diff --git a/C1_Browser-based-TF-JS/W2/assignment/C1_W2_Assignment.js b/C1_Browser-based-TF-JS/W2/assignment/C1_W2_Assignment.js
deleted file mode 100755
index 8c7d1557..00000000
--- a/C1_Browser-based-TF-JS/W2/assignment/C1_W2_Assignment.js
+++ /dev/null
@@ -1,137 +0,0 @@
-import {FMnistData} from './fashion-data.js';
-var canvas, ctx, saveButton, clearButton;
-var pos = {x:0, y:0};
-var rawImage;
-var model;
-
-function getModel() {
-    
-    // In the space below create a convolutional neural network that can classify the 
-    // images of articles of clothing in the Fashion MNIST dataset. Your convolutional
-    // neural network should only use the following layers: conv2d, maxPooling2d,
-    // flatten, and dense. Since the Fashion MNIST has 10 classes, your output layer
-    // should have 10 units and a softmax activation function. You are free to use as
-    // many layers, filters, and neurons as you like.  
-    // HINT: Take a look at the MNIST example.
-    model = tf.sequential();
-    
-    // YOUR CODE HERE
-    
-    
-    // Compile the model using the categoricalCrossentropy loss,
-    // the tf.train.adam() optimizer, and `acc` for your metrics.
-    model.compile(// YOUR CODE HERE);
-    
-    return model;
-}
-
-async function train(model, data) {
-        
-    // Set the following metrics for the callback: 'loss', 'val_loss', 'acc', 'val_acc'.
-    const metrics = // YOUR CODE HERE    
-
-        
-    // Create the container for the callback. Set the name to 'Model Training' and 
-    // use a height of 1000px for the styles. 
-    const container = // YOUR CODE HERE   
-    
-    
-    // Use tfvis.show.fitCallbacks() to setup the callbacks. 
-    // Use the container and metrics defined above as the parameters.
-    const fitCallbacks = // YOUR CODE HERE
-    
-    const BATCH_SIZE = 512;
-    const TRAIN_DATA_SIZE = 6000;
-    const TEST_DATA_SIZE = 1000;
-    
-    // Get the training batches and resize them. Remember to put your code
-    // inside a tf.tidy() clause to clean up all the intermediate tensors.
-    // HINT: Take a look at the MNIST example.
-    const [trainXs, trainYs] = // YOUR CODE HERE
-
-    
-    // Get the testing batches and resize them. Remember to put your code
-    // inside a tf.tidy() clause to clean up all the intermediate tensors.
-    // HINT: Take a look at the MNIST example.
-    const [testXs, testYs] = // YOUR CODE HERE
-
-    
-    return model.fit(trainXs, trainYs, {
-        batchSize: BATCH_SIZE,
-        validationData: [testXs, testYs],
-        epochs: 10,
-        shuffle: true,
-        callbacks: fitCallbacks
-    });
-}
-
-function setPosition(e){
-    pos.x = e.clientX-100;
-    pos.y = e.clientY-100;
-}
-    
-function draw(e) {
-    if(e.buttons!=1) return;
-    ctx.beginPath();
-    ctx.lineWidth = 24;
-    ctx.lineCap = 'round';
-    ctx.strokeStyle = 'white';
-    ctx.moveTo(pos.x, pos.y);
-    setPosition(e);
-    ctx.lineTo(pos.x, pos.y);
-    ctx.stroke();
-    rawImage.src = canvas.toDataURL('image/png');
-}
-    
-function erase() {
-    ctx.fillStyle = "black";
-    ctx.fillRect(0,0,280,280);
-}
-    
-function save() {
-    var raw = tf.browser.fromPixels(rawImage,1);
-    var resized = tf.image.resizeBilinear(raw, [28,28]);
-    var tensor = resized.expandDims(0);
-    
-    var prediction = model.predict(tensor);
-    var pIndex = tf.argMax(prediction, 1).dataSync();
-    
-    var classNames = ["T-shirt/top", "Trouser", "Pullover", 
-                      "Dress", "Coat", "Sandal", "Shirt",
-                      "Sneaker",  "Bag", "Ankle boot"];
-            
-            
-    alert(classNames[pIndex]);
-}
-    
-function init() {
-    canvas = document.getElementById('canvas');
-    rawImage = document.getElementById('canvasimg');
-    ctx = canvas.getContext("2d");
-    ctx.fillStyle = "black";
-    ctx.fillRect(0,0,280,280);
-    canvas.addEventListener("mousemove", draw);
-    canvas.addEventListener("mousedown", setPosition);
-    canvas.addEventListener("mouseenter", setPosition);
-    saveButton = document.getElementById('sb');
-    saveButton.addEventListener("click", save);
-    clearButton = document.getElementById('cb');
-    clearButton.addEventListener("click", erase);
-}
-
-
-async function run() {
-    const data = new FMnistData();
-    await data.load();
-    const model = getModel();
-    tfvis.show.modelSummary({name: 'Model Architecture'}, model);
-    await train(model, data);
-    await model.save('downloads://my_model');
-    init();
-    alert("Training is done, try classifying your drawings!");
-}
-
-document.addEventListener('DOMContentLoaded', run);
-
-
-
diff --git a/C1_Browser-based-TF-JS/W2/assignment/fashion-mnist.html b/C1_Browser-based-TF-JS/W2/assignment/fashion-mnist.html
index 52f6254a..efe696d8 100755
--- a/C1_Browser-based-TF-JS/W2/assignment/fashion-mnist.html
+++ b/C1_Browser-based-TF-JS/W2/assignment/fashion-mnist.html
@@ -1,3 +1,4 @@
+<!DOCTYPE html>
 <html>
 <head>
     <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@4.1.0"></script>