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Update wdbc_exercice.HTML #178

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35 changes: 24 additions & 11 deletions TensorFlow Deployment/Course 1 - TensorFlow-JS/Week 1/Exercise/wdbc_exercise.html
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Original file line number Diff line number Diff line change
Expand Up @@ -12,17 +12,21 @@
// 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{xs:Object.values(xs), ys: Object.values(ys)};
}).batch(10);
const testingUrl = 'wdbc-test.csv';

// Take a look at the 'wdbc-test.csv' file and specify the column
Expand All @@ -31,7 +35,11 @@
// 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
}
}

});

Expand All @@ -40,14 +48,16 @@
// 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(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

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
Expand All @@ -59,13 +69,16 @@
// hidden layers should be enough to get a high accuracy.
const model = tf.sequential();

// YOUR CODE HERE
model.add(tf.layers.dense({inputShape: [numOfFeatures],activation:"sigmoid",units:31 }))
model.add(tf.layers.dense({activation:"sigmoid",units:15 }))

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



// 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.rmsprop(0.05)});


await model.fitDataset(convertedTrainingData,
Expand All @@ -82,4 +95,4 @@
</script>
<body>
</body>
</html>
</html>