Comments (5)
I set the arg optimizer_parameters=model.fc.parameters() in trainer.compile.
But i guess it is being passed to optimizer initialization:
line 133, in set_optimizer
TypeError: init() got an unexpected keyword argument 'parameters'
from torchsample.
added kwargs.pop('parameters',None) after line 128 and it works as expected
from torchsample.
Oh I see. Wait can you tell me where in this function you added that line (i'd appreciate it):
def set_optimizer(self, optimizer, **kwargs):
if type(optimizer) is type or isinstance(optimizer, str):
if 'parameters' in kwargs:
parameters = kwargs['parameters']
else:
parameters = self.model.parameters()
optimizer = _validate_optimizer_input(optimizer)
self._optimizer = optimizer(parameters, **kwargs)
else:
self._optimizer = optimizer
from torchsample.
I guess there is a better way, but the following works for me:
# 1. Set some layers of the model to be non-trainable (param.requires_grad = False)
# 2. Monkey patch the instance parameters() method to return trainable weights only
import types
def parameters(self):
p = filter(lambda p: p.requires_grad, nn.Module.parameters(self))
return p
model.parameters = types.MethodType(parameters, model)
# 3. Profit
trainer.fit_loader(train_loader, val_loader=val_loader, nb_epoch=1)
from torchsample.
def set_optimizer(self, optimizer, **kwargs):
if type(optimizer) is type or isinstance(optimizer, str):
if 'parameters' in kwargs:
parameters = kwargs['parameters']
kwargs.pop('parameters',None)
from torchsample.
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from torchsample.