Aimbot-PPO/Aimbot-PPO-Python/Pytorch/Archive/test2.ipynb

{
 "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": {
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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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "i =  0\n",
      "i =  1\n",
      "i =  2\n",
      "i =  3\n",
      "i =  4\n",
      "i =  5\n",
      "i =  6\n",
      "i =  7\n",
      "i =  8\n",
      "i =  9\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "import threading\n",
    "\n",
    "num = 0\n",
    "\n",
    "def print_numers():\n",
    "    global num\n",
    "    for i in range(10):\n",
    "        num +=1\n",
    "        print(\"i = \",i)\n",
    "\n",
    "thread = threading.Thread(target=print_numers)\n",
    "\n",
    "print(num)\n",
    "thread.start()\n",
    "thread.join()\n",
    "print(num)"
   ]
  }
 ],
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   "file_extension": ".py",
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