def test_passes_filtered_cgroups(
self, filtered_optimizer, filter_requires_grad):
pgroups = [{
'params': [torch.zeros(1, requires_grad=True),
torch.zeros(1, requires_grad=False)],
'lr': 0.1
}, {
'params': [torch.zeros(1, requires_grad=True)]
}]
opt = filtered_optimizer(torch.optim.SGD, filter_requires_grad)
filtered_opt = opt(pgroups, lr=0.2)
assert isinstance(filtered_opt, torch.optim.SGD)
assert len(list(filtered_opt.param_groups[0]['params'])) == 1
assert len(list(filtered_opt.param_groups[1]['params'])) == 1
assert filtered_opt.param_groups[0]['lr'] == 0.1
assert filtered_opt.param_groups[1]['lr'] == 0.2
def test_passes_kwargs_to_neuralnet_optimizer(
self, filtered_optimizer, filter_requires_grad):
from skorch import NeuralNetClassifier
class MyModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.dense0 = torch.nn.Linear(1, 1)
def forward(self, X):
return self.dense0(X)
opt = filtered_optimizer(torch.optim.SGD, filter_requires_grad)
net = NeuralNetClassifier(
MyModule, optimizer=opt, optimizer__momentum=0.9)
net.initialize()
assert isinstance(net.optimizer_, torch.optim.SGD)
assert len(net.optimizer_.param_groups) == 1
assert net.optimizer_.param_groups[0]['momentum'] == 0.9
def test_passes_kwargs_to_neuralnet_optimizer(
self, filtered_optimizer, filter_requires_grad):
from skorch import NeuralNetClassifier
from skorch.toy import make_classifier
module_cls = make_classifier(
input_units=1,
num_hidden=0,
output_units=1,
)
with pytest.warns(DeprecationWarning):
opt = filtered_optimizer(torch.optim.SGD, filter_requires_grad)
net = NeuralNetClassifier(
module_cls, optimizer=opt, optimizer__momentum=0.9)
net.initialize()
assert isinstance(net.optimizer_, torch.optim.SGD)
assert len(net.optimizer_.param_groups) == 1
assert net.optimizer_.param_groups[0]['momentum'] == 0.9
def test_pickle(self, filtered_optimizer, filter_requires_grad):
with pytest.warns(DeprecationWarning):
opt = filtered_optimizer(torch.optim.SGD, filter_requires_grad)
# Does not raise
pickle.dumps(opt)
def test_pickle(self, filtered_optimizer, filter_requires_grad):
opt = filtered_optimizer(torch.optim.SGD, filter_requires_grad)
# Does not raise
pickle.dumps(opt)
