tensorflow-2-public/C1_Browser-based-TF-JS/W1/assignment/C1_W1_Assignment.html at d46e02dc14a2e997a35fe3cb6127f695d12d4b00 · https-deeplearning-ai/tensorflow-2-public · GitHub

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<html>
<head></head>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
    <script lang="js">

        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, {
                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 = 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';

            // 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, {
                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 = 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 = (await trainingData.columnNames()).length - 1; // YOUR CODE HERE


            // 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
            // sigmoid activation function. You are free to use as many hidden layers and
            // neurons as you like.
            // HINT: Make sure your input layer has the correct input shape. We also suggest
            // 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(loss:"binarycrossentropy", optimizer:tf.train.adam(0.0612)});// YOUR CODE HERE);


            await model.fitDataset(convertedTrainingData,
                             {epochs:100,
                              validationData: convertedTestingData,
                              callbacks:{
                                  onEpochEnd: async(epoch, logs) =>{
                                      console.log("Epoch: " + epoch + " Loss: " + logs.loss + " Accuracy: " + logs.acc);
                                  }
                              }});
            await model.save('downloads://my_model');
        }
        run();
    </script>
<body>
</body>
</html>