Code viewer [Ancient Brain]

Code viewer for World: New World (clone by Luke S...

// Use JS to write whatever HTML and data you want to the page 

// One way of using JS to write HTML and data is with a multi-line string
// https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Template_literals




document.write ( `


<!DOCTYPE html>
<html>
<head>
  <meta charset="utf-8">
  <meta http-equiv="X-UA-Compatible" content="IE=edge">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>TensorFlow.js Tutorial</title>

  <!-- Import TensorFlow.js -->
  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.0.0/dist/tf.min.js"></script>
  <!-- Import tfjs-vis -->
  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis@1.0.2/dist/tfjs-vis.umd.min.js"></script>

  <!-- Import the data file -->
  <script src="uploads/lukescales/data.js" type="module"></script>

  <!-- Import the main script file -->
  <script src="script.js" type="module"></script>

</head>

<body>
</body>
</html>



` );

const IMAGE_SIZE = 784;
const NUM_CLASSES = 10;
const NUM_DATASET_ELEMENTS = 65000;

const TRAIN_TEST_RATIO = 5 / 6;

const NUM_TRAIN_ELEMENTS = Math.floor(TRAIN_TEST_RATIO * NUM_DATASET_ELEMENTS);
const NUM_TEST_ELEMENTS = NUM_DATASET_ELEMENTS - NUM_TRAIN_ELEMENTS;

const MNIST_IMAGES_SPRITE_PATH =
    'https://storage.googleapis.com/learnjs-data/model-builder/mnist_images.png';
const MNIST_LABELS_PATH =
    'https://storage.googleapis.com/learnjs-data/model-builder/mnist_labels_uint8';

/**
 * A class that fetches the sprited MNIST dataset and returns shuffled batches.
 *
 * NOTE: This will get much easier. For now, we do data fetching and
 * manipulation manually.
 */
class MnistData {
  constructor() {
    this.shuffledTrainIndex = 0;
    this.shuffledTestIndex = 0;
  }

  async load() {
    // Make a request for the MNIST sprited image.
    const img = new Image();
    const canvas = document.createElement('canvas');
    const ctx = canvas.getContext('2d');
    const imgRequest = new Promise((resolve, reject) => {
      img.crossOrigin = '';
      img.onload = () => {
        img.width = img.naturalWidth;
        img.height = img.naturalHeight;

        const datasetBytesBuffer =
            new ArrayBuffer(NUM_DATASET_ELEMENTS * IMAGE_SIZE * 4);

        const chunkSize = 5000;
        canvas.width = img.width;
        canvas.height = chunkSize;

        for (let i = 0; i < NUM_DATASET_ELEMENTS / chunkSize; i++) {
          const datasetBytesView = new Float32Array(
              datasetBytesBuffer, i * IMAGE_SIZE * chunkSize * 4,
              IMAGE_SIZE * chunkSize);
          ctx.drawImage(
              img, 0, i * chunkSize, img.width, chunkSize, 0, 0, img.width,
              chunkSize);

          const imageData = ctx.getImageData(0, 0, canvas.width, canvas.height);

          for (let j = 0; j < imageData.data.length / 4; j++) {
            // All channels hold an equal value since the image is grayscale, so
            // just read the red channel.
            datasetBytesView[j] = imageData.data[j * 4] / 255;
          }
        }
        this.datasetImages = new Float32Array(datasetBytesBuffer);

        resolve();
      };
      img.src = MNIST_IMAGES_SPRITE_PATH;
    });

    const labelsRequest = fetch(MNIST_LABELS_PATH);
    const [imgResponse, labelsResponse] =
        Promise.all([imgRequest, labelsRequest]);

    this.datasetLabels = new Uint8Array(labelsResponse.arrayBuffer());

    // Create shuffled indices into the train/test set for when we select a
    // random dataset element for training / validation.
    this.trainIndices = tf.util.createShuffledIndices(NUM_TRAIN_ELEMENTS);
    this.testIndices = tf.util.createShuffledIndices(NUM_TEST_ELEMENTS);

    // Slice the the images and labels into train and test sets.
    this.trainImages =
        this.datasetImages.slice(0, IMAGE_SIZE * NUM_TRAIN_ELEMENTS);
    this.testImages = this.datasetImages.slice(IMAGE_SIZE * NUM_TRAIN_ELEMENTS);
    this.trainLabels =
        this.datasetLabels.slice(0, NUM_CLASSES * NUM_TRAIN_ELEMENTS);
    this.testLabels =
        this.datasetLabels.slice(NUM_CLASSES * NUM_TRAIN_ELEMENTS);
  }

  nextTrainBatch(batchSize) {
    return this.nextBatch(
        batchSize, [this.trainImages, this.trainLabels], () => {
          this.shuffledTrainIndex =
              (this.shuffledTrainIndex + 1) % this.trainIndices.length;
          return this.trainIndices[this.shuffledTrainIndex];
        });
  }

  nextTestBatch(batchSize) {
    return this.nextBatch(batchSize, [this.testImages, this.testLabels], () => {
      this.shuffledTestIndex =
          (this.shuffledTestIndex + 1) % this.testIndices.length;
      return this.testIndices[this.shuffledTestIndex];
    });
  }

  nextBatch(batchSize, data, index) {
    const batchImagesArray = new Float32Array(batchSize * IMAGE_SIZE);
    const batchLabelsArray = new Uint8Array(batchSize * NUM_CLASSES);

    for (let i = 0; i < batchSize; i++) {
      const idx = index();

      const image =
          data[0].slice(idx * IMAGE_SIZE, idx * IMAGE_SIZE + IMAGE_SIZE);
      batchImagesArray.set(image, i * IMAGE_SIZE);

      const label =
          data[1].slice(idx * NUM_CLASSES, idx * NUM_CLASSES + NUM_CLASSES);
      batchLabelsArray.set(label, i * NUM_CLASSES);
    }

    const xs = tf.tensor2d(batchImagesArray, [batchSize, IMAGE_SIZE]);
    const labels = tf.tensor2d(batchLabelsArray, [batchSize, NUM_CLASSES]);

    return {xs, labels};
  }
}