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   "execution_count": 1,
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     "end_time": "2025-01-20T07:32:36.354335Z",
     "start_time": "2025-01-20T07:32:35.224080Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 设置使用gpu7 cuda\n",
    "device = torch.device(\"cuda:7\" if torch.cuda.is_available() and torch.cuda.get_device_properties(0).total_memory >= 6*1024**3 else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-20T07:32:38.297401Z",
     "start_time": "2025-01-20T07:32:38.261009Z"
    }
   },
   "outputs": [],
   "source": [
    "# 设置使用mps mps设备当前未支持限制内存\n",
    "device = torch.device(\"mps\" if torch.backends.mps.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-20T07:32:39.972353Z",
     "start_time": "2025-01-20T07:32:39.958549Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiLayerPerceptron(\n",
      "  (hidden_layer1): Linear(in_features=10, out_features=20, bias=True)\n",
      "  (hidden_layer2): Linear(in_features=20, out_features=10, bias=True)\n",
      "  (output_layer): Linear(in_features=10, out_features=2, bias=True)\n",
      "  (activation1): ReLU()\n",
      "  (activation2): Sigmoid()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# 定义一个简单的神经元层\n",
    "class MultiLayerPerceptron(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size1, hidden_size2, output_size):\n",
    "        super(MultiLayerPerceptron, self).__init__()\n",
    "        self.hidden_layer1 = nn.Linear(input_size, hidden_size1)\n",
    "        self.hidden_layer2 = nn.Linear(hidden_size1, hidden_size2)\n",
    "        self.output_layer = nn.Linear(hidden_size2, output_size)\n",
    "        \n",
    "        # 定义不同的激活函数\n",
    "        self.activation1 = nn.ReLU()  # 第一个隐藏层使用 ReLU\n",
    "        self.activation2 = nn.Sigmoid()  # 第二个隐藏层使用 Sigmoid\n",
    "\n",
    "    def forward(self, x):\n",
    "        # 第一个隐藏层及其激活函数\n",
    "        x = self.hidden_layer1(x)\n",
    "        x = self.activation1(x)\n",
    "        \n",
    "        # 第二个隐藏层及其激活函数\n",
    "        x = self.hidden_layer2(x)\n",
    "        x = self.activation2(x)\n",
    "        \n",
    "        # 输出层\n",
    "        x = self.output_layer(x)\n",
    "        return x\n",
    "\n",
    "# 创建一个MLP实例\n",
    "mlp = MultiLayerPerceptron(input_size=10, hidden_size1=20, hidden_size2=10, output_size=2)\n",
    "\n",
    "# 打印模型结构\n",
    "print(mlp)"
   ]
  }
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