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