TensorFlow.js is a JavaScript library for training and deploying machine learning models in the browser and on Node.js.
In this tutorial, we'll learn how to shuffle and batch data with TensorFlow.js. We'll cover the following topics:
When training machine learning models, it's important to shuffle the data before each epoch. This is because, if the data is not shuffled, the model might overfit on the first few samples in the dataset.
Data shuffling is the process of randomly reordering the samples in a dataset. This can be done either before or after each epoch.
There are two main benefits of shuffling data:
To shuffle data with TensorFlow.js, we first need to convert our data into a tf.data.Dataset
.
We can do this by using the tf.data.array()
or tf.data.tensor()
methods:
// Convert an array into a dataset
const dataset = tf.data.array([1, 2, 3, 4, 5]);
// Convert a tensor into a dataset
const dataset = tf.data.tensor([1, 2, 3, 4, 5]);
Once we have our dataset, we can use the tf.data. shuffle()
method to shuffle the data:
const shuffledDataset = dataset.shuffle();
To batch data with TensorFlow.js, we first need to convert our data into a tf.data.Dataset
.
We can do this by using the tf.data.array()
or tf.data.tensor()
methods:
// Convert an array into a dataset
const dataset = tf.data.array([1, 2, 3, 4, 5]);
// Convert a tensor into a dataset
const dataset = tf.data.tensor([1, 2, 3, 4, 5]);
Once we have our dataset, we can use the tf.data. batch()
method to batch the data:
const batchedDataset = dataset.batch(2);
To shuffle and batch data together with TensorFlow.js, we first need to convert our data into a tf.data.Dataset
.
We can do this by using the tf.data.array()
or tf.data.tensor()
methods:
// Convert an array into a dataset
const dataset = tf.data.array([1, 2, 3, 4, 5]);
// Convert a tensor into a dataset
const dataset = tf.data.tensor([1, 2, 3, 4, 5]);
Once we have our dataset, we can use the tf.data. shuffle()
and tf.data. batch()
methods to shuffle and batch the data:
const shuffledAndBatchedDataset = dataset.shuffle().batch(2);