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v0.3.4 & changelog
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Borda committed Jun 17, 2021
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9 changes: 4 additions & 5 deletions .pre-commit-config.yaml
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Expand Up @@ -26,9 +26,8 @@ repos:
rev: 'v0.31.0'
hooks:
- id: yapf
args: [ --in-place, --parallel ]

# - repo: https://github.com/pre-commit/mirrors-mypy
# rev: 'v0.902'
# hooks:
# - id: mypy
- repo: https://github.com/PyCQA/flake8
rev: 3.9.2
hooks:
- id: flake8
18 changes: 5 additions & 13 deletions CHANGELOG.md
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Expand Up @@ -5,28 +5,20 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).


## [unReleased] - 2021-MM-DD

### Added

## [0.3.4] - 2021-06-17

### Changed

- Replaced `load_boston` with `load_diabetes` in the docs and tests ([#629](https://github.com/PyTorchLightning/lightning-bolts/pull/629))


- Added base encoder and MLP dimension arguments to BYOL constructor ([#637](https://github.com/PyTorchLightning/lightning-bolts/pull/637))


### Deprecated


### Removed


### Fixed

- Fixed the MNIST download giving HTTP 503 ([#633](https://github.com/PyTorchLightning/lightning-bolts/pull/633))
- Fixed type annotation of `ExperienceSource.__iter__` ([#645](https://github.com/PyTorchLightning/lightning-bolts/pull/645))
- Fixed `pretrained_urls` on Windows ([#652](https://github.com/PyTorchLightning/lightning-bolts/pull/652))
- Fixed logistic regression ([#655](https://github.com/PyTorchLightning/lightning-bolts/pull/655), [#664](https://github.com/PyTorchLightning/lightning-bolts/pull/664))
- Fixed double softmax in `SSLEvaluator` ([#663](https://github.com/PyTorchLightning/lightning-bolts/pull/663))


## [0.3.3] - 2021-04-17
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2 changes: 1 addition & 1 deletion pl_bolts/__about__.py
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@@ -1,4 +1,4 @@
__version__ = '0.4.0dev'
__version__ = '0.3.4'
__author__ = 'PyTorchLightning et al.'
__author_email__ = 'name@pytorchlightning.ai'
__license__ = 'Apache-2.0'
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1 change: 1 addition & 0 deletions pl_bolts/models/regression/logistic_regression.py
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Expand Up @@ -90,6 +90,7 @@ def validation_epoch_end(self, outputs: List[Dict[str, Tensor]]) -> Dict[str, Te
return {'val_loss': val_loss, 'log': tensorboard_logs, 'progress_bar': progress_bar_metrics}

def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Dict[str, Tensor]:
x, y = batch
x = x.view(x.size(0), -1)
y_hat = self.linear(x)
acc = accuracy(F.softmax(y_hat, -1), y)
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