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/**
* This is a code for Google App Script to see if there is a line for QBHouse at Kojimachi.
* main(): Access to qb house kojimachi homepage and check the number of people in line.
* Replace an email with yours.
* doGet(): This function is for publishing a web service as API (only one endpoint).
*/

function main() {
  var url = 'http://www.qbhouse.co.jp/search/detail.php?id=475'; // QB HOUSE Kojimachi
  var text = UrlFetchApp.fetch(url).getContentText('utf-8');

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var CHANNEL_ACCESS_TOKEN = 'xxx=';
var line_endpoint = 'https://api.line.me/v2/bot/message/reply';

function doGet(e) {
  return ContentService.createTextOutput(UrlFetchApp.fetch("http://ip-api.com/json"));
}

function doPost(e) {
  var contents = JSON.parse(e.postData.contents);

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nwatab

import matplotlib.pyplot as plt
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, BatchNormalization
from keras.layers.advanced_activations import LeakyReLU
from keras.layers import Conv2D, MaxPooling2D, Reshape, UpSampling2D, InputLayer
from keras.optimizers import Adam
import os

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1. Fine-tuning with DAGM2007 dataset. 109 Epochs was enough. It took 8 hours with a Tesla K80 (12GB Memory, 61GB RAM, 100GB SSD)

https://resources.mpi-inf.mpg.de/conference/dagm/2007/prizes.html

2. python heatmap.py image.jpg

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