Sten Goes stengoes

## cosine_decay_with_warmup.py
class CosineDecayWithWarmup(tf.keras.optimizers.schedules.LearningRateSchedule):

    def __init__(
        self,
        warmup_steps,
        total_steps,
        base_lr=0.001,
    ):
        super(CosineDecayWithWarmup, self).__init__()
        self.warmup_steps = warmup_steps

## models.py
import tensorflow as tf

# Reset states (to be sure)
tf.keras.backend.clear_session()

# Method 1
x = tf.keras.Input(shape=(224, 224, 3), dtype=tf.uint8)
c = tf.keras.layers.Flatten()(x)
y = tf.keras.layers.Dense(units=2, activation="softmax")(c)
model1 = tf.keras.Model(inputs=x, outputs=y)

## image_pad_or_crop.js
function image_pad_or_crop(image, output_height, output_width) {
  // Pads or crops an image so that the height and width dimensions match output_height and output_width.
  // This function is compatible with tf.image.resize_with_crop_or_pad(image, target_height, target_width)

  // Validate inputs
  const image_size = image.height * image.width * image.depth;
  if (image.data.length != image_size) {
    console.log("Error: image dimensions do not match rgba buffer size!");
    return null;
  }

## zeropad.js
function zeropad(image, top, bottom, left, right) {
    // Pads an image with a specified amount of zeros on the top, left, bottom and right.

    // Validate inputs
    const image_size = image.height * image.width * image.depth;
    if (image.data.length != image_size) {
        console.log("Error: image dimensions do not match rgba buffer size!");
        return null;
    }
    if (image.data.constructor !== Uint8ClampedArray && image.data.constructor !== Float32Array) {

## example_static_library_in_c++.md

      
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                / example_static_library_in_c++.md
            
            
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                A minimal example for creating, compiling and using a static library in C++.
              
          
    A minimal example for creating, compiling and using a static library in C++.
1. Create a test library

// test.h
#pragma once 
int add(int a, int b);
int mult(int a, int b);

  
## example_dynamic_library_in_c++.md

      
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                stengoes
                / example_dynamic_library_in_c++.md
            
            
              Last active
              July 4, 2020 17:51
            
              
                A minimal example for creating, compiling and using a dynamic library in C++.
              
          
    A minimal example for creating, compiling and using a dynamic library in C++.
1. Create a test library

// test.h
#pragma once 
int add(int a, int b);
int mult(int a, int b);

  
## balance.js
function distribute_recordings_over_lines(num_recordings, distribution, lines) {
  // This function distributes number of recordings optimally
  // given the current distribution and the lines on which you want to record.

  function find_level(n, distribution, lines, low, high) {
    // Compute the level
    const level = (low + high) / 2;

    // Compute search statistic
    let s = 0;

## resize.js
 function resizeBicubic(image, newHeight, newWidth) {

      /* Helper functions */
      function extrapolateImageBoundaries(image) {

          const height = image.length;
          const width = image[0].length;

          // Linearly extrapolate top and bottom of the image
          image[-2] = [];

## demosaic.js
function demosaic(raw) {
    // Demosaics a Color Filter Array (CFA) into a RGBA color array.
    // For implementation details see: http://research.microsoft.com/pubs/102068/demosaicing_icassp04.pdf

    // Validate inputs
    const raw_size = raw.height * raw.width * raw.depth;
    if (raw.data.length != raw_size) {
        console.log("Error: raw image dimensions do not match raw buffer size!");
        return null;
    }

## index.html
<html>
    <body>
        <canvas id="mycanvas" width="2048" height="512"></canvas>

    </body>

</html>

<script>
    const height = 512;
	class CosineDecayWithWarmup(tf.keras.optimizers.schedules.LearningRateSchedule):

	def __init__(
	self,
	warmup_steps,
	total_steps,
	base_lr=0.001,
	):
	super(CosineDecayWithWarmup, self).__init__()
	self.warmup_steps = warmup_steps
	import tensorflow as tf

	# Reset states (to be sure)
	tf.keras.backend.clear_session()

	# Method 1
	x = tf.keras.Input(shape=(224, 224, 3), dtype=tf.uint8)
	c = tf.keras.layers.Flatten()(x)
	y = tf.keras.layers.Dense(units=2, activation="softmax")(c)
	model1 = tf.keras.Model(inputs=x, outputs=y)
	function image_pad_or_crop(image, output_height, output_width) {
	// Pads or crops an image so that the height and width dimensions match output_height and output_width.
	// This function is compatible with tf.image.resize_with_crop_or_pad(image, target_height, target_width)

	// Validate inputs
	const image_size = image.height * image.width * image.depth;
	if (image.data.length != image_size) {
	console.log("Error: image dimensions do not match rgba buffer size!");
	return null;
	}
	function zeropad(image, top, bottom, left, right) {
	// Pads an image with a specified amount of zeros on the top, left, bottom and right.

	// Validate inputs
	const image_size = image.height * image.width * image.depth;
	if (image.data.length != image_size) {
	console.log("Error: image dimensions do not match rgba buffer size!");
	return null;
	}
	if (image.data.constructor !== Uint8ClampedArray && image.data.constructor !== Float32Array) {
	function distribute_recordings_over_lines(num_recordings, distribution, lines) {
	// This function distributes number of recordings optimally
	// given the current distribution and the lines on which you want to record.

	function find_level(n, distribution, lines, low, high) {
	// Compute the level
	const level = (low + high) / 2;

	// Compute search statistic
	let s = 0;
	function resizeBicubic(image, newHeight, newWidth) {

	/* Helper functions */
	function extrapolateImageBoundaries(image) {

	const height = image.length;
	const width = image[0].length;

	// Linearly extrapolate top and bottom of the image
	image[-2] = [];
	function demosaic(raw) {
	// Demosaics a Color Filter Array (CFA) into a RGBA color array.
	// For implementation details see: http://research.microsoft.com/pubs/102068/demosaicing_icassp04.pdf

	// Validate inputs
	const raw_size = raw.height * raw.width * raw.depth;
	if (raw.data.length != raw_size) {
	console.log("Error: raw image dimensions do not match raw buffer size!");
	return null;
	}
	<html>
	<body>
	<canvas id="mycanvas" width="2048" height="512"></canvas>

	</body>

	</html>

	<script>
	const height = 512;