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stick with one cycle for now

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Dr. Kashif Rasul
2021-01-03 21:51:27 +01:00
parent 4a90f84c16
commit dee1478463
1 changed files with 2 additions and 15 deletions
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pts/trainer.py
+2 -15
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@@ -23,10 +23,7 @@ class Trainer:
num_batches_per_epoch: int = 50,
learning_rate: float = 1e-3,
weight_decay: float = 1e-6,
learning_rate_decay_factor: float = 0.5,
patience: int = 10,
minimum_learning_rate: float = 5e-5,
maximum_learning_rate: float = 0.01,
maximum_learning_rate: float = 1e-2,
clip_gradient: Optional[float] = None,
device: Optional[Union[torch.device, str]] = None,
**kwargs,
@@ -36,9 +33,6 @@ class Trainer:
self.num_batches_per_epoch = num_batches_per_epoch
self.learning_rate = learning_rate
self.weight_decay = weight_decay
self.learning_rate_decay_factor = learning_rate_decay_factor
self.patience = patience
self.minimum_learning_rate = minimum_learning_rate
self.maximum_learning_rate = maximum_learning_rate
self.clip_gradient = clip_gradient
self.device = device
@@ -64,13 +58,6 @@ class Trainer:
steps_per_epoch=self.num_batches_per_epoch,
epochs=self.epochs,
)
# lr_scheduler = ReduceLROnPlateau(
# optimizer,
# mode='min',
# factor=self.learning_rate_decay_factor,
# patience=self.patience,
# min_lr=self.minimum_learning_rate,
# )
for epoch_no in range(self.epochs):
# mark epoch start time
@@ -101,11 +88,11 @@ class Trainer:
loss.backward()
if self.clip_gradient is not None:
nn.utils.clip_grad_norm_(net.parameters(), self.clip_gradient)
optimizer.step()
lr_scheduler.step()
if self.num_batches_per_epoch == batch_no:
# lr_scheduler.step(avg_epoch_loss / batch_no)
break
# mark epoch end time and log time cost of current epoch