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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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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 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, data, FullConnPara):
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weights = FullConnPara["weights"] # 获取权重矩阵
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bias = FullConnPara["bias"] # 偏置向量
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# 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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data = data.reshape(1, data.shape[1] * data.shape[2])
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# 计算全连接层的线性变换：data与权重矩阵w进行乘法，再加上偏置向量b
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output = np.dot(data, weights.T) + bias
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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
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def setfullconn_para(self, poolH, poolW):
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height = poolH
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width = poolW # 获取池化后的图片数组的长度和宽度
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num_outputs = int(input("请输入全连接层的输出节点数量: "))
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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,
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"num_outputs": num_outputs}
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return FullConnPara
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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 = 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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