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36 lines
1.7 KiB
36 lines
1.7 KiB
11 months ago
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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'],
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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[
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'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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if __name__ == '__main__':
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AjFullconn = AjFullconn_Class("AjFullconn1", 9,
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"全连接调整1", [], 510, 120)
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···
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AjFCPara = AjFullconn.SetAjFCPara(loss, FullConnPara)
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weight, bias = AjFullconn.AjFullconnProc(AjFCPara)
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FullConnPara['weights'] = weight
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FullConnPara['bias'] = bias
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