Pytorch on RaspberryPi

Finally, I successfully installed Pytorch on my RaspberryPi 3B. I followed the instructions on How to install PyTorch v0.3.1 on RaspberryPi 3B and a blog post (in Chinese) 在 RaspberryPi 上编译 PyTorch. I am running latest raspian image 2018-04-18-raspbian-stretch and a self-compiled Python 3.6.5. If you are interested in compile Python 3.6.5 or above on your own, check out Installing Python 3.6 on Raspbian. In this post, I will write down steps to my successful installation, and share my compiled wheel file at the end.

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I recently built a flask app that run on my raspberry pi. Then I turned it down. Why? In order to access the app from outside the home internet, I had to expose my pi to public. After only one day of exposure, my pi received several attack attempts and the flask app then always replied 301. Here are some attack logs: - "GET / HTTP/1.1" 404 - - "GET / HTTP/1.0" 404 - - "GET / HTTP/1.0" 404 - - code 400, message Bad HTTP/0.9 request type ('\x03\x00\x00/*à\x00\x00\x00\x00\x00Cookie:') - "/*àCookie: mstshash=Administr" HTTPStatus.BAD_REQUEST - - "GET HTTP/1.1" 404 - - "GET / HTTP/1.1" 404 -

To be more precise, the flask app was still running, but the home router seemed not forwarding the requests to my pi. Anyway, I have no idea of what have happened. I just shut the app down, flashed a fresh new image to my pi.

I want a way to communicate with my pi without exposing it. That is the motivation I start the project chapi, which means "chat with pi".

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在这篇文章中,我将使用朴素贝叶斯分类器(Naive Bayes Classifier)打造一个简单的个人推荐系统:给定一篇中文文章(article),返回我喜欢(like)这篇文章的概率。用数学的语言来描述的话,我们要计算的是



$$P(like|aritcle) = \frac{P(article|like)P(like)}{P(article|like)P(like)+P(article|dislike)P(dislike)}$$

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Les misérables



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Digit recognition, CNN

最近看了一些关于卷积网络(Convolution Neural Network)的内容,我想用mnist数据集重复一下网上的教程,好让自己能更好地理解并使用卷积网络。比较了一下TensorflowPytorch,个人比较喜欢Pytorch,所以以后基本就用它了。Pytorch里是包含获取mnist数据集的api的,使用这api我们可以很简单就能准备好数据。但是出于学习Pytorch的目的,我决定自己准备数据。


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LeetCode Contest 78


第一题Subdomain Visit Count





class Solution:
    def subdomainVisits(self, cpdomains):
        :type cpdomains: List[str]
        :rtype: List[str]
        cnt = collections.defaultdict(int)
        for cp in cpdomains:
            n, addr = cp.split(' ')
            n, addr, p = int(n), '.'+addr, 0
            while p != -1:
                addr = addr[p+1:]
                cnt[addr] += n
                p = addr.find('.')
        return ['{} {}'.format(v, k) for k, v in cnt.items()]

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Markov Decision Process: Finite horizon

This post is considered to the notes on finite horizon Markov decision process for lecture 18 in Andrew Ng's lecture series. In my previous two notes ([1], [2]) about Markov decision process (MDP), only state rewards are considered. We can easily generalize MDP to state-action reward.

State-Action Reward

Our reward function now is a function in terms of both states and actions. More precisely, the reward function is a function

$$R: S \times A \to \R.$$

All other requirements in definition of MDP will remain intact. For completeness, we include the definition here. We shall pay attention to the fifth components.

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Markov Decision Process: Continuous states

I wrote this post for lecture 17 in Andrew Ng's lecture collections on Machine Learning. In my previous post, we discussed Markov Decision Process (MDP) in its simplest form, where the set of states and the set of actions are both finite. But in real world application, states and actions can be infinite and even continuous. For example, if we want to model states of a self-driving car in a 2D plane, we must at least have the position \((x, y)\), the direction \(\theta\) of the car pointing to, its velocity \((v_x, v_y)\) and the rate \(r\) of change in \(\theta\). So the states of a car is at least a 6 dimensional space. For actions of a car, we can control how fast it goes in direction \(\theta\) and we can also control \(r\). Thus the actions have dimension 2.

In this post, we consider only continuous states with finite actions. Indeed, actions space usually has much lower dimension than states space, so in case of continuous actions, we might just discretize the actions spaces to get a finite set of representatives of actions. One may argue that we can also discretize the states space. Yes, we can do it, but only when the dimension \(n\) of state space is small enough: if we discretize each dimension into \(k\) parts, then there would be \(k^n\) many states. If \(n\) is large, then \(k^n\) is not feasible. This is so called the curse of dimensionality. Moreover, discretizing states space usually results in lack of smoothness.

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Markov Decision Process

This article is my notes for 16th lecture in Machine Learning by Andrew Ng on Markov Decision Process (MDP). MDP is a typical way in machine learning to formulate reinforcement learning, whose tasks roughly speaking are to train agents to take actions in order to get maximal rewards in some settings. One example of reinforcement learning would be developing a game bot to play Super Mario on its own.

Another simple example is used in the lecture, and I will use it throughout the post as well. Since the example is really simple, so MDP shown below is not of the most general form, but only good enough to solve the example and give the idea of what MDP and reinforcement learning are. The example begins with a 3 by 4 grid as below.

the grid

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