(images, sess, batch_size=50, verbose=True)
return mu, sigma
Calculation of the statistics used by the FID.

    Args:
        images (ndarray): Numpy array of shape (N, H, W, 3) and values in
            the range [0, 255].
        sess (Session): TensorFlow session object.
        batch_size (int): Batch size for inference.
        verbose (bool): If True, prints out logging information.

    Returns:
        ndarray: Mean of inception features from samples.
        ndarray: Covariance of inception features from samples.
Calculation of the statistics used by the FID.
Calculation of the statistics used by the FID .
def calculate_activation_statistics(images, sess, batch_size=50, verbose=True):
    """
    Calculation of the statistics used by the FID.

    Args:
        images (ndarray): Numpy array of shape (N, H, W, 3) and values in
            the range [0, 255].
        sess (Session): TensorFlow session object.
        batch_size (int): Batch size for inference.
        verbose (bool): If True, prints out logging information.

    Returns:
        ndarray: Mean of inception features from samples.
        ndarray: Covariance of inception features from samples.
    """
    act = inception_utils.get_activations(images, sess, batch_size, verbose)
    mu = np.mean(act, axis=0)
    sigma = np.cov(act, rowvar=False)

    return mu, sigma
def calculate_activation_statistics ( images , sess , batch_size = 50 , verbose = True ) : act = inception_utils . get_activations ( images , sess , batch_size , verbose ) mu = np . mean ( act , axis = 0 ) sigma = np . cov ( act , rowvar = False ) return mu , sigma