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def serialize_example(feature0, feature1, feature2, feature3):
  """
  Creates a tf.train.Example message ready to be written to a file.
  """
  # Create a dictionary mapping the feature name to the tf.train.Example-compatible
  # data type.
  feature = {
      'feature0': _int64_feature(feature0),
      'feature1': _int64_feature(feature1),
      'feature2': _bytes_feature(feature2),

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import tensorflow_transform as tft

def preprocessing_fn(inputs):
    """Preprocess input columns into transformed columns."""
    x = inputs['x']
    y = inputs['y']
    s = inputs['s']
    x_centered = x - tft.mean(x)
    y_normalized = tft.scale_to_0_1(y)
    s_integerized = tft.compute_and_apply_vocabulary(s)

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async function toggleAction(action) {
    setActionText(action)
    switch(action) {
        case 'jump':
            console.log('jump')
            var e = new KeyboardEvent('keydown',{'keyCode':32, 'which':32});
            document.dispatchEvent(e);
            break;
        case 'crouch':
            var e = new KeyboardEvent('keydown',{'keyCode':40, 'which':40});

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h = imageElement.height
pos = h - hip.position.y;

if (pos < h * 0.2) {
    toggleAction('crouch')
} else if (pos < h * 0.7) {
    toggleAction('idle')
} else {
    toggleAction('jump')
}

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 async function poseHandler(pose) {
        l_hip = pose.keypoints[11];
        r_hip = pose.keypoints[12];

        hip = undefined;
        if (l_hip &&  !r_hip) {
            hip = l_hip;
        }else if (r_hip && !l_hip) {
            hip = r_hip;
        }else{

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var recording = true;
imageElement.onclick = function() {
    recording = !recording;
    console.log('recording: ' + recording);
};

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var imageElement = document.getElementById('webcam');
imageElement.width = 640 // css is not enough for h/w
imageElement.height = 480 // add those here so that posenet produces non-zero outputs

async function poseHandler(pose) {
    // do stuff 
}

imageElement.addEventListener('loadeddata', async (event) => {
    const net = await posenet.load()

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<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/posenet"></script>

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=== quantizable_default_no_regularizer ===
    threshold = 0
        32 bits => 0.9137 top5_acc
        4 bits => 0.9155 top5_acc
        3 bits => 0.9146 top5_acc
        2 bits => 0.9123 top5_acc
    threshold = 1e-06
        32 bits => 0.9137 top5_acc
        4 bits => 0.9155 top5_acc

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def uniform_q(x, bits=32):
    if bits == 32:
        return x

    n = float(2 ** bits)
    out = np.clip(np.round(x * n - 0.5), 0, n-1) / (n-1)
    return out
    
def quantize_weights(x, bits=32):
    if bits == 32: