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Update C1_W1_Assignment.html #59

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46 changes: 39 additions & 7 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 @@ -13,6 +13,9 @@
const trainingData = tf.data.csv(trainingUrl, {

// YOUR CODE HERE
columnConfigs: {
diagnosis: { isLabel: true }
}

});

Expand All @@ -21,7 +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 = // YOUR CODE HERE
const convertedTrainingData = trainingData.map(({ xs, ys }) => {
return {
xs: Object.values(xs),
ys: Object.values(ys)
};
}).batch(32); // YOUR CODE HERE

const testingUrl = '/data/wdbc-test.csv';

Expand All @@ -32,6 +40,9 @@
const testingData = tf.data.csv(testingUrl, {

// YOUR CODE HERE
columnConfigs: {
diagnosis: { isLabel: true }
}

});

Expand All @@ -40,13 +51,18 @@
// 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); // YOUR CODE HERE


// 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; // YOUR CODE HERE


// In the space below create a neural network that predicts 1 if the diagnosis is malignant
Expand All @@ -60,13 +76,29 @@
const model = tf.sequential();

// YOUR CODE HERE
model.add(tf.layers.dense({
inputShape: [numOfFeatures],
units: 16,
activation: 'relu'
}));


model.add(tf.layers.dense({
units: 8,
activation: 'relu'
}));

model.add(tf.layers.dense({
units: 1,
activation: 'sigmoid'
}));

// 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']
}) // YOUR CODE HERE);

await model.fitDataset(convertedTrainingData,
{epochs:100,
Expand All @@ -82,4 +114,4 @@
</script>
<body>
</body>
</html>
</html>