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import numpy as np
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import pandas as pd
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import lqmtest_x3_5_pool
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import lqmtest_x3_2_load_data
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import lqmtest_x3_4_conv_proc
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import lqmtest_x3_6_fullconn
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import lqmtest_x3_7_nonlinear
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import lqmtest_x3_8_classify
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import lqmtest_x3_9_label
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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 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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# 创建一个单位矩阵，大小为标签类别的个数
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one_hot_matrix = np.eye(len(label_list))
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index = [x for x in label]
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label_one_hot = np.take(one_hot_matrix, index, axis=0)
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# 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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if loss_type == "CEE": # 使用交叉熵损失函数
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loss=-np.sum(label_one_hot*np.log(input))/len(input)
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elif loss_type == "MSE": # 使用均方误差损失函数
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loss = np.sum((input-label_one_hot)**2)/len(input)
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elif loss_type == "MAE": # 使用平均绝对误差损失函数
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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")
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return loss # 返回误差值loss
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def seterror_para(self): # 定义设置误差参数的函数
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label_list = input("请输入标签类别列表（用逗号分隔）：")
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label_list = [int(x) for x in label_list.split(',')] # 确定参数信息: 标签类别
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loss_type = input("请输入损失函数类型（CEE/MSE/MAE）：") # 假设损失函数类型为交叉熵 CEE
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# 返回ErrorProc参数，这里用一个元组来存储
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ErrorPara = (label_list, loss_type)
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return ErrorPara # 返回ErrorPara参数
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if __name__ == '__main__':
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DataSet = lqmtest_x3_2_load_data.Data_Class("DataSet1", 1, "数据集1", [], 120, 330)
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# setload_data()函数，获取加载数据集的参数
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DataPara = DataSet.SetDataPara()
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train_images, test_images = DataSet.LoadData(DataPara)
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Conv = lqmtest_x3_4_conv_proc.Conv_Class("Conv1", 2, "卷积1", [], 250, 330)
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ConvPara = Conv.SetConvPara()
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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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conv_images = [] # 存储卷积处理后的图片的列表
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for image in images: # 获取训练集的图片数据
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dim = len(image.shape) # 获取矩阵的维度
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if dim == 2: # 如果是二维矩阵,则转化为三维矩阵
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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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conv_images.append(output) # 将卷积结果存储到列表
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elif dim == 3: # 若为三维矩阵，则保持不变直接卷积处理
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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 = lqmtest_x3_5_pool.Pool_Class("Pool1", 3, "最大池化1", [], 380, 330)
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PoolPara = Pool.SetPollPara()
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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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# 将池化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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pool_images = np.array(pool_images)
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_, _, poolH, poolW = pool_images.shape
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FullConn = lqmtest_x3_6_fullconn.FullConn_Class("FullConn1", 4, "全连接1", [], 510, 330)
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FullConnPara = FullConn.SetFullConnPara(poolH, poolW)
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fullconn_images = [] # 存储全连接处理后的图片的列表
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for image in pool_images: # 获取池化后的图片数据
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output = FullConn.FullConnProc(image, FullConnPara)
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fullconn_images.append(output) # 将全连接处理后的结果存储到列表
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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
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fullconn_images = np.array(fullconn_images)
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Nonline = lqmtest_x3_7_nonlinear.Nonline_Class("Nonline1", 5, "非线性函数1", [], 640, 330)
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NonLPara = Nonline.SetNonLPara()
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# 存储非线性处理后的图片的列表
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nonlinear_images = []
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for image in fullconn_images: # 获取全连接处理后的图片数据
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output = Nonline.NonlinearProc(image, NonLPara)
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# 将非线性处理后的结果存储到列表
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nonlinear_images.append(output)
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# 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
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nonlinear_images = np.array(nonlinear_images)
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Classifier=lqmtest_x3_8_classify.Classifier_Class("Classifier1", 6, "分类1", [], 780, 330)
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ClassifyPara = Classifier.SetClassifyPara()
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classifier_images = [] # 存储分类处理后的图片的列表
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prob_images = [] # 存储分类处理后的概率向量
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for image in nonlinear_images: # 获取非线性处理后的图片数据
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# 调用softmax函数，得到概率分布向量
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prob = Classifier.softmax(image)
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prob_images.append(prob) # 将概率向量结果存储到列表
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output = Classifier.ClassifierProc(image, ClassifyPara)
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classifier_images.append(output) # 将分类结果存储到列表
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# 将分类的结果列表转换为数组形式，方便后续处理
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classifier_images = np.array(classifier_images)
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LabelPara = lqmtest_x3_9_label.Label()
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label_dict = LabelPara.setlabel_para()
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right_label = LabelPara.label_array(i)
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labeled_samples = LabelPara.label_proc(images,
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right_label, label_dict)
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Error = Error_Class("Error1", 7, "误差计算1", [], 710, 124)
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ErrorPara = Error.SetErrorPara()
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# 输入值是一个概率矩阵，真实标签值是一个类别列表
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prob_images = np.squeeze(prob_images)
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loss = Error.ErrorProc(prob_images, right_label, ErrorPara)
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