Great code guys! Can I ask a question at this code?
https://github.com/locuslab/TCN/blob/master/TCN/adding_problem/model.py#L17
Usually when I implement CNN-series model, the calculation of the last layer CNN dimension was always a problem.
In your code, Line17, it looks the final linear layer just catches a part of the conv-layer output. Is this understanding correct? Does it ignore many other parameters that in the conv-layer output?
What shocks me is, when I test such implementation on other traditional CNN models, they also works. (I mean just use like self.linear(y1[:, :, -1])) Does this mean the task is simple for the designed CNN because we just dropped a lot of neurons in it?
Will be highly appreciated if someone could advice.
Great code guys! Can I ask a question at this code?
https://github.com/locuslab/TCN/blob/master/TCN/adding_problem/model.py#L17
Usually when I implement CNN-series model, the calculation of the last layer CNN dimension was always a problem.
In your code, Line17, it looks the final linear layer just catches a part of the conv-layer output. Is this understanding correct? Does it ignore many other parameters that in the conv-layer output?
What shocks me is, when I test such implementation on other traditional CNN models, they also works. (I mean just use like
self.linear(y1[:, :, -1])) Does this mean the task is simple for the designed CNN because we just dropped a lot of neurons in it?Will be highly appreciated if someone could advice.