PyTorch provide a simple DQN implementation to solve the cartpole game. However, the code is incorrect, it diverges after training (It has been discussed here).

The official code’s training data is below, it’s high score is about 50 and finally diverges.

There are many reason that lead to divergence.

First it use the difference of two frame as input in the tutorial, not only it loss the cart’s absolute information(This information is useful, as game will terminate if cart moves too far from centre), but also confused the agent when difference is the same but the state is varied.

Recently, I found a really good example code for Python circular import, and I’d like to record it here.

Here is the code:

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# X.py
def X1():
    return "x1"

from Y import Y2

def X2():
    return "x2"

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# Y.py
def Y1():
    return "y1"

from X import X1

def Y2():
    return "y2"

Guess what will happen if you run python X.py and python Y.py?

Overview

1. CPython allocation memory to save dictionary, the initial table size is 8, entries are saved as <hash,key,value> in each slot(The slot content changed after Python 3.6).
2. When a new key is added, python use i = hash(key) & mask where mask=table_size-1 to calculate which slot it should be placed. If the slot is occupied, CPython using a probing algorithm to find the empty slot to store new item.
3. When 2/3 of the table is full, the table will be resized.
4. When getting item from dictionary, both hash and key must be equal.

Resizing

When elements size is below 50000, the table size will increase by a factor of 4 based on used slots. Otherwise, it will increase by a factor of 2. The dictionary size is always \(2^{n}\).

PyTorch is a really powerful framework to build the machine learning models. Although some features is missing when compared with TensorFlow (For example, the early stop function, History to draw plot), its code style is more intuitive.

Torchtext is a NLP package which is also made by pytorch team. It provide a way to read text, processing and iterate the texts.

Google Colab is a Jupyter notebook environment host by Google, you can use free GPU and TPU to run your modal.

CSRF(Cross-site request forgery) is a way to generate fake user request to target website. For example, on a malicious website A, there is a button, click it will send request to www.B.com/logout. When the user click this button, he will logout from website B unconsciously. Logout is not a big problem, but malicious website can generate more dangerous request like money transfer.

Django CSRF protection

Each web framework has different approach to do CSRF protection. In Django, the validation process is below:

Recently, I’m working on a neo4j project. I use Py2neo to interact with graph db. Although Py2neo is a very Pythonic and easy to use, its performance is really poor. Sometimes I have to manually write cypher statement by myself if I can’t bear with the slow execution. Here is a small script which I use to compare the performance of 4 different ways to insert nodes.

import time

from graph_db import graph

from py2neo.data import Node, Subgraph


def delete_label(label):
    graph.run('MATCH (n:{}) DETACH DELETE n'.format(label))


def delete_all():
    print('delete all')
    graph.run('match (n) detach delete n')


def count_label(label):
    return len(graph.nodes.match(label))


def bench_create1():
    print('Using py2neo one by one')
    delete_label('test')
    start = time.time()
    tx = graph.begin()
    for i in range(100000):
        n = Node('test', id=i)
        tx.create(n)
    tx.commit()
    print(time.time() - start)
    print(count_label('test'))
    delete_label('test')


def bench_create2():
    print('Using cypher one by one')
    delete_label('test')
    start = time.time()
    tx = graph.begin()
    for i in range(100000):
        tx.run('create (n:test {id: $id})', id=i)
        if i and i % 1000 == 0:
            tx.commit()
            tx = graph.begin()
    tx.commit()
    print(time.time() - start)
    print(count_label('test'))
    delete_label('test')


def bench_create3():
    print('Using Subgraph')
    delete_label('test')
    start = time.time()
    tx = graph.begin()
    nodes = []
    for i in range(100000):
        nodes.append(Node('test', id=i))
    s = Subgraph(nodes=nodes)
    tx.create(s)
    tx.commit()
    print(time.time() - start)
    print(count_label('test'))
    delete_label('test')



def bench_create4():
    print('Using unwind')
    delete_label('test')
    start = time.time()
    tx = graph.begin()
    ids = list(range(100000))
    tx.run('unwind $ids as id create (n:test {id: id})', ids=ids)
    tx.commit()
    print(time.time() - start)
    print(count_label('test'))
    delete_label('test')


def bench_create():
    create_tests = [bench_create1, bench_create2, bench_create3, bench_create4]

    print('testing create')
    for i in create_tests:
        i()


if __name__ == '__main__':
    bench_create()

Apparently, using cypher with unwind keyword is the fastest way to batch insert nodes.