Zhao

Qihong, have you work out how to update the parameters in LocNet yet?

Thanks for your favorable and prompt reply. And do you think the implementation that you mentioned above realize the parameters updates as Mnih's paper describes?

Yeah, I found that the work done by Zhongwen drops check point and summary(tensorboard), its will be helpful if they are added. And there are a question that still confuses...

There is no error message but just don't show the window as it should be. So I doubt it may because of my platform's problem.

@QihongL found the reason. It's because that the matplotlib has something wrong in my platform, when I $sudo pip uninstall matplotlib, it worked! I may be because I had install...

@GodOfProbability @QihongL We make an assumption that we don't sampling but use the mean_loc straightly, just similar with the soft attention in "show, attend and tell" Then, the question are:...

@GodOfProbability Yes, you are right! The parameters at location generation module rely on the the derivative of log[P(sample_loc|mean_loc,sigma)] w.r.t. parameters_loc to update, which is actually the derivative of mean_loc w.r.t....

@QihongL @GodOfProbability @jlindsey15 @Lzc6996 It proves working well when I comment the line mean_loc = tf.stop_gradient(mean_loc) as Gopal described above. When using the bandwidth = 12, it converge at more...

@jtkim-kaist The baseline tech is very important to location prediction. It is learnable as an extra term of cost function as is shown in the source code below: ``` J...

@jtkim-kaist b shouldn't be updated from this term: `J = tf.concat(1, [tf.log(p_y + SMALL_NUM) * (onehot_labels_placeholder), tf.log(p_loc + SMALL_NUM) * (R - no_grad_b)])` where no_grad_b = tf.stop_gradient(b) can prevent it...