73 lines
2.8 KiB
Python
73 lines
2.8 KiB
Python
from abc import ABC
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from torch import optim
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class ConfigureOptimizersPool(ABC):
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def configure_optimizers(self):
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"""
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return whatever optimizers we want here.
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:return: list of optimizers
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"""
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optimizer = optim.Adam(self.parameters(), lr=self.learning_rate)
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return optimizer
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def configure_optimizers__empty(self):
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return None
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def configure_optimizers__lbfgs(self):
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"""
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return whatever optimizers we want here.
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:return: list of optimizers
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"""
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optimizer = optim.LBFGS(self.parameters(), lr=self.learning_rate)
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return optimizer
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def configure_optimizers__multiple_optimizers(self):
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"""
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return whatever optimizers we want here.
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:return: list of optimizers
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"""
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# try no scheduler for this model (testing purposes)
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optimizer1 = optim.Adam(self.parameters(), lr=self.learning_rate)
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optimizer2 = optim.Adam(self.parameters(), lr=self.learning_rate)
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return optimizer1, optimizer2
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def configure_optimizers__single_scheduler(self):
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optimizer = optim.Adam(self.parameters(), lr=self.learning_rate)
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lr_scheduler = optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.1)
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return [optimizer], [lr_scheduler]
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def configure_optimizers__multiple_schedulers(self):
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optimizer1 = optim.Adam(self.parameters(), lr=self.learning_rate)
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optimizer2 = optim.Adam(self.parameters(), lr=self.learning_rate)
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lr_scheduler1 = optim.lr_scheduler.StepLR(optimizer1, 1, gamma=0.1)
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lr_scheduler2 = optim.lr_scheduler.StepLR(optimizer2, 1, gamma=0.1)
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return [optimizer1, optimizer2], [lr_scheduler1, lr_scheduler2]
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def configure_optimizers__mixed_scheduling(self):
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optimizer1 = optim.Adam(self.parameters(), lr=self.learning_rate)
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optimizer2 = optim.Adam(self.parameters(), lr=self.learning_rate)
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lr_scheduler1 = optim.lr_scheduler.StepLR(optimizer1, 4, gamma=0.1)
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lr_scheduler2 = optim.lr_scheduler.StepLR(optimizer2, 1, gamma=0.1)
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return [optimizer1, optimizer2], \
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[{'scheduler': lr_scheduler1, 'interval': 'step'}, lr_scheduler2]
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def configure_optimizers__reduce_lr_on_plateau(self):
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optimizer = optim.Adam(self.parameters(), lr=self.learning_rate)
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lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer)
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return [optimizer], [lr_scheduler]
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def configure_optimizers__param_groups(self):
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param_groups = [
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{'params': list(self.parameters())[:2], 'lr': self.learning_rate * 0.1},
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{'params': list(self.parameters())[2:], 'lr': self.learning_rate}
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]
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optimizer = optim.Adam(param_groups)
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lr_scheduler = optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.1)
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return [optimizer], [lr_scheduler]
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