TensorFlow Data Collection
The data used in Example 1, is a list of car objects like this:
{
"Name": "chevrolet chevelle malibu",
"Miles_per_Gallon": 18,
"Cylinders": 8,
"Displacement": 307,
"Horsepower": 130,
"Weight_in_lbs": 3504,
"Acceleration": 12,
"Year": "1970-01-01",
"Origin": "USA"
},
{
"Name": "buick skylark 320",
"Miles_per_Gallon": 15,
"Cylinders": 8,
"Displacement": 350,
"Horsepower": 165,
"Weight_in_lbs": 3693,
"Acceleration": 11.5,
"Year": "1970-01-01",
"Origin": "USA"
},The dataset is a JSON file stored at:
[https://storage.googleapis.com/tfjs-tutorials/carsData.json
Cleaning Data
When preparing for machine learning, it is always important to:
- Remove the data you don't need
- Clean the data from errors
Remove Data
A smart way to remove unnecessary data, is to extract only the data you need .
This can be done by iterating (looping over) your data with a map function .
The function below takes an object and returns only x and y from the object's Horsepower and Miles_per_Gallon properties:
function extractData(obj) {
return {x:obj.Horsepower, y:obj.Miles_per_Gallon};
}Remove Errors
Most datasets contain some type of errors.
A smart way to remove errors is to use a filter function to filter out the errors.
The code below returns false if one of the properties (x or y) contains a null value:
function removeErrors(obj) {
return obj.x != null && obj.y != null;
}Fetching Data
When you have your map and filter functions ready, you can write a function to fetch the data.
Runnable example
async function runTF() {
const jsonData = await fetch("cardata.json");
let values = await jsonData.json();
values = values.map(extractData).filter(removeErrors);
}Plotting the Data
Here is some code you can use to plot the data:
Runnable example
function tfPlot(values, surface) {
tfvis.render.scatterplot(surface, {values:values, series:['Original','Predicted']}, {xLabel:'Horsepower', yLabel:'MPG'});
}