Keras Normalize - Introduction

Keras is a popular deep learning framework written in Python. It provides high-level APIs for building and training neural networks. One important preprocessing step in deep learning is data normalization, which helps to scale and standardize the input data. Keras provides various methods for data normalization, including the normalize function.

What is Data Normalization?

Data normalization is the process of transforming input data to have a standard scale or distribution. This is important because it helps to avoid issues caused by different features having different scales or units. Normalizing the data can make the training process more efficient and improve the model's performance.

Keras normalize Function

The normalize function in Keras is a preprocessing method specifically designed to normalize data. It operates on a dataset or a single data sample and scales the values to a specific range. The function syntax is as follows:

keras.utils.normalize(x, axis=-1, order=2)

Here, x is the input data, axis specifies the axis along which normalization is performed, and order specifies the normalization order (often referred to as the p-norm).

The normalize function calculates the L2-norm (Euclidean norm) for each data sample along the specified axis and scales the values accordingly. This normalization order is commonly used in machine learning tasks where the magnitude of the features matters.

Example Usage

Here is an example of how to use the normalize function in Keras:

from keras.utils import normalize
import numpy as np

data = np.array([[1, 2, 3],
                 [4, 5, 6]])

normalized_data = normalize(data, axis=1)

print(normalized_data)

Output:

[[0.26726124 0.53452248 0.80178373]
 [0.45584231 0.56980288 0.68376346]]

In this example, we have a 2-dimensional array data representing two data samples. The normalize function is applied along axis=1, which means each row is individually normalized. The output is the normalized array where each row has unit L2-norm.

Conclusion

Data normalization is an essential preprocessing step in deep learning. Keras provides the normalize function to easily normalize input data. By scaling and standardizing the values, normalization can improve the performance and efficiency of the neural network.