How to print the “actual” learning rate in Adadelta in pytorch
In short:
I can't draw lr/epoch curve when using adadelta optimizer in pytorch because optimizer.param_groups[0]['lr'] always return the same value.
In detail:
Adadelta can dynamically adapts over time using only first order information and
has minimal computational overhead beyond vanilla stochastic gradient descent [1].
In pytorch, the source code of Adadelta is here https://pytorch.org/docs/stable/_modules/torch/optim/adadelta.html#Adadelta
Since it requires no manual tuning of learning rate, in my knowledge, we don't have to set any schedular after declare the optimizer
self.optimizer = torch.optim.Adadelta(self.model.parameters(), lr=1)
The way to check learning rate is
current_lr = self.optimizer.param_groups[0]['lr']
The problem is it always return 1 (the initial lr).
Could anyone tell me how can I get the true learning rate so that can I draw a lr/epch curve?
[1] https://arxiv.org/pdf/1212.5701.pdf
python optimization neural-network deep-learning pytorch
asked Nov 21 '18 at 5:47
王智寬王智寬
10210
add a comment |
In short:
I can't draw lr/epoch curve when using adadelta optimizer in pytorch because optimizer.param_groups[0]['lr'] always return the same value.
In detail:
Adadelta can dynamically adapts over time using only first order information and
has minimal computational overhead beyond vanilla stochastic gradient descent [1].
In pytorch, the source code of Adadelta is here https://pytorch.org/docs/stable/_modules/torch/optim/adadelta.html#Adadelta
Since it requires no manual tuning of learning rate, in my knowledge, we don't have to set any schedular after declare the optimizer
self.optimizer = torch.optim.Adadelta(self.model.parameters(), lr=1)
The way to check learning rate is
current_lr = self.optimizer.param_groups[0]['lr']
The problem is it always return 1 (the initial lr).
Could anyone tell me how can I get the true learning rate so that can I draw a lr/epch curve?
[1] https://arxiv.org/pdf/1212.5701.pdf
python optimization neural-network deep-learning pytorch
asked Nov 21 '18 at 5:47
王智寬王智寬
10210
add a comment |
In short:
I can't draw lr/epoch curve when using adadelta optimizer in pytorch because optimizer.param_groups[0]['lr'] always return the same value.
In detail:
Adadelta can dynamically adapts over time using only first order information and
has minimal computational overhead beyond vanilla stochastic gradient descent [1].
In pytorch, the source code of Adadelta is here https://pytorch.org/docs/stable/_modules/torch/optim/adadelta.html#Adadelta
Since it requires no manual tuning of learning rate, in my knowledge, we don't have to set any schedular after declare the optimizer
self.optimizer = torch.optim.Adadelta(self.model.parameters(), lr=1)
The way to check learning rate is
current_lr = self.optimizer.param_groups[0]['lr']
The problem is it always return 1 (the initial lr).
Could anyone tell me how can I get the true learning rate so that can I draw a lr/epch curve?
[1] https://arxiv.org/pdf/1212.5701.pdf
python optimization neural-network deep-learning pytorch
asked Nov 21 '18 at 5:47
王智寬王智寬
10210
In short:
I can't draw lr/epoch curve when using adadelta optimizer in pytorch because optimizer.param_groups[0]['lr'] always return the same value.
In detail:
Adadelta can dynamically adapts over time using only first order information and
has minimal computational overhead beyond vanilla stochastic gradient descent [1].
In pytorch, the source code of Adadelta is here https://pytorch.org/docs/stable/_modules/torch/optim/adadelta.html#Adadelta
Since it requires no manual tuning of learning rate, in my knowledge, we don't have to set any schedular after declare the optimizer
self.optimizer = torch.optim.Adadelta(self.model.parameters(), lr=1)
The way to check learning rate is
current_lr = self.optimizer.param_groups[0]['lr']
The problem is it always return 1 (the initial lr).
Could anyone tell me how can I get the true learning rate so that can I draw a lr/epch curve?
[1] https://arxiv.org/pdf/1212.5701.pdf
python optimization neural-network deep-learning pytorch
python optimization neural-network deep-learning pytorch
asked Nov 21 '18 at 5:47
王智寬王智寬
10210
asked Nov 21 '18 at 5:47
王智寬王智寬
10210
asked Nov 21 '18 at 5:47
王智寬王智寬
10210
asked Nov 21 '18 at 5:47
王智寬王智寬
10210
asked Nov 21 '18 at 5:47
王智寬王智寬
10210
10210
add a comment |
add a comment |
1 Answer
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Check: self.optimizer.state. This is optimized with the lr and used in optimization process.
From documentation a lr is just:
lr (float, optional): coefficient that scale delta before it is
applied
to the parameters (default: 1.0)
https://pytorch.org/docs/stable/_modules/torch/optim/adadelta.html
Edited: you may find acc_delta values in self.optimizer.state values but you need to go through dictionaries contained by this dictionary:
dict_with_acc_delta = [self.optimizer.state[i] for i in self.optimizer.state.keys() if "acc_delta" in self.optimizer.state[i].keys()]
acc_deltas = [i["acc_delta"] for i in dict_with_acc_delta]
I have eight layers and shapes of elements in the acc_deltas list are following
[torch.Size([25088]),
torch.Size([25088]),
torch.Size([4096, 25088]),
torch.Size([4096]),
torch.Size([1024, 4096]),
torch.Size([1024]),
torch.Size([102, 1024]),
torch.Size([102])]
answered Nov 21 '18 at 7:40
artonaartona
71247
But...self.optimizer.state['acc_delta']always returns an empty dictionary {} in every epoch.
– 王智寬
Nov 21 '18 at 8:08
I have edited my post
– artona
Nov 21 '18 at 10:18
Thank you! I got this.
– 王智寬
Nov 22 '18 at 8:21
add a comment |
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Check: self.optimizer.state. This is optimized with the lr and used in optimization process.
From documentation a lr is just:
lr (float, optional): coefficient that scale delta before it is
applied
to the parameters (default: 1.0)
https://pytorch.org/docs/stable/_modules/torch/optim/adadelta.html
Edited: you may find acc_delta values in self.optimizer.state values but you need to go through dictionaries contained by this dictionary:
dict_with_acc_delta = [self.optimizer.state[i] for i in self.optimizer.state.keys() if "acc_delta" in self.optimizer.state[i].keys()]
acc_deltas = [i["acc_delta"] for i in dict_with_acc_delta]
I have eight layers and shapes of elements in the acc_deltas list are following
[torch.Size([25088]),
torch.Size([25088]),
torch.Size([4096, 25088]),
torch.Size([4096]),
torch.Size([1024, 4096]),
torch.Size([1024]),
torch.Size([102, 1024]),
torch.Size([102])]
answered Nov 21 '18 at 7:40
artonaartona
71247
But...self.optimizer.state['acc_delta']always returns an empty dictionary {} in every epoch.
– 王智寬
Nov 21 '18 at 8:08
I have edited my post
– artona
Nov 21 '18 at 10:18
Thank you! I got this.
– 王智寬
Nov 22 '18 at 8:21
add a comment |
Check: self.optimizer.state. This is optimized with the lr and used in optimization process.
From documentation a lr is just:
lr (float, optional): coefficient that scale delta before it is
applied
to the parameters (default: 1.0)
https://pytorch.org/docs/stable/_modules/torch/optim/adadelta.html
Edited: you may find acc_delta values in self.optimizer.state values but you need to go through dictionaries contained by this dictionary:
dict_with_acc_delta = [self.optimizer.state[i] for i in self.optimizer.state.keys() if "acc_delta" in self.optimizer.state[i].keys()]
acc_deltas = [i["acc_delta"] for i in dict_with_acc_delta]
I have eight layers and shapes of elements in the acc_deltas list are following
[torch.Size([25088]),
torch.Size([25088]),
torch.Size([4096, 25088]),
torch.Size([4096]),
torch.Size([1024, 4096]),
torch.Size([1024]),
torch.Size([102, 1024]),
torch.Size([102])]
answered Nov 21 '18 at 7:40
artonaartona
71247
But...self.optimizer.state['acc_delta']always returns an empty dictionary {} in every epoch.
– 王智寬
Nov 21 '18 at 8:08
I have edited my post
– artona
Nov 21 '18 at 10:18
Thank you! I got this.
– 王智寬
Nov 22 '18 at 8:21
add a comment |
Check: self.optimizer.state. This is optimized with the lr and used in optimization process.
From documentation a lr is just:
lr (float, optional): coefficient that scale delta before it is
applied
to the parameters (default: 1.0)
https://pytorch.org/docs/stable/_modules/torch/optim/adadelta.html
Edited: you may find acc_delta values in self.optimizer.state values but you need to go through dictionaries contained by this dictionary:
dict_with_acc_delta = [self.optimizer.state[i] for i in self.optimizer.state.keys() if "acc_delta" in self.optimizer.state[i].keys()]
acc_deltas = [i["acc_delta"] for i in dict_with_acc_delta]
I have eight layers and shapes of elements in the acc_deltas list are following
[torch.Size([25088]),
torch.Size([25088]),
torch.Size([4096, 25088]),
torch.Size([4096]),
torch.Size([1024, 4096]),
torch.Size([1024]),
torch.Size([102, 1024]),
torch.Size([102])]
answered Nov 21 '18 at 7:40
artonaartona
71247
Check: self.optimizer.state. This is optimized with the lr and used in optimization process.
From documentation a lr is just:
lr (float, optional): coefficient that scale delta before it is
applied
to the parameters (default: 1.0)
https://pytorch.org/docs/stable/_modules/torch/optim/adadelta.html
Edited: you may find acc_delta values in self.optimizer.state values but you need to go through dictionaries contained by this dictionary:
dict_with_acc_delta = [self.optimizer.state[i] for i in self.optimizer.state.keys() if "acc_delta" in self.optimizer.state[i].keys()]
acc_deltas = [i["acc_delta"] for i in dict_with_acc_delta]
I have eight layers and shapes of elements in the acc_deltas list are following
[torch.Size([25088]),
torch.Size([25088]),
torch.Size([4096, 25088]),
torch.Size([4096]),
torch.Size([1024, 4096]),
torch.Size([1024]),
torch.Size([102, 1024]),
torch.Size([102])]
answered Nov 21 '18 at 7:40
artonaartona
71247
edited Nov 21 '18 at 10:17
answered Nov 21 '18 at 7:40
artonaartona
71247
answered Nov 21 '18 at 7:40
artonaartona
71247
answered Nov 21 '18 at 7:40
artonaartona
71247
71247
But...self.optimizer.state['acc_delta']always returns an empty dictionary {} in every epoch.
– 王智寬
Nov 21 '18 at 8:08
I have edited my post
– artona
Nov 21 '18 at 10:18
Thank you! I got this.
– 王智寬
Nov 22 '18 at 8:21
add a comment |
But...self.optimizer.state['acc_delta']always returns an empty dictionary {} in every epoch.
– 王智寬
Nov 21 '18 at 8:08
I have edited my post
– artona
Nov 21 '18 at 10:18
Thank you! I got this.
– 王智寬
Nov 22 '18 at 8:21
But...
self.optimizer.state['acc_delta'] always returns an empty dictionary {} in every epoch.– 王智寬
Nov 21 '18 at 8:08
But...
self.optimizer.state['acc_delta'] always returns an empty dictionary {} in every epoch.– 王智寬
Nov 21 '18 at 8:08
I have edited my post
– artona
Nov 21 '18 at 10:18
I have edited my post
– artona
Nov 21 '18 at 10:18
Thank you! I got this.
– 王智寬
Nov 22 '18 at 8:21
Thank you! I got this.
– 王智寬
Nov 22 '18 at 8:21
add a comment |
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