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Update C1_W1_Assignment.html #47

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44 changes: 30 additions & 14 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 @@ -11,17 +11,27 @@
// 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, {

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

});

// 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}) => {
const labels = [
ys.species == "malignant" ? 1:0,
ys.species == "benign" ? 1:0
]
return{ xs:Object.values(xs), ys:Object.values(labels)};
}).batch(10);
// YOUR CODE HERE

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

Expand All @@ -30,23 +40,31 @@
// 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: {
species: {
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}) => {
const labels = [
ys.species == "malignant" ? 1:0,
ys.species == "benign" ? 1:0
]
return { xs:Obhect.values(xs), ys:Object.values(labels)};
}).batch(10);// 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 = (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 @@ -58,14 +76,12 @@
// 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();

model.add(tf.layers.dense({inputShape:[numofFeatures],activation="relu", units:10}))
model.add(tf.layers.dense({activation:"sigmoid", units : 2}));
// YOUR CODE HERE



// 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.adam(0.0612)});// YOUR CODE HERE);


await model.fitDataset(convertedTrainingData,
Expand All @@ -82,4 +98,4 @@
</script>
<body>
</body>
</html>
</html>