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import numpy as np
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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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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 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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def nonlinear_proc(self, inputdata, NonLPara): # 定义非线性函数
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# 从NonLPara参数中获取非线性函数类型
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nonlinearmode = NonLPara["nonlinearmode"]
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if nonlinearmode == "Sigmoid":
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# 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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output = 1 / (1 + np.exp(-inputdata))
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elif nonlinearmode == "ReLU":
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# ReLU函数，将负数输入置为0，而正数输入保持不变
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output = np.maximum(inputdata, 0)
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elif nonlinearmode == "Tanh":
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# 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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output = np.tanh(inputdata)
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else:
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# 非法的非线性类型，抛出异常
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raise ValueError("Invalid nonlinear mode")
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return output # 返回计算后的值
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def setnonl_para(self): # 定义设置非线性参数的函数
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# 确定参数信息：非线性函数的类型。可以选择"Sigmoid", "ReLU" 或 "Tanh"
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nonlinearmode = input("请输入非线性函数的类型（Sigmoid/ReLU/Tanh）：")
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# 返回NonLPara参数，这里用一个字典来存储
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NonLPara = {"nonlinearmode": nonlinearmode}
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return NonLPara # 返回NonLPara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 = 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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