(start, end, index):
+    smooth = [False, True]
+    width = [2, 4]
+    if start[0] == end[0]:
+        Viewcanvas.create_line(start[0], start[1] + 30,  end[0] , end[1] - 30, arrow=tk.LAST,
+                               arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index], width=width[index])
+    elif start[1] == end[1]:
+        Viewcanvas.create_line(start[0] + 30, start[1], end[0] - 30, end[1], arrow=tk.LAST,
+                               arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index], width=width[index])
+    else:
+        if abs(start[0]-end[0]) > abs(start[1]-end[1]):
+            # 创建数据线箭头
+            Viewcanvas.create_line(start[0]-15, start[1], int((start[0] + end[0])/2), end[1], end[0] + 30, end[1], arrow=tk.LAST,
+                                   arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index], width=width[index])
+        else:
+            # 创建数据线箭头
+            Viewcanvas.create_line(start[0], start[1] - 20, start[0], end[1], end[0] + 30, end[1], arrow=tk.LAST, arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index], width=width[index])
+
+def creating_elements():
+    text_record = [(0, -50), (0, 50), (-80, 0)]
+    # 遍历AllModelObj列表，在窗口左侧创建图元菜单
+    for obj in AllModelObj:
+        # 并且要根据需求调整每个对象的位置
+        obj_x = obj.ObjX  # 根据对象的id计算x坐标
+        obj_y = obj.ObjY  # 根据对象的id计算y坐标
+        Item_Record.append((obj_x, obj_y))
+        Item_Name.append(obj.ObjID)
+        # 根据对象的类型，绘制相应的图形
+        if 'Error' in obj.ObjID:
+            connecting_lines(obj_x, obj_y, obj.ObjLable, text_record[0], list_image[obj.ObjType - 1])
+        elif 'Aj' in obj.ObjID:
+            connecting_lines(obj_x, obj_y, obj.ObjLable, text_record[2], list_image[-1])
+        else:
+            connecting_lines(obj_x, obj_y, obj.ObjLable, text_record[1], list_image[obj.ObjType - 1])
+
+def ligature(): # 连接线
+    # print(Item_Record)
+    for conn in AllModelConn:
+        starting = Item_Name.index(conn[2])
+        # print(starting)
+        ending = Item_Name.index(conn[3])
+        if conn[1] == 1:
+            # print(Item_Record[starting])
+            conn_lines(Item_Record[starting], Item_Record[ending], 1)
+        else:
+            conn_lines(Item_Record[starting], Item_Record[ending], 0)
+
+
+if __name__ == '__main__':
+    global AllModelObj
+    Item_Record = []
+    Item_Name = []
+    window()
+    create_instance()
+    connect_class()
+    img_path = ["img/data.png", "img/conv.png", "img/pool.png", "img/full_connect.png", "img/nonlinear.png",
+                "img/classifier.png", "img/error.png", "img/adjust.png"]
+    list_image = []
+    for path in img_path:
+        list_image.append(element(path))
+    creating_elements()
+    ligature()
+    Root.mainloop()
+    # print(Item_Record)

X2_new2.py

@@ -0,0 +1,128 @@
+import tkinter as tk
+from PIL import Image, ImageTk
+from X1 import *
+def create_instance():
+    global AllModelObj
+    global DataSet, Conv, Pool, FullConn, Nonline, Classifier, Error, AjConv, AjFullconn
+    DataSet = Data_Class("DataSet1", 1, "数据集1", ".", 120, 330)
+    Conv = Conv_Class("Conv1", 2, "卷积1", ".", 250, 330)
+    Pool = Pool_Class("Pool1", 3, "最大池化1", ".", 380, 330)
+    FullConn = FullConn_Class("FullConn1", 4, "全连接1", ".", 510, 330)
+    Nonline = Nonline_Class("Nonline1", 5, "非线性函数1", ".", 640, 330)
+    Classifier = Classifier_Class("Classifier1", 6, "分类1", ".", 780, 330)
+    Error = Error_Class("Error1", 7, "误差计算1", ".", 710, 124)
+    AjConv = AjConv_Class("AjConv1", 8, "卷积调整1", ".", 250, 70)
+    AjFullconn = AjFullconn_Class("AjFullconn1", 9, "全连接调整1", ".", 510, 120)
+    AllModelObj = [DataSet, Conv, Pool, FullConn, Nonline, Classifier, Error, AjConv, AjFullconn]
+
+def connect_class():
+    global AllModelConn
+    # 创建连接对象实例
+    Line1 = ModelConn(1, 1, DataSet.ObjID, Conv.ObjID).output()
+    Line2 = ModelConn(2, 1, Conv.ObjID, Pool.ObjID).output()
+    Line3 = ModelConn(3, 1, Pool.ObjID, FullConn.ObjID).output()
+    Line4 = ModelConn(4, 1, FullConn.ObjID, Nonline.ObjID).output()
+    Line5 = ModelConn(5, 1, Nonline.ObjID, Classifier.ObjID).output()
+    Line6 = ModelConn(6, 1, Classifier.ObjID, Error.ObjID).output()
+    Line7 = ModelConn(7, 2, Error.ObjID, AjFullconn.ObjID).output()
+    Line8 = ModelConn(8, 2, Error.ObjID, AjConv.ObjID).output()
+    Line9 = ModelConn(9, 2, AjFullconn.ObjID, FullConn.ObjID).output()
+    Line10 = ModelConn(10, 2, AjConv.ObjID, Conv.ObjID).output()
+    # 网络连接对象总表
+    AllModelConn = [Line1, Line2, Line3, Line4,
+                    Line5, Line6, Line7, Line8,
+                    Line9, Line10]
+class Networking:
+    def __init__(self):
+        self.Root = tk.Tk()  # 创建一个主窗口
+        # 设置窗口的大小为1200*750
+        self.window_width = 900  # 窗口的宽度
+        self.window_height = 550  # 窗口的高度
+        self.list_image = []
+        self.Item_Record = [[], []]  # 记录图元坐标与图元号
+    def window(self):
+        self.Root.title("神经网络可视化")
+        self.Root.geometry("900x550")  # 设置窗口的大小和位置
+        # 创建一个画布，用于绘制矩形框，设置画布的大小和背景色
+        self.Viewcanvas = tk.Canvas(self.Root, width=self.window_width, height=self.window_height, bg="white")
+        # 将画布添加到主窗口中
+        self.Viewcanvas.pack()
+        # 绘制矩形框，使用不同的颜色和线宽，指定矩形框的左上角和右下角坐标，填充色，边框色和边框宽度
+        self.Viewcanvas.create_rectangle(5, 5, 895, 545, fill=None, outline="lightblue", width=2)
+    def connecting_lines(self, obj):
+        obj_x = obj.ObjX  # 根据对象的id计算x坐标
+        obj_y = obj.ObjY  # 根据对象的id计算y坐标
+        text = obj.ObjLable
+        if 'Error' in obj.ObjID:
+            x, y = 0, -50
+        elif 'Aj' in obj.ObjID:
+            x, y = -80, 0
+        else:
+            x, y = 0, 50
+        self.Viewcanvas.create_image(obj_x, obj_y, image=self.list_image[obj.ObjType - 1])  # 创建图元对象
+        self.Viewcanvas.create_text(obj_x + x, obj_y + y, text=text, font=("黑体", 14))  # 创建图元对象的标签
+
+    def conn_lines(self, conn):
+        starting = self.Item_Record[1].index(conn[2])
+        ending = self.Item_Record[1].index(conn[3])
+        smooth = [False, True]
+        width = [2, 4]
+        start, end = self.Item_Record[0][starting], self.Item_Record[0][ending]
+        index = 1 if conn[1] == 1 else 0
+        if start[0] == end[0]:
+            self.Viewcanvas.create_line(start[0], start[1] + 30, end[0], end[1] - 30, arrow=tk.LAST,
+                                   arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index], width=width[index])
+        elif start[1] == end[1]:
+            self.Viewcanvas.create_line(start[0] + 30, start[1], end[0] - 30, end[1], arrow=tk.LAST,
+                                   arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index], width=width[index])
+        else:
+            if abs(start[0] - end[0]) > abs(start[1] - end[1]):
+                # 创建数据线箭头
+                self.Viewcanvas.create_line(start[0] - 15, start[1], int((start[0] + end[0]) / 2), end[1], end[0] + 30,
+                                       end[1], arrow=tk.LAST,
+                                       arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index],
+                                       width=width[index])
+            else:
+                # 创建数据线箭头
+                self.Viewcanvas.create_line(start[0], start[1] - 20, start[0], end[1], end[0] + 30, end[1], arrow=tk.LAST,
+                                       arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth[index],
+                                       width=width[index])
+    def creating_elements(self):
+        text_record = [(0, -50), (0, 50), (-80, 0)]
+        # 遍历AllModelObj列表，在窗口左侧创建图元菜单
+        for obj in self.AllModelObj:
+            # 并且要根据需求调整每个对象的位置
+            obj_x = obj.ObjX  # 根据对象的id计算x坐标
+            obj_y = obj.ObjY  # 根据对象的id计算y坐标
+            self.Item_Record.append((obj_x, obj_y))
+            self.connecting_lines(obj,)
+
+    def element(self, path):
+        img = Image.open(path)  # 加载图元对应的图片文件
+        img = img.resize((60, 50))  # 使用resize方法调整图片
+        img = ImageTk.PhotoImage(img)  # 把Image对象转换成PhotoImage对象
+        self.Root.img = img  # 保存图片的引用，防止被垃圾回收
+        return img
+
+    def read_element(self):
+        img_path = ["img/data.png", "img/conv.png", "img/pool.png", "img/full_connect.png", "img/nonlinear.png",
+                    "img/classifier.png", "img/error.png", "img/adjust.png", "img/adjust.png"]
+        for path in img_path:
+            self.list_image.append(self.element(path))
+
+    def visual_output(self, AllModelObj, AllModelConn):
+        for obj in AllModelObj:  # 遍历AllModelObj列表，在窗口创建图元
+            self.Item_Record[0].append((obj.ObjX, obj.ObjY))  # 记录图元坐标
+            self.Item_Record[1].append(obj.ObjID)  # 记录图元号
+            self.connecting_lines(obj)  # 根据图元对象信息在画布上画图元
+        for conn in AllModelConn:  # 遍历AllModelConn列表，在窗口连线图元
+            self.conn_lines(conn)
+
+if __name__ == '__main__':
+    create_instance()
+    connect_class()
+    Net = Networking()
+    Net.window()
+    Net.read_element()
+    Net.visual_output(AllModelObj, AllModelConn)
+    Net.Root.mainloop()

X3.py

@@ -1,4 +1,5 @@
 import os
+import cv2
 import numpy as np
 import tkinter as tk
 from tkinter import filedialog
@@ -52,13 +53,20 @@ def setload_data(): # 定义加载数据集的参数SetLoadData()
 def load_data(DataPara):
     imgPaths=["train_imgPath","test_imgPath"] # DataPara的键名
     listimages=[]    # 存储图片的列表
+    list_path = []
     for datapath in imgPaths:
         filenames = os.listdir(DataPara[datapath])
-        images = []  # 存储图片的列表
+        Path = []
         for tfname in filenames:
             if os.path.isdir(tfname) == False:      # 如果不是子文件夹，就是图片文件
-                path = DataPara[datapath] + tfname  # 拼接图片路径
-	            # 引用X1里面的image_to_array()方法
+                path = os.path.join(DataPara[datapath], tfname)  # 使用os.path.join()拼接图片路径
+                Path.append(path)
+        file_paths = sorted(Path, key=lambda x: int(x.split("/")[-1].split(".")[0]))
+        list_path.append(file_paths)
+    for data_path in list_path:
+        images = []  # 存储图片的列表
+        for path in data_path:
+            # print(path)
             img = image_to_array(path, DataPara["img_width"], DataPara["img_height"])  # 读取图片数据
             img = img.T  # 转置，图像的行和列将互换位置。
             images.append(img)  # 将图片数组添加到训练集图片列表中
@@ -118,7 +126,6 @@ def conv_proc(image, ConvPara): # 定义卷积函数ConvProc()
             for j in range(out_w):
                 stride = ConvPara["stride"] # 获得步长
                 output[k, i, j] = np.sum(image[k, i * stride:i * stride + 3, j * stride:j * stride + 3] * kernel)# 计算卷积操作
-
     return output # 返回卷积计算后的数组（特征向量）
 
 # if __name__ == '__main__':
@@ -382,7 +389,6 @@ def classify(nonlinear_images):
         output = classifier_proc(image, ClassifyPara) # 进行分类处理
         classifier_images.append(output)             # 将分类结果存储到列表
     classifier_images = np.array(classifier_images)  # 将分类的结果列表转换为数组形式，方便后续处理
-    print(classifier_images)
     return classifier_images, prob_images
 '''
 【编程16.12】编制程序：设置标签类别SetLabelPara()及为样本标记标签函数LabelProc()。
@@ -474,7 +480,7 @@ def loss_count(prob_images):
     input = prob_images # print(len(input))
     array = np.arange(1, 11) # 定义一个一维数组，包含1到10的整数
     # 从a中随机抽取100个元素，有放回，每个元素的概率相同
-    right_label = np.random.choice(array, DataPara['batch_size'])
+    right_label = np.random.choice(array, 900)
     loss = error_proc(input, right_label, ErrorPara) # 计算误差值
     return loss
 
@@ -486,41 +492,27 @@ SetAjConvPara()实现的具体要求为:（1）确定卷积调整的参数：卷
                              （2）返回参数AjConvPara。4.028404024493524
 AjConvProc()的具体实现要求为：（1）使用误差值计算卷积层的误差项和梯度；（2）梯度下降算法更新卷积层参数；（3）返回新的卷积层参数。
 '''
-def conv_ajconv(loss,images):
-    global ConvPara
-    kernel = ConvPara["kernel"]
-    bias = 0.5
-    loss = np.array([[loss]])
-    new_kernel, new_bias = ajconv_proc(images, kernel, bias, loss)
-    ConvPara["kernel"] = new_kernel
-    ConvPara["bias"] = new_bias
-    print('卷积参数更新后的卷积核：')
-    print(new_kernel)
-    print('卷积参数更新后的偏置向量：')
-    print(new_bias)
-def ajconv_proc(input, kernel, bias, loss):
-    kernel_grad_list = []
-    bias_grad = 0  # 初始化偏置项梯度为零
+def setajconv_para():
+    kernel = np.array([[1.0,2.0],[3.0,4.0]])  # 卷积核信息
+    learning_rate = 0.01  # 学习率
+    bias = 0.5 # 偏置
+    loss = np.array([[0.1, 0.2], [0.3, 0.4]])
+    AjConvPara={'kernel_info': kernel,'learning_rate': learning_rate,'bias': bias,'loss': loss}
+    return AjConvPara
+def ajconv_proc(input, AjConvPara):
+    k = [] # 计算卷积核和偏置项的梯度
+    bias_grad = np.sum(AjConvPara['loss'])  # 计算偏置项梯度为误差值之和
     for c in input:
-        # 计算卷积核和偏置项的梯度
-        kernel_grad = np.zeros_like(kernel)  # 初始化卷积核梯度为零矩阵
-        for i in range(loss.shape[0]):  # 遍历误差值矩阵的行
-            for j in range(loss.shape[1]):  # 遍历误差值矩阵的列
-                # 将输入数据数组中对应的子矩阵旋转180度，与误差值相乘，累加到卷积核梯度上
-                kernel_grad += np.rot90(c[i:i + kernel_grad.shape[0], j:j + kernel_grad.shape[0]], 2) * loss[i, j]
-                # 将误差值累加到偏置项梯度上
-                bias_grad += loss[i, j]
-        kernel_grad_list.append(kernel_grad)
-    # 使用stack函数沿着第0个轴把一百个a数组堆叠起来
-    result = np.stack(kernel_grad_list, axis=0)
-    kernel_grad = np.sum(result, axis=0) / len(input)  # 沿着第0个维度求和
-    # 设置学习率为0.01
-    learning_rate = 0.01
-    # 更新卷积核和偏置项参数
-    kernel = kernel - learning_rate * kernel_grad  # 卷积核参数减去学习率乘以卷积核梯度
-    bias = bias - learning_rate * bias_grad  # 偏置项参数减去学习率乘以偏置项梯度
-    # 返回更新后的卷积核、偏置
-    return kernel, bias
+        kernel_grad = np.zeros_like(AjConvPara['kernel_info'])  # 初始化卷积核梯度为零矩阵
+        for i in range(AjConvPara['loss'].shape[0]):      # 遍历误差值矩阵的每一行
+            for j in range(AjConvPara['loss'].shape[1]):  # 遍历误差值矩阵的每一列
+                kernel_grad += np.rot90(c[i:i + 2, j:j + 2], 2) * AjConvPara['loss'][i, j]
+        k.append(kernel_grad)
+    result = np.stack(k, axis=0)
+    kernel_grad = np.sum(result, axis=0) / len(input)
+    kernel = AjConvPara['kernel_info'] - AjConvPara['learning_rate'] * kernel_grad
+    bias = AjConvPara['bias'] - AjConvPara['learning_rate'] * bias_grad
+    return kernel, bias# 返回更新后的卷积核和偏置项
 # if __name__ == '__main__':
 #     input = np.array([[[1,2,3],[4,5,6],[7,8,9]],
 #                       [[10,11,12],[13,14,15],[16,17,18]],
@@ -531,29 +523,24 @@ def ajconv_proc(input, kernel, bias, loss):
 #     print("\nkernel_new =\n", kernel_new,"\n")
 #     print("bias_new =", bias_new)
 
-# 定义一个全连接层的权重矩阵和偏置向量的调整函数，可以选择不同的激活函数
-def update_parameters(weight_matrix, bias_vector, loss):
-    # 根据激活函数的参数选择相应的函数和导数
-    # 计算权重矩阵和偏置向量的梯度，使用链式法则
-    learning_rate = 0.01
-    gradient_weights = np.outer(loss, learning_rate)
-    # 更新权重矩阵和偏置向量
-    weight_matrix = weight_matrix - gradient_weights
-    bias_vector = bias_vector - learning_rate * bias_vector
-    # 返回更新后的权重矩阵和偏置向量
-    return weight_matrix, bias_vector
-def ajfullconn_proc(loss):
-    global FullConnPara
-    weight1 = FullConnPara["weights"]
-    bias1 = FullConnPara["bias"]
-    loss = np.array([loss])
-    new_weight1, new_bias1 = update_parameters(weight1, bias1, loss)
-    FullConnPara["weights"] = new_weight1
-    FullConnPara["bias"] = new_bias1
-    print('全连接参数更新后的权重矩阵：')
-    print(new_weight1)
-    print('全连接参数更新后的偏置向量：')
-    print(new_bias1)
+def setajfc_para():
+    AjFCPara = {
+        'weights': np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]),  # 全连接权重
+        'bias': np.array([0.5, 0.6]),  # 全连接偏置
+        'learning_rate': 0.01,  # 学习率
+        'error': np.array([0.001, 0.002])  # 误差值
+    }
+    return AjFCPara
+def ajfullconn_proc(AjFCPara):
+    error_value = AjFCPara['error']
+    learning_rate = AjFCPara['learning_rate']
+    error_term = error_value  # 全连接层的误差项等于误差值
+    gradient_weights = np.outer(error_term, learning_rate)  # 计算权重梯度
+    gradient_bias = error_term * learning_rate  # 计算偏置梯度
+    # 更新全连接层参数（梯度下降算法）
+    AjFCPara['weights'] -= gradient_weights
+    AjFCPara['bias'] -= gradient_bias
+    return AjFCPara
 # if __name__ == '__main__':
 #     AjFCPara = setajfc_para()# 调用SetAjFCPara()设置参数
 #     print("初始化的全连接层参数：", AjFCPara)
@@ -581,9 +568,7 @@ def main():
     parameter()
     global DataPara
     train_images, test_images = load_data(DataPara)  # 调用LoadData()函数，根据参数加载数据集
-    for i in range(len(train_images) // DataPara['batch_size']):
-        train_image=train_images[i * DataPara['batch_size']:(i + 1) * DataPara['batch_size']]
-        conv_images = convolutional_operation(train_image)  # 存储卷积处理后的图片的列表
+    conv_images = convolutional_operation(train_images)  # 存储卷积处理后的图片的列表
     pool_images = pooling_treatment(conv_images)  # 存储还回池化处理后的图片的列表
     global FullConnPara
     FullConnPara = setfullconn_para(pool_images[0])  # 调用设置全连接层参数的函数，获取全连接参数
@@ -593,9 +578,7 @@ def main():
     labeled_samples = label_proc(nonlinear_images, prob_images, classifier_images)  # 读取之前的分类，每一行是一个样本，每一列是一个特征，将分类标签放在最后一列
     loss = loss_count(prob_images)
     # 打印结果
-        print('loss值：{}'.format(loss))
-        conv_ajconv(loss,train_image)
-        ajfullconn_proc(loss)
+    print(loss)
 
 if __name__ == '__main__':
     main()

X3_new.py

@@ -0,0 +1,485 @@
+import os
+import cv2
+import numpy as np
+import tkinter as tk
+from tkinter import filedialog
+from read_data import *
+from PIL import Image
+
+class ModelObj: # 网络对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        self.ObjID = ObjID  # 图元号
+        self.ObjType = ObjType  # 图元类别
+        self.ObjLable = ObjLable  # 对象标签
+        self.ParaString = ParaString  # 参数字符串
+        self.ObjX = ObjX  # 对象位置x坐标
+        self.ObjY = ObjY  # 对象位置y坐标
+
+class Data_Class(ModelObj):  # 数据集网络对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.LoadData = self.load_data  # 基本操作函数
+        self.SetDataPara = self.set_data_para  # 参数设置函数
+
+    # 定义加载数据集load_data()
+    def load_data(self, DataPara):
+        global SubFolders
+        listimages = []  # 存储图片的列表
+        list_path = []
+        SubFolders, train_Path = read_folders(DataPara["train_imgPath"])
+        list_path.append(train_Path)
+        _, path_list = read_folders(DataPara["test_imgPath"])
+        list_path.append(path_list)
+        for data_path in list_path:
+            images = []  # 存储图片的列表
+            for path in data_path:
+                # print(path)
+                img = self.image_to_array(path, DataPara["img_width"], DataPara["img_height"])  # 读取图片数据
+                img = img.T  # 转置，图像的行和列将互换位置。
+                images.append(img)  # 将图片数组添加到训练集图片列表中
+            listimages.append(np.array(images))  # 返回转换后的数组
+        return listimages[0], listimages[1]
+
+    def set_data_para(self):# 定义加载数据集的参数SetLoadData()
+        # 设置数据集路径信息
+        train_imgPath = 'data_classification/train/'  # 训练集文件夹的位置
+        test_imgPath = 'data_classification/test/'  # 测试集文件夹的位置
+        img_width = 48  # 图片宽度
+        img_height = 48  # 图片高度
+        # 设置每批次读入图片的数量
+        batch_size = 32  # 批次大小
+        # 返回DataPara参数，这里用一个字典来存储
+        DataPara = {"train_imgPath": train_imgPath,
+                    "test_imgPath": test_imgPath,
+                    "img_width": img_width,
+                    "img_height": img_height,
+                    "batch_size": batch_size}
+        return DataPara
+
+    # 定义一个函数，实现图片转换为数组的功能
+    def image_to_array(self, path, height, width):
+        img = Image.open(path).convert("L")  # 转换为灰度模式
+        img = img.resize((height, width))  # 将图片缩放为height*width的固定大小
+        data = np.array(img)  # 将图片转换为数组格式
+        data = data / 255.0  # 将数组中的数值归一化，除以255
+        return data
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.LoadData,
+                  self.SetDataPara, self.ParaString, self.ObjX, self.ObjY]
+        return result
+# if __name__ == '__main__':
+#     DataSet = Data_Class("DataSet1", 1, "数据集1", ".", 120, 330)
+#     print(DataSet)
+class Conv_Class(ModelObj):  # 卷积对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.ConvProc = self.conv_proc  # 基本操作函数
+        self.SetConvPara = self.setconv_para  # 参数设置函数
+    # 定义卷积函数ConvProc()
+    def conv_proc(self, image, ConvPara):
+        # 获取输入数据的大小，这里假设是单通道的图片
+        c, image_h, image_w = image.shape
+        kernel_h = ConvPara["kernel_h"]  # 获取卷积核的大小和卷积核
+        kernel_w = ConvPara["kernel_w"]
+        kernel = ConvPara["kernel"]
+        out_h = (image_h - kernel_h) // ConvPara["stride"] + 1  # 计算输出数据的大小
+        out_w = (image_w - kernel_w) // ConvPara["stride"] + 1
+        output = np.zeros((c, out_h, out_w))  # 初始化输出数据为零矩阵
+        for k in range(c):  # 遍历每个通道
+            for i in range(out_h):  # 遍历每个输出位置
+                for j in range(out_w):
+                    stride = ConvPara["stride"]  # 获得步长
+                    output[k, i, j] = np.sum(
+                        image[k, i * stride:i * stride + 3, j * stride:j * stride + 3] * kernel)  # 计算卷积操作
+        return output  # 返回卷积计算后的数组（特征向量）
+
+    def setconv_para(self):# 定义设置卷积参数的函数SetConvPara()
+        kernel_h = 3  # 设置卷积核大小，这里假设是3x3
+        kernel_w = 3
+        kernel = [[1.289202, -1.471377, -0.238452],  # 卷积核为3x3的单位矩阵(高斯分布)
+                  [-0.562343, -0.019988, -0.441446],
+                  [1.627381, 1.390266, 0.812486]]
+        stride = 1  # 设置步长，这里假设是1
+        padding = 0  # 设置填充，这里假设是0
+        ConvPara = {"kernel": kernel,  # 返回ConvPara参数，这里用一个字典来存储
+                    "kernel_h": kernel_h,
+                    "kernel_w": kernel_w,
+                    "stride": stride,
+                    "padding": padding}
+        return ConvPara
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.ConvProc, self.SetConvPara, self.ParaString, self.ObjX,
+                  self.ObjY]
+        return result
+
+
+class Pool_Class(ModelObj):  # 池化对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.MaxPoolProc = self.pool_proc  # 基本操作函数
+        self.SetPollPara = self.setpool_para  # 参数设置函数
+
+    def pool_proc(self, image, PoolPara):
+        pool_mode = PoolPara["pool_mode"]
+        pool_size = PoolPara["pool_size"]
+        stride = PoolPara["stride"]
+        c, h, w = image.shape  # 获取输入特征图的高度和宽度
+        out_h = int((h - pool_size) / stride) + 1  # 计算输出特征图的高度
+        out_w = int((w - pool_size) / stride) + 1  # 计算输出特征图的宽度
+        out = np.zeros((c, out_h, out_w))  # 初始化输出特征图为全零数组
+        for k in range(c):  # 对于输出的每一个位置上计算：
+            for i in range(out_h):
+                for j in range(out_w):
+                    window = image[k, i * stride:i * stride + pool_size, j * stride:j * stride + pool_size]
+                    if pool_mode == "max":  # 最大池化
+                        out[k][i][j] = np.max(window)
+                    elif pool_mode == "avg":  # 平均池化
+                        out[k][i][j] = np.mean(window)
+                    elif pool_mode == "min":  # 最小池化
+                        out[k][i][j] = np.min(window)
+                    else:  # 无效的池化类型
+                        raise ValueError("Invalid pooling mode")
+        return out  # 返回特征图。
+
+    # 定义设置池化参数的函数
+    def setpool_para(self):
+        pool_mode = "max"  # 设置池大小和池类型，这里假设是2x2最大池化
+        pool_size = 2
+        stride = 2  # 设置步长，这里假设是2
+        PoolPara = {"pool_mode": pool_mode, "pool_size": pool_size, "stride": stride}  # 返回PoolPara参数，这里用一个字典来存储
+        return PoolPara  # 返回PoolPara参数
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.MaxPoolProc, self.SetPollPara, self.ParaString, self.ObjX,
+                  self.ObjY]
+        return result
+class FullConn_Class(ModelObj):  # 全连接对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.FullConnProc = self.fullconn_proc  # 基本操作函数
+        self.SetFullConnPara = self.setfullconn_para  # 参数设置函数
+
+    def fullconn_proc(self, inputdata, FullConnPara):
+        weights = FullConnPara["weights"]  # 从FullConnPara参数中获取权重矩阵
+        bias = FullConnPara["bias"]  # 偏置向量
+        inputdata = inputdata.reshape(1, inputdata.shape[1] * inputdata.shape[2])  # 对输入进行展平处理，变换为单通道的一维数组格式
+        output = np.dot(inputdata, weights.T) + bias  # 计算全连接层的线性变换：inputdata与权重矩阵w进行乘法，再加上偏置向量b
+        return output  # 返回全连接计算后的数组
+
+    # 定义一个函数来设置全连接层的相关参数，这里可以根据实际情况修改或随机生成
+    def setfullconn_para(self, data, num_outputs):
+        # 获取池化后的图片数组的长度和宽度
+        c, height, width = data
+        num_outputs = num_outputs
+        weights = np.random.randn(num_outputs, height * width)
+        bias = np.random.randn(1, num_outputs)
+        # 返回FullConnPara参数，这里用一个字典来存储
+        FullConnPara = {"weights": weights, "bias": bias, "num_outputs": num_outputs}
+        return FullConnPara
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.FullConnProc, self.SetFullConnPara, self.ParaString, self.ObjX,
+                  self.ObjY]
+        return result
+
+class Nonline_Class(ModelObj):  # 非线性对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.NonlinearProc = self.nonlinear_proc  # 基本操作函数
+        self.SetNonLPara = self.setnonl_para  # 参数设置函数
+
+    # 定义非线性函数
+    def nonlinear_proc(self, inputdata, NonLPara):
+        nonlinearmode = NonLPara["nonlinearmode"]  # 从NonLPara参数中获取非线性函数类型
+        if nonlinearmode == "Sigmoid":  # 判断nonlinearmode，进行相应的计算
+            output = 1 / (1 + np.exp(-inputdata))  # Sigmoid函数，将任何实数的输入映射到0和1之间的输出
+        elif nonlinearmode == "ReLU":
+            output = np.maximum(inputdata, 0)  # ReLU函数，将负数输入置为0，而正数输入保持不变
+        elif nonlinearmode == "Tanh":
+            output = np.tanh(inputdata)  # Tanh函数，将任何实数的输入映射到-1和1之间的输出
+        else:
+            raise ValueError("Invalid nonlinear mode")  # 非法的非线性类型，抛出异常
+        return output  # 返回计算后的值
+
+    # 定义设置非线性参数的函数
+    def setnonl_para(self):
+        # 可以选择"Sigmoid", "ReLU" 或 "Tanh"
+        nonlinearmode = "ReLU"  # 确定参数信息:非线性函数的类型
+        NonLPara = {"nonlinearmode": nonlinearmode}  # 返回NonLPara参数，这里用一个字典来存储
+        return NonLPara  # 返回NonLPara参数
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.NonlinearProc, self.SetNonLPara, self.ParaString, self.ObjX,
+                  self.ObjY]
+        return result
+class Label:  #  标签
+    # 设置标签类别列表并将其转化为one-hot向量的形式
+    def setlabel_para(self, label_list):
+        num_classes = len(label_list)
+        identity_matrix = np.eye(num_classes)
+        label_dict = {label: identity_matrix[i] for i, label in enumerate(label_list)}
+        return label_dict
+    # 读取样本数据集，遍历每个样本，并将样本和对应的标签组成元组，返回标记好标签的样本列表
+    def label_proc(self, samples, labels, label_dict):
+        labeled_samples = [(sample, label_dict[label]) for sample, label in zip(samples, labels)]
+        return labeled_samples
+    def label_array(self, i):
+        # 读取标签数据
+        path_csv = 'train.csv'
+        df = pd.read_csv(path_csv, header=None, skiprows=range(0, i * 32), nrows=(i + 1) * 32 - i * 32)
+        # print(df)
+        # 将标签数据转化成数组
+        right_label = df.iloc[:, 0].tolist()
+        right_label = list(map(int, right_label))
+        right_label = [x for x in right_label]
+        # print(right_label)
+        return right_label
+
+class Classifier_Class(ModelObj):  # 分类对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.ClassifierProc = self.classifier_proc  # 基本操作函数
+        self.SetClassifyPara = self.setclassify_para  # 参数设置函数
+
+    def classifier_proc(self, inputdata, ClassifyPara):
+        def softmax(x):  # 定义softmax函数
+            x -= np.max(x)  # 减去最大值，防止数值溢出
+            return np.exp(x) / np.sum(np.exp(x))  # 计算指数和归一化
+
+        threshold = ClassifyPara["threshold"]  # 从ClassifyPara参数中获取阈值
+        output = -1  # 初始化输出为-1
+        prob = softmax(inputdata)  # 调用softmax函数，得到概率分布向量
+        prob1 = prob[prob >= threshold]  # 如果概率高于阈值，就将该类别加入输出结果
+        index = np.where(prob == max(prob1))  # 使用where()函数来返回等于概率最大值的元素的索引
+        output = index[1].item(0) + 1  # 使用item()方法来将索引转换为标准Python标量
+        return output  # 返回分类标签
+
+    # 定义设置分类函数参数的函数
+    def setclassify_para(self):
+        threshold = 0.1     # 设定阈值,可以根据你的数据和任务来调整阈值
+        ClassifyPara = {"threshold": threshold} # 返回ClassifyPara参数，这里用一个字典来存储
+        return ClassifyPara # 返回ClassifyPara参数
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.ClassifierProc, self.SetClassifyPara, self.ParaString, self.ObjX,
+                  self.ObjY]
+        return result
+
+class Error_Class(ModelObj):  # 误差计算对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.ErrorProc = self.error_proc  # 基本操作函数
+        self.SetErrorPara = self.seterror_para  # 参数设置函数
+
+    def error_proc(self, input, label, ErrorPara):
+        label_list, loss_type = ErrorPara  # 读取标签列表和损失函数类型
+        one_hot_matrix = np.eye(len(label_list))  # 创建一个单位矩阵，大小为标签类别的个数
+        index = [x for x in label]
+        # print(label)
+        label_one_hot = np.take(one_hot_matrix, index, axis=0)  # 从one-hot矩阵中取出对应的向量
+        # print(label_one_hot)
+        if loss_type == "CEE":  # 确定损失函数类别，实现不同的损失函数，计算输入值与label之间的误差
+            # 使用交叉熵损失函数，公式为：-sum(label_one_hot * log(input)) / n
+            loss = -np.sum(label_one_hot * np.log(input)) / len(input)
+        elif loss_type == "MSE":
+            # 使用均方误差损失函数，公式为：sum((input - label_one_hot) ** 2) / n
+            loss = np.sum((input - label_one_hot) ** 2) / len(input)
+        elif loss_type == "MAE":
+            # 使用平均绝对误差损失函数，公式为：sum(abs(input - label_one_hot)) / n
+            loss = np.sum(np.abs(input - label_one_hot)) / len(input)
+        else:
+            raise ValueError("Invalid loss type")  # 如果损失函数类型不在以上三种中，抛出异常
+        return loss  # 返回误差值loss
+
+    # 定义设置误差参数的函数
+    def seterror_para(self):
+        label_list = [0, 1, 2, 3, 4, 5, 6] # 确定参数信息: 标签类别，损失函数类型
+        loss_type = "CEE" # 假设损失函数类型为交叉熵（Cross Entropy Error，CEE）
+        ErrorPara = (label_list, loss_type) # 返回ErrorProc参数，这里用一个元组来存储
+        return ErrorPara # 返回ErrorPara参数
+
+    def output(self):  # 输出方法
+        # 创建一个空列表
+        result = [self.ObjID, self.ObjType, self.ObjLable, self.ErrorProc, self.SetErrorPara, self.ParaString, self.ObjX,
+                  self.ObjY]
+        return result
+
+class AjConv_Class(ModelObj):  # 卷积调整对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.AjConvProc = self.ajconv_proc  # 基本操作函数
+        self.SetAjConvPara = self.setajconv_para  # 参数设置函数
+
+    def ajconv_proc(self, images, AjConvPara):
+        kernel_grad_list = []
+        bias_grad = 0  # 初始化偏置项梯度为零
+        for c in images:
+            # 计算卷积核和偏置项的梯度
+            kernel_grad = np.zeros_like(AjConvPara['kernel_info'])  # 初始化卷积核梯度为零矩阵
+            for i in range(AjConvPara['loss'].shape[0]):  # 遍历误差值矩阵的行
+                for j in range(AjConvPara['loss'].shape[1]):  # 遍历误差值矩阵的列
+                    # 将输入数据数组中对应的子矩阵旋转180度，与误差值相乘，累加到卷积核梯度上
+                    kernel_grad += np.rot90(c[i:i + kernel_grad.shape[0], j:j + kernel_grad.shape[0]], 2) * AjConvPara['loss'][i, j]
+                    # 将误差值累加到偏置项梯度上
+                    bias_grad += AjConvPara['loss'][i, j]
+            kernel_grad_list.append(kernel_grad)
+        # 使用stack函数沿着第0个轴把一百个a数组堆叠起来
+        result = np.stack(kernel_grad_list, axis=0)
+        kernel_grad = np.sum(result, axis=0) / len(images)  # 沿着第0个维度求和
+        # 更新卷积核和偏置项参数
+        kernel = AjConvPara['kernel_info'] - AjConvPara['learning_rate'] * kernel_grad  # 卷积核参数减去学习率乘以卷积核梯度
+        return kernel  # 返回更新后的卷积核
+    def setajconv_para(self, loss,  ConvPara):
+        kernel = ConvPara['kernel']  # 卷积核信息
+        learning_rate = 0.01  # 学习率
+        loss = np.array([[loss]])
+        AjConvPara = {'kernel_info': kernel, 'learning_rate': learning_rate, 'loss': loss}
+        return AjConvPara
+
+class AjFullconn_Class(ModelObj):  # 全连接调整对象
+    def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
+        super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
+        self.AjFullconnProc = self.ajfullconn_proc  # 基本操作函数
+        self.SetAjFCPara = self.setajfc_para  # 参数设置函数
+    def ajfullconn_proc(self, AjFCPara):
+        # 根据激活函数的参数选择相应的函数和导数
+        # 计算权重矩阵和偏置向量的梯度，使用链式法则
+        gradient_weights = np.outer(AjFCPara['loss'], AjFCPara['learning_rate'])
+        # 更新权重矩阵和偏置向量
+        weight_matrix = AjFCPara['weights'] - gradient_weights
+        bias_vector = AjFCPara['bias'] - AjFCPara['learning_rate'] * AjFCPara['bias']
+        # 返回更新后的权重矩阵和偏置向量
+        return weight_matrix, bias_vector
+
+    def setajfc_para(self, loss, FullConnPara):
+        weights = FullConnPara["weights"]
+        bias = FullConnPara["bias"]
+        loss = np.array([loss])
+        AjFCPara = {
+            'weights': weights,  # 全连接权重
+            'bias': bias,  # 全连接偏置
+            'learning_rate': 0.01,  # 学习率
+            'loss': loss  # 误差值
+        }
+        return AjFCPara
+
+def main():
+    DataPara = DataSet.SetDataPara()  # setload_data()函数，获取加载数据集的参数
+    train_images, test_images = DataSet.LoadData(DataPara)
+    ConvPara = Conv.SetConvPara()  # 调用SetConvPara()函数，获取卷积层参数
+    PoolPara = Pool.SetPollPara()
+    FullConnPara = FullConn.SetFullConnPara((1, 23, 23), 7)
+    NonLPara = Nonline.SetNonLPara()
+    ClassifyPara = Classifier.SetClassifyPara()
+    ErrorPara = Error.SetErrorPara()
+    LabelPara = Label()
+    # AjFCPara = AjFullconn.SetAjFCPara()
+    for i in range(len(train_images) // 32):
+        images = train_images[i * 32:(i + 1) * 32]
+        # 存储卷积处理后的图片的列表
+        conv_images = []
+        # 获取训练集的图片数据
+        for image in images:
+            # 获取矩阵的维度
+            dim = len(image.shape)
+            # 如果是二维矩阵,则转化为三维矩阵
+            if dim == 2:
+                image_h, image_w = image.shape
+                image = np.reshape(image, (1, image_h, image_w))
+                # 调用ConvProc()函数，根据ConvPara参数完成卷积计算
+                output = Conv.ConvProc(image, ConvPara)
+                # 将卷积结果存储到列表
+                conv_images.append(output)
+            # 若为三维矩阵，则保持不变直接卷积处理
+            elif dim == 3:
+                output = Conv.ConvProc(image, ConvPara)
+                conv_images.append(output)
+        # 将卷积处理后的图片列表转换为数组形式，方便后续处理
+        conv_images = np.array(conv_images)
+
+
+        pool_images = []  # 存储池化处理后的图片的列表
+        for image in conv_images:  # 获取卷积后的图片数据
+            output = Pool.MaxPoolProc(image, PoolPara)
+            pool_images.append(output)  # 将池化结果存储到列表
+        pool_images = np.array(pool_images)  # 将池化处理后的图片列表转换为数组形式，方便后续处理
+        # print(conv_images)
+
+        # print(pool_images[0].shape)
+        # 存储全连接处理后的图片的列表
+        fullconn_images = []
+        # 获取池化后的图片数据
+        for image in pool_images:
+            output = FullConn.FullConnProc(image, FullConnPara)
+            # 将全连接处理后的结果存储到列表
+            fullconn_images.append(output)
+        # 将全连接处理后的图片列表转换为数组形式，方便后续处理
+        fullconn_images = np.array(fullconn_images)
+        # print(fullconn_images)
+
+        # 存储非线性处理后的图片的列表
+        nonlinear_images = []
+        for image in fullconn_images:  # 获取全连接处理后的图片数据
+            output = Nonline.NonlinearProc(image, NonLPara)
+            # 将非线性处理后的结果存储到列表
+            nonlinear_images.append(output)
+        # 将非线性处理后的图片列表转换为数组形式，方便后续处理
+        nonlinear_images = np.array(nonlinear_images)
+        # print(nonlinear_images)
+
+
+        classifier_images = []  # 存储分类处理后的图片的列表
+        prob_images = []  # 存储分类处理后的概率向量
+        def softmax(x):  # 定义softmax函数
+            x -= np.max(x)  # 减去最大值，防止数值溢出
+            return np.exp(x) / np.sum(np.exp(x))  # 计算指数和归一化
+        for image in nonlinear_images:  # 获取非线性处理后的图片数据
+            prob = softmax(image)  # 调用softmax函数，得到概率分布向量
+            prob_images.append(prob)  # 将概率向量结果存储到列表
+            output = Classifier.ClassifierProc(image, ClassifyPara)  # 进行分类处理
+            classifier_images.append(output)  # 将分类结果存储到列表
+        classifier_images = np.array(classifier_images)  # 将分类的结果列表转换为数组形式，方便后续处理
+        print(classifier_images)
+        # print(setlabel_para())
+        label_dict = LabelPara.setlabel_para([0, 1, 2, 3, 4, 5, 6])
+        right_label = LabelPara.label_array(i)
+        labeled_samples = LabelPara.label_proc(images, right_label, label_dict)
+        print(right_label)
+
+
+        # 假设有以下输入值和真实标签值，输入值是一个概率矩阵，真实标签值是一个类别列表
+        prob_images = np.squeeze(prob_images)
+        # print(prob_images)
+        loss = Error.ErrorProc(prob_images, right_label, ErrorPara)  # 计算误差值
+        print(loss)
+        AjConvPara = AjConv.SetAjConvPara(loss, ConvPara)
+        ConvPara['kernel'] = AjConv.AjConvProc(images, AjConvPara)
+        print(ConvPara['kernel'])
+
+        AjFCPara = AjFullconn.SetAjFCPara(loss, FullConnPara)
+        weight, bias = AjFullconn.AjFullconnProc(AjFCPara)
+        FullConnPara['weights'] = weight
+        FullConnPara['bias'] = bias
+        # print(weight, bias)
+
+
+if __name__ == '__main__':
+    DataSet = Data_Class("DataSet1", 1, "数据集1", [], 120, 330)
+    Conv = Conv_Class("Conv1", 2, "卷积1", [], 250, 330)
+    Pool = Pool_Class("Pool1", 3, "最大池化1", [], 380, 330)
+    FullConn = FullConn_Class("FullConn1", 4, "全连接1", [], 510, 330)
+    Nonline = Nonline_Class("Nonline1", 5, "非线性函数1", [], 640, 330)
+    Classifier = Classifier_Class("Classifier1", 6, "分类1", [], 780, 330)
+    Error = Error_Class("Error1", 7, "误差计算1", [], 710, 124)
+    AjConv = AjConv_Class("AjConv1", 8, "卷积调整1", [], 250, 70)
+    AjFullconn = AjFullconn_Class("AjFullconn1", 9, "全连接调整1", [], 510, 120)
+    # AllModelObj = [DataSet, Conv, Pool, FullConn, Nonline, Classifier, Error, AjConv, AjFullconn]
+    main()

X4.py

@@ -2,10 +2,9 @@ import sys
 import pickle
 import subprocess
 import tkinter as tk
-import ttkbootstrap as ttk
+from tkinter import ttk
 from tkinter import messagebox
 import tkinter.scrolledtext as st
-from ttkbootstrap import *
 from PIL import Image, ImageTk
 from tkinter import Canvas, Frame, BOTH, PhotoImage, filedialog
 from X3 import load_data
@@ -38,7 +37,7 @@ Fullconn3 = None
 global Viewcanvas  # 定义画布
 global Text  # 带滚动条
 global Root  # 主窗口
-global AllModelObj #网络对象
+global AllModelObj  # 网络对象
 
 List_image = []  # 存储图元的列表
 Prob_images = []  # 存储图元的列表
@@ -59,20 +58,20 @@ Original_y = 0  # 记录图元原来的y坐标
 Offset_x = 0  # 记录鼠标相对于图元的x偏移量
 Offset_y = 0  # 记录鼠标相对于图元的y偏移量
 Yt = 0  # 新图元的y坐标
-Batch = 0 #批次
+Batch = 0  # 批次
 
 def create_instance():
     # 创建图元对象实例
-    DataSet = ModelObj("DataSet", 1, "数据集", "LoadData", "SetDataPara", ".", 120, 70).output()
-    Conv = ModelObj("Conv", 2, "卷积", "ConvProc", "SetConvPara", ".", 280, 70).output()
-    Pool = ModelObj("Pool", 3, "最大池化", "MaxPoolProc", "SetPollPara", ".", 120, 165).output()
-    FullConn = ModelObj("FullConn", 4, "全连接", "FullConnProc", "SetFullConnPara", ".", 280, 165).output()
-    Nonline = ModelObj("Nonline", 5, "非线性函数", "NonlinearProc", "SetNonLPara", ".", 120, 260).output()
-    Classifier = ModelObj("Classifier", 6, "分类", "ClassifierProc", "SetClassifyPara", ".", 280, 260).output()
-    Error = ModelObj("Error", 7, "误差计算", "ErrorProc", "SetErrorPara", ".", 120, 355).output()
-    AjConv = ModelObj("AjConv", 8, "卷积调整", "AjConvProc", "SetAjConvPara", ".", 280, 355).output()
-    AjFullconn = ModelObj("AjFullconn", 9, "全连接调整", "AjFullconnProc", "SetAjFCPara", ".", 120, 450).output()
-    AjNonline = ModelObj("AjNonline", 10, "非线性调整", "AjNonlineProc", "SetAjNLPara", ".", 280, 450).output()
+    DataSet = ModelObj("DataSet", 1, "数据集", "LoadData", "SetDataPara", ".", 90, 40).output()
+    Conv = ModelObj("Conv", 2, "卷积", "ConvProc", "SetConvPara", ".", 200, 40).output()
+    Pool = ModelObj("Pool", 3, "最大池化", "MaxPoolProc", "SetPollPara", ".", 90, 105).output()
+    FullConn = ModelObj("FullConn", 4, "全连接", "FullConnProc", "SetFullConnPara", ".", 200, 105).output()
+    Nonline = ModelObj("Nonline", 5, "非线性函数", "NonlinearProc", "SetNonLPara", ".", 90, 170).output()
+    Classifier = ModelObj("Classifier", 6, "分类", "ClassifierProc", "SetClassifyPara", ".", 200, 170).output()
+    Error = ModelObj("Error", 7, "误差计算", "ErrorProc", "SetErrorPara", ".", 90, 235).output()
+    AjConv = ModelObj("AjConv", 8, "卷积调整", "AjConvProc", "SetAjConvPara", ".", 200, 235).output()
+    AjFullconn = ModelObj("AjFullconn", 9, "全连接调整", "AjFullconnProc", "SetAjFCPara", ".", 90, 300).output()
+    AjNonline = ModelObj("AjNonline", 10, "非线性调整", "AjNonlineProc", "SetAjNLPara", ".", 200, 300).output()
     listinstance = [DataSet, Conv, Pool, FullConn, Nonline, Classifier, Error, AjConv, AjFullconn, AjNonline]
     return listinstance  # 还回图元对象实例列表
 
@@ -81,19 +80,19 @@ def picture_frame(window_width,window_height):
     # 计算矩形框的坐标和尺寸
     rect1_x = gap                                        # 第一个矩形框的左上角x坐标
     rect1_y = gap                                        # 第一个矩形框的左上角y坐标
-    rect1_width = window_width * 1 / 3 - gap-50          # 第一个矩形框的宽度
-    rect1_height = window_height * 3 / 4 - gap * 2-50    # 第一个矩形框的高度
-    rect2_x = window_width * 1 / 3 + gap - 50            # 第二个矩形框的左上角x坐标
+    rect1_width = window_width * 1 / 3 - gap-30          # 第一个矩形框的宽度
+    rect1_height = window_height * 3 / 4 - gap * 2       # 第一个矩形框的高度
+    rect2_x = window_width * 1 / 3 + gap - 30            # 第二个矩形框的左上角x坐标
     rect2_y = gap                                        # 第二个矩形框的左上角y坐标
-    rect2_width = window_width * 2 / 3 - gap + 50        # 第二个矩形框的宽度
-    rect2_height = window_height * 3 / 4 - gap * 2 -50   # 第二个矩形框的高度
+    rect2_width = window_width * 2 / 3 - gap + 30        # 第二个矩形框的宽度
+    rect2_height = window_height * 3 / 4 - gap * 2       # 第二个矩形框的高度
     rect3_x = gap                                        # 第三个矩形框的左上角x坐标
-    rect3_y = window_height * 3 / 4 + gap - 50           # 第三个矩形框的左上角y坐标
+    rect3_y = window_height * 3 / 4 + gap                # 第三个矩形框的左上角y坐标
     rect3_width = window_width - gap * 2                 # 第三个矩形框的宽度
-    rect3_height = window_height * 1 / 4 - gap * 2 + 50  # 第三个矩形框的高度
+    rect3_height = window_height * 1 / 4 - gap * 2       # 第三个矩形框的高度
     global Viewcanvas
     # 创建一个画布，用于绘制矩形框，设置画布的大小和背景色
-    Viewcanvas = tk.Canvas(Root, width=window_width, height=window_height, bg="blue")
+    Viewcanvas = tk.Canvas(Root, width=window_width, height=window_height, bg="white")
     Viewcanvas.pack()    # 将画布添加到主窗口中
     # 绘制矩形框，使用不同的颜色和线宽，指定矩形框的左上角和右下角坐标，填充色，边框色和边框宽度
     Viewcanvas.create_rectangle(rect1_x, rect1_y, rect1_x + rect1_width, rect1_y + rect1_height,
@@ -104,7 +103,7 @@ def picture_frame(window_width,window_height):
                             fill=None, outline="lightblue", width=2)
     global Text
     Text = st.ScrolledText(Root) # 创建一个带滚动条的文本框，用于展示计算过程
-    Text.place(x=rect3_x, y=rect3_y, width=1160, height=rect3_height - 1)
+    Text.place(x=rect3_x, y=rect3_y, width=760, height=rect3_height - 1)
 # if __name__ == '__main__':
 #     Root = tk.Tk()
 #     # 设置窗口的大小为1200*750
@@ -119,7 +118,7 @@ def element(path):
     # 加载图元对应的图片文件
     img=Image.open(path)
     # 使用resize方法调整图片
-    img = img.resize((70, 60))
+    img = img.resize((40, 30))
     # 把Image对象转换成PhotoImage对象
     img = ImageTk.PhotoImage(img)
     # 保存图片的引用，防止被垃圾回收
@@ -128,47 +127,46 @@ def element(path):
 
 def push_button():
     button = {}
-    # 指定按钮主题为 litera
-    style = Style(theme='litera')
+    style = ttk.Style(Root)
     # 设置blue.TButton样式的背景色为亮蓝灰，前景色为白色
-    style.configure("blue.TButton", background="LightSlateGray", foreground="white", font=("黑体", 13))
+    style.configure("blue.TButton", background="LightSlateGray", foreground="black", font=("黑体", 10))
     style.map("blue.TButton",
               background=[('active', 'PowderBlue'), ('pressed', 'red')],  # 设置激活状态下的背景色为粉蓝色，按下状态下的背景色为红色
               foreground=[('active', 'black'), ('pressed', 'white')])     # 设置激活状态下的前景色为黑色，按下状态下的前景色为白色
     # 设置green.TButton样式的背景色为中海洋绿，前景色为白色
-    style.configure("green.TButton", background="MediumSeaGreen", foreground="white", font=("黑体", 13))
+    style.configure("green.TButton", background="MediumSeaGreen", foreground="black", font=("黑体", 8))
     style.map("green.TButton",
               background=[('active', 'PaleGreen'), ('pressed', 'red')],  # 设置激活状态下的背景色为弱绿色，按下状态下的背景色为红色
               foreground=[('active', 'black'), ('pressed', 'white')])     # 设置激活状态下的前景色为黑色，按下状态下的前景色为白色
 
     # 创建三个按钮，用于触发训练参数的设置、训练、输出模型程序的操作
     button1 = ttk.Button(Root, text=" 训练参数设置 ", style="blue.TButton")
-    button1.place(x=20, y=500)
+    button1.place(x=20, y=330)
     button['button1'] = button1
     button2 = ttk.Button(Root, text=" 训练 ", style="blue.TButton")
-    button2.place(x=190, y=500)
+    button2.place(x=140, y=330)
     button['button2'] = button2
     button3 = ttk.Button(Root, text=" 输出模型程序 ", style="blue.TButton")
-    button3.place(x=300, y=500)
+    button3.place(x=240, y=330)
     button['button3'] = button3
     # 创建五个按钮，用于触发操作
     button4 = ttk.Button(Root, text=" 加载已有模型 ", style="green.TButton")
-    button4.place(x=370, y=5)
+    button4.place(x=255, y=10)
     button['button4'] = button4
     button5 = ttk.Button(Root, text=" 新建模型 ", style="green.TButton")
-    button5.place(x=530, y=5)
+    button5.place(x=355, y=10)
     button['button5'] = button5
     button6 = ttk.Button(Root, text=" 图元参数设置 ", style="green.TButton")
-    button6.place(x=660, y=5)
+    button6.place(x=435, y=10)
     button['button6'] = button6
     button7 = ttk.Button(Root, text=" 保存模型 ", style="green.TButton")
-    button7.place(x=820, y=5)
+    button7.place(x=535, y=10)
     button['button7'] = button7
     button8 = ttk.Button(Root, text=" 模型正确性检查 ", style="green.TButton")
-    button8.place(x=940, y=5)
+    button8.place(x=615, y=10)
     button['button8'] = button8
     button9 = ttk.Button(Root, text="退出", style="green.TButton")
-    button9.place(x=1120, y=5)
+    button9.place(x=725, y=10)
     button['button9'] = button9
     return button
 
@@ -330,14 +328,15 @@ def fully_connected(event):
     event.widget.coords(Dragged_item, Original_x, Original_y)
     # 将新图元的id和坐标添加到列表中
     Items1.append((new_item1, x, Yt))
-    # 创建弹出窗口
-    top = creat_window("全连接参数配置")
-    fully_size_entry = create_input_box(top, "全连接输出维度：", 10)  # 创建一个输入框，用于输入池大小
+    top = tk.Toplevel(Root)  # 创建弹出窗口
+    top.geometry("300x250")
+    top.title("全连接参数配置")
+    num_outputs_entry = create_input_box(top, "全连接输出维度：", 10)
     # 创建
     def get_input():
         global Listinstance
         # 从下拉框中获取输出维度
-        num = int(fully_size_entry.get())
+        num = int(num_outputs_entry.get())
         # 复制对象的标签
         Viewcanvas.create_text(x, Yt + 40, text=str(num) + "--全连接")
         if Listinstance[3][5] == ".":
@@ -394,12 +393,20 @@ def class_ification(event):
     event.widget.coords(Dragged_item, Original_x, Original_y)
     # 将新图元的id和坐标添加到列表中
     Items1.append((new_item1, x, Yt))
-    top = creat_window("分类参数配置")
-    class_size_entry = create_input_box(top, "阈值：", 0.001)  # 创建一个输入框，用于分类大小
+    top = tk.Toplevel(Root)  # 创建弹出窗口
+    top.geometry("300x250")
+    top.title("分类参数配置")
+    # 创建一个输入框，用于输入阈值
+    threshold_label = tk.Label(top, text="阈值：")
+    threshold_label.pack(padx=10, pady=10)
+    threshold = tk.DoubleVar(top, value=0.001)  # 创建一个DoubleVar对象，并设置默认值为0.001
+    threshold_entry = tk.Entry(top, textvariable=threshold)  # 关联DoubleVar对象
+    threshold_entry.pack(padx=20, pady=20)
+
     # 创建
     def get_input():
         # 从输入框中获取阈值
-        threshold = float(class_size_entry.get())
+        threshold = float(threshold_entry.get())
         # 复制对象的标签
         Viewcanvas.create_text(x, Yt + 40, text="输出分类结果")
         if Listinstance[5][5] == ".":
@@ -623,7 +630,7 @@ def element_binding(obj_x, obj_y, image, y, text):
     global Viewcanvas
     l1 = Viewcanvas.create_image(obj_x, obj_y, image=image) # 创建图元对象
     # 创建图元对象的标签
-    Viewcanvas.create_text(obj_x - 70, obj_y + y, text=text, font=("黑体", 14))
+    Viewcanvas.create_text(obj_x - 40, obj_y + y, text=text, font=("黑体", 10))
     return l1
 
 def create_left_element(AllModelObj,List_image):
@@ -716,7 +723,7 @@ def number_judgments(lsts):
         if type(lll) == list:
             count.append(lll[1])
             sequence.append(index)
-            if lll[1]==9:
+            if lll[1]==7:
                 break
     count_2,count_3,count_4=0,0,0
     list_count=[]
@@ -756,18 +763,18 @@ def load_file():
 
 def connecting_lines(obj_x, obj_y, index, x, y, x1, x2, y1, y2, y3 , image, text, smooth, width):
     # 创建图元对象
-    Viewcanvas.create_image(obj_x+(index + x) * 110, obj_y, image=image)
+    Viewcanvas.create_image(obj_x+(index + x) * 80, obj_y, image=image)
     # 创建图元对象的标签
-    Viewcanvas.create_text(obj_x+(index + x) * 110, obj_y + y1, text=text, font=("黑体", 14))
+    Viewcanvas.create_text(obj_x+(index + x) * 80, obj_y + y1, text=text, font=("黑体", 8))
     # 创建数据线箭头
-    Viewcanvas.create_line(obj_x+(index + x) * 110 + x1, obj_y + y2, obj_x+(index + y) * 110 + x2, obj_y + y3, arrow=tk.LAST,
+    Viewcanvas.create_line(obj_x+(index + x) * 80 + x1, obj_y + y2, obj_x+(index + y) * 80 + x2, obj_y + y3, arrow=tk.LAST,
                        arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth, width=1)
 
 def connecting(obj_x, obj_y, index, x, y1, image, text):
     # 创建图元对象
-    Viewcanvas.create_image(obj_x+(index + x) * 110, obj_y, image=image)
+    Viewcanvas.create_image(obj_x+(index + x) * 80, obj_y, image=image)
     # 创建图元对象的标签
-    Viewcanvas.create_text(obj_x+(index + x) * 110, obj_y + y1, text=text, font=("黑体", 14))
+    Viewcanvas.create_text(obj_x+(index + x) * 80, obj_y + y1, text=text, font=("黑体", 8))
 # 加载已有模型
 def load_model():
     import copy
@@ -778,6 +785,7 @@ def load_model():
     global Viewcanvas
     lsts = load_file()
     DataPara = setload_data()
+    Viewcanvas.delete('all')
     create_left_element(AllModelObj, List_image)
     Train_images, Test_images = load_data(DataPara)  # load_data()函数，根据参数加载数据集
     # 打印一些信息，检查是否正确加载
@@ -785,25 +793,25 @@ def load_model():
     print("测试集图片的形状：", Test_images.shape)
     # 根据pkl文件的前向传播顺序列表，依次识别卷积层、池化层、全连接层、非线性层和分类层的操作
     count,counts,sequence = number_judgments(lsts)
-    x,y = 420,300
+    x,y = 290,200
     connecting_lines(x, y, 0, 0, 0, 25, 60, 45, 0, 0, List_image[0], '数据集', True, 3)
     for layer in range(len(count)):
         if count[layer] == 2: # 如果是卷积层，将读取的卷积层的信息存储到Conv实例
-            if counts[layer]%10 == 1:
+            if counts[layer] % 10 == 1:
                 global Conv1
                 Listinstance[1] = copy.deepcopy(lsts[sequence[layer]])
                 Conv1 = lsts[sequence[layer]]
                 connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[1], '卷积层1', True, 3)
-            if counts[layer]%10 == 2:
+            if counts[layer] % 10 == 2:
                 global Conv2
                 Conv2 = lsts[sequence[layer]]
                 connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[1], '卷积层2', True, 3)
-            if counts[layer]%10 == 3:
+            if counts[layer] % 10 == 3:
                 global Conv3
                 Conv3 = lsts[sequence[layer]]
                 connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[1], '卷积层3', True, 3)
         if count[layer] == 3: # 如果是池化层，将读取的池化层的信息存储到Pool实例
-            if counts[layer]%10 == 1:
+            if counts[layer] % 10 == 1:
                 global Pool1
                 Listinstance[2] =copy.deepcopy(lsts[sequence[layer]])
                 Pool1 = lsts[sequence[layer]]
@@ -842,25 +850,9 @@ def load_model():
         if count[layer] == 7:  # 如果是计算误差层，将读取的信息存储到Error实例
             Listinstance[6] = lsts[sequence[layer]]
             connecting(x-30, y-120, layer, 0, 45, List_image[6], '计算误差')
-            Viewcanvas.create_line(x + layer * 110 + 25, y, x + layer * 110 + 10, y - 60,
-                                   x + 5 * 110 - 20, y - 120, arrow=tk.LAST, arrowshape=(16, 20, 4),
-                                   fill='lightblue', smooth=True, width=2)
-            # 创建图元对象
-            Viewcanvas.create_image(600, 180, image=List_image[7])
-            # 创建图元对象的标签
-            Viewcanvas.create_text(600, 230, text='调整1', font=("黑体", 14))
-            Viewcanvas.create_line(x + layer * 110 - 50, y - 140, 630, 165, arrow=tk.LAST, arrowshape=(16, 20, 4),
-                                   fill='lightblue', smooth=True, width=2)
-            Viewcanvas.create_line(600, 200, x + 1 * 110, y - 20, arrow=tk.LAST, arrowshape=(16, 20, 4),
-                                   fill='lightblue', smooth=True, width=2)
-            # 创建图元对象
-            Viewcanvas.create_image(860, 200, image=List_image[7])
-            # 创建图元对象的标签
-            Viewcanvas.create_text(860, 250, text='调整2', font=("黑体", 14))
-            Viewcanvas.create_line(x + layer * 110 - 60, y - 120, 875, 200, arrow=tk.LAST, arrowshape=(16, 20, 4),
-                                   fill='lightblue', smooth=True, width=2)
-            Viewcanvas.create_line(835, 200, x + (layer - 2) * 110 + 10, y - 20, arrow=tk.LAST, arrowshape=(16, 20, 4),
-                                   fill='lightblue', smooth=True, width=2)
+            Viewcanvas.create_line(x + 5 * 80 + 25, y, x + 5 * 80 + 20, y - 80,
+                                   x + 5 * 80 -20 , y-120, arrow=tk.LAST, arrowshape=(16, 20, 4),
+                                   fill='lightblue', smooth=True, width=1)
 
 def create_window(number,count,list1,list2,list3,list4,list5,list6):
     global Listinstance
@@ -1108,6 +1100,7 @@ def create_window(number,count,list1,list2,list3,list4,list5,list6):
             para = float(para)
             window = creat_window('分类参数调整')  # 创建一个新的窗口
             threshold_entry = create_input_box(window, "阈值：", para)  # 创建一个输入框，用于输入阈值
+
             def modify_para():
                 # 获取输入框的内容
                 threshold = threshold_entry.get()
@@ -1191,10 +1184,10 @@ if __name__ == '__main__':
     # 创建一个主窗口
     Root = tk.Tk()
     # 设置窗口的大小为1200*750
-    window_width = 1200  # 窗口的宽度
-    window_height = 750  # 窗口的高度
+    window_width = 800  # 窗口的宽度
+    window_height = 450  # 窗口的高度
     Root.title("神经网络可视化")
-    Root.geometry("1200x750")  # 设置窗口的大小和位置
+    Root.geometry("800x450")  # 设置窗口的大小和位置
     picture_frame(window_width,window_height)
     Listinstance = create_instance()
     listclass = connect_class(Listinstance)

X5.py

@@ -7,14 +7,12 @@ import functools
 import numpy as np
 import pandas as pd
 import tkinter as tk
-import ttkbootstrap as ttk
+from tkinter import ttk
 from tkinter import messagebox
 import tkinter.scrolledtext as st
 from PIL import Image, ImageTk
-from ttkbootstrap import *
 from tkinter import Canvas, Frame, BOTH, PhotoImage, filedialog
-from X4 import setload_data
-from X3 import load_data
+from X3 import setload_data,load_data
 from X2 import ModelObj
 # 网络模型正确的图元和连接关系的数据结构，用一个字典表示
 RIGHT_MODEL = [
@@ -55,6 +53,7 @@ Items3 = [] # 存储被拖拽的图元的列表
 Items4 = [] # 存储新图元文字标签的列表
 Counts = {} # 用来存储Items3每个数字出现过的次数
 Conv_xy_list = [] # 存储卷积图元的坐标
+Pool_xy_list = [] # 存储全连接图元的坐标
 Fullconn_xy_list = [] # 存储全连接图元的坐标
 Train_list = [] # 存储训练设置参数
 Listinstance = [] # 图元对象实例
@@ -68,16 +67,16 @@ Batch = 0 #批次
 
 def create_instance():
     # 创建图元对象实例
-    DataSet = ModelObj("DataSet", 1, "数据集", "LoadData", "SetDataPara", ".", 120, 70).output()
-    Conv = ModelObj("Conv", 2, "卷积", "ConvProc", "SetConvPara", ".", 280, 70).output()
-    Pool = ModelObj("Pool", 3, "最大池化", "MaxPoolProc", "SetPollPara", ".", 120, 165).output()
-    FullConn = ModelObj("FullConn", 4, "全连接", "FullConnProc", "SetFullConnPara", ".", 280, 165).output()
-    Nonline = ModelObj("Nonline", 5, "非线性函数", "NonlinearProc", "SetNonLPara", ".", 120, 260).output()
-    Classifier = ModelObj("Classifier", 6, "分类", "ClassifierProc", "SetClassifyPara", ".", 280, 260).output()
-    Error = ModelObj("Error", 7, "误差计算", "ErrorProc", "SetErrorPara", ".", 120, 355).output()
-    AjConv = ModelObj("AjConv", 8, "卷积调整", "AjConvProc", "SetAjConvPara", ".", 280, 355).output()
-    AjFullconn = ModelObj("AjFullconn", 9, "全连接调整", "AjFullconnProc", "SetAjFCPara", ".", 120, 450).output()
-    AjNonline = ModelObj("AjNonline", 10, "非线性调整", "AjNonlineProc", "SetAjNLPara", ".", 280, 450).output()
+    DataSet = ModelObj("DataSet", 1, "数据集", "LoadData", "SetDataPara", ".", 90, 40).output()
+    Conv = ModelObj("Conv", 2, "卷积", "ConvProc", "SetConvPara", ".", 200, 40).output()
+    Pool = ModelObj("Pool", 3, "最大池化", "MaxPoolProc", "SetPollPara", ".", 90, 105).output()
+    FullConn = ModelObj("FullConn", 4, "全连接", "FullConnProc", "SetFullConnPara", ".", 200, 105).output()
+    Nonline = ModelObj("Nonline", 5, "非线性函数", "NonlinearProc", "SetNonLPara", ".", 90, 170).output()
+    Classifier = ModelObj("Classifier", 6, "分类", "ClassifierProc", "SetClassifyPara", ".", 200, 170).output()
+    Error = ModelObj("Error", 7, "误差计算", "ErrorProc", "SetErrorPara", ".", 90, 235).output()
+    AjConv = ModelObj("AjConv", 8, "卷积调整", "AjConvProc", "SetAjConvPara", ".", 200, 235).output()
+    AjFullconn = ModelObj("AjFullconn", 9, "全连接调整", "AjFullconnProc", "SetAjFCPara", ".", 90, 300).output()
+    AjNonline = ModelObj("AjNonline", 10, "非线性调整", "AjNonlineProc", "SetAjNLPara", ".", 200, 300).output()
     listinstance = [DataSet, Conv, Pool, FullConn, Nonline, Classifier, Error, AjConv, AjFullconn, AjNonline]
     return listinstance  # 还回图元对象实例列表
 
@@ -86,16 +85,16 @@ def picture_frame(window_width,window_height):
     # 计算矩形框的坐标和尺寸
     rect1_x = gap                                        # 第一个矩形框的左上角x坐标
     rect1_y = gap                                        # 第一个矩形框的左上角y坐标
-    rect1_width = window_width * 1 / 3 - gap-50          # 第一个矩形框的宽度
-    rect1_height = window_height * 3 / 4 - gap * 2-50    # 第一个矩形框的高度
-    rect2_x = window_width * 1 / 3 + gap - 50            # 第二个矩形框的左上角x坐标
+    rect1_width = window_width * 1 / 3 - gap-30          # 第一个矩形框的宽度
+    rect1_height = window_height * 3 / 4 - gap * 2       # 第一个矩形框的高度
+    rect2_x = window_width * 1 / 3 + gap - 30            # 第二个矩形框的左上角x坐标
     rect2_y = gap                                        # 第二个矩形框的左上角y坐标
-    rect2_width = window_width * 2 / 3 - gap +50         # 第二个矩形框的宽度
-    rect2_height = window_height * 3 / 4 - gap * 2 -50   # 第二个矩形框的高度
+    rect2_width = window_width * 2 / 3 - gap + 30        # 第二个矩形框的宽度
+    rect2_height = window_height * 3 / 4 - gap * 2       # 第二个矩形框的高度
     rect3_x = gap                                        # 第三个矩形框的左上角x坐标
-    rect3_y = window_height * 3 / 4 + gap -50            # 第三个矩形框的左上角y坐标
+    rect3_y = window_height * 3 / 4 + gap                # 第三个矩形框的左上角y坐标
     rect3_width = window_width - gap * 2                 # 第三个矩形框的宽度
-    rect3_height = window_height * 1 / 4 - gap * 2  +50  # 第三个矩形框的高度
+    rect3_height = window_height * 1 / 4 - gap * 2       # 第三个矩形框的高度
     global Viewcanvas
     # 创建一个画布，用于绘制矩形框，设置画布的大小和背景色
     Viewcanvas = tk.Canvas(Root, width=window_width, height=window_height, bg="white")
@@ -109,7 +108,7 @@ def picture_frame(window_width,window_height):
                                 fill=None, outline="lightblue", width=2)
     global Text
     Text = st.ScrolledText(Root) # 创建一个带滚动条的文本框，用于展示计算过程
-    Text.place(x=rect3_x, y=rect3_y, width=1160, height=rect3_height - 1)
+    Text.place(x=rect3_x, y=rect3_y, width=760, height=rect3_height - 1)
 # if __name__ == '__main__':
 #     Root = tk.Tk()
 #     # 设置窗口的大小为1200*750
@@ -124,7 +123,7 @@ def element(path):
     # 加载图元对应的图片文件
     img=Image.open(path)
     # 使用resize方法调整图片
-    img = img.resize((70, 60))
+    img = img.resize((40, 30))
     # 把Image对象转换成PhotoImage对象
     img = ImageTk.PhotoImage(img)
     # 保存图片的引用，防止被垃圾回收
@@ -133,47 +132,45 @@ def element(path):
 
 def push_button():
     button = {}
-    # 指定按钮主题为 litera
-    style = Style(theme='litera')
-    # 设置blue.TButton样式的背景色为亮蓝灰，前景色为白色
-    style.configure("blue.TButton", background="LightSlateGray", foreground="white", font=("黑体", 13))
-    style.map("blue.TButton",
-              background=[('active', 'PowderBlue'), ('pressed', 'red')],  # 设置激活状态下的背景色为粉蓝色，按下状态下的背景色为红色
-              foreground=[('active', 'black'), ('pressed', 'white')])     # 设置激活状态下的前景色为黑色，按下状态下的前景色为白色
-    # 设置green.TButton样式的背景色为中海洋绿，前景色为白色
-    style.configure("green.TButton", background="MediumSeaGreen", foreground="white", font=("黑体", 13))
-    style.map("green.TButton",
-              background=[('active', 'PaleGreen'), ('pressed', 'red')],  # 设置激活状态下的背景色为弱绿色，按下状态下的背景色为红色
-              foreground=[('active', 'black'), ('pressed', 'white')])     # 设置激活状态下的前景色为黑色，按下状态下的前景色为白色
+    style = ttk.Style(Root)
+    # 设置blue.TButton样式
+    style.configure("blue.TButton", background="LightSlateGray", foreground="black", font=("黑体", 8))
+    style.map("blue.TButton", background=[('active', 'PowderBlue'), ('pressed', 'red')],
+              foreground=[('active', 'black'), ('pressed', 'white')])
+
+    # 设置green.TButton样式
+    style.configure("green.TButton", background="MediumSeaGreen", foreground="black", font=("黑体", 8))
+    style.map("green.TButton", background=[('active', 'PaleGreen'), ('pressed', 'red')],
+              foreground=[('active', 'black'), ('pressed', 'white')])
 
     # 创建三个按钮，用于触发训练参数的设置、训练、输出模型程序的操作
     button1 = ttk.Button(Root, text=" 训练参数设置 ", style="blue.TButton")
-    button1.place(x=20, y=500)
+    button1.place(x=20, y=330)
     button['button1'] = button1
     button2 = ttk.Button(Root, text=" 训练 ", style="blue.TButton")
-    button2.place(x=190, y=500)
+    button2.place(x=140, y=330)
     button['button2'] = button2
     button3 = ttk.Button(Root, text=" 输出模型程序 ", style="blue.TButton")
-    button3.place(x=300, y=500)
+    button3.place(x=240, y=330)
     button['button3'] = button3
     # 创建五个按钮，用于触发操作
     button4 = ttk.Button(Root, text=" 加载已有模型 ", style="green.TButton")
-    button4.place(x=370, y=5)
+    button4.place(x=255, y=10)
     button['button4'] = button4
     button5 = ttk.Button(Root, text=" 新建模型 ", style="green.TButton")
-    button5.place(x=530, y=5)
+    button5.place(x=355, y=10)
     button['button5'] = button5
     button6 = ttk.Button(Root, text=" 图元参数设置 ", style="green.TButton")
-    button6.place(x=660, y=5)
+    button6.place(x=435, y=10)
     button['button6'] = button6
     button7 = ttk.Button(Root, text=" 保存模型 ", style="green.TButton")
-    button7.place(x=820, y=5)
+    button7.place(x=535, y=10)
     button['button7'] = button7
     button8 = ttk.Button(Root, text=" 模型正确性检查 ", style="green.TButton")
-    button8.place(x=940, y=5)
+    button8.place(x=615, y=10)
     button['button8'] = button8
     button9 = ttk.Button(Root, text="退出", style="green.TButton")
-    button9.place(x=1120, y=5)
+    button9.place(x=725, y=10)
     button['button9'] = button9
     return button
 
@@ -271,6 +268,7 @@ def setpool_para(numbers):
     pool_list = Listinstance[2][5].split()
     # 池大小和池类型
     pool_mode, pool_size = pool_list[numbers-1].split(',')
+    print(pool_size)
     pool_size = int(pool_size)
     stride = 2 # 设置步长，这里假设是2
     PoolPara = {numbers:{"pool_mode": pool_mode, # 返回PoolPara参数，这里用一个字典来存储
@@ -533,113 +531,20 @@ def update_conv_layer(input, kernel, bias, loss):
     bias = bias - learning_rate * bias_grad        # 偏置项参数减去学习率乘以偏置项梯度
     # 返回更新后的卷积核、偏置
     return kernel, bias
-def conv_one(loss,images):
-    global Conv1
-    kernel = Conv1[5][1]["kernel"]
-    bias = 0.5
-    loss = np.array([[loss]])
-    new_kernel, new_bias = update_conv_layer(images, kernel, bias, loss)
-    Conv1[5][1]["kernel"] = new_kernel
-    Conv1[5][1]["bias"] = new_bias
-
-def conv_two(loss, images):
-    global Conv1
-    global Conv2
-    kernel1 = Conv1[5][1]["kernel"]
-    bias1 = 0.5
-    loss = np.array([[loss]])
-    new_kernel1, new_bias1 = update_conv_layer(images, kernel1, bias1, loss)
-    Conv1[5][1]["kernel"] = new_kernel1
-    Conv1[5][1]["bias"] = new_bias1
-
-    kernel2 = Conv2[5][2]["kernel"]
-    bias2 = 0.5
-    loss = np.array([[loss]])
-    new_kernel2, new_bias2 = update_conv_layer(Pool1[-1], kernel2, bias2, loss)
-    Conv2[5][2]["kernel"] = new_kernel2
-    Conv2[5][2]["bias"] = new_bias2
-
-def conv_three(loss, images):
-    global Conv1
-    global Conv2
-    global Conv3
-    kernel1 = Conv1[5][1]["kernel"]
-    bias1 = 0.5
-    loss = np.array([[loss]])
-    new_kernel1, new_bias1 = update_conv_layer(images, kernel1, bias1, loss)
-    Conv1[5][1]["kernel"] = new_kernel1
-    Conv1[5][1]["bias"] = new_bias1
-    kernel2 = Conv2[5][2]["kernel"]
-    bias2 = 0.5
-    loss = np.array([[loss]])
-    new_kernel2, new_bias2 = update_conv_layer(Pool1[-1], kernel2, bias2, loss)
-    Conv2[5][2]["kernel"] = new_kernel2
-    Conv2[5][2]["bias"] = new_bias2
-    kernel3 = Conv3[5][3]["kernel"]
-    bias3 = 0.5
-    loss = np.array([[loss]])
-    new_kernel3, new_bias3 = update_conv_layer(Pool2[-1], kernel3, bias3, loss)
-    Conv3[5][3]["kernel"] = new_kernel3
-    Conv3[5][3]["bias"] = new_bias3
 
 # 定义一个全连接层的权重矩阵和偏置向量的调整函数，可以选择不同的激活函数
-def update_parameters(weight_matrix, bias_vector, loss):
+def update_parameters(input_data, weight_matrix, bias_vector, loss, output_derivative):
     # 根据激活函数的参数选择相应的函数和导数
     # 计算权重矩阵和偏置向量的梯度，使用链式法则
+    weight_matrix_grad = np.dot(input_data.T, loss * output_derivative)
+    bias_vector_grad = np.sum(loss * output_derivative, axis=0)
     learning_rate = 0.01
-    gradient_weights = np.outer(loss, learning_rate)
     # 更新权重矩阵和偏置向量
-    weight_matrix = weight_matrix - gradient_weights
-    bias_vector = bias_vector - learning_rate * bias_vector
+    weight_matrix = weight_matrix - learning_rate * weight_matrix_grad
+    bias_vector = bias_vector - learning_rate * bias_vector_grad
     # 返回更新后的权重矩阵和偏置向量
     return weight_matrix, bias_vector
-def fully_one(loss):
-    global Fullconn1
-    weight1 = Fullconn1[5][1]["weights"]
-    bias1 = Fullconn1[5][1]["bias"]
-    loss = np.array([loss])
-    new_weight1, new_bias1 = update_parameters(weight1, bias1, loss)
-    Fullconn1[5][1]["weights"] = new_weight1
-    Fullconn1[5][1]["bias"] = new_bias1
-
-def fully_two(loss):
-    global Fullconn1
-    global Fullconn2
-    weight1 = Fullconn1[5][1]["weights"]
-    bias1 = Fullconn1[5][1]["bias"]
-    loss = np.array([loss])
-    new_weight1, new_bias1 = update_parameters(weight1, bias1, loss)
-    Fullconn1[5][1]["weights"] = new_weight1
-    Fullconn1[5][1]["bias"] = new_bias1
-
-    weight2 = Fullconn2[5][1]["weights"]
-    bias2 = Fullconn2[5][1]["bias"]
-    new_weight2, new_bias2 = update_parameters(weight2, bias2, loss)
-    Fullconn2[5][1]["weights"] = new_weight2
-    Fullconn2[5][1]["bias"] = new_bias2
-
-def fully_three(loss):
-    global Fullconn1
-    global Fullconn2
-    global Fullconn3
-    weight1 = Fullconn1[5][1]["weights"]
-    bias1 = Fullconn1[5][1]["bias"]
-    loss = np.array([loss])
-    new_weight1, new_bias1 = update_parameters(weight1, bias1, loss)
-    Fullconn1[5][1]["weights"] = new_weight1
-    Fullconn1[5][1]["bias"] = new_bias1
-
-    weight2 = Fullconn2[5][1]["weights"]
-    bias2 = Fullconn2[5][1]["bias"]
-    new_weight2, new_bias2 = update_parameters(weight2, bias2, loss)
-    Fullconn2[5][1]["weights"] = new_weight2
-    Fullconn2[5][1]["bias"] = new_bias2
-
-    weight3 = Fullconn3[5][3]["weights"]
-    bias3 = Fullconn3[5][1]["bias"]
-    new_weight3, new_bias3 = update_parameters(weight3, bias3, loss)
-    Fullconn3[5][3]["weights"] = new_weight3
-    Fullconn3[5][3]["bias"] = new_bias3
+
 # X4交互式构建卷积神经网络模型
 # 定义图元拖拽开始的函数
 def on_drag_start(event):
@@ -783,7 +688,9 @@ def fully_connected(event):
     event.widget.coords(Dragged_item, Original_x, Original_y)
     # 将新图元的id和坐标添加到列表中
     Items1.append((new_item1, x, Yt))
-    top = creat_window("全连接参数配置")
+    top = tk.Toplevel(Root)  # 创建弹出窗口
+    top.geometry("300x250")
+    top.title("全连接参数配置")
     num_outputs_entry = create_input_box(top, "全连接输出维度：", 10)
     # 创建
     def get_input():
@@ -846,13 +753,20 @@ def class_ification(event):
     event.widget.coords(Dragged_item, Original_x, Original_y)
     # 将新图元的id和坐标添加到列表中
     Items1.append((new_item1, x, Yt))
-    top = creat_window('分类参数配置')  # 创建弹出窗口
-    # 创建一个下拉框，用于选择非线性类型
-    class_box_combobox = create_input_box(top, "阈值：", 0.001)  # 创建一个下拉框，用于选择池化类型
+    top = tk.Toplevel(Root)  # 创建弹出窗口
+    top.geometry("300x250")
+    top.title("分类参数配置")
+    # 创建一个输入框，用于输入阈值
+    threshold_label = tk.Label(top, text="阈值：")
+    threshold_label.pack(padx=10, pady=10)
+    threshold = tk.DoubleVar(top, value=0.001)  # 创建一个DoubleVar对象，并设置默认值为0.001
+    threshold_entry = tk.Entry(top, textvariable=threshold)  # 关联DoubleVar对象
+    threshold_entry.pack(padx=20, pady=20)
+
     # 创建
     def get_input():
         # 从输入框中获取阈值
-        threshold = float(class_box_combobox.get())
+        threshold = float(threshold_entry.get())
         # 复制对象的标签
         Viewcanvas.create_text(x, Yt + 40, text="输出分类结果")
         if Listinstance[5][5] == ".":
@@ -919,6 +833,7 @@ def adjust_one(event):
     # 将新图元的id和坐标添加到列表中
     Items1.append((new_item1, x, y))
 
+
 def adjust_two(event):
     global Viewcanvas
     global Items1
@@ -1055,7 +970,7 @@ def element_binding(obj_x, obj_y, image, y, text):
     # 创建图元对象
     l1 = Viewcanvas.create_image(obj_x, obj_y, image=image)
     # 创建图元对象的标签
-    Viewcanvas.create_text(obj_x - 70, obj_y + y, text=text, font=("黑体", 14))
+    Viewcanvas.create_text(obj_x - 40, obj_y + y, text=text, font=("黑体", 8))
     return l1
 
 def create_left_element(AllModelObj,List_image):
@@ -1120,21 +1035,21 @@ def train_setting():
     button = tk.Button(top, text="获取信息", command=get_input)
     button.pack()
 
-def conn_forward(count,layer,outputs):
+def conn_forward(counts,layer,outputs):
     # 若为第一层卷积层且该层的图元实例Conv1还未创建
-    if count[layer] == 1 and Conv1 == None:
+    if counts[layer] == 1 and Conv1 is None:
         # 调用SetConvPara()函数，获取卷积层1参数
-        ConvPara = setconv_para(1)
+        ConvPara = setconv_para(counts[layer])
     # 若图元实例Conv1已经被定义，说明需要根据反向传播调整卷积核
-    if count[layer] == 1 and Conv1 != None:
+    if counts[layer] == 1 and Conv1 is not None:
         ConvPara = Conv1[5][1]
-    if count[layer] == 2 and Conv2 == None:
-        ConvPara = setconv_para(count[layer])
-    if count[layer] == 2 and Conv2 != None:
+    if counts[layer] == 2 and Conv2 is None:
+        ConvPara = setconv_para(counts[layer])
+    if counts[layer] == 2 and Conv2 is not None:
         ConvPara = Conv2[5][2]
-    if count[layer] == 3 and Conv3 == None:
-        ConvPara = setconv_para(count[layer])
-    if count[layer] == 3 and Conv3 != None:
+    if counts[layer] == 3 and Conv3 is None:
+        ConvPara = setconv_para(counts[layer])
+    if counts[layer] == 3 and Conv3 is not None:
         ConvPara = Conv3[5][3]
     # 初始化一个空列表来存储池化后的结果
     new_outputs = []
@@ -1160,22 +1075,22 @@ def conn_forward(count,layer,outputs):
     print(outputs[0])
     print("")
     return outputs
-def pool_forward(count,layer,outputs):
+def pool_forward(counts,layer,outputs):
     global Pool1
     global Pool2
     global Pool3
-    if count[layer] == 1 and Pool1 == None:
+    if counts[layer] == 1 and Pool1 is None:
         # 调用设置池化层参数的函数，获取池化参数
-        PoolPara = setpool_para(count[layer])
-    if count[layer] == 1 and Pool1 != None:
+        PoolPara = setpool_para(counts[layer])
+    if counts[layer] == 1 and Pool1 is not None:
         PoolPara = Pool1[5][1]
-    if count[layer] == 2 and Pool2 == None:
-        PoolPara = setpool_para(count[layer])
-    if count[layer] == 2 and Pool2 != None:
+    if counts[layer] == 2 and Pool2 is None:
+        PoolPara = setpool_para(counts[layer])
+    if counts[layer] == 2 and Pool2 is not None:
         PoolPara = Pool2[5][2]
-    if count[layer] == 3 and Pool3 == None:
-        PoolPara = setpool_para(count[layer])
-    if count[layer] == 3 and Pool3 != None:
+    if counts[layer] == 3 and Pool3 is None:
+        PoolPara = setpool_para(counts[layer])
+    if counts[layer] == 3 and Pool3 is not None:
         PoolPara = Pool3[5][3]
     # 初始化一个空列表来存储池化后的结果
     new_outputs = []
@@ -1184,33 +1099,33 @@ def pool_forward(count,layer,outputs):
         new_outputs.append(pool_proc(output, PoolPara))
     # 将new_outputs赋值给outputs
     outputs = new_outputs
-    if count[layer] == 1:
+    if counts[layer] == 1:
         Pool1.append(outputs)
-    if count[layer] == 2:
+    if counts[layer] == 2:
         Pool2.append(outputs)
-    if count[layer] == 3:
+    if counts[layer] == 3:
         Pool3.append(outputs)
     print("池化处理结果：")
     print(outputs[0])
     print("")
     return outputs
 
-def fullconn_forward(count,layer,outputs):
+def fullconn_forward(counts,layer,outputs):
     output = outputs[0]
     # 若图元实例Fullconn1未被定义
-    if count[layer] == 1 and Fullconn1 == None:
+    if counts[layer] == 1 and Fullconn1 is None:
         # 调用设置全连接层参数的函数，获取全连接参数
-        FullConnPara = setfullconn_para(output, count[layer])
+        FullConnPara = setfullconn_para(output, counts[layer])
     # 若图元实例Fullconn1已经被定义，说明需要根据反向传播调整权重矩阵和偏置向量
-    if count[layer] == 1 and Fullconn1 != None:
+    if counts[layer] == 1 and Fullconn1 is not None:
         FullConnPara = Fullconn1[5][1]
-    if count[layer] == 2 and Fullconn2 == None:
-        FullConnPara = setfullconn_para(output, count[layer])
-    if count[layer] == 2 and Fullconn2 != None:
+    if counts[layer] == 2 and Fullconn2 is None:
+        FullConnPara = setfullconn_para(output, counts[layer])
+    if counts[layer] == 2 and Fullconn2 is not None:
         FullConnPara = Fullconn2[5][2]
-    if count[layer] == 3 and Fullconn3 == None:
-        FullConnPara = setfullconn_para(output, count[layer])
-    if count[layer] == 3 and Fullconn3 != None:
+    if counts[layer] == 3 and Fullconn3 is None:
+        FullConnPara = setfullconn_para(output, counts[layer])
+    if counts[layer] == 3 and Fullconn3 is not None:
         FullConnPara = Fullconn3[5][3]
     # 初始化一个空列表来存储池化后的结果
     new_outputs = []
@@ -1257,6 +1172,7 @@ def classifier_forward(outputs):
         x -= np.max(x)
         # 计算指数和归一化
         return np.exp(x) / np.sum(np.exp(x))
+
     # 对outputs列表中的每个元素调用ClassifierProc函数，并将结果添加到new_outputs列表中
     for output in outputs:
         new_outputs.append(classifier_proc(output, ClassifyPara))
@@ -1295,6 +1211,55 @@ def forward_propagation(Train_images):
     outputs = np.array(outputs)# 将输出结果列表转换为数组形式，方便后续处理
     return outputs# 返回输出结果数组
 
+def conv_one(loss,images):
+    global Conv1
+    kernel = Conv1[5][1]["kernel"]
+    bias = 0.5
+    loss = np.array([[loss]])
+    new_kernel, new_bias = update_conv_layer(images, kernel, bias, loss)
+    Conv1[5][1]["kernel"] = new_kernel
+    Conv1[5][1]["bias"] = new_bias
+
+def conv_two(loss, images):
+    global Conv1
+    global Conv2
+    kernel1 = Conv1[5][1]["kernel"]
+    bias1 = 0.5
+    loss = np.array([[loss]])
+    new_kernel1, new_bias1 = update_conv_layer(images, kernel1, bias1, loss)
+    Conv1[5][1]["kernel"] = new_kernel1
+    Conv1[5][1]["bias"] = new_bias1
+
+    kernel2 = Conv2[5][2]["kernel"]
+    bias2 = 0.5
+    loss = np.array([[loss]])
+    new_kernel2, new_bias2 = update_conv_layer(Pool1[-1], kernel2, bias2, loss)
+    Conv2[5][2]["kernel"] = new_kernel2
+    Conv2[5][2]["bias"] = new_bias2
+
+def conv_three(loss, images):
+    global Conv1
+    global Conv2
+    global Conv3
+    kernel1 = Conv1[5][1]["kernel"]
+    bias1 = 0.5
+    loss = np.array([[loss]])
+    new_kernel1, new_bias1 = update_conv_layer(images, kernel1, bias1, loss)
+    Conv1[5][1]["kernel"] = new_kernel1
+    Conv1[5][1]["bias"] = new_bias1
+    kernel2 = Conv2[5][2]["kernel"]
+    bias2 = 0.5
+    loss = np.array([[loss]])
+    new_kernel2, new_bias2 = update_conv_layer(Pool1[-1], kernel2, bias2, loss)
+    Conv2[5][2]["kernel"] = new_kernel2
+    Conv2[5][2]["bias"] = new_bias2
+    kernel3 = Conv3[5][3]["kernel"]
+    bias3 = 0.5
+    loss = np.array([[loss]])
+    new_kernel3, new_bias3 = update_conv_layer(Pool2[-1], kernel3, bias3, loss)
+    Conv3[5][3]["kernel"] = new_kernel3
+    Conv3[5][3]["bias"] = new_bias3
+
 def determine(Train_list,Items3):
     if len(Train_list) == 0:
         messagebox.showerror("错误", "请设置训练参数！")
@@ -1331,33 +1296,23 @@ def train():# 训练
             loss = error_proc(Prob_images, right_label, ErrorPara) * 0.1 # 计算误差值
             print("当前epoch:",first_epoch,i * batch_size,":",(i + 1) * batch_size,"误差值：",loss)
             print("")
-            if (Conv2 == None) and (Conv3 == None):
-                conv_one(loss,images)
-            elif (Conv2 != None) and (Conv3 == None):
-                conv_two(loss,images)
-            elif (Conv3 != None):
-                conv_three(loss,images)
-            if (Fullconn2 == None) and (Fullconn3 == None):
-                fully_one(loss)
-            elif (Fullconn2 != None) and (Fullconn3 == None):
-                fully_two(loss)
-            elif (Fullconn3 != None):
-                fully_three(loss)
+            if Conv2 and Conv3 is None:
+                conv_one(loss, images)
+            elif Conv2 is not None and Conv3 is None:
+                conv_two(loss, images)
+            elif Conv3 is not None:
+                conv_three(loss, images)
 # 输出模型程序
 def model_show():
-    pixel = ['数据集','卷积','池化','全连接','非线性','分类','误差','调整1','调整2','调整3']
-    pixel_items3 = []
-    for i in Items3:
-        pixel_items3.append(pixel[i])
-    print(pixel_items3)
+    print(Items3)
 
 def connecting_lines(obj_x, obj_y, index, x, y, x1, x2, y1, y2, y3 , image, text, smooth, width):
     # 创建图元对象
-    Viewcanvas.create_image(obj_x+(index + x) * 110, obj_y, image=image)
+    Viewcanvas.create_image(obj_x+(index + x) * 80, obj_y, image=image)
     # 创建图元对象的标签
-    Viewcanvas.create_text(obj_x+(index + x) * 110, obj_y + y1, text=text, font=("黑体", 14))
+    Viewcanvas.create_text(obj_x+(index + x) * 80, obj_y + y1, text=text, font=("黑体", 8))
     # 创建数据线箭头
-    Viewcanvas.create_line(obj_x+(index + x) * 110 +x1, obj_y + y2, obj_x+(index + y) * 110 +x2, obj_y + y3, arrow=tk.LAST,
+    Viewcanvas.create_line(obj_x+(index + x) * 80 +x1, obj_y + y2, obj_x+(index + y) * 80 +x2, obj_y + y3, arrow=tk.LAST,
                        arrowshape=(16, 20, 4), fill='lightblue', smooth=smooth, width=width)
 
 def number_judgments(lsts):
@@ -1407,9 +1362,9 @@ def load_file():
 
 def connecting(obj_x, obj_y, index, x, y1, image, text):
     # 创建图元对象
-    Viewcanvas.create_image(obj_x+(index + x) * 110, obj_y, image=image)
+    Viewcanvas.create_image(obj_x+(index + x) * 80, obj_y, image=image)
     # 创建图元对象的标签
-    Viewcanvas.create_text(obj_x+(index + x) * 110, obj_y + y1, text=text, font=("黑体", 14))
+    Viewcanvas.create_text(obj_x+(index + x) * 80, obj_y + y1, text=text, font=("黑体", 8))
 # 加载已有模型
 def load_model():
     import copy
@@ -1420,6 +1375,7 @@ def load_model():
     global Viewcanvas
     global Listinstance
     global Viewcanvas
+    Viewcanvas.delete('all')
     create_left_element(AllModelObj, List_image)
     Train_images, Test_images = load_data(DataPara)  # load_data()函数，根据参数加载数据集
     # 打印一些信息，检查是否正确加载
@@ -1427,82 +1383,66 @@ def load_model():
     print("测试集图片的形状：", Test_images.shape)
     # 根据pkl文件的前向传播顺序列表，依次识别卷积层、池化层、全连接层、非线性层和分类层的操作
     count,counts,sequence = number_judgments(lsts)
-    x,y = 420,300
-    connecting_lines(x, y, 0, 0, 0, 25, 60, 45, 0, 0, List_image[0], '数据集', True, 2)
+    x,y = 290,200
+    connecting_lines(x, y, 0, 0, 0, 25, 60, 45, 0, 0, List_image[0], '数据集', True, 1)
     for layer in range(len(count)):
         if count[layer] == 2: # 如果是卷积层，将读取的卷积层的信息存储到Conv实例
             if counts[layer]%10 == 1:
                 global Conv1
                 Listinstance[1] = copy.deepcopy(lsts[sequence[layer]])
                 Conv1 = lsts[sequence[layer]]
-                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[1], '卷积层1', True, 2)
+                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[1], '卷积层1', True, 1)
             if counts[layer]%10 == 2:
                 global Conv2
                 Conv2 = lsts[sequence[layer]]
-                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[1], '卷积层2', True, 2)
+                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[1], '卷积层2', True, 1)
             if counts[layer]%10 == 3:
                 global Conv3
                 Conv3 = lsts[sequence[layer]]
-                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[1], '卷积层3', True, 2)
+                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[1], '卷积层3', True, 3)
         if count[layer] == 3: # 如果是池化层，将读取的池化层的信息存储到Pool实例
             if counts[layer]%10 == 1:
                 global Pool1
                 Listinstance[2] =copy.deepcopy(lsts[sequence[layer]])
                 Pool1 = lsts[sequence[layer]]
-                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[2], '池化1', True, 2)
+                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[2], '池化1', True, 3)
             if counts[layer]%10 == 2:
                 global Pool2
                 Pool2 = lsts[sequence[layer]]
-                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[2], '池化2', True, 2)
+                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[2], '池化2', True, 3)
             if counts[layer]%10 == 3:
                 global Pool3
                 Pool3 = lsts[sequence[layer]]
-                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[2], '池化3', True, 2)
+                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[2], '池化3', True, 3)
         if count[layer] == 4: # 如果是全连接层，将读取的全连接层的信息存储到Pool实例
             if counts[layer]%10 == 1:
                 global Fullconn1
                 Listinstance[3] = lsts[sequence[layer]]
                 Fullconn1 = copy.deepcopy(lsts[sequence[layer]])# 深拷贝创建了一个全新的对象,要使用深拷贝，不然报错
                 Listinstance[3][5] = str(Fullconn1[5][1]["num_outputs"])
-                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[3], '全连接1', True, 2)
+                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[3], '全连接1', True, 3)
             if counts[layer]%10 == 2:
                 global Fullconn2
                 Fullconn2 = lsts[sequence[layer]]
                 # FullConn[5] = FullConn[5] + "  " + str(Fullconn2[5][2]["num_outputs"])
-                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[3], '全连接3', True, 2)
+                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[3], '全连接3', True, 3)
             if counts[layer]%10 == 3:
                 global Fullconn3
                 Fullconn3 = lsts[sequence[layer]]
                 # FullConn[5] = FullConn[5] + "  " + str(Fullconn3[5][3]["num_outputs"])
-                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[3], '全连接3', True, 2)
+                connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[3], '全连接3', True, 3)
         if count[layer] == 5: # 如果是非线性层，将读取的非线性层的信息存储到Nonline实例
             Listinstance[4] = lsts[sequence[layer]]
-            connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[4], '非线性', True, 2)
+            connecting_lines(x, y, layer, 1, 2, 25, -25, 45, 0, 0, List_image[4], '非线性', True, 3)
         if count[layer] == 6: # 如果是分类输出层，将读取的信息存储到Nonline实例
             Listinstance[5] = lsts[sequence[layer]]
             connecting(x, y, layer, 1, 45,List_image[5], '分类')
         if count[layer] == 7:  # 如果是计算误差层，将读取的信息存储到Error实例
             Listinstance[6] = lsts[sequence[layer]]
             connecting(x - 30, y - 120, layer, 0, 45, List_image[6], '计算误差')
-            Viewcanvas.create_line(x + layer * 110 + 25, y, x + layer * 110 + 10, y - 60,
-                                   x + 5 * 110 - 20, y - 120, arrow=tk.LAST, arrowshape=(16, 20, 4),
-                                   fill='lightblue', smooth=True, width=2)
-            # 创建图元对象
-            Viewcanvas.create_image(600, 180, image=List_image[7])
-            # 创建图元对象的标签
-            Viewcanvas.create_text(600, 230, text='调整1', font=("黑体", 14))
-            Viewcanvas.create_line(x + layer * 110 -50, y - 140, 630, 165,arrow=tk.LAST, arrowshape=(16, 20, 4),
-                                   fill='lightblue', smooth=True, width=2)
-            Viewcanvas.create_line(600, 200, x + 1 * 110 ,y-20, arrow=tk.LAST, arrowshape=(16, 20, 4),
-                                   fill='lightblue', smooth=True, width=2)
-            # 创建图元对象
-            Viewcanvas.create_image(860, 200, image=List_image[7])
-            # 创建图元对象的标签
-            Viewcanvas.create_text(860, 250, text='调整2', font=("黑体", 14))
-            Viewcanvas.create_line(x + layer * 110 -60, y - 120, 875, 200,arrow=tk.LAST, arrowshape=(16, 20, 4),
-                                   fill='lightblue', smooth=True, width=2)
-            Viewcanvas.create_line(835, 200, x + (layer-2) * 110 + 10, y - 20, arrow=tk.LAST, arrowshape=(16, 20, 4),
-                                   fill='lightblue', smooth=True, width=2)
+            Viewcanvas.create_line(x + 5 * 80 + 25, y, x + 5 * 80 + 20, y - 80,
+                                   x + 5 * 80 - 20, y - 120, arrow=tk.LAST, arrowshape=(16, 20, 4),
+                                   fill='lightblue', smooth=True, width=1)
 
 def create_window(number,count,list1,list2,list3,list4,list5,list6):
     global Listinstance
@@ -1884,16 +1824,12 @@ def count_model(layer):
     if layer == 6:
         with open("list.pkl", "ab") as f:
             pickle.dump(Listinstance[6], f)
+
 # 保存网络模型
 def save_model():
     if len(Items3) == 0:
         messagebox.showerror("错误", "请添加图元！")
         return
-    file_path = 'list.pkl'
-    # 检查文件是否存在
-    if os.path.exists(file_path):
-        # 删除文件
-        os.remove(file_path)
     # 生成试验数据集，用于保存模型
     Test_images = np.random.rand(100, 48, 48) * 0.01
     # 对试验数据集进行前向传播的计算
@@ -1934,12 +1870,12 @@ def button_link():
     global button
     # 替每个主界面的按钮绑定各自的函数
     button['button1'].config(command=train_setting)
-    button['button2'].config(command=train)
-    button['button3'].config(command=model_show)
-    button['button4'].config(command=load_model)
-    button['button5'].config(command=restart_new_model)
-    button['button6'].config(command=setpara_func)
-    button['button7'].config(command=save_model)
+    button['button2'].config(command=train)  # 训练
+    button['button3'].config(command=model_show)  # 训练
+    button['button4'].config(command=load_model)  # 训练
+    button['button5'].config(command=restart_new_model)  # 训练
+    button['button6'].config(command=setpara_func)  # 训练
+    button['button7'].config(command=save_model)  # 保存模型
     button['button8'].config(command=check_connection)
     button['button9'].config(command=close_program)
 
@@ -1953,10 +1889,10 @@ def main():
     global button
     Root = tk.Tk()
     # 设置窗口的大小为1200*750
-    window_width = 1200  # 窗口的宽度
-    window_height = 750  # 窗口的高度
+    window_width = 800  # 窗口的宽度
+    window_height = 450  # 窗口的高度
     Root.title("神经网络可视化")
-    Root.geometry("1200x750")  # 设置窗口的大小和位置
+    Root.geometry("800x450")  # 设置窗口的大小和位置
     picture_frame(window_width, window_height)
     Listinstance = create_instance()
     # 创建网络对象总表和网络连接对象总表

classification.py

@@ -0,0 +1,54 @@
+import os
+import csv
+import shutil
+import pandas as pd
+
+def index_label(photo_folder, csv_name):
+    # 读取照片数据文件夹
+    photo_files = os.listdir(photo_folder)
+    # 提取数字作为索引
+    indexes = [int(os.path.splitext(file)[0]) for file in photo_files]
+    # 打开原始CSV文件和新的CSV文件
+    csv_file = 'data/emotion.csv'
+    new_csv_file = 'data/'+csv_name+'.csv'
+    with open(csv_file, 'r') as file:
+        reader = csv.reader(file)
+        data = list(reader)
+    # 添加索引列
+    header = ['索引', '标签']
+    result = [header] + [[index, data[index][0]] + data[index][1:] for index in indexes if index < len(data)]
+    # 将结果保存为新的CSV文件
+    # new_csv_file = '新的CSV文件路径'
+    with open(new_csv_file, 'w', newline='') as file:
+        writer = csv.writer(file)
+        writer.writerows(result)
+
+def image_classification(photo_folder, csv_name):
+    # 读取包含索引和标签的数据文件
+    # 定义要删除的文件夹路径
+    target_folder = 'data_classification/' + csv_name  # 目标文件夹路径
+
+    # 遍历文件夹中的文件并删除
+    for filename in os.listdir(target_folder):
+        file_path = os.path.join(target_folder, filename)
+        try:
+            if os.path.isfile(file_path) or os.path.islink(file_path):
+                os.unlink(file_path)  # 删除文件
+            elif os.path.isdir(file_path):
+                shutil.rmtree(file_path)  # 递归删除文件夹及其内容
+        except Exception as e:
+            print(f'无法删除文件：{file_path}')
+
+    new_csv_file = 'data/' + csv_name + '.csv'
+    df = pd.read_csv(new_csv_file, encoding='GBK')
+    # 设置原始文件夹路径和目标文件夹路径
+    # 遍历 DataFrame
+    for index, row in df.iterrows():
+        file_index = row['索引']  # 索引对应的列名
+        file_label = row['标签']  # 标签对应的列名
+        source_file = os.path.join(photo_folder, f'{file_index}.jpg')  # 原始文件路径
+        target_folder_label = os.path.join(target_folder, str(file_label))  # 目标文件夹路径
+        # 创建对应标签的目标文件夹（如果不存在）
+        os.makedirs(target_folder_label, exist_ok=True)
+        # 复制文件到目标文件夹
+        shutil.copy(source_file, target_folder_label)

data/data.csv

@@ -0,0 +1,901 @@
+索引,标签
+0,0
+1,0
+10,0
+100,3
+101,2
+102,6
+103,3
+104,4
+105,3
+106,2
+107,0
+108,2
+109,3
+11,6
+110,2
+111,3
+112,6
+113,6
+114,3
+115,4
+116,4
+117,0
+118,3
+119,3
+12,6
+120,0
+121,2
+122,0
+123,0
+124,3
+125,3
+126,0
+127,4
+128,5
+129,2
+13,6
+130,4
+131,2
+132,5
+133,3
+134,0
+135,6
+136,6
+137,2
+138,2
+139,3
+14,3
+140,2
+141,3
+142,3
+143,3
+144,3
+145,3
+146,4
+147,2
+148,2
+149,0
+15,5
+150,0
+151,2
+152,6
+153,3
+154,4
+155,5
+156,3
+157,0
+158,2
+159,4
+16,3
+160,5
+161,2
+162,3
+163,4
+164,6
+165,3
+166,4
+167,3
+168,6
+169,0
+17,2
+170,4
+171,4
+172,4
+173,2
+174,6
+175,5
+176,5
+177,6
+178,5
+179,2
+18,6
+180,3
+181,2
+182,3
+183,4
+184,0
+185,2
+186,0
+187,3
+188,0
+189,2
+19,4
+190,2
+191,5
+192,3
+193,5
+194,5
+195,4
+196,3
+197,0
+198,6
+199,5
+2,2
+20,4
+200,2
+201,6
+202,0
+203,0
+204,4
+205,5
+206,6
+207,3
+208,5
+209,5
+21,2
+210,3
+211,2
+212,2
+213,3
+214,3
+215,3
+216,3
+217,6
+218,3
+219,6
+22,0
+220,3
+221,0
+222,6
+223,3
+224,2
+225,0
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