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from PIL import Image

img = Image.open('./icon.png')

sizes = [16, 32, 128, 256, 512]

for s in sizes:
    img_resized = img.resize((s, s))
    img_resized.save(f'./dist/icon-{s}x{s}@1x.png')

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#!/usr/bin/env python3
import re
import sys

f = open(sys.argv[-1], 'r+')
document = f.read()

build = ''

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class MulticlassAverageRecall(Layer):
    def __init__(self, name='multiclass_recall', classes=4,
                 output_idx=0, **kwargs):
        super(MulticlassAverageRecall, self).__init__(name=name, **kwargs)
        self.stateful = True
        self.classes = classes

        self.output_idx = output_idx

        self.t = K.variable(np.zeros(classes), dtype='int32')

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import math
import pandas as pd
import numpy as np

from keras.models import Model
from keras.callbacks import Callback

from sklearn.metrics import confusion_matrix

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import os
import time
import argparse
import GPUtil

from multiprocessing import Process

parser = argparse.ArgumentParser(description='GPU Bastard, control the resource belong to you!')
parser.add_argument('--index',
                    help='GPU Index',

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'''
Automatic Keras callback class for:
- Confusion matrix calculation
- Weighted accuracy(AKA. WA)
- Unweighted accuracy(AKA. UA)
- Label distribution
- Predict correctness

Dependences: pandas, numpy

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var func1 = function() {
    return new Promise(function(resolve) {
        setTimeout(function() {
            console.log('func1');
            resolve();
        }, 500);
    });
};
var func2 = function() {
    return new Promise(function(resolve) {

YiqinZhao

#include <iostream>

int boxes[10] = {5, 3, 2, 7, 10, 9, 1, 6, 4, 8};
int lemons[10] = {2, 6, 7, 1, 10, 5, 4, 3, 9, 8};

int main() {
    int globalMax = -1;
    
    for (int k = 0; k < 10; k++) {
        int i = k, j = 0;

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function * selfIncreasement() {
  let i = 0
  while (true) {
    i++
    yield console.log(i)
  }
}

let a = selfIncreasement()

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let result = window.location.href
  .split('?')[1]
  .split('&')
  .map(v => v.split('='))
  .reduce((obj, v) => { obj[v[0]] = v[1]; return obj }, {}) 

console.log(result)