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Applied Machine Learning: Deep Learning for Industry

Dataset Iteration

Iterate through a dataset to extract individual data observations.

We'll cover the following...

Chapter Goals:

  • Learn how to iterate through a dataset and extract values from data observations
  • Implement a function that iterates through a NumPy-based dataset and extracts the feature data

A. Iterator

The previous few chapters focused on creating and configuring datasets. In this chapter, we’ll discuss how to iterate through a dataset and extract the data.

To iterate through a dataset, we need to create an Iterator object. There are a few different ways to create an Iterator, but we’ll focus on the simplest and most commonly used method, which is the make_one_shot_iterator function.

Python 3.5
import numpy as np
import tensorflow as tf
data = np.array([[1., 2.],
       [3., 4.]])
dataset = tf.compat.v1.data.Dataset.from_tensor_slices(data)
dataset = dataset.batch(1)
it = tf.compat.v1.data.make_one_shot_iterator(dataset)
next_elem = it.get_next()
print(next_elem)
added = next_elem + 1
print(added)

In the example, it represents an Iterator for dataset. The get_next function returns something we’ll refer to as the next-element tensor.

The next-element tensor represents the batched data observation(s) at each iteration through the dataset. We can even apply operations or transformations to the next-element tensor. In the example ...