test sciweaver

Author: joaogui1

g3doc/tutorials/framework.md

@@ -15,7 +15,8 @@ Convolutional NNs and Recurrent NNs).
 
 Note that the generalized neighbor loss equation is flexible and can have other
 forms besides the one illustrated above. For example, we can also select
-\\(\sum_{x_j \in \mathcal{N}(x_i)}\mathcal{E}(y_i,g_\theta(x_j)\\) to be the
+![equation](http://www.sciweavers.org/tex2img.php?eq=%5C%28%5Csum_%7Bx_j%20%5Cin%20%5Cmathcal%7BN%7D%28x_i%29%7D%5Cmathcal%7BE%7D%28y_i%2Cg_%5Ctheta%28x_j%29%5C%29&bc=White&fc=Black&im=jpg&fs=12&ff=arev&edit=0)
+ to be the
 neighbor loss, which calculates the distance between the ground truth \\(y_i\\)
 and the prediction from the neighbor \\(g_\theta(x_j)\\). This is commonly used
 in adversarial learning