Tensorflow Data splitting with input data pipeline

In our previous series, we explored the concept of data splitting when working with TensorFlow data. In this new series, we will delve into two pivotal concepts: "take" and "skip." These functions serve as the foundational tools for effectively splitting and manipulating data within the TensorFlow framework.

For instance, let's consider a scenario where we have generated a data pipeline containing ten sequences, and we aim to split it into training, testing, and validation sets.

he TensorFlow take function is a critical component when working with data pipelines and datasets in machine learning and deep learning applications. It is used to extract a specified number of elements from a dataset, effectively allowing you to "take" a subset of your data. This function is particularly valuable when dividing your data into training, testing, and validation sets or when you need to extract a specific batch of data for training your model.

Here's a more detailed explanation of how the take function works:

• Input: The take function operates on a TensorFlow dataset, which is a fundamental data structure used for managing and processing data. Typically, you create a dataset from your raw data using functions like tf.data.Dataset.from_tensor_slices() or tf.data.Dataset.from_generator().

• Usage: The take function is used to create a new dataset that contains a specified number of elements from the original dataset. You specify the number of elements you want to extract as an argument to the take function.

• Output: The output of the take function is a new dataset containing the desired subset of elements. This new dataset can then be used for various purposes, such as training, testing, or validation.

Here's an example of how to use the take function in TensorFlow

import tensorflow as tf

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
dataset = tf.data.Dataset.from_tensor_slices(data)


subset_dataset = dataset.take(5)

for element in subset_dataset:
    print(element.numpy())

In this example, dataset.take(5) creates a new dataset that contains the first 5 elements of the original dataset. This allows you to work with a smaller portion of your data, which is often used for tasks like training and testing machine learning models.

The take function is a versatile tool in TensorFlow that can be combined with other functions in the data pipeline to create efficient and flexible data processing workflows.

The TensorFlow skip function, which is more commonly referred to as skip() or skip(n), is used to skip a specified number of elements at the beginning of a TensorFlow dataset. This function is valuable for situations where you want to ignore or discard a certain number of initial data points in your dataset.

Here's a more detailed explanation of how the skip function works:

• Input: The skip function operates on a TensorFlow dataset, which is a core data structure for handling and processing data in machine learning and deep learning workflows. You usually create a dataset from your raw data using functions like tf.data.Dataset.from_tensor_slices() or tf.data.Dataset.from_generator().

• Usage: The skip function is applied to the dataset and accepts an integer argument (n) that specifies the number of elements you want to skip at the beginning of the dataset.

• Output: The output of the skip function is a new dataset that excludes the first n elements from the original dataset. This new dataset can be used for various purposes, such as training, testing, or validation after the initial elements have been skipped.

Here's an example of how to use the skip function in TensorFlow:

python

import tensorflow as tf


data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
dataset = tf.data.Dataset.from_tensor_slices(data)


subset_dataset = dataset.skip(3)


for element in subset_dataset:
    print(element.numpy())  

In this example, dataset.skip(3) creates a new dataset that starts from the fourth element of the original dataset, effectively skipping the first three elements.

The skip function is particularly useful when you need to discard an initial "burn-in" period or remove unwanted data points from your dataset. When combined with other functions in the data pipeline, it enables you to build flexible and customized data processing workflows in TensorFlow.

Simple function to split

def split(data,TRAIN_RATIO=0.6,VAL_RATIO=0.2,TEST_RATIO=0.2):
    DATA_SIZE = len(data)
    train_data = data.take(int(TRAIN_RATIO * DATA_SIZE  ))
    val_data = data.skip(int(TRAIN_RATIO * DATA_SIZE  ))
    val_test = val_data.take(int(VAL_RATIO * DATA_SIZE  ))
    test_data = val_data.skip(int(VAL_RATIO * DATA_SIZE  ))

    return train_data,val_data,test_data

Thanks for reading .

Co writter: Ebele Precious Okemba