Simplification about grip HOT 14 CLOSED

@jmercat Thank you for your interest in our work.
Your simplification code is easier to understand and will be helpful to others.
Thanks.

from grip.

@xincoder I realize that I haven't thanked you for your great work and for sharing the code. So thanks a lot, I am grateful. I am working on an idea that might improve your model with minimal changes to it. I'll keep you informed.

from grip.

@jmercat Thank you very much. It makes my day.😄
I really hope to see that our work can inspire others in both academy and industry.

from grip.

hi! @xincoder ,could you tell me what tool you used to draw that algorithm architecture in the paper, thanks in advance!

from grip.

Hi @tinmodeHuang, thank you for your interest in our work.
Microsoft PowerPoint was used to draw the model architecture.

from grip.

Hi @tinmodeHuang, thank you for your interest in our work.
Microsoft PowerPoint was used to draw the model architecture.

well, I also wonder how to visualize prediction trajectories after I had a general knowlegde of dependency among all scripts and haven't found out dedicated utility to visualize it.

from grip.

Hi @tinmodeHuang, thank you for your interest in our work.
Microsoft PowerPoint was used to draw the model architecture.

well, I also wonder how to visualize prediction trajectories after I had a general knowlegde of dependency among all scripts and haven't found out dedicated utility to visualize it.

The visualized results reported in our paper were generated using Plotly. It is very easy to draw it using any library that you are familiar with, e.g., Matplotlib, etc.

from grip.

Hi @tinmodeHuang, thank you for your interest in our work.
Microsoft PowerPoint was used to draw the model architecture.

well, I also wonder how to visualize prediction trajectories after I had a general knowlegde of dependency among all scripts and haven't found out dedicated utility to visualize it.

The visualized results reported in our paper were generated using Plotly. It is very easy to draw it using any library that you are familiar with, e.g., Matplotlib, etc.

thanks for instant reply! I'm very new to Plotly-like library, if possible, are you willing to share your visualizing script with me? and then I can use it as introduction to relavent library

from grip.

@xincoder while I have taken an attempt to visualizing it, I agree with you, here I'm sorry for the thought of trying to get it without any efforts. By the way, I have confused about that you do the plane rotation transformation at a random angle in the script xin_feeder_baidu.py, is it to do so for add noise to improve robustness?

from grip.

@xincoder while I have taken an attempt to visualizing it, I agree with you, here I'm sorry for the thought of trying to get it without any efforts. By the way, I have confused about that you do the plane rotation transformation at a random angle in the script xin_feeder_baidu.py, is it to do so for add noise to improve robustness?

@tinmodeHuang , yes. The rotation is one kind of data augmentation during training. The benefit by doing so is reported in our GRIP++ paper (B12 VS B13 in Table3).

from grip.

@xincoder while I have taken an attempt to visualizing it, I agree with you, here I'm sorry for the thought of trying to get it without any efforts. By the way, I have confused about that you do the plane rotation transformation at a random angle in the script xin_feeder_baidu.py, is it to do so for add noise to improve robustness?

@tinmodeHuang , yes. The rotation is one kind of data augmentation during training. The benefit by doing so is reported in our GRIP++ paper (B12 VS B13 in Table3).

thanks for the reminder! well, would you or even people accept the interruption from questioners' appreciation? I have wondered so ever.

from grip.

@xincoder while I have taken an attempt to visualizing it, I agree with you, here I'm sorry for the thought of trying to get it without any efforts. By the way, I have confused about that you do the plane rotation transformation at a random angle in the script xin_feeder_baidu.py, is it to do so for add noise to improve robustness?

@tinmodeHuang , yes. The rotation is one kind of data augmentation during training. The benefit by doing so is reported in our GRIP++ paper (B12 VS B13 in Table3).

thanks for the reminder! well, would you or even people accept the interruption from questioners' appreciation? I have wondered so ever.

@tinmodeHuang ? Sorry, I did not get your point. Would you please provide more details about your question. Thanks.

from grip.

  # compute hop steps

@xincoder @jmercat Thanks you，your code is very helpful for me.
But I don't know the code to # compute hop steps is for what? What is max_hop mean? Could you help me, thanks!
# compute hop steps transfer_mat = [np.linalg.matrix_power(A, d) for d in range(self.max_hop + 1)]

from grip.

@moriartyjack0520 Thank you for your question. The hop is a concept of Graph theory. It counts how many paths from one node to others (of a certain length). The code you highlighted above is used to calculate it (by multiplying the "adjacency matrix" several times, this is a simple way to achieve this goal).

from grip.