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| class Node: | |
| def __init__(self, element = None, next_node = None): | |
| self._element = element | |
| self._next = next_node | |
| def get_element(self): | |
| return self._element | |
| def get_next(self): | |
| return self._next |
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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 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(): |