Data Preparation for Deep Learning

Deep learning models often require a large amount of data, making data preparation a crucial step. In this article, we will delve into the process of data preparation for deep learning models, providing a detailed explanation of each step. We will also include example code using the TensorFlow library to perform data preparation tasks.

Data Collection and Preprocessing

Data collection and preprocessing are fundamental steps in data preparation for deep learning models.

Data Collection

The data collection process aims to create representative and diverse datasets for the deep learning model. The dataset should include samples that the model will be working on (representative dataset) as well as additional examples and classes to improve the model’s generalization (generalization dataset).

For example, when working on an image classification model, you can create a dataset that consists of a large number of images from different categories. This dataset should encompass various objects, scenes, and other types of images. The data collection process involves gathering resources and employing methods to create such a dataset.

Data Preprocessing

Data preprocessing involves organizing and cleaning the collected data. The specific preprocessing steps depend on the characteristics of the dataset and the requirements of the deep learning model.

In many cases, when working with image data, common preprocessing steps include resizing the images to a consistent size and normalizing the pixel values. This ensures that the pixel values of the images are within a certain range, facilitating better learning by the model.

Deep learning libraries such as TensorFlow provide various functions and tools to perform data preprocessing tasks. Here’s an example code snippet that demonstrates image resizing and normalization:

In the above code, the preprocess_image function takes an image as input, resizes it to 224x224 pixels, and normalizes the pixel values to the range of 0 to 1.

Data Splitting

It is common practice to divide the dataset into training, validation, and testing partitions when training deep learning models. The training partition contains the data used for model learning, the validation partition is used to evaluate the model’s performance during training and assist in hyperparameter tuning, and the testing partition is reserved for evaluating the model’s real-world performance.

Scikit-learn provides functions for splitting the data into these partitions. Here’s an example code snippet that splits the data into training, validation, and testing sets:

In the above code, the train_test_split function from the scikit-learn library is used to split the data into training, validation, and testing sets based on the specified proportions. The resulting data and labels are assigned to separate variables.

In this article, we have explored the important steps involved in data preparation for deep learning. Proper execution of data collection, preprocessing, data splitting, and data augmentation steps can contribute to improved performance of deep learning models. Libraries like TensorFlow offer a variety of functions and tools that can be utilized to implement these steps.

I will talk about Data Augmentation in the next article.