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Update C1_W1_Assignment.html #43

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68 changes: 40 additions & 28 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
@@ -1,54 +1,61 @@
<!DOCTYPE html>
<html>
<head></head>
<head>
<title>Diagnosis Classifier</title>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
<script lang="js">

<script lang="javascript">
async function run(){
const trainingUrl = '/data/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
}
}
});

// 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 = // YOUR CODE HERE

const convertedTrainingData = trainingData.map(({xs, ys}) => {
return [Object.values(xs), ys.diagnosis === 'M' ? 1 : 0];
});

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
}
}
});

// 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}) => {
return [Object.values(xs), ys.diagnosis === 'M' ? 1 : 0];
});

// 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;

// 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
Expand All @@ -58,16 +65,19 @@
// 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({units: 32, activation: 'relu', inputShape: [numOfFeatures]}));
model.add(tf.layers.dense({units: 16, 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']
});

await model.fitDataset(convertedTrainingData,
{epochs:100,
validationData: convertedTestingData,
Expand All @@ -76,10 +86,12 @@
console.log("Epoch: " + epoch + " Loss: " + logs.loss + " Accuracy: " + logs.acc);
}
}});

await model.save('downloads://my_model');
}
run();
</script>
</head>
<body>
</body>
</html>
</html>