How I can use returned values of 'tf.data.csv' in the evaluation method 'evaluate()' in Tensorflow.js?
I wanted to train a simple model on TFJS. First I read the data from a CSV file. Then I trained the model and finally I calculated the loss and accuracy. But I could not to measure accuracy on the test dataset which is imported by 'tf.data.csv'.
<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 numOfFeatures = (await trainingData.columnNames()).length - 1;
const numOfSamples= 455
const convertedData =
trainingData.map(({xs, ys}) => {
const labels = [
ys.diagnosis == 1 ? 1 : 0
]
return{ xs: Object.values(xs), ys: Object.values(labels)};
}).batch(20);
const testingUrl = 'wdbc-test.csv';
const testingData = tf.data.csv(testingUrl, {
columnConfigs: {
diagnosis:{
isLabel: true
}
}
});
const convertedTestingData = // YOUR CODE HERE
testingData.map(({xs, ys}) => {
const labels = [
ys.diagnosis == 1 ? 1 : 0
]
return{ xs: Object.values(xs), ys: Object.values(labels)};
}).batch(10);
const numOfTestFeatures = (await testingData.columnNames()).length - 1;
const a =testingData.toArray()
console.log(a)
const model = tf.sequential();
model.add(tf.layers.dense({inputShape: [numOfFeatures], activation: "relu", units: 10}));
model.add(tf.layers.dense({inputShape: 10 , activation: "relu", units: 10}));
model.add(tf.layers.dense({activation: "sigmoid", units: 1}));
model.compile({loss: "binaryCrossentropy", optimizer: tf.train.rmsprop(0.05),metrics: "accuracy"});
await model.fitDataset(convertedData,
{epochs:2,
callbacks:{
onEpochEnd: async(epoch, logs) =>{
console.log("Epoch: " + epoch + " Loss: " + logs.loss );
}
}});
const result = model.evaluateDataset(convertedData,{batchSize: 10});
console.log("Accuracy: " + result);
await model.save('downloads://my_model');
}
run();
</script>
<body>
</body>
</html>tf.data.csv returns a tf.data.Dataset. The evaluate method expect a tensor or an array of tensor. If you would like to evaluate a tf.data.Dataset, the method evaluateDataset can be used instead.
evaluateDataset returns a promise.
const data = await model.evaluateDataset(testingData)
// data can be a tf.Scalar or an array of tf.Scalar