From ccd5b401f2ca8cc0202a7be39db3382d5b958b9f Mon Sep 17 00:00:00 2001
From: Mirandabangkitm197b4kx250 <m197b4kx2506@bangkit.academy>
Date: Fri, 15 Nov 2024 17:15:15 +0700
Subject: [PATCH 1/9] Update C1_W1_Assignment.html

---
 .../W1/assignment/C1_W1_Assignment.html       | 67 +++++++++++++------
 1 file changed, 48 insertions(+), 19 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..5c01faf1 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
@@ -11,9 +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 }
+                }
             });
             
             // Convert the training data into arrays in the space below.
@@ -21,18 +21,23 @@
             // 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)
+                };
+            }).batch(32);
+
             const testingUrl = '/data/wdbc-test.csv';
-            
+
             // Take a look at the 'wdbc-test.csv' file and specify the column
             // that should be treated as the label in the space below..
             // 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 }
+                }
             });
             
             // Convert the testing data into arrays in the space below.
@@ -40,13 +45,18 @@
             // 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 }) => {
+                return {
+                    xs: Object.values(xs),
+                    ys: Object.values(ys)
+                };
+            }).batch(32); 
 
             // 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 = 30; 
+
             
                 
             // In the space below create a neural network that predicts 1 if the diagnosis is malignant
@@ -58,15 +68,34 @@
             // using ReLu activation functions where applicable. For this dataset only a few
             // hidden layers should be enough to get a high accuracy.  
             const model = tf.sequential();
-            
-            // YOUR CODE HERE
 
-            
-            
+            // Input layer dan hidden layer pertama
+            model.add(tf.layers.dense({
+                inputShape: [numOfFeatures], // Sesuaikan jumlah fitur
+                units: 16,
+                activation: 'relu'
+            }));
+
+            // Hidden layer kedua
+            model.add(tf.layers.dense({
+                units: 8,
+                activation: 'relu'
+            }));
+
+            // Output layer
+            model.add(tf.layers.dense({
+                units: 1,
+                activation: 'sigmoid' // Aktivasi sigmoid untuk output biner
+            }));
+
+
             // Compile the model using the binaryCrossentropy loss, 
             // the rmsprop optimizer, and accuracy for your metrics. 
-            model.compile(// YOUR CODE HERE);
-            
+            model.compile({
+                loss: 'binaryCrossentropy',
+                optimizer: 'rmsprop',
+                metrics: ['accuracy']
+            });
             
             await model.fitDataset(convertedTrainingData, 
                              {epochs:100,
@@ -82,4 +111,4 @@
     </script>
 <body>
 </body>
-</html>
\ No newline at end of file
+</html>

From 6f13d180c29f8d7401b5236ff2aeae898b07e9ab Mon Sep 17 00:00:00 2001
From: Mirandabangkitm197b4kx250 <m197b4kx2506@bangkit.academy>
Date: Fri, 15 Nov 2024 17:52:27 +0700
Subject: [PATCH 2/9] Update C1_W1_Assignment.html


From f00fdbbb396ad0d94d305abe3c22e2dc4208db2c Mon Sep 17 00:00:00 2001
From: Mirandabangkitm197b4kx250 <m197b4kx2506@bangkit.academy>
Date: Fri, 15 Nov 2024 17:53:00 +0700
Subject: [PATCH 3/9] Update C1_W1_Assignment.html

---
 C1_Browser-based-TF-JS/W1/assignment/C1_W1_Assignment.html | 1 +
 1 file changed, 1 insertion(+)

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 5c01faf1..de471a7a 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
@@ -112,3 +112,4 @@
 <body>
 </body>
 </html>
+</html>

From e47d24c468669bcb00075b303fe6b6f548416257 Mon Sep 17 00:00:00 2001
From: Mirandabangkitm197b4kx250 <m197b4kx2506@bangkit.academy>
Date: Fri, 15 Nov 2024 17:53:21 +0700
Subject: [PATCH 4/9] Update C1_W1_Assignment.html

---
 C1_Browser-based-TF-JS/W1/assignment/C1_W1_Assignment.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

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 de471a7a..c5178a5d 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
@@ -111,5 +111,5 @@
     </script>
 <body>
 </body>
-</html>
+
 </html>

From 5390a37cd854cb552e620ec6574cee84d70c34e8 Mon Sep 17 00:00:00 2001
From: Mirandabangkitm197b4kx250 <m197b4kx2506@bangkit.academy>
Date: Fri, 15 Nov 2024 17:54:27 +0700
Subject: [PATCH 5/9] Update C1_W1_Assignment.html

---
 .../W1/assignment/C1_W1_Assignment.html       | 88 ++++++-------------
 1 file changed, 29 insertions(+), 59 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 c5178a5d..3532d507 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
@@ -1,26 +1,19 @@
 <html>
 <head></head>
+<body>
     <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
-    <script lang="js">
-        
-        async function run(){
+    <script>
+        async function run() {
             const trainingUrl = '/data/wdbc-train.csv';
-            
-            // Take a look at the 'wdbc-train.csv' file and specify the column
-            // that should be treated as the label in the space below.
-            // HINT: Remember that you are trying to build a classifier that 
-            // can predict from the data whether the diagnosis is malignant or benign.
+
+            // Specify the column that should be treated as the label
             const trainingData = tf.data.csv(trainingUrl, {
                 columnConfigs: {
                     diagnosis: { isLabel: true }
                 }
             });
-            
-            // Convert the training data into arrays in the space below.
-            // Note: In this case, the labels are integers, not strings.
-            // 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. 
+
+            // Convert the training data into arrays
             const convertedTrainingData = trainingData.map(({ xs, ys }) => {
                 return {
                     xs: Object.values(xs),
@@ -30,53 +23,35 @@
 
             const testingUrl = '/data/wdbc-test.csv';
 
-            // Take a look at the 'wdbc-test.csv' file and specify the column
-            // that should be treated as the label in the space below..
-            // HINT: Remember that you are trying to build a classifier that 
-            // can predict from the data whether the diagnosis is malignant or benign.
+            // Specify the column that should be treated as the label for testing data
             const testingData = tf.data.csv(testingUrl, {
-               columnConfigs: {
+                columnConfigs: {
                     diagnosis: { isLabel: true }
                 }
             });
-            
-            // Convert the testing data into arrays in the space below.
-            // Note: In this case, the labels are integers, not strings.
-            // 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. 
+
+            // Convert the testing data into arrays
             const convertedTestingData = testingData.map(({ xs, ys }) => {
                 return {
                     xs: Object.values(xs),
                     ys: Object.values(ys)
                 };
-            }).batch(32); 
+            }).batch(32);
 
-            // 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 = 30; 
+            // Specify the number of features (columns excluding the label)
+            const numOfFeatures = 30;
 
-            
-                
-            // In the space below create a neural network that predicts 1 if the diagnosis is malignant
-            // and 0 if the diagnosis is benign. Your neural network should only use dense
-            // layers and the output layer should only have a single output unit with a
-            // sigmoid activation function. You are free to use as many hidden layers and
-            // neurons as you like.  
-            // HINT: Make sure your input layer has the correct input shape. We also suggest
-            // using ReLu activation functions where applicable. For this dataset only a few
-            // hidden layers should be enough to get a high accuracy.  
+            // Create a neural network model
             const model = tf.sequential();
 
-            // Input layer dan hidden layer pertama
+            // Input layer and first hidden layer
             model.add(tf.layers.dense({
-                inputShape: [numOfFeatures], // Sesuaikan jumlah fitur
+                inputShape: [numOfFeatures], // Adjust input shape based on the number of features
                 units: 16,
                 activation: 'relu'
             }));
 
-            // Hidden layer kedua
+            // Second hidden layer
             model.add(tf.layers.dense({
                 units: 8,
                 activation: 'relu'
@@ -85,31 +60,26 @@
             // Output layer
             model.add(tf.layers.dense({
                 units: 1,
-                activation: 'sigmoid' // Aktivasi sigmoid untuk output biner
+                activation: 'sigmoid' // Sigmoid activation for binary classification
             }));
 
-
-            // Compile the model using the binaryCrossentropy loss, 
-            // the rmsprop optimizer, and accuracy for your metrics. 
+            // Compile the model with binaryCrossentropy loss, rmsprop optimizer, and accuracy metric
             model.compile({
                 loss: 'binaryCrossentropy',
                 optimizer: 'rmsprop',
                 metrics: ['accuracy']
             });
-            
-            await model.fitDataset(convertedTrainingData, 
-                             {epochs:100,
-                              validationData: convertedTestingData,
-                              callbacks:{
-                                  onEpochEnd: async(epoch, logs) =>{
-                                      console.log("Epoch: " + epoch + " Loss: " + logs.loss + " Accuracy: " + logs.acc);
-                                  }
-                              }});
-            await model.save('downloads://my_model');
+
+            // Fit the model on the training data
+            await model.fitDataset(convertedTrainingData, {
+                epochs: 100,
+                validationData: convertedTestingData
+            });
+
+            // Model is now trained and ready for evaluation or prediction
         }
+
         run();
     </script>
-<body>
 </body>
-
 </html>

From c3b68a4644760c2d1fe9eb28893ac0160880cd5d Mon Sep 17 00:00:00 2001
From: Mirandabangkitm197b4kx250 <m197b4kx2506@bangkit.academy>
Date: Fri, 15 Nov 2024 18:01:45 +0700
Subject: [PATCH 6/9] Update C1_W1_Assignment.html

---
 .../W1/assignment/C1_W1_Assignment.html       | 94 +++++++++++++------
 1 file changed, 66 insertions(+), 28 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 3532d507..d458e65e 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
@@ -1,19 +1,32 @@
 <html>
 <head></head>
-<body>
+    
     <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
-    <script>
-        async function run() {
+    <script lang="js">
+        
+        async function run(){
             const trainingUrl = '/data/wdbc-train.csv';
-
-            // Specify the column that should be treated as the label
+            
+            // Take a look at the 'wdbc-train.csv' file and specify the column
+            // that should be treated as the label in the space below.
+            // 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 }
                 }
             });
 
-            // Convert the training data into arrays
+            // Convert the training data into arrays in the space below.
+            // Note: In this case, the labels are integers, not strings.
+            // 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),
@@ -22,36 +35,61 @@
             }).batch(32);
 
             const testingUrl = '/data/wdbc-test.csv';
+            
 
-            // Specify the column that should be treated as the label for testing data
+            // Take a look at the 'wdbc-test.csv' file and specify the column
+            // that should be treated as the label in the space below..
+            // 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, {
-                columnConfigs: {
+                
+                // YOUR CODE HERE
+
+               columnConfigs: {
                     diagnosis: { isLabel: true }
                 }
             });
 
-            // Convert the testing data into arrays
+            // Convert the testing data into arrays in the space below.
+            // Note: In this case, the labels are integers, not strings.
+            // 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 }) => {
                 return {
                     xs: Object.values(xs),
                     ys: Object.values(ys)
                 };
-            }).batch(32);
+            }).batch(32); 
+
+            // 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 = 30; 
+
 
-            // Specify the number of features (columns excluding the label)
-            const numOfFeatures = 30;
 
-            // Create a neural network model
+            // In the space below create a neural network that predicts 1 if the diagnosis is malignant
+@@ -58,15 +68,34 @@
+            // using ReLu activation functions where applicable. For this dataset only a few
+            // hidden layers should be enough to get a high accuracy.  
             const model = tf.sequential();
 
-            // Input layer and first hidden layer
+            // YOUR CODE HERE
+
+
+
+            // Input layer dan hidden layer pertama
             model.add(tf.layers.dense({
-                inputShape: [numOfFeatures], // Adjust input shape based on the number of features
+                inputShape: [numOfFeatures], // Sesuaikan jumlah fitur
                 units: 16,
                 activation: 'relu'
             }));
 
-            // Second hidden layer
+            // Hidden layer kedua
             model.add(tf.layers.dense({
                 units: 8,
                 activation: 'relu'
@@ -60,26 +98,26 @@
             // Output layer
             model.add(tf.layers.dense({
                 units: 1,
-                activation: 'sigmoid' // Sigmoid activation for binary classification
+                activation: 'sigmoid' // Aktivasi sigmoid untuk output biner
             }));
 
-            // Compile the model with binaryCrossentropy loss, rmsprop optimizer, and accuracy metric
+
+            // Compile the model using the binaryCrossentropy loss, 
+            // the rmsprop optimizer, and accuracy for your metrics. 
+            model.compile(// YOUR CODE HERE);
+
             model.compile({
                 loss: 'binaryCrossentropy',
                 optimizer: 'rmsprop',
                 metrics: ['accuracy']
             });
 
-            // Fit the model on the training data
-            await model.fitDataset(convertedTrainingData, {
-                epochs: 100,
-                validationData: convertedTestingData
-            });
-
-            // Model is now trained and ready for evaluation or prediction
-        }
-
-        run();
+            await model.fitDataset(convertedTrainingData, 
+                             {epochs:100,
+@@ -82,4 +111,5 @@
     </script>
+<body>
 </body>
 </html>
+</html>
+</html>

From db63336e0f10645d460ceb275e27927f42f4dd78 Mon Sep 17 00:00:00 2001
From: Mirandabangkitm197b4kx250 <m197b4kx2506@bangkit.academy>
Date: Fri, 15 Nov 2024 18:02:37 +0700
Subject: [PATCH 7/9] Update C1_W1_Assignment.html

---
 C1_Browser-based-TF-JS/W1/assignment/C1_W1_Assignment.html | 3 ---
 1 file changed, 3 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 d458e65e..34f63764 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
@@ -12,9 +12,6 @@
             // 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 }
                 }

From b85d1b4706060868479bdba56eccd1755dd4038d Mon Sep 17 00:00:00 2001
From: Mirandabangkitm197b4kx250 <m197b4kx2506@bangkit.academy>
Date: Fri, 15 Nov 2024 18:12:25 +0700
Subject: [PATCH 8/9] Update C1_W1_Assignment.html

---
 .../W1/assignment/C1_W1_Assignment.html       | 75 +++++++++----------
 1 file changed, 36 insertions(+), 39 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 34f63764..8b56fddd 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
@@ -1,6 +1,5 @@
 <html>
 <head></head>
-    
     <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
     <script lang="js">
         
@@ -12,28 +11,28 @@
             // 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 }
                 }
+                
             });
-
+            
             // Convert the training data into arrays in the space below.
             // Note: In this case, the labels are integers, not strings.
             // 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 }) => {
+            const convertedTrainingData = trainingData.map(({ xs, ys }) => { // YOUR CODE HERE 
                 return {
                     xs: Object.values(xs),
                     ys: Object.values(ys)
                 };
             }).batch(32);
-
+                  
             const testingUrl = '/data/wdbc-test.csv';
             
-
             // Take a look at the 'wdbc-test.csv' file and specify the column
             // that should be treated as the label in the space below..
             // HINT: Remember that you are trying to build a classifier that 
@@ -41,80 +40,78 @@
             const testingData = tf.data.csv(testingUrl, {
                 
                 // YOUR CODE HERE
-
-               columnConfigs: {
+                columnConfigs: {
                     diagnosis: { isLabel: true }
                 }
+                
             });
-
+            
             // Convert the testing data into arrays in the space below.
             // Note: In this case, the labels are integers, not strings.
             // 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 }) => {
+            const convertedTestingData = testingData.map(({ xs, ys }) => { // YOUR CODE HERE
                 return {
                     xs: Object.values(xs),
                     ys: Object.values(ys)
                 };
-            }).batch(32); 
+            }).batch(32);
+                  
 
             // 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 = 30; 
-
-
-
+            const numOfFeatures = 30; // YOUR CODE HERE 
+            
+                
             // In the space below create a neural network that predicts 1 if the diagnosis is malignant
-@@ -58,15 +68,34 @@
+            // and 0 if the diagnosis is benign. Your neural network should only use dense
+            // layers and the output layer should only have a single output unit with a
+            // sigmoid activation function. You are free to use as many hidden layers and
+            // neurons as you like.  
+            // HINT: Make sure your input layer has the correct input shape. We also suggest
             // using ReLu activation functions where applicable. For this dataset only a few
             // hidden layers should be enough to get a high accuracy.  
             const model = tf.sequential();
-
+            
             // YOUR CODE HERE
-
-
-
-            // Input layer dan hidden layer pertama
             model.add(tf.layers.dense({
-                inputShape: [numOfFeatures], // Sesuaikan jumlah fitur
+                inputShape: [numOfFeatures], 
                 units: 16,
                 activation: 'relu'
             }));
 
-            // Hidden layer kedua
             model.add(tf.layers.dense({
                 units: 8,
                 activation: 'relu'
             }));
 
-            // Output layer
             model.add(tf.layers.dense({
                 units: 1,
-                activation: 'sigmoid' // Aktivasi sigmoid untuk output biner
+                activation: 'sigmoid' 
             }));
-
-
+            
             // Compile the model using the binaryCrossentropy loss, 
             // the rmsprop optimizer, and accuracy for your metrics. 
-            model.compile(// YOUR CODE HERE);
-
-            model.compile({
+            model.compile( {
                 loss: 'binaryCrossentropy',
                 optimizer: 'rmsprop',
                 metrics: ['accuracy']
-            });
-
+            }); // YOUR CODE HERE);
+            
             await model.fitDataset(convertedTrainingData, 
                              {epochs:100,
-@@ -82,4 +111,5 @@
+                              validationData: convertedTestingData,
+                              callbacks:{
+                                  onEpochEnd: async(epoch, logs) =>{
+                                      console.log("Epoch: " + epoch + " Loss: " + logs.loss + " Accuracy: " + logs.acc);
+                                  }
+                              }});
+            await model.save('downloads://my_model');
+        }
+        run();
     </script>
 <body>
 </body>
 </html>
-</html>
-</html>

From 943780b27b341adee4ad4deeb6971a4fe2e1f2f9 Mon Sep 17 00:00:00 2001
From: Mirandabangkitm197b4kx250 <m197b4kx2506@bangkit.academy>
Date: Fri, 15 Nov 2024 18:16:44 +0700
Subject: [PATCH 9/9] Update C1_W1_Assignment.html

---
 .../W1/assignment/C1_W1_Assignment.html                | 10 +++++-----
 1 file changed, 5 insertions(+), 5 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 8b56fddd..1e3a3aee 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
@@ -24,12 +24,12 @@
             // 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 = trainingData.map(({ xs, ys }) => { // YOUR CODE HERE 
+            const convertedTrainingData = trainingData.map(({ xs, ys }) => { 
                 return {
                     xs: Object.values(xs),
                     ys: Object.values(ys)
                 };
-            }).batch(32);
+            }).batch(32); // YOUR CODE HERE
                   
             const testingUrl = '/data/wdbc-test.csv';
             
@@ -51,12 +51,12 @@
             // 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 = testingData.map(({ xs, ys }) => { // YOUR CODE HERE
+            const convertedTestingData = testingData.map(({ xs, ys }) => { 
                 return {
                     xs: Object.values(xs),
                     ys: Object.values(ys)
                 };
-            }).batch(32);
+            }).batch(32); // YOUR CODE HERE
                   
 
             // Specify the number of features in the space below.
@@ -98,7 +98,7 @@
                 loss: 'binaryCrossentropy',
                 optimizer: 'rmsprop',
                 metrics: ['accuracy']
-            }); // YOUR CODE HERE);
+            }) // YOUR CODE HERE);
             
             await model.fitDataset(convertedTrainingData, 
                              {epochs:100,