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| author | aktersnurra <gustaf.rydholm@gmail.com> | 2020-09-20 01:15:23 +0200 |
|---|---|---|
| committer | aktersnurra <gustaf.rydholm@gmail.com> | 2020-09-20 01:15:23 +0200 |
| commit | 2b743f2f9046a2e2930647d234dc7392d71efa66 (patch) | |
| tree | 237b5729cfbae90877a44baa52ac2ebaec388f79 /src/training | |
| parent | e181195a699d7fa237f256d90ab4dedffc03d405 (diff) | |
Fixed some bash scripts.
Diffstat (limited to 'src/training')
| -rw-r--r-- | src/training/experiments/line_ctc_experiment.yml | 36 | ||||
| -rw-r--r-- | src/training/run_experiment.py | 2 |
2 files changed, 19 insertions, 19 deletions
diff --git a/src/training/experiments/line_ctc_experiment.yml b/src/training/experiments/line_ctc_experiment.yml index 432d1cc..337c830 100644 --- a/src/training/experiments/line_ctc_experiment.yml +++ b/src/training/experiments/line_ctc_experiment.yml @@ -1,7 +1,7 @@ experiment_group: Lines Experiments experiments: - train_args: - batch_size: 42 + batch_size: 64 max_epochs: &max_epochs 32 dataset: type: IamLinesDataset @@ -17,18 +17,18 @@ experiments: network: type: LineRecurrentNetwork args: + # backbone: ResidualNetwork + # backbone_args: + # in_channels: 1 + # num_classes: 64 # Embedding + # depths: [2,2] + # block_sizes: [32, 64] + # activation: selu + # stn: false backbone: ResidualNetwork backbone_args: - in_channels: 1 - num_classes: 64 # Embedding - depths: [2,2] - block_sizes: [32,64] - activation: selu - stn: false - # encoder: ResidualNetwork - # encoder_args: - # pretrained: training/experiments/CharacterModel_EmnistDataset_ResidualNetwork/0917_203601/model/best.pt - # freeze: false + pretrained: training/experiments/CharacterModel_EmnistDataset_ResidualNetwork/0920_010806/model/best.pt + freeze: false flatten: false input_size: 64 hidden_size: 64 @@ -67,20 +67,20 @@ experiments: # args: # T_max: *max_epochs swa_args: - start: 24 + start: 48 lr: 5.e-2 - callbacks: [Checkpoint, ProgressBar, WandbCallback, WandbImageLogger] # EarlyStopping] + callbacks: [Checkpoint, ProgressBar, WandbCallback, WandbImageLogger, EarlyStopping] callback_args: Checkpoint: monitor: val_loss mode: min ProgressBar: epochs: *max_epochs - # EarlyStopping: - # monitor: val_loss - # min_delta: 0.0 - # patience: 10 - # mode: min + EarlyStopping: + monitor: val_loss + min_delta: 0.0 + patience: 10 + mode: min WandbCallback: log_batch_frequency: 10 WandbImageLogger: diff --git a/src/training/run_experiment.py b/src/training/run_experiment.py index a347d9f..cc882ad 100644 --- a/src/training/run_experiment.py +++ b/src/training/run_experiment.py @@ -116,7 +116,7 @@ def load_modules_and_arguments(experiment_config: Dict) -> Tuple[Callable, Dict] # Learning rate scheduler lr_scheduler_ = None lr_scheduler_args = None - if experiment_config["lr_scheduler"] is not None: + if "lr_scheduler" in experiment_config: lr_scheduler_ = getattr( torch.optim.lr_scheduler, experiment_config["lr_scheduler"]["type"] ) |