1

神经网络/main.py

@@ -6,6 +6,7 @@
 # 导入模块
 from typing import List, Literal, Optional, Dict, Tuple
 import numpy
+import pickle
 
 
 class NeuralNetwork:
@@ -144,6 +145,7 @@ class NeuralNetwork:
             raise RuntimeError(
                 "输入和真实输出应为数组，其中输入维度应为[输入神经元数, 样本数]，真实输出维度应为[输出神经元数, 样本数]，样本数应需相同"
             )
+        self.training = True
         # 归一化输入
         self.parameters[0].update({"activation": self._normalize(input=X)})
 
@@ -172,6 +174,13 @@ class NeuralNetwork:
 
             epoch += 1
 
+        try:
+            with open("neural_network.pkl", "wb") as file:
+                pickle.dump(self, file, protocol=pickle.HIGHEST_PROTOCOL)
+            print(f"模型保存成功")
+        except Exception as exception:
+            raise RuntimeError(f"模型保存失败：{str(exception)}") from exception
+
     def _normalize(
         self,
         input: numpy.ndarray,
@@ -376,6 +385,18 @@ class NeuralNetwork:
                 }
             )
 
+    def _reason(self, input: numpy.ndarray) -> numpy.ndarray:
+        """
+        推理
+        :param input: 输入，维度为[输入神经元数, 样本数]
+        :return: 输出，维度为[输出神经元数, 样本数]
+        """
+        self.training = False
+        # 归一化输入
+        self.parameters[0].update({"activation": self._normalize(input=input)})
+
+        return self._forward_propagate()
+
 
 # 测试代码
 if __name__ == "__main__":
@@ -391,5 +412,5 @@ if __name__ == "__main__":
 
     # 训练
     neural_network.train(
-        X=X, y_true=y_true, target_loss=0.01, epochs=50_000, learning_rate=0.05
+        X=X, y_true=y_true, target_loss=0.1, epochs=1_000, learning_rate=0.05
     )