Cell 11

This code normalizes feature values in training and testing datasets by dividing them by 255.0, effectively converting pixel values from a range of [0, 255] to [0, 1]. This is a common normalization technique in deep learning, suitable for image classification problems.

Cell 11

What the code could have been:

import numpy as np

def normalize_features(train_features, test_features):
    """
    Normalizes the training and testing features by dividing them by a scalar value.

    Args:
        train_features (numpy.ndarray): Training features.
        test_features (numpy.ndarray): Testing features.

    Returns:
        x_train (numpy.ndarray): Normalized training features.
        x_test (numpy.ndarray): Normalized testing features.
    """

    # Ensure input data are numpy arrays
    train_features = np.asarray(train_features)
    test_features = np.asarray(test_features)

    # Define the normalization scalar value
    NORMALIZATION_SCALAR = 255.0

    # Normalize the features
    x_train = train_features / NORMALIZATION_SCALAR
    x_test = test_features / NORMALIZATION_SCALAR

    return x_train, x_test

# Example usage:
x_train, x_test = normalize_features(train_features, test_features)

Normalizing Feature Values

Code Snippet

Description

This code normalizes the feature values in the training and testing datasets by dividing them by 255.0.

Cell 11

What the code could have been:

Normalizing Feature Values

Code Snippet

Description

Assumed Context