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License: MIT License
The way you write code is really hard to understand... why write in this way... is your project in work written in this way too? what's the benifit~, just curious~~
I really like the picture you draw about your net because it can make parameters Visual.I want to know how you do it?Thank you.
in your original code opt = SGD(trainable_params(model), momentum=0.9, weight_decay=5e-4*batch_size, nesterov=True)
multiply 5e-4 by batch_size is redundant.
Hi,
I ran your code on a single GPU, which is NVIDIA GeForce GTX 1080Ti, but training 24 epochs took me nearly 4 minutes.
I did not change anything in your code except two paths.
Is that we have to configure something before we run your model ?
Thank you.
Hi there, I have a question about the nesterov update
def nesterov_update(w, dw, v, lr, weight_decay, momentum):
dw.add_(weight_decay, w).mul_(-lr)
v.mul_(momentum).add_(dw)
w.add_(dw.add_(momentum, v))
If I had to sketch the logic of this, I see it as saying
dw = -lr*(dw + weight_decay*w)
v = v*momentum + dw
w = w + dw + momentum*v
due to the in-place use of add_
etc.
Q: Can you explain how this is implementing nesterov? I don't see how it is calculating the gradient at the shifted weight.
basic_net classifier uses bias=False, but blog diagrams show bias=True. Also, if it is indeed set to False, why so?
File "/usr/local/lib/python3.5/dist-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(input, **kwargs)
File "/home/clarence/cifar10-fast/torch_backend.py", line 126, in forward
self.cache[n] = getattr(self, n)([self.cache[x] for x in i])
File "/home/clarence/cifar10-fast/torch_backend.py", line 126, in
self.cache[n] = getattr(self, n)(*[self.cache[x] for x in i])
KeyError: 'classifier'
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