{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MyNet(\n",
      "  (fc1): Linear(in_features=10, out_features=20, bias=True)\n",
      "  (fc2): Linear(in_features=20, out_features=10, bias=True)\n",
      ")\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 创建一个神经网络\n",
    "class MyNet(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = torch.nn.Linear(10, 20)\n",
    "        self.fc2 = torch.nn.Linear(20, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "net = MyNet()\n",
    "\n",
    "# 打印神经网络结构\n",
    "print(net)\n",
    "\n",
    "# 获取第一层权重张量\n",
    "weights = net.state_dict()['fc1.weight']\n",
    "\n",
    "# 将权重张量转换为numpy数组，并可视化\n",
    "plt.imshow(weights.numpy())\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python version:  3.11.3 | packaged by Anaconda, Inc. | (main, Apr 19 2023, 23:46:34) [MSC v.1916 64 bit (AMD64)]\n"
     ]
    }
   ],
   "source": [
    "# print python version\n",
    "import sys\n",
    "print('python version: ', sys.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "import wandb\n",
    "import time\n",
    "import numpy as np\n",
    "import random\n",
    "import uuid\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "from AimbotEnv import Aimbot\n",
    "from tqdm import tqdm\n",
    "from torch.distributions.normal import Normal\n",
    "from torch.distributions.categorical import Categorical\n",
    "from distutils.util import strtobool\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from mlagents_envs.environment import UnityEnvironment\n",
    "from mlagents_envs.side_channel.side_channel import (\n",
    "    SideChannel,\n",
    "    IncomingMessage,\n",
    "    OutgoingMessage,\n",
    ")\n",
    "from typing import List\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'aaa' object has no attribute 'outa'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 14\u001b[0m\n\u001b[0;32m     12\u001b[0m asd \u001b[39m=\u001b[39m aaa(outa, outb)\n\u001b[0;32m     13\u001b[0m asd\u001b[39m.\u001b[39mfunc()\n\u001b[1;32m---> 14\u001b[0m \u001b[39mprint\u001b[39m(asd\u001b[39m.\u001b[39;49mouta)  \u001b[39m# 输出 100\u001b[39;00m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'aaa' object has no attribute 'outa'"
     ]
    }
   ],
   "source": [
    "class aaa():\n",
    "    def __init__(self, a, b):\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "\n",
    "    def func(self):\n",
    "        global outa\n",
    "        outa = 100\n",
    "\n",
    "outa = 1\n",
    "outb = 2\n",
    "asd = aaa(outa, outb)\n",
    "asd.func()\n",
    "print(asd.outa)  # 输出 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "usage: ipykernel_launcher.py [-h] [--seed SEED]\n",
      "ipykernel_launcher.py: error: unrecognized arguments: --ip=127.0.0.1 --stdin=9003 --control=9001 --hb=9000 --Session.signature_scheme=\"hmac-sha256\" --Session.key=b\"46ef9317-59fb-4ab6-ae4e-6b35744fc423\" --shell=9002 --transport=\"tcp\" --iopub=9004 --f=c:\\Users\\UCUNI\\AppData\\Roaming\\jupyter\\runtime\\kernel-v2-311926K1uko38tdWb.json\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "2",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 2\n"
     ]
    }
   ],
   "source": [
    "import argparse\n",
    "\n",
    "def parse_args():\n",
    "    parser = argparse.ArgumentParser()\n",
    "    parser.add_argument(\"--seed\", type=int, default=11,\n",
    "                        help=\"seed of the experiment\")\n",
    "    args = parser.parse_args()\n",
    "    return args\n",
    "\n",
    "arggg = parse_args()\n",
    "print(type(arggg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.2, 3.2)\n",
      "1.2\n"
     ]
    }
   ],
   "source": [
    "aaa = (1.2,3.2)\n",
    "print(aaa)\n",
    "print(aaa[0])"
   ]
  }
 ],
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