Explain with an example how to do feature preprocessing using keras?
This recipe explains with an example how to do feature preprocessing using keras
Recipe Objective
With an example, elaborate how to do feature preprocessing using keras
The Keras preprocessing layers API gives an option to developers to build Keras-native input processing pipelines. These pipelines can be used as independent preprocessing code in non-Keras workflows, they can be combined directly with Keras models.
Learn to Build a Multi Class Image Classification Model in Python from Scratch
Step 1- Importing Library
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers.experimental import preprocessing
Step 2- Creating a 2d array.
Preprocessing a 2D array.
X = np.array([[4,8,10], [40,100, 120], [200, 300, 1000],[1000,5000,10000]])
layer = preprocessing.Normalization()
layer.adapt(data)
normalized_X = layer(X)
Step 3- Printing the mean and Standard Deviation of Data.
print("Features mean: %.2f" % (normalized_X.numpy().mean()))
print("Features std: %.2f" % (normalized_X.numpy().std()))
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