diff --git a/parameters.pkl b/parameters.pkl
index 6472348..b051368 100644
Binary files a/parameters.pkl and b/parameters.pkl differ
diff --git a/神经网络/main.py b/神经网络/main.py
index 185bbc6..1af045f 100644
--- a/神经网络/main.py
+++ b/神经网络/main.py
@@ -4,7 +4,7 @@
 """
 
 # 导入模块
-from typing import List, Literal, Optional, Dict, Tuple
+from typing import List, Literal
 import numpy
 import pickle
 
@@ -43,6 +43,8 @@ class NeuralNetwork:
             raise RuntimeError("神经网络结构应为列表，长度大于等于3且元素均为正整数")
         # 初始化神经网络结构
         self.structure = structure
+        # 神经网络层数（定义，第0层为输入层，第l层为隐含层（l=1,2,...,L-1），第L层为输出层（L为神经网络层数），深度为L+1）
+        self.layer_counts = len(structure) - 1
 
         if hidden_activate not in self.HIDDEN_ACTIVATES:
             raise RuntimeError(f"该隐含层激活函数 {hidden_activate} 暂不支持")
@@ -51,9 +53,6 @@ class NeuralNetwork:
             raise RuntimeError(f"该输出层激活函数 {output_activate} 暂不支持")
         self.output_activate = output_activate
 
-        # 神经网络层数（定义，第0层为输入层，第l层为隐含层（l=1,2,...,L-1），第L层为输出层（L为神经网络层数），深度为L+1）
-        self.layer_counts = len(structure) - 1
-
         # 初始化神经网络参数
         self.parameters = {}
 
@@ -414,7 +413,17 @@ class NeuralNetwork:
         """
         with open("parameters.pkl", "wb") as file:
             pickle.dump(
-                obj=self.parameters,
+                obj={
+                    layer_index: {
+                        key: value
+                        for key, value in layer_parameters.items()
+                        if layer_index == 0
+                        and key in ["mean", "variance"]
+                        or layer_index != 0
+                        and key in ["weight", "bias", "activate"]
+                    }
+                    for layer_index, layer_parameters in self.parameters.items()
+                },
                 file=file,
                 protocol=pickle.HIGHEST_PROTOCOL,
             )
@@ -456,17 +465,25 @@ class NeuralNetwork:
                 == (self.structure[layer_index], self.structure[layer_index - 1])
                 and self.parameters[layer_index]["bias"].shape
                 == (self.structure[layer_index], 1)
+                and (
+                    self.parameters[layer_index]["activate"] in self.output_activate
+                    if layer_index == self.layer_counts
+                    else self.parameters[layer_index]["activate"]
+                    in self.hidden_activate
+                )
                 if isinstance(self.parameters[layer_index]["weight"], numpy.ndarray)
                 and isinstance(self.parameters[layer_index]["bias"], numpy.ndarray)
                 else False
             ):
-                raise RuntimeError("神经网络参数中权重和偏置的维度与神经网络结构不匹配")
+                raise RuntimeError(
+                    "神经网络参数中权重和偏置的维度与神经网络结构不匹配、或激活函数不匹配"
+                )
 
 
 # 测试代码
 if __name__ == "__main__":
 
-    X = numpy.random.randn(2, 5000)
+    X = numpy.random.randn(2, 1000)
     # 真实函数：y = 2*x1 + 3*x2 + 1
     y_true = 2 * X[0:1, :] ** 2 + 3 * X[1:2, :] + 1
 
@@ -476,9 +493,7 @@ if __name__ == "__main__":
     )
 
     # 训练
-    neural_network.train(
-        X=X, y_true=y_true, target_loss=0.01, epochs=1_000, learning_rate=0.1
-    )
+    #neural_network.train(X=X, y_true=y_true, target_loss=0.01, epochs=1000_000, learning_rate=0.05)
 
     print(f"推理结果：{y_true[:, 0:5]}")