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FC_ML_Optim_Function/Optimizer_Selector.py

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+import torch
+import torch.optim as optim
+
+
+class OptimizerSelector:
+    def __init__(self):
+        self.available_optimizers = {
+            'sgd': '随机梯度下降',
+            'adam': '自适应矩估计',
+            'rmsprop': '均方根传播',
+            'adagrad': '自适应梯度',
+            'adamw': 'Adam权重衰减版'
+        }
+
+    def get_optimizer(self, model_params, name='sgd', **kwargs):
+        """获取配置好的优化器实例
+
+        参数:
+            model_params: 模型参数(可通过model.parameters()获取)
+            name: 优化器类型
+            **kwargs: 优化器专属参数(lr, weight_decay等)
+        """
+        if name == 'sgd':
+            return optim.SGD(model_params, **kwargs)
+        elif name == 'adam':
+            return optim.Adam(model_params, **kwargs)
+        elif name == 'rmsprop':
+            return optim.RMSprop(model_params, **kwargs)
+        elif name == 'adagrad':
+            return optim.Adagrad(model_params, **kwargs)
+        elif name == 'adamw':
+            return optim.AdamW(model_params, **kwargs)
+        else:
+            raise ValueError(f"不支持的优化器，可选: {list(self.available_optimizers.keys())}")
+
+    def print_available(self):
+        """打印支持的优化器列表"""
+        print("可用优化器：")
+        for k, v in self.available_optimizers.items():
+            print(f"{k.ljust(10)} -> {v}")
+
+
+# 使用示例
+if __name__ == "__main__":
+    model = torch.nn.Linear(10, 2)  # 示例模型
+    selector = OptimizerSelector()
+    optimizer = selector.get_optimizer(
+        model.parameters(),
+        name='adamw',
+        lr=0.001,
+        weight_decay=0.01
+    )