lmoroney · JulizarHw31 · May 30, 2021
diff --git a/TensorFlow Deployment/Course 1 - TensorFlow-JS/Week 1/Exercise/example b/TensorFlow Deployment/Course 1 - TensorFlow-JS/Week 1/Exercise/example
@@ -0,0 +1,114 @@
+<html>
+<head></head>
+    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
+    <script lang="js">
+
+        async function run(){
+            const trainingUrl = '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.
+            const trainingData = tf.data.csv(trainingUrl, {
+
+                // YOUR CODE HERE
+                columnConfigs: {
+                    diagnosis: {
+                        isLabel: true
+                    }
+                }
+
+            });
+
+            const numOfFeatures = (await trainingData.columnNames()).length - 1
+            console.log(numOfFeatures);
+
+            // 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 = trainingData.map(
+                ({xs, ys}) => {
+                    return {xs:Object.values(xs), ys:Object.values(ys)};
+                }
+
+            ).batch(10);
+
+
+            // console.log(convertedTrainingData);
+
+            const testingUrl = '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.
+            // 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 = testingData.map(
+                ({xs, ys}) => {
+                    return {xs:Object.values(xs), ys:Object.values(ys)};
+                }
+
+            ).batch(10);
+
+
+            // 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. 
+            // console.log(numOfFeatures);
+
+            // 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.  
+            const model = tf.sequential();
+
+            // YOUR CODE HERE
+            model.add(tf.layers.dense({inputShape: [numOfFeatures],units: 32, activation: "relu"}));
+            model.add(tf.layers.dense({units: 1, activation: "sigmoid"}));
+
+
+            console.log(model.summary());
+
+            // Compile the model using the binaryCrossentropy loss, 
+            // the rmsprop optimizer, and accuracy for your metrics. 
+            model.compile({loss: "binaryCrossentropy", optimizer: tf.train.rmsprop(0.05), 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');
+
+        }
+        run();
+    </script>
+<body>
+</body>
+</html>