From a7fd8e80d6cb7322b7580a0137c689f0e9bbef57 Mon Sep 17 00:00:00 2001
From: wildansibil <wildansibil2@gmail.com>
Date: Sat, 16 Nov 2024 21:34:59 +0700
Subject: [PATCH] Update C1_W1_Assignment.html

---
 .../W1/assignment/C1_W1_Assignment.html       | 54 ++++++++++++++-----
 1 file changed, 41 insertions(+), 13 deletions(-)

diff --git a/C1_Browser-based-TF-JS/W1/assignment/C1_W1_Assignment.html b/C1_Browser-based-TF-JS/W1/assignment/C1_W1_Assignment.html
index 023c6143..1993169c 100755
--- a/C1_Browser-based-TF-JS/W1/assignment/C1_W1_Assignment.html
+++ b/C1_Browser-based-TF-JS/W1/assignment/C1_W1_Assignment.html
@@ -11,8 +11,9 @@
             // HINT: Remember that you are trying to build a classifier that 
             // can predict from the data whether the diagnosis is malignant or benign.
             const trainingData = tf.data.csv(trainingUrl, {
-                
-                // YOUR CODE HERE
+                 columnConfigs: {
+                    diagnosis: {isLabel: true} // Treat 'diagnosis' column as the label
+                }
                 
             });
             
@@ -21,8 +22,10 @@
             // Therefore, there is no need to convert string labels into
             // a one-hot encoded array of label values like we did in the
             // Iris dataset example. 
-            const convertedTrainingData = // YOUR CODE HERE
-                  
+            const convertedTrainingData = trainingData.map(({xs, ys}) => {
+                return {xs: Object.values(xs), ys: Object.values(ys)[0]};
+            }).batch(32);
+
             const testingUrl = '/data/wdbc-test.csv';
             
             // Take a look at the 'wdbc-test.csv' file and specify the column
@@ -30,9 +33,9 @@
             // HINT: Remember that you are trying to build a classifier that 
             // can predict from the data whether the diagnosis is malignant or benign.
             const testingData = tf.data.csv(testingUrl, {
-                
-                // YOUR CODE HERE
-                
+                columnConfigs: {
+                    diagnosis: {isLabel: true} // Treat 'diagnosis' column as the label
+                }
             });
             
             // Convert the testing data into arrays in the space below.
@@ -40,13 +43,19 @@
             // Therefore, there is no need to convert string labels into
             // a one-hot encoded array of label values like we did in the
             // Iris dataset example. 
-            const convertedTestingData = // YOUR CODE HERE
+            const convertedTestingData = testingData.map(({xs, ys}) => {
+                // Normalize the feature values and return
+                return {xs: Object.values(xs), ys: Object.values(ys)[0]};
+            }).batch(32);
+
+            // Specify the number of features
+            const sample = await trainingData.take(1).toArray();
                   
 
             // Specify the number of features in the space below.
             // HINT: You can get the number of features from the number of columns
             // and the number of labels in the training data. 
-            const numOfFeatures = // YOUR CODE HERE
+            const numOfFeatures = Object.keys(sample[0].xs).length;
             
                 
             // In the space below create a neural network that predicts 1 if the diagnosis is malignant
@@ -59,13 +68,32 @@
             // hidden layers should be enough to get a high accuracy.  
             const model = tf.sequential();
             
-            // YOUR CODE HERE
+    // Input layer
+            model.add(tf.layers.dense({
+                inputShape: [numOfFeatures],
+                units: 32,
+                activation: 'relu'
+            }));
 
-            
+            // Hidden layers
+            model.add(tf.layers.dense({
+                units: 16,
+                activation: 'relu'
+            }));
+
+            // Output layer
+            model.add(tf.layers.dense({
+                units: 1,
+                activation: 'sigmoid' // For binary classification
+            }));
             
             // Compile the model using the binaryCrossentropy loss, 
             // the rmsprop optimizer, and accuracy for your metrics. 
-            model.compile(// YOUR CODE HERE);
+            model.compile({
+                optimizer: 'rmsprop',
+                loss: 'binaryCrossentropy',
+                metrics: ['accuracy']
+            });
             
             
             await model.fitDataset(convertedTrainingData, 
@@ -82,4 +110,4 @@
     </script>
 <body>
 </body>
-</html>
\ No newline at end of file
+</html>