How do I create synthetic datasets using TensorFlow for anomaly detection

Question

Can you explain how I create synthetic datasets using TensorFlow for anomaly detection with the help of Python programming?

Answer 1

In order to create synthetic datasets for anomaly detection in TensorFlow, you can use random data generation with controlled anomalies injected into the dataset. Here is the code snippet you can refer:

The steps we are using above are:

• Generate Normal Data: Use tf.random.normal() for regular patterns.
• Generate Anomalies: Use tf.random.uniform() or another distribution to simulate outliers.
• Combine and Label: Merge normal and anomalous data, labeling anomalies distinctly.
• Shuffle and Batch: Create a tf.data.Dataset for training.

Hence, this synthetic dataset can be used to train and evaluate anomaly detection models.