288 lines
15 KiB
Plaintext
288 lines
15 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"MyNet(\n",
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" (fc1): Linear(in_features=10, out_features=20, bias=True)\n",
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" (fc2): Linear(in_features=20, out_features=10, bias=True)\n",
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")\n"
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]
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},
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{
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"data": {
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"image/png": 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",
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"text/plain": [
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"<Figure size 432x288 with 2 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"import torch\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# 创建一个神经网络\n",
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"class MyNet(torch.nn.Module):\n",
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" def __init__(self):\n",
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" super().__init__()\n",
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" self.fc1 = torch.nn.Linear(10, 20)\n",
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" self.fc2 = torch.nn.Linear(20, 10)\n",
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"\n",
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" def forward(self, x):\n",
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" x = torch.relu(self.fc1(x))\n",
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" x = self.fc2(x)\n",
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" return x\n",
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"\n",
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"net = MyNet()\n",
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"\n",
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"# 打印神经网络结构\n",
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"print(net)\n",
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"\n",
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"# 获取第一层权重张量\n",
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"weights = net.state_dict()['fc1.weight']\n",
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"\n",
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"# 将权重张量转换为numpy数组，并可视化\n",
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"plt.imshow(weights.numpy())\n",
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"plt.colorbar()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"python version: 3.11.3 | packaged by Anaconda, Inc. | (main, Apr 19 2023, 23:46:34) [MSC v.1916 64 bit (AMD64)]\n"
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]
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}
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],
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"source": [
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"# print python version\n",
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"import sys\n",
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"print('python version: ', sys.version)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import argparse\n",
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"import wandb\n",
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"import time\n",
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"import numpy as np\n",
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"import random\n",
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"import uuid\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.optim as optim\n",
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"\n",
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"from AimbotEnv import Aimbot\n",
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"from tqdm import tqdm\n",
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"from torch.distributions.normal import Normal\n",
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"from torch.distributions.categorical import Categorical\n",
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"from distutils.util import strtobool\n",
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"from torch.utils.tensorboard import SummaryWriter\n",
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"from mlagents_envs.environment import UnityEnvironment\n",
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"from mlagents_envs.side_channel.side_channel import (\n",
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" SideChannel,\n",
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" IncomingMessage,\n",
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" OutgoingMessage,\n",
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")\n",
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"from typing import List\n"
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]
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},
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||
{
|
||
"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": 4,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"3"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y=\"a;b;c\"\n",
|
||
"len(y.split(\";\"))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[2]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"a = np.array([1,2,3,4])\n",
|
||
"print(a[[False,True,False,False]])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{1, 2, 3, 4}"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"a = {1,2,3}\n",
|
||
"a.add(4)\n",
|
||
"a"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"array([3, 4])"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"a = np.array([[1,3],[2,4]])\n",
|
||
"a.max(axis=1)\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.9.17"
|
||
},
|
||
"orig_nbformat": 4
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|