Data Loaders and Stuff
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.gitignore.txt
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161
.gitignore.txt
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# My Local Settings
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/data/
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# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
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# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
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/.idea/
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# CMake
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cmake-build-*/
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# File-based project format
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*.iws
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# IntelliJ
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out/
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# mpeltonen/sbt-idea plugin
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.idea_modules/
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# JIRA plugin
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atlassian-ide-plugin.xml
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# Crashlytics plugin (for Android Studio and IntelliJ)
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com_crashlytics_export_strings.xml
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crashlytics.properties
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crashlytics-build.properties
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fabric.properties
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# Created by https://www.gitignore.io/api/python
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# Edit at https://www.gitignore.io/?templates=python
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### Python ###
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# celery beat schedule file
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celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# End of https://www.gitignore.io/api/python
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@ -31,8 +31,8 @@ class LightningModuleOverrides:
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return self.network.forward(x)
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return self.network.forward(x)
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@data_loader
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@data_loader
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def tng_dataloader(self):
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def train_dataloader(self):
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num_workers = 0 # os.cpu_count() // 2
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num_workers = 0 # os.cpu_count() // 2
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return DataLoader(DataContainer(os.path.join('data', 'training'), self.size, self.step),
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return DataLoader(DataContainer(os.path.join('data', 'training'), self.size, self.step),
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shuffle=True, batch_size=10000, num_workers=num_workers)
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shuffle=True, batch_size=10000, num_workers=num_workers)
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"""
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"""
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# REQUIRED
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# REQUIRED
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raise NotImplementedError
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raise NotImplementedError
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@abstractmethod
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def configure_optimizers(self):
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# REQUIRED
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raise NotImplementedError
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@pl.data_loader
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def train_dataloader(self):
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# REQUIRED
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raise NotImplementedError
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"""
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def validation_step(self, batch, batch_nb):
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def validation_step(self, batch, batch_nb):
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# OPTIONAL
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# OPTIONAL
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pass
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pass
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# OPTIONAL
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# OPTIONAL
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pass
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pass
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@abstractmethod
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def configure_optimizers(self):
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# REQUIRED
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raise NotImplementedError
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@pl.data_loader
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def tng_dataloader(self):
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# REQUIRED
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raise NotImplementedError
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# return DataLoader(MNIST(os.getcwd(), train=True, download=True,
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# transform=transforms.ToTensor()), batch_size=32)
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"""
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@pl.data_loader
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@pl.data_loader
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def val_dataloader(self):
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def val_dataloader(self):
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# OPTIONAL
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# OPTIONAL
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{"name": "default", "version": 0, "tags_path": "C:\\Users\\steff\\Google Drive\\LMU\\Research\\ae_toolbox_torch\\output\\AE_Model\\Sun_Sep_29_12-35-27_2019\\default\\version_0/meta_tags.csv", "metrics_path": "C:\\Users\\steff\\Google Drive\\LMU\\Research\\ae_toolbox_torch\\output\\AE_Model\\Sun_Sep_29_12-35-27_2019\\default\\version_0/metrics.csv", "autosave": false, "description": null, "created_at": "2019-09-29 10:35:27.965484", "exp_hash": "default_v0"}
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key,value
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step,5
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features,6
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size,9
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latent_dim,2
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model,AE_Model
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refresh,False
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future_predictions,False
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loss,epoch,created_at
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{"name": "default", "version": 0, "tags_path": "C:\\Users\\steff\\Google Drive\\LMU\\Research\\ae_toolbox_torch\\output\\AE_Model\\Sun_Sep_29_12-44-13_2019\\default\\version_0/meta_tags.csv", "metrics_path": "C:\\Users\\steff\\Google Drive\\LMU\\Research\\ae_toolbox_torch\\output\\AE_Model\\Sun_Sep_29_12-44-13_2019\\default\\version_0/metrics.csv", "autosave": false, "description": null, "created_at": "2019-09-29 10:44:13.614075", "exp_hash": "default_v0"}
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key,value
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step,5
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features,6
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size,9
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latent_dim,2
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model,AE_Model
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refresh,False
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future_predictions,True
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{"name": "default", "version": 0, "tags_path": "C:\\Users\\steff\\Google Drive\\LMU\\Research\\ae_toolbox_torch\\output\\AE_Model\\Sun_Sep_29_12-44-29_2019\\default\\version_0/meta_tags.csv", "metrics_path": "C:\\Users\\steff\\Google Drive\\LMU\\Research\\ae_toolbox_torch\\output\\AE_Model\\Sun_Sep_29_12-44-29_2019\\default\\version_0/metrics.csv", "autosave": false, "description": null, "created_at": "2019-09-29 10:44:29.534657", "exp_hash": "default_v0"}
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key,value
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step,5
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features,6
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size,9
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latent_dim,2
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model,AE_Model
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refresh,False
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future_predictions,True
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loss,epoch,created_at
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1.372,0.0,2019-09-29 10:44:34.492200
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0.267,1.0,2019-09-29 10:54:22.294891
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{"name": "default", "version": 0, "tags_path": "C:\\Users\\steff\\Google Drive\\LMU\\Research\\ae_toolbox_torch\\output\\SAAE_Model\\Sun_Sep_29_12-54-18_2019\\default\\version_0/meta_tags.csv", "metrics_path": "C:\\Users\\steff\\Google Drive\\LMU\\Research\\ae_toolbox_torch\\output\\SAAE_Model\\Sun_Sep_29_12-54-18_2019\\default\\version_0/metrics.csv", "autosave": false, "description": null, "created_at": "2019-09-29 10:54:18.863108", "exp_hash": "default_v0"}
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key,value
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step,5
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features,6
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size,9
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latent_dim,2
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model,SAAE_Model
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refresh,False
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future_predictions,True
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loss,epoch,created_at
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args.add_argument('--features', default=6)
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args.add_argument('--features', default=6)
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args.add_argument('--size', default=9)
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args.add_argument('--size', default=9)
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args.add_argument('--latent_dim', default=2)
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args.add_argument('--latent_dim', default=2)
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args.add_argument('--model', default='AE_Model')
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args.add_argument('--model', default='SAAE_Model')
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args.add_argument('--refresh', type=strtobool, default=False)
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args.add_argument('--refresh', type=strtobool, default=False)
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args.add_argument('--future_predictions', type=strtobool, default=False)
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args.add_argument('--future_predictions', type=strtobool, default=True)
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class AE_Model(AutoEncoder_LO, LightningModule):
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class AE_Model(AutoEncoder_LO, LightningModule):
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trainer = Trainer(experiment=exp,
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trainer = Trainer(experiment=exp,
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max_nb_epochs=250,
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max_nb_epochs=250,
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gpus=[0],
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gpus=[0],
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add_log_row_interval=1000,
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row_log_interval=1000,
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# checkpoint_callback=checkpoint_callback
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# checkpoint_callback=checkpoint_callback
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)
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)
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