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54 changes: 41 additions & 13 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 @@ -11,8 +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} // Treat 'diagnosis' column as the label
}

});

Expand All @@ -21,32 +22,40 @@
// 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)[0]};
}).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} // Treat 'diagnosis' column as the label
}
});

// 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}) => {
// Normalize the feature values and return
return {xs: Object.values(xs), ys: Object.values(ys)[0]};
}).batch(32);

// Specify the number of features
const sample = await trainingData.take(1).toArray();


// 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 = Object.keys(sample[0].xs).length;


// In the space below create a neural network that predicts 1 if the diagnosis is malignant
Expand All @@ -59,13 +68,32 @@
// hidden layers should be enough to get a high accuracy.
const model = tf.sequential();

// YOUR CODE HERE
// Input layer
model.add(tf.layers.dense({
inputShape: [numOfFeatures],
units: 32,
activation: 'relu'
}));


// Hidden layers
model.add(tf.layers.dense({
units: 16,
activation: 'relu'
}));

// Output layer
model.add(tf.layers.dense({
units: 1,
activation: 'sigmoid' // For binary classification
}));

// Compile the model using the binaryCrossentropy loss,
// the rmsprop optimizer, and accuracy for your metrics.
model.compile(// YOUR CODE HERE);
model.compile({
optimizer: 'rmsprop',
loss: 'binaryCrossentropy',
metrics: ['accuracy']
});


await model.fitDataset(convertedTrainingData,
Expand All @@ -82,4 +110,4 @@
</script>
<body>
</body>
</html>
</html>