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data_final_ver2.js
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/**
----This is modified version of the original code licensed as below---
----Modifications made by Erol Ozan-----------------------------------
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
//selected_size = parseInt(document.getElementById("selected_size").value);
number_of_images = parseInt(document.getElementById("myText").value);
number_of_classes = parseInt(document.getElementById("number_of_classes").value);
selected_size = parseInt(document.getElementById("selected_size").value);
file_name = document.getElementById("file_name").value;
file_extension = document.getElementById("file_extension").value;
number_of_training_images = parseInt(document.getElementById("number_of_training_images").value);
number_of_test_images = parseInt(document.getElementById("number_of_test_images").value);
epochs_value = parseInt(document.getElementById("epochs_value").value);
var IMAGE_SIZE = selected_size*selected_size;
var NUM_TEST_ELEMENTS = number_of_test_images;
//const NUM_TEST_ELEMENTS = 270;
//const MNIST_IMAGES_SPRITE_PATH =
// 'training_imgs.png';
// '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.
*/
export class MnistData {
constructor() {
this.shuffledTrainIndex = 0;
this.shuffledTestIndex = 0;
}
async load() {
// Make a request for the MNIST sprited image.
// const img = new Image();
var final_image_array = [0,1,0];//
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(number_of_images * IMAGE_SIZE * 4);
const chunkSize = number_of_images;
canvas.width = selected_size;
canvas.height = chunkSize*selected_size;
for (let i = 0; i < 1; i++) {
const datasetBytesView = new Float32Array(
datasetBytesBuffer, i * IMAGE_SIZE * chunkSize * 4,
IMAGE_SIZE * chunkSize);
// ctx.drawImage(img,0,0);
// const imageData = ctx.getImageData(0, 0, 56, 56*400);
var imageDataX = image_array;
console.log("imageDataX");
console.log(imageDataX);
for (let j = 0; j < imageDataX.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;
datasetBytesView[j] = ((parseInt(imageDataX[j * 4]) + parseInt(imageDataX[j * 4 +1])+ parseInt(imageDataX[j * 4 +2]) )/3)/ 255;
//console.log(j, ",", datasetBytesView[j]);
}
final_image_array=datasetBytesView;
}
console.log("image data set loading started");
this.datasetImages = new Float32Array(final_image_array);
console.log("loading is succesful");
resolve();
});
const [imgResponse, labelsResponse] =
await Promise.all([imgRequest]);
var dataLabels = [0,1];
var k = 0;
var ratio = parseInt(number_of_images/number_of_classes);
var i=0;
for (i = 0; i < (ratio); i++) {
k=number_of_classes * i;
dataLabels[k] = 1;
dataLabels[k +1] = 0;
if (number_of_classes>2){dataLabels[k +2] = 0;}
if (number_of_classes>3){dataLabels[k +3] = 0;}
if (number_of_classes>4){dataLabels[k +4] = 0;}
if (number_of_classes>5){dataLabels[k +5] = 0;}
if (number_of_classes>6){dataLabels[k +6] = 0;}
if (number_of_classes>7){dataLabels[k +7] = 0;}
if (number_of_classes>8){dataLabels[k +8] = 0;}
if (number_of_classes>9){dataLabels[k +9] = 0;}
}
for (i = ratio; i < (2*(ratio)); i++) {
k=number_of_classes* i;
dataLabels[k] = 0;
dataLabels[k +1] = 1;
if (number_of_classes>2){dataLabels[k +2] = 0;}
if (number_of_classes>3){dataLabels[k +3] = 0;}
if (number_of_classes>4){dataLabels[k +4] = 0;}
if (number_of_classes>5){dataLabels[k +5] = 0;}
if (number_of_classes>6){dataLabels[k +6] = 0;}
if (number_of_classes>7){dataLabels[k +7] = 0;}
if (number_of_classes>8){dataLabels[k +8] = 0;}
if (number_of_classes>9){dataLabels[k +9] = 0;}
}
if (number_of_classes>2){
for (i = (2*(ratio)); i < (3*(ratio)); i++) {
k=number_of_classes* i;
dataLabels[k] = 0;
dataLabels[k +1] = 0;
if (number_of_classes>2){dataLabels[k +2] = 1;}
if (number_of_classes>3){dataLabels[k +3] = 0;}
if (number_of_classes>4){dataLabels[k +4] = 0;}
if (number_of_classes>5){dataLabels[k +5] = 0;}
if (number_of_classes>6){dataLabels[k +6] = 0;}
if (number_of_classes>7){dataLabels[k +7] = 0;}
if (number_of_classes>8){dataLabels[k +8] = 0;}
if (number_of_classes>9){dataLabels[k +9] = 0;}
}
}
if (number_of_classes>3){
for (i = (3*(ratio)); i < (4*(ratio)); i++) {
k=number_of_classes* i;
dataLabels[k] = 0;
dataLabels[k +1] = 0;
if (number_of_classes>2){dataLabels[k +2] = 0;}
if (number_of_classes>3){dataLabels[k +3] = 1;}
if (number_of_classes>4){dataLabels[k +4] = 0;}
if (number_of_classes>5){dataLabels[k +5] = 0;}
if (number_of_classes>6){dataLabels[k +6] = 0;}
if (number_of_classes>7){dataLabels[k +7] = 0;}
if (number_of_classes>8){dataLabels[k +8] = 0;}
if (number_of_classes>9){dataLabels[k +9] = 0;}
}
}
if (number_of_classes>4){
for (i = (4*(ratio)); i < (5*(ratio)); i++) {
k=number_of_classes* i;
dataLabels[k] = 0;
dataLabels[k +1] = 0;
if (number_of_classes>2){dataLabels[k +2] = 0;}
if (number_of_classes>3){dataLabels[k +3] = 0;}
if (number_of_classes>4){dataLabels[k +4] = 1;}
if (number_of_classes>5){dataLabels[k +5] = 0;}
if (number_of_classes>6){dataLabels[k +6] = 0;}
if (number_of_classes>7){dataLabels[k +7] = 0;}
if (number_of_classes>8){dataLabels[k +8] = 0;}
if (number_of_classes>9){dataLabels[k +9] = 0;}
}
}
if (number_of_classes>5){
for (i = (5*(ratio)); i < (6*(ratio)); i++) {
k=number_of_classes* i;
dataLabels[k] = 0;
dataLabels[k +1] = 0;
if (number_of_classes>2){dataLabels[k +2] = 0;}
if (number_of_classes>3){dataLabels[k +3] = 0;}
if (number_of_classes>4){dataLabels[k +4] = 0;}
if (number_of_classes>5){dataLabels[k +5] = 1;}
if (number_of_classes>6){dataLabels[k +6] = 0;}
if (number_of_classes>7){dataLabels[k +7] = 0;}
if (number_of_classes>8){dataLabels[k +8] = 0;}
if (number_of_classes>9){dataLabels[k +9] = 0;}
}
}
if (number_of_classes>6){
for (i = (6*(ratio)); i < (7*(ratio)); i++) {
k=number_of_classes* i;
dataLabels[k] = 0;
dataLabels[k +1] = 0;
if (number_of_classes>2){dataLabels[k +2] = 0;}
if (number_of_classes>3){dataLabels[k +3] = 0;}
if (number_of_classes>4){dataLabels[k +4] = 0;}
if (number_of_classes>5){dataLabels[k +5] = 0;}
if (number_of_classes>6){dataLabels[k +6] = 1;}
if (number_of_classes>7){dataLabels[k +7] = 0;}
if (number_of_classes>8){dataLabels[k +8] = 0;}
if (number_of_classes>9){dataLabels[k +9] = 0;}
}
}
if (number_of_classes>7){
for (i = (7*(ratio)); i < (8*(ratio)); i++) {
k=number_of_classes* i;
dataLabels[k] = 0;
dataLabels[k +1] = 0;
if (number_of_classes>2){dataLabels[k +2] = 0;}
if (number_of_classes>3){dataLabels[k +3] = 0;}
if (number_of_classes>4){dataLabels[k +4] = 0;}
if (number_of_classes>5){dataLabels[k +5] = 0;}
if (number_of_classes>6){dataLabels[k +6] = 0;}
if (number_of_classes>7){dataLabels[k +7] = 1;}
if (number_of_classes>8){dataLabels[k +8] = 0;}
if (number_of_classes>9){dataLabels[k +9] = 0;}
}
}
if (number_of_classes>8){
for (i = (8*(ratio)); i < (9*(ratio)); i++) {
k=number_of_classes* i;
dataLabels[k] = 0;
dataLabels[k +1] = 0;
if (number_of_classes>2){dataLabels[k +2] = 0;}
if (number_of_classes>3){dataLabels[k +3] = 0;}
if (number_of_classes>4){dataLabels[k +4] = 0;}
if (number_of_classes>5){dataLabels[k +5] = 0;}
if (number_of_classes>6){dataLabels[k +6] = 0;}
if (number_of_classes>7){dataLabels[k +7] = 0;}
if (number_of_classes>8){dataLabels[k +8] = 1;}
if (number_of_classes>9){dataLabels[k +9] = 0;}
}
}
if (number_of_classes>9){
for (i = (9*(ratio)); i < (10*(ratio)); i++) {
k=number_of_classes* i;
dataLabels[k] = 0;
dataLabels[k +1] = 0;
if (number_of_classes>2){dataLabels[k +2] = 0;}
if (number_of_classes>3){dataLabels[k +3] = 0;}
if (number_of_classes>4){dataLabels[k +4] = 0;}
if (number_of_classes>5){dataLabels[k +5] = 0;}
if (number_of_classes>6){dataLabels[k +6] = 0;}
if (number_of_classes>7){dataLabels[k +7] = 0;}
if (number_of_classes>8){dataLabels[k +8] = 0;}
if (number_of_classes>9){dataLabels[k +9] = 1;}
}
}
console.log("**********************");
console.log("dataLabels");
console.log(dataLabels);
console.log("**********************");
this.datasetLabels = new Uint8Array(dataLabels);
console.log("this.datasetLabels", this.datasetLabels);
// Create shuffled indices into the train/test set for when we select a
// random dataset element for training / validation.
this.trainIndices = tf.util.createShuffledIndices(number_of_training_images);
console.log("this.trainIndices");
console.log(this.trainIndices);
this.testIndices = tf.util.createShuffledIndices(number_of_test_images);//attention
// Slice the the images and labels into train and test sets.
this.trainImages =
this.datasetImages.slice(0, IMAGE_SIZE * number_of_training_images);
this.testImages = this.datasetImages.slice(IMAGE_SIZE * number_of_test_images);
this.trainLabels =
this.datasetLabels.slice(0, number_of_classes * number_of_training_images);
console.log("this.trainLabels");
console.log(this.trainLabels);
this.testLabels =
this.datasetLabels.slice(number_of_classes * number_of_test_images);
console.log("this.testLabels");
console.log(this.testLabels);
}
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 * number_of_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 * number_of_classes, idx * number_of_classes + number_of_classes);
batchLabelsArray.set(label, i * number_of_classes);
}
const xs = tf.tensor2d(batchImagesArray, [batchSize, IMAGE_SIZE]);
const labels = tf.tensor2d(batchLabelsArray, [batchSize, number_of_classes]);
return {xs, labels};
}
}