blzq

def gaussian_blur(img, kernel_size=11, sigma=5):
    def gauss_kernel(channels, kernel_size, sigma):
        ax = tf.range(-kernel_size // 2 + 1.0, kernel_size // 2 + 1.0)
        xx, yy = tf.meshgrid(ax, ax)
        kernel = tf.exp(-(xx ** 2 + yy ** 2) / (2.0 * sigma ** 2))
        kernel = kernel / tf.reduce_sum(kernel)
        kernel = tf.tile(kernel[..., tf.newaxis], [1, 1, channels])
        return kernel

    gaussian_kernel = gauss_kernel(tf.shape(img)[-1], kernel_size, sigma)

blzq

def add_axis_angle_rotations(rv1, rv2):
    """ Given angle*axis rotation vectors a*l and b*m, result angle*axis: c*n
    cos(c/2) = cos(a/2)cos(b/2) - sin(a/2)sin(b/2) (l . m)
    sin(c/2) (n) =
      sin(a/2)cos(b/2) (l) + cos(a/2)sin(b/2) (m) + sin(a/2)sin(b/2) (l x m)
    Args:
        rv1, rv2: Axis-angle rotation Tensors with shape [batch_size, 3]
    Returns:
        Axis-angle rotation in [batch_size, 3] Tensor which when applied is 
        equivalent to application of rv2 then rv1

blzq

www.netflix.com##.episodeSynopsis
www.netflix.com##.nfa-w-60.nfa-m-0.nfa-c-gray-80.nfa-fs-1-6-em
www.netflix.com##.synopsis
www.netflix.com##.WatchNext-episode-synopsis