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
index 8c7d1557..24cc2fb0 100755
--- a/C1_Browser-based-TF-JS/W2/assignment/C1_W2_Assignment.js
+++ b/C1_Browser-based-TF-JS/W2/assignment/C1_W2_Assignment.js
@@ -1,61 +1,70 @@
 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);
-    
+    model.add(tf.layers.conv2d({
+        inputShape: [28, 28, 1],
+        kernelSize: 3,
+        filters: 16,
+        activation: 'relu'
+    }));
+
+    model.add(tf.layers.maxPooling2d({
+        poolSize: [2, 2]
+    }));
+
+    model.add(tf.layers.conv2d({
+        kernelSize: 3,
+        filters: 32,
+        activation: 'relu'
+    }));
+
+    model.add(tf.layers.flatten());
+
+    model.add(tf.layers.dense({
+        units: 128,
+        activation: 'relu'
+    }));
+
+    model.add(tf.layers.dense({
+        units: 10,
+        activation: 'softmax'
+    }));
+
+    model.compile({
+        loss: 'categoricalCrossentropy',
+        optimizer: tf.train.adam(),
+        metrics: ['acc']
+    });
+
     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 metrics = ['loss', 'val_loss', 'acc', 'val_acc'];
+    const container = document.getElementById('training');
+    const fitCallbacks = tfvis.show.fitCallbacks(container, metrics);
+
     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
+    const [trainXs, trainYs] = tf.tidy(() => {
+        const batch = data.nextTrainBatch(TRAIN_DATA_SIZE);
+        return [batch.xs.reshape([TRAIN_DATA_SIZE, 28, 28, 1]), batch.labels];
+    });
+
+    const [testXs, testYs] = tf.tidy(() => {
+        const batch = data.nextTestBatch(TEST_DATA_SIZE);
+        return [batch.xs.reshape([TEST_DATA_SIZE, 28, 28, 1]), batch.labels];
+    });
 
-    
     return model.fit(trainXs, trainYs, {
         batchSize: BATCH_SIZE,
         validationData: [testXs, testYs],
@@ -100,7 +109,6 @@ function save() {
                       "Dress", "Coat", "Sandal", "Shirt",
                       "Sneaker",  "Bag", "Ankle boot"];
             
-            
     alert(classNames[pIndex]);
 }
     
@@ -132,6 +140,3 @@ async function run() {
 }
 
 document.addEventListener('DOMContentLoaded', run);
-
-
-