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Commit b6bb4bfc authored by lukas leufen's avatar lukas leufen
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add docs for some plots, include sample plots

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3 merge requests!125Release v0.10.0,!124Update Master to new version v0.10.0,!91WIP: Resolve "create sphinx docu"
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docs/_source/_plots/conditional_quantiles_cali-ref_plot-1.png

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docs/_source/_plots/conditional_quantiles_cali-ref_plot-2.png

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docs/_source/_plots/conditional_quantiles_cali-ref_plot-3.png

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docs/_source/_plots/conditional_quantiles_like-bas_plot-1.png

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docs/_source/_plots/conditional_quantiles_like-bas_plot-2.png

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docs/_source/_plots/conditional_quantiles_like-bas_plot-3.png

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docs/_source/_plots/data_availability.png

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docs/_source/_plots/data_availability_combined.png

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docs/_source/_plots/data_availability_summary.png

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docs/_source/_plots/monthly_summary_box_plot.png

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docs/_source/_plots/skill_score_bootstrap.png

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docs/_source/_plots/skill_score_clim_CNN.png

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docs/_source/_plots/skill_score_clim_all_terms_CNN.png

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docs/_source/_plots/skill_score_competitive.png

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docs/_source/_plots/station_map.png

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docs/_source/_plots/testrun_network_daily_history_learning_rate-1.png

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docs/_source/_plots/testrun_network_daily_history_loss-1.png

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docs/_source/_plots/testrun_network_daily_history_main_mse-1.png

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......@@ -45,6 +45,7 @@ extensions = [
'sphinx_rtd_theme',
'sphinx.ext.githubpages',
'recommonmark',
'sphinx.ext.autosectionlabel',
]
# 2020-02-19 Begin
......
......@@ -69,7 +69,8 @@ How to create a customised model?
def set_loss(self):
self.loss = keras.losses.mean_squared_error
* If you have a branched model with multiple outputs, you need to consider the right ordering. E.g.
* If you have a branched model with multiple outputs, you need either set only a single loss for all branch outputs or
to provide the same number of loss functions considering the right order. E.g.
.. code-block:: python
......@@ -102,11 +103,8 @@ You can treat the instance of your model as instance but also as the model itsel
the model instead of the model instance, you can directly apply the command on the instance instead of adding the model
parameter call.
>>> MyCustomisedModel().model.compile()
is therefore equal to the command
>>> MyCustomisedModel().compile()
>>> MyCustomisedModel().model.compile(**kwargs) == MyCustomisedModel().compile(**kwargs)
True
"""
......
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