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Update C1_W1_Assignment.html #40

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29 changes: 21 additions & 8 deletions C1_Browser-based-TF-JS/W1/assignment/C1_W1_Assignment.html
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,10 @@
const trainingData = tf.data.csv(trainingUrl, {

// YOUR CODE HERE

columnConfigs: {
diagnosis: {
isLabel: true
}
});

// Convert the training data into arrays in the space below.
Expand All @@ -22,7 +25,9 @@
// a one-hot encoded array of label values like we did in the
// Iris dataset example.
const convertedTrainingData = // YOUR CODE HERE

trainingData.map(({xs, ys}) => {
return{ xs: Object.values(xs), ys: Object.values(ys)};
}).batch(10);
const testingUrl = '/data/wdbc-test.csv';

// Take a look at the 'wdbc-test.csv' file and specify the column
Expand All @@ -32,7 +37,10 @@
const testingData = tf.data.csv(testingUrl, {

// YOUR CODE HERE

columnConfigs: {
diagnosis: {
isLabel: true
}
});

// Convert the testing data into arrays in the space below.
Expand All @@ -41,12 +49,14 @@
// a one-hot encoded array of label values like we did in the
// Iris dataset example.
const convertedTestingData = // YOUR CODE HERE

trainingData.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.
const numOfFeatures = // YOUR CODE HERE
const numOfFeatures = (await trainingData.columnNames()).length - 1; // YOUR CODE HERE


// In the space below create a neural network that predicts 1 if the diagnosis is malignant
Expand All @@ -61,11 +71,14 @@

// YOUR CODE HERE


model.add(tf.layers.dense({inputShape: [numOfFeatures], activation: "relu", units: 32}))
model.add(tf.layers.dense({activation: "relu", units: 64}));
model.add(tf.layers.dense({activation: "relu", units: 32}));
model.add(tf.layers.dense({activation: "sigmoid", units: 1}));

// 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: tf.train.rmsprop(0.01), metrics: ['accuracy']}) // YOUR CODE HERE);


await model.fitDataset(convertedTrainingData,
Expand All @@ -82,4 +95,4 @@
</script>
<body>
</body>
</html>
</html>