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Notebooks for learning deep learning
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tfjs@latest:2 Uncaught (in promise) Error: Argument 'b' passed to 'mul' must be a Tensor or TensorLike, but got 'null' #58

Closed sayakpaul closed 4 years ago

sayakpaul commented 4 years ago

I am currently taking the Browser-based Models with TensorFlow.js course. I am only stuck in the Week 1 exercise.

Here's the code: 

<html>
<head></head>
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
    <script lang="js">
        async function run(){
            const trainingUrl = 'wdbc-train.csv';
            const trainingData = tf.data.csv(trainingUrl, {
                columnConfigs: {
                    diagnosis: {
                        isLabel: true
                    }
                }
            });

            const convertedTrainingData = 
                trainingData.map(({xs, ys}) => {
                      // console.log(trainingData);
                      return{ xs: Object.values(xs), ys: Object.values(ys)};
                  }).batch(10);

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

            // const testingData = tf.data.csv(testingUrl, {
            //     columnConfigs: {
            //         diagnosis: {
            //             isLabel: true
            //         }
            //     }

            // });

            // const convertedTestingData = 
            //     testingData.map(({xs, ys}) => {
            //           return{ xs: Object.values(xs), ys: Object.values(ys)};
            //       }).batch(10);

            const numOfFeatures = 30;
            // console.log(numOfFeatures);

            const model = tf.sequential();
            model.add(tf.layers.dense({inputShape: [numOfFeatures], activation: "relu", units: 20}))
            model.add(tf.layers.dense({activation: "relu", units: 20}))
            model.add(tf.layers.dense({activation: "relu", units: 10}))
            model.add(tf.layers.dense({activation: "relu", units: 5}))
            model.add(tf.layers.dense({activation: "sigmoid", units: 1}));

            model.compile({loss: "binaryCrossentropy", optimizer: tf.train.rmsprop(), metrics: ["accuracy"]});

            model.summary();

            //console.log(convertedTrainingData);

            await model.fitDataset(convertedTrainingData, 
                             {epochs:100,
                              callbacks:{
                                  onEpochEnd: async(epoch, logs) =>{
                                      console.log("Epoch: " + epoch + " Loss: " + logs.loss);
                                  }
                              }});

            // 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>

And here's the console log:

tfjs@latest:2 _________________________________________________________________
tfjs@latest:2 Layer (type)                 Output shape              Param #   
tfjs@latest:2 =================================================================
tfjs@latest:2 dense_Dense1 (Dense)         [null,20]                 620       
tfjs@latest:2 _________________________________________________________________
tfjs@latest:2 dense_Dense2 (Dense)         [null,20]                 420       
tfjs@latest:2 _________________________________________________________________
tfjs@latest:2 dense_Dense3 (Dense)         [null,10]                 210       
tfjs@latest:2 _________________________________________________________________
tfjs@latest:2 dense_Dense4 (Dense)         [null,5]                  55        
tfjs@latest:2 _________________________________________________________________
tfjs@latest:2 dense_Dense5 (Dense)         [null,1]                  6         
tfjs@latest:2 =================================================================
tfjs@latest:2 Total params: 1311
tfjs@latest:2 Trainable params: 1311
tfjs@latest:2 Non-trainable params: 0
tfjs@latest:2 _________________________________________________________________
tfjs@latest:2 Uncaught (in promise) Error: Argument 'b' passed to 'mul' must be a Tensor or TensorLike, but got 'null'
    at Ke (tfjs@latest:2)
    at mul_ (tfjs@latest:2)
    at Object.mul (tfjs@latest:2)
    at t.mul (tfjs@latest:2)
    at tfjs@latest:2
    at tfjs@latest:2
    at t.scopedRun (tfjs@latest:2)
    at t.tidy (tfjs@latest:2)
    at We (tfjs@latest:2)
    at tfjs@latest:2
Ke @ tfjs@latest:2
mul_ @ tfjs@latest:2
mul @ tfjs@latest:2
t.mul @ tfjs@latest:2
(anonymous) @ tfjs@latest:2
(anonymous) @ tfjs@latest:2
t.scopedRun @ tfjs@latest:2
t.tidy @ tfjs@latest:2
We @ tfjs@latest:2
(anonymous) @ tfjs@latest:2
e.applyGradients @ tfjs@latest:2
e.minimize @ tfjs@latest:2
(anonymous) @ tfjs@latest:2
(anonymous) @ tfjs@latest:2
(anonymous) @ tfjs@latest:2
(anonymous) @ tfjs@latest:2
o @ tfjs@latest:2
async function (async)
run @ wdbc_exercise.html:53
(anonymous) @ wdbc_exercise.html:73

I am absolutely running out of options here to debug this. Help would be appreciated. Thanks!