Eric Tang randcode-generator

## animation2.html
<html>
  <script src="https://code.jquery.com/jquery-3.5.1.slim.js" integrity="sha256-DrT5NfxfbHvMHux31Lkhxg42LY6of8TaYyK50jnxRnM=" crossorigin="anonymous"></script>
  <body>
    <div id="table1"></div>
    <br>
    <button onclick="next()">next</button>
    <div id="stackid"></div>
    <div id="dictid"></div>
  </body>
  <script>

## animation.html
<html>
  <script src="https://code.jquery.com/jquery-3.5.1.slim.js" integrity="sha256-DrT5NfxfbHvMHux31Lkhxg42LY6of8TaYyK50jnxRnM=" crossorigin="anonymous"></script>
  <body>
    x: <input type="text" id="x_t" value="0"><br>
    y: <input type="text" id="y_t" value="0"><br>
    len: <input type="text" id="len_t" value="2"><br>
    <button onclick="zerozero()">0, 0</button>
    <button onclick="count=0;reset();apply();addcount()">apply</button>
    <button onclick="reset();next();apply();addcount()">next</button><br>
    counter: <div id="counter"></div><br>

## vgg.py
# Based on the following sample
# https://github.com/tensorflow/models/blob/master/research/slim/slim_walkthrough.ipynb

import tensorflow as tf
import os
import sys
import tarfile
from tensorflow.contrib import slim as contrib_slim

import urllib

## vgg.py
import tensorflow as tf
import os
from tensorflow.contrib import slim as contrib_slim

import urllib2

slim = contrib_slim
image_size = 224

_R_MEAN = 123.68

## tf_linear_regression_continue_part2.py
import tensorflow as tf
import numpy

# Parameters
learning_rate = 0.01
training_epochs = 3500
datapoints_count = 100

# Training Data
train_X = []

## tf_linear_regression_part2.py
import tensorflow as tf
import numpy

# Parameters
learning_rate = 0.01
training_epochs = 500
datapoints_count = 100

# Training Data
train_X = []

## tfboard.py
import tensorflow as tf

with tf.name_scope("myvars"):
  a = tf.Variable(0.0, name="a")
  tf.summary.scalar('apple', a)
  b = tf.Variable(0.0, name="b")
  tf.summary.scalar('bananas', b)

training_epochs = 10
with tf.Session() as sess:

## iou.py
import tensorflow as tf

anchor = \
[
 [0.0, 0.0, 100.0, 100.0],
 [100.0, 100.0, 160.0, 200.0],
 [100.0, 0.0, 200.0, 100.0]
]

ground_truth = \

## convolution_strides_same.py
import tensorflow as tf
k = tf.constant([
    [0, 1, 2],
    [1, 2, 3],
    [2, 3, 4]
], dtype=tf.float32, name='k')
i = tf.constant([
    [0, 1, 2, 3, 4],
    [1, 2, 3, 4, 5],
    [2, 3, 4, 5, 6],

## convolution_strides_valid.py
import tensorflow as tf
k = tf.constant([
    [0, 1, 2],
    [1, 2, 3],
    [2, 3, 4]
], dtype=tf.float32, name='k')
i = tf.constant([
    [0, 1, 2, 3, 4],
    [1, 2, 3, 4, 5],
    [2, 3, 4, 5, 6],
	<html>
	<script src="https://code.jquery.com/jquery-3.5.1.slim.js" integrity="sha256-DrT5NfxfbHvMHux31Lkhxg42LY6of8TaYyK50jnxRnM=" crossorigin="anonymous"></script>
	<body>
	<div id="table1"></div>
	<br>
	<button onclick="next()">next</button>
	<div id="stackid"></div>
	<div id="dictid"></div>
	</body>
	<script>
	# Based on the following sample
	# https://github.com/tensorflow/models/blob/master/research/slim/slim_walkthrough.ipynb

	import tensorflow as tf
	import os
	import sys
	import tarfile
	from tensorflow.contrib import slim as contrib_slim

	import urllib
	import tensorflow as tf
	import numpy

	# Parameters
	learning_rate = 0.01
	training_epochs = 3500
	datapoints_count = 100

	# Training Data
	train_X = []
	import tensorflow as tf

	with tf.name_scope("myvars"):
	a = tf.Variable(0.0, name="a")
	tf.summary.scalar('apple', a)
	b = tf.Variable(0.0, name="b")
	tf.summary.scalar('bananas', b)

	training_epochs = 10
	with tf.Session() as sess:
	import tensorflow as tf

	anchor = \
	[
	[0.0, 0.0, 100.0, 100.0],
	[100.0, 100.0, 160.0, 200.0],
	[100.0, 0.0, 200.0, 100.0]
	]

	ground_truth = \
	import tensorflow as tf
	k = tf.constant([
	[0, 1, 2],
	[1, 2, 3],
	[2, 3, 4]
	], dtype=tf.float32, name='k')
	i = tf.constant([
	[0, 1, 2, 3, 4],
	[1, 2, 3, 4, 5],
	[2, 3, 4, 5, 6],