ZJUGuoShuai

\documentclass[8pt, t, aspectratio=169, compress]{beamer}
\usetheme{Berlin}

\usepackage{xeCJK}
\setCJKmainfont{Noto Sans CJK SC}

\usefonttheme[onlymath]{serif}
\usepackage{txfonts}
\usepackage[T1]{fontenc}

ZJUGuoShuai

"""
获取最新 IP 地址

运行需要 requests 库：

$ pip install requests

"""

import requests

ZJUGuoShuai

1. 从压缩包恢复镜像 执行下面的命令将镜像读入本地：

$ docker load < chatbot_image.tar.gz

2. 从镜像运行容器 执行下面的命令，从镜像运行一个容器示例：

$ docker run -it --rm --name chatbot -p 8080:8080 -p 5000:5000 chatbot:v0.3 /run.sh

3. 打开本机浏览器开始使用 打开浏览器，输入地址 127.0.0.1:8080 开始使用聊天界面。

ZJUGuoShuai

import itchat
import os
import math
from PIL import Image

itchat.auto_login(hotReload=True)  # 扫码登录微信

if not os.path.exists('img'):  # 如果同目录没有img目录
    os.mkdir('img')  # 创建img目录

ZJUGuoShuai

#!/bin/bash
LENGTH=1
DELAY=5
while true
do
    for ANGLE in 0 90 180 270
    do
        xdotool mousemove_relative --polar $ANGLE $LENGTH
        sleep $DELAY
    done

ZJUGuoShuai

x = tf.constant([[1], [2], [3], [4]], dtype=tf.float32)
y_true = tf.constant([[0], [-1], [-2], [-3]], dtype=tf.float32)

linear_model = tf.layers.Dense(units=1)

y_pred = linear_model(x)
loss = tf.losses.mean_squared_error(labels=y_true, predictions=y_pred)

optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)

ZJUGuoShuai

<pseudocode>
<pre style="display:none;">
\begin{algorithm}
\caption{Quicksort}
\begin{algorithmic}
\PROCEDURE{Quicksort}{$A, p, r$}
\end{algorithmic}
\end{algorithm}
</pre>
</pseudocode>

ZJUGuoShuai

from collections import namedtuple
from operator import itemgetter
from pprint import pformat
import numpy as np


class Node(namedtuple('Node', 'location left_child right_child')):
    def __repr__(self):
        return pformat(tuple(self))

ZJUGuoShuai

import torch
import torch.nn as nn
import numpy as np

def make_context_vector(context, word_to_ix):
    idxs = [word_to_ix[w] for w in context]
    return torch.tensor(idxs, dtype=torch.long)

def get_index_of_max(input):
    index = 0

ZJUGuoShuai

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

torch.manual_seed(1)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

CONTEXT_SIZE = 2
EMBEDDING_DIM = 10