Hinaser

# References
# https://u2pia.medium.com/ubuntu-20-04-nvidia-gpu-control-through-ssh-terminal-bb136f447e11
# https://unix.stackexchange.com/questions/367584/how-to-adjust-nvidia-gpu-fan-speed-on-a-headless-node

# This gist is for GPU configuration for mining on Ubuntu 20.04
# This worked with dual GPU machine (GTX1070/RTX3070).

# Set coolbits which enables to control all gpu fan/clock via command line
sudo nvidia-xconfig -a --cool-bits=28

Hinaser

function getWordWithKanaTrimmed(el) {
    const cloneEl = el.cloneNode(true);
    const kanaEls = Array.from(cloneEl.querySelectorAll("span.kana"));
    kanaEls.forEach(kanaEl => {
        cloneEl.removeChild(kanaEl);
    });
    return cloneEl.innerText;
}
function generateWordList() {
    const allRows = document.querySelectorAll("#wordlist > tbody > tr:not(:first-child)");

Hinaser

/**
 * Requirement:
 *   sudo npm install -g babel-cli
 *   sudo npm install -g babel-preset-es2015
 *   sudo npm install -g babel-preset-es2017
 *   sudo npm install -g parse
 *   sudo npm install -g randomstring
 *   sudo npm install -g shuffle-seed
 *
 * To execute:

Hinaser

/**
 * Sample parse-server/parse-dashboard script for Node.js v6.
 *
 * In order to use this script you must follow the procedure below.
 *
 * 1) Create folder for this snippet.
 *    $ cd <A folder you like to host script files>
 *    $ npm init
 *      # You don't need to answer all of the questions. You can safely skip this mess.
 *    $ npm install --save-dev babel-cli babel-preset-es2015 deepmerge express parse-dashboard parse-server simple-parse-smtp-adapter

Hinaser

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import numpy as np
import tensorflow as tf
import time
import datetime as dt

"""
じゃんけん用学習データを生成するクラス。
本来であればプログラムでデータを自動生成するのではなく、

Hinaser

"""
Learn arbitrary function from training data

Architecture: MLP
Number of hidden layers: You can configure via `N_neurons_in_layers`
"""

import math
import time
import datetime as dt

Hinaser

"""
Learn arbitrary function from training data

Architecture: MLP
Number of hidden layers: You can configure via `N_neurons_in_layers`
"""

import math
import time
from inspect import signature

Hinaser

import numpy as np
import tensorflow as tf

# セッションを生成
sess = tf.InteractiveSession()

# トレーニングするパラメータを定義
A = tf.Variable(tf.zeros(shape=[3, 3]), dtype=tf.float32, name="A")
b = tf.Variable(tf.zeros(shape=[3, 1]), dtype=tf.float32, name="b")
sess.run(tf.global_variables_initializer())