#!/usr/bin/env python3
"""This modual contains the function for task 13"""
import numpy as np


def batch_norm(Z, gamma, beta, epsilon):
    """
        normalizes an unactivated output of a neural network using
         batch normalization:

        @Z
            is a numpy.ndarray of shape (m, n) that should be normalized
            m
                is the number of data points
            n
                is the number of features in Z
        @gamma
            is a numpy.ndarray of shape (1, n) containing the scales used for
             batch normalization
        @beta
            is a numpy.ndarray of shape (1, n) containing the offsets
             used for batch normalization
        @epsilon
            is a small number used to avoid division by zero
        Returns: the normalized Z matrix
    """
    mean = np.mean(Z, axis=0)
    var = np.var(Z, axis=0)

    Znorm = (Z - mean)/(var + epsilon)**.5
    ZN = (gamma * Znorm) + beta

    return ZN

381c079dab05aa7009dbd924e53d08bdda5f72b0
<mask token>

<mask token>


def batch_norm(Z, gamma, beta, epsilon):
    """
        normalizes an unactivated output of a neural network using
         batch normalization:

        @Z
            is a numpy.ndarray of shape (m, n) that should be normalized
            m
                is the number of data points
            n
                is the number of features in Z
        @gamma
            is a numpy.ndarray of shape (1, n) containing the scales used for
             batch normalization
        @beta
            is a numpy.ndarray of shape (1, n) containing the offsets
             used for batch normalization
        @epsilon
            is a small number used to avoid division by zero
        Returns: the normalized Z matrix
    """
    mean = np.mean(Z, axis=0)
    var = np.var(Z, axis=0)
    Znorm = (Z - mean) / (var + epsilon) ** 0.5
    ZN = gamma * Znorm + beta
    return ZN

<mask token>
import numpy as np


def batch_norm(Z, gamma, beta, epsilon):
    """
        normalizes an unactivated output of a neural network using
         batch normalization:

        @Z
            is a numpy.ndarray of shape (m, n) that should be normalized
            m
                is the number of data points
            n
                is the number of features in Z
        @gamma
            is a numpy.ndarray of shape (1, n) containing the scales used for
             batch normalization
        @beta
            is a numpy.ndarray of shape (1, n) containing the offsets
             used for batch normalization
        @epsilon
            is a small number used to avoid division by zero
        Returns: the normalized Z matrix
    """
    mean = np.mean(Z, axis=0)
    var = np.var(Z, axis=0)
    Znorm = (Z - mean) / (var + epsilon) ** 0.5
    ZN = gamma * Znorm + beta
    return ZN

#!/usr/bin/env python3
"""This modual contains the function for task 13"""
import numpy as np


def batch_norm(Z, gamma, beta, epsilon):
    """
        normalizes an unactivated output of a neural network using
         batch normalization:

        @Z
            is a numpy.ndarray of shape (m, n) that should be normalized
            m
                is the number of data points
            n
                is the number of features in Z
        @gamma
            is a numpy.ndarray of shape (1, n) containing the scales used for
             batch normalization
        @beta
            is a numpy.ndarray of shape (1, n) containing the offsets
             used for batch normalization
        @epsilon
            is a small number used to avoid division by zero
        Returns: the normalized Z matrix
    """
    mean = np.mean(Z, axis=0)
    var = np.var(Z, axis=0)

    Znorm = (Z - mean)/(var + epsilon)**.5
    ZN = (gamma * Znorm) + beta

    return ZN
