Method getData_

mnist-acgan/data.js:152–186

(isTrainingData)

  }

  getData_(isTrainingData) {
    let imagesIndex;
    let labelsIndex;
    if (isTrainingData) {
      imagesIndex = 0;
      labelsIndex = 1;
    } else {
      imagesIndex = 2;
      labelsIndex = 3;
    }
    const size = this.dataset[imagesIndex].length;
    tf.util.assert(
        this.dataset[labelsIndex].length === size,
        `Mismatch in the number of images (${size}) and ` +
            `the number of labels (${this.dataset[labelsIndex].length})`);

    // Only create one big array to hold batch of images.
    const imagesShape = [size, IMAGE_HEIGHT, IMAGE_WIDTH, 1];
    const images = new Float32Array(tf.util.sizeFromShape(imagesShape));
    const labels = new Int32Array(tf.util.sizeFromShape([size, 1]));

    let imageOffset = 0;
    let labelOffset = 0;
    for (let i = 0; i < size; ++i) {
      images.set(this.dataset[imagesIndex][i], imageOffset);
      labels.set(this.dataset[labelsIndex][i], labelOffset);
      imageOffset += IMAGE_FLAT_SIZE;
      labelOffset += 1;
    }

    return {
      images: tf.tensor4d(images, imagesShape),
      labels: tf.oneHot(tf.tensor1d(labels, 'int32'), LABEL_FLAT_SIZE).toFloat()
    };
  }
}

module.exports = new MnistDataset();