(images, epsilon=1.0e-8)
return images * tf.rsqrt(
      tf.reduce_mean(tf.square(images), axis=3, keepdims=True) + epsilon)
Pixel normalization.

  For each pixel a[i,j,k] of image in HWC format, normalize its value to
  b[i,j,k] = a[i,j,k] / SQRT(SUM_k(a[i,j,k]^2) / C + eps).

  Args:
    images: A 4D `Tensor` of NHWC format.
    epsilon: A small positive number to avoid division by zero.

  Returns:
    A 4D `Tensor` with pixel-wise normalized channels.
Pixel normalization.
Pixel normalization .
def pixel_norm(images, epsilon=1.0e-8):
  """Pixel normalization.

  For each pixel a[i,j,k] of image in HWC format, normalize its value to
  b[i,j,k] = a[i,j,k] / SQRT(SUM_k(a[i,j,k]^2) / C + eps).

  Args:
    images: A 4D `Tensor` of NHWC format.
    epsilon: A small positive number to avoid division by zero.

  Returns:
    A 4D `Tensor` with pixel-wise normalized channels.
  """
  return images * tf.rsqrt(
      tf.reduce_mean(tf.square(images), axis=3, keepdims=True) + epsilon)
def pixel_norm ( images , epsilon = 1.0e-8 ) : return images * tf . rsqrt ( tf . reduce_mean ( tf . square ( images ) , axis = 3 , keepdims = True ) + epsilon )