mahasak

from platform import python_version
print(python_version())

mahasak

const banList = ['avenger', 'infinity', 'endgame'];
const rule = new RegExp(banList.join('|'),'i')
setInterval(function() {  
  Array.from(document.querySelectorAll('[role="article"]'))
    .filter(d => rule.test(d.innerText))
    .forEach(d => d.remove())
}, 2000)

mahasak

docker run -u root --rm -d -p 8080:8080 -p 50000:50000 -v $HOME/dockers/jenkins:/var/jenkins_home -v /var/run/docker.sock:/var/run/docker.sock --name jenkins_dev jenkinsci/blueocean

mahasak

import * as vscode from 'vscode';

export function activate(context: vscode.ExtensionContext) {
	console.log('Congratulations, your extension "idenExt" is now active!');
	
	let disposable = vscode.commands.registerCommand('extension.helloWorld', () => {
		vscode.window.showInformationMessage('Hello World!');
	});

	context.subscriptions.push(disposable);

mahasak

{
	"name": "bigbears-toolbox",
	"displayName": "BigBears Toolbox",
	"description": "BigBears Toolbox",
	"version": "0.0.1",
	"publisher": "bigbears",
	"author": {
		"name": "Mahasak Pijittum"
	},
	"repository": {"url": "https://github.com/mahasak/bigbear-toolbox-vscode"},

mahasak

from rx import Observable, Observer
import json
import unittest
from rx.testing import TestScheduler, ReactiveTest
from rx.core import Observer, ObservableBase, Observable
from rx.subjects import Subject

MAX_LIVE = 7

class game_status:

mahasak

def hangman(secret_word, letters):
  max_score = 7
  return max_score - len([ch for ch in letters if ch not in secret_word]) > 0


secret = "bigbears"
tries = ['a','b', 'c', 'd', 'e', 'f', 'g', 'h']
print(hangman("bigbears",tries))

mahasak

Keybase proof

I hereby claim:

• I am mahasak on github.
• I am mahasak (https://keybase.io/mahasak) on keybase.
• I have a public key ASCt9xcks3C7Mt7Q6t5hdIrr4H-unFSMgnUJdX56qg6HIAo

To claim this, I am signing this object:

mahasak

# Resize the input image nad preprocess it.
image = T.Resize(target_size=(224, 224))(image)
image = T.ToTensor()(image)

# Convert to Torch.Tensor and normalize.
image = T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(image)

with torch.no_grad():
    image = torch.autograd.Variable(image[None])
      

mahasak

import torch
from torch import nn
from torchvision.models import resnet50

def load_model():
    global model
    model = resnet50(pretrained=False)
    model_path = "./models/resnet50-19c8e357.pth"
    checkpoint = torch.load(model_path)
    model.load_state_dict(checkpoint)