def psnr(prediction, ground_truth, maxp=None, name='psnr'):
    """`Peak Signal to Noise Ratio <https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio>`_.

    .. math::

        PSNR = 20 \cdot \log_{10}(MAX_p) - 10 \cdot \log_{10}(MSE)

    Args:
        prediction: a :class:`tf.Tensor` representing the prediction signal.
        ground_truth: another :class:`tf.Tensor` with the same shape.
        maxp: maximum possible pixel value of the image (255 in in 8bit images)

    Returns:
        A scalar tensor representing the PSNR
    """

    maxp = float(maxp)

    def log10(x):
        with tf.name_scope("log10"):
            numerator = tf.log(x)
            denominator = tf.log(tf.constant(10, dtype=numerator.dtype))
            return numerator / denominator

    mse = tf.reduce_mean(tf.square(prediction - ground_truth))
    if maxp is None:
        psnr = tf.multiply(log10(mse), -10., name=name)
    else:
        psnr = tf.multiply(log10(mse), -10.)
        psnr = tf.add(tf.multiply(20., log10(maxp)), psnr, name=name)

    return psnr
`Peak Signal to Noise Ratio <https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio>`_.

    .. math::

        PSNR = 20 \cdot \log_{10}(MAX_p) - 10 \cdot \log_{10}(MSE)

    Args:
        prediction: a :class:`tf.Tensor` representing the prediction signal.
        ground_truth: another :class:`tf.Tensor` with the same shape.
        maxp: maximum possible pixel value of the image (255 in in 8bit images)

    Returns:
        A scalar tensor representing the PSNR
    
tensorpack/tfutils/symbolic_functions.py