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..b6a77b00 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
@@ -14,13 +14,21 @@ function getModel() {
     // many layers, filters, and neurons as you like.  
     // HINT: Take a look at the MNIST example.
     model = tf.sequential();
-    
-    // YOUR CODE HERE
-    
+    model.add(tf.layers.conv2d({inputShape: [28, 28, 1], filters: 32, kernelSize: 3, activation: 'relu'}));
+    model.add(tf.layers.maxPooling2d({poolSize: [2, 2]}));
+    model.add(tf.layers.conv2d({filters: 64, kernelSize: 3, activation: 'relu'}));
+    model.add(tf.layers.maxPooling2d({poolSize: [2, 2]}));
+    model.add(tf.layers.flatten());
+    model.add(tf.layers.dense({units: 128, activation: 'relu'}));
+    model.add(tf.layers.dense({units: 10, activation: 'softmax'}));
     
     // Compile the model using the categoricalCrossentropy loss,
     // the tf.train.adam() optimizer, and `acc` for your metrics.
-    model.compile(// YOUR CODE HERE);
+    model.compile({
+        optimizer: tf.train.adam(),
+        loss: 'categoricalCrossentropy',
+        metrics: ['accuracy']
+    });
     
     return model;
 }
@@ -28,17 +36,17 @@ function getModel() {
 async function train(model, data) {
         
     // Set the following metrics for the callback: 'loss', 'val_loss', 'acc', 'val_acc'.
-    const metrics = // YOUR CODE HERE    
+    const metrics = ['loss', 'val_loss', 'acc', 'val_acc'];  
 
         
     // 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   
+    const container = { name: 'Model Training', styles: { height: '1000px' } };
     
     
     // Use tfvis.show.fitCallbacks() to setup the callbacks. 
     // Use the container and metrics defined above as the parameters.
-    const fitCallbacks = // YOUR CODE HERE
+    const fitCallbacks = tfvis.show.fitCallbacks(container, metrics);
     
     const BATCH_SIZE = 512;
     const TRAIN_DATA_SIZE = 6000;
@@ -47,15 +55,25 @@ async function train(model, data) {
     // 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
+    const [trainXs, trainYs] = tf.tidy(() => {
+        const d = data.nextTrainBatch(TRAIN_DATA_SIZE);
+        return [
+            d.xs.reshape([TRAIN_DATA_SIZE, 28, 28, 1]),
+            d.labels
+        ];
+    });
 
-    
     // 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 [testXs, testYs] = tf.tidy(() => {
+        const d = data.nextTestBatch(TEST_DATA_SIZE);
+        return [
+            d.xs.reshape([TEST_DATA_SIZE, 28, 28, 1]),
+            d.labels
+        ];
+    });
 
-    
     return model.fit(trainXs, trainYs, {
         batchSize: BATCH_SIZE,
         validationData: [testXs, testYs],