Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

wdbc_exercise.html #180

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

wdbc_exercise.html #180

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
30 changes: 23 additions & 7 deletions TensorFlow Deployment/Course 1 - TensorFlow-JS/Week 1/Exercise/wdbc_exercise.html
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -13,15 +13,22 @@
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';

Expand All @@ -32,6 +39,11 @@
const testingData = tf.data.csv(testingUrl, {

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

});

Expand All @@ -40,13 +52,15 @@
// 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 = trainingData.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
Expand All @@ -61,11 +75,13 @@

// 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 +98,4 @@
</script>
<body>
</body>
</html>
</html>