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Update C1_W1_Assignment.html #61

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43 changes: 38 additions & 5 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 +13,11 @@
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 @@ -32,7 +36,11 @@
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 @@ -41,13 +49,18 @@
// a one-hot encoded array of label values like we did in the
// Iris dataset example.
const convertedTestingData = // YOUR CODE HERE

testingData.map(({xs,ys}) =>
{// Convert xs(features) and ys(labels) from object form (keyed by
// column name) to array form.
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

(await trainingData.columnNames()).length - 1;

// 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
Expand All @@ -60,6 +73,26 @@
const model = tf.sequential();

// YOUR CODE HERE
// input layer + first layer
model.add(tf.layers.dense
({
inputShape: [numOfFeatures],
activation: "sigmoid",
units: 5
}));
// hidden layer
model.add(tf.layers.dense
({
units: 10,
activation: "relu"
}));
// output layer
model.add(tf.layers.dense
({
units: 1,
activation: "sigmoid"
}));




Expand All @@ -82,4 +115,4 @@
</script>
<body>
</body>
</html>
</html>