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<!DOCTYPE html> | |
<html lang="en"> | |
<head> | |
<meta charset="utf-8"/> | |
<title>Translate a Triangle</title> | |
<body onload="main()"> | |
<canvas id="webgl" width="400" height="400"> | |
Please use a browser that supports "canvas" | |
</canvas> |
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<!doctype html> | |
<html> | |
<body> | |
<canvas width = "570" height = "570" id = "my_Canvas"></canvas> | |
<script> | |
/*============== Creating a canvas ====================*/ | |
var canvas = document.getElementById('my_Canvas'); | |
gl = canvas.getContext('experimental-webgl'); | |
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<!doctype html> | |
<html> | |
<body> | |
<canvas width = "570" height = "570" id = "my_Canvas"></canvas> | |
<script> | |
/*================Creating a canvas=================*/ | |
var canvas = document.getElementById('my_Canvas'); | |
gl = canvas.getContext('experimental-webgl'); |
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import time | |
from absl import app, flags, logging | |
from absl.flags import FLAGS | |
import cv2 | |
import tensorflow as tf | |
from yolov3_tf2.models import ( | |
YoloV3, YoloV3Tiny | |
) | |
from yolov3_tf2.dataset import transform_images | |
from yolov3_tf2.utils import draw_outputs |
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#! /usr/bin/env python | |
# coding=utf-8 | |
# ================================================================ | |
# Copyright (C) 2020 Mina Gabriel All rights reserved. | |
# | |
# Editor : PyCharm | |
# File name : preprocessing.py | |
# Author : Mina Gabriel | |
# Created date: 5/23/20 06:55 | |
# Description : |
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print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU'))) | |
#input image is 5 X 5 and 1 channel | |
input_shape = (1, 1, 5, 5) | |
tf.summary.trace_on(graph=True, profiler=True) | |
x = tf.random.normal(input_shape) | |
print(x.shape) | |
y = tf.keras.layers.Conv2D( |
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import os | |
from datetime import datetime | |
import coloredlogs, logging | |
import tensorflow as tf | |
import tensorflow.python.util.deprecation as deprecation | |
tf.debugging.set_log_device_placement(True) | |
coloredlogs.install() |
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import sys | |
from flask import Flask, jsonify, request | |
from keras.datasets import mnist | |
import MySQLdb | |
app = Flask(__name__) | |
@app.route("/", methods=["GET", "POST"]) | |
def hello(): |
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import keras | |
from keras.datasets import mnist | |
from keras.layers import Input, Dense | |
from keras.models import load_model | |
from skimage.util import invert | |
import numpy as np | |
import matplotlib.pyplot as plt | |
(X_train, y_train), (X_test, y_test) = mnist.load_data() |
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import os | |
import pandas as pd | |
import mysql.connector | |
from sqlalchemy import create_engine | |
# ====== Connection ====== # | |
# Connecting to mysql by providing a sqlachemy engine | |
engine = create_engine('mysql+mysqlconnector://myhu:password@192.168.68.102/dbname', echo=True) |