docs (#4107)
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William Falcon
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@ -7,8 +7,9 @@
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.. _metrics:
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#######
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Metrics
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=======
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#######
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``pytorch_lightning.metrics`` is a Metrics API created for easy metric development and usage in
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PyTorch and PyTorch Lightning. It is rigorously tested for all edge cases and includes a growing list of
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@ -104,8 +105,9 @@ This metrics API is independent of PyTorch Lightning. Metrics can directly be us
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# total accuracy over all validation batches
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total_valid_accuracy = valid_accuracy.compute()
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*********************
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Implementing a Metric
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---------------------
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*********************
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To implement your custom metric, subclass the base ``Metric`` class and implement the following methods:
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@ -142,35 +144,40 @@ Example implementation:
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def compute(self):
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return self.correct.float() / self.total
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Metric
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^^^^^^
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**********
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Metric API
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**********
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.. autoclass:: pytorch_lightning.metrics.Metric
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:noindex:
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*************
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Class metrics
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*************
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Classification Metrics
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----------------------
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Accuracy
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^^^^^^^^
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~~~~~~~~
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.. autoclass:: pytorch_lightning.metrics.classification.Accuracy
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:noindex:
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Precision
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^^^^^^^^^
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~~~~~~~~~
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.. autoclass:: pytorch_lightning.metrics.classification.Precision
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:noindex:
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Recall
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^^^^^^
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~~~~~~
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.. autoclass:: pytorch_lightning.metrics.classification.Recall
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:noindex:
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Fbeta
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^^^^^
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~~~~~
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.. autoclass:: pytorch_lightning.metrics.classification.Fbeta
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:noindex:
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@ -179,35 +186,35 @@ Regression Metrics
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------------------
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MeanSquaredError
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^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_lightning.metrics.regression.MeanSquaredError
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:noindex:
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MeanAbsoluteError
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^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_lightning.metrics.regression.MeanAbsoluteError
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:noindex:
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MeanSquaredLogError
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^^^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_lightning.metrics.regression.MeanSquaredLogError
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:noindex:
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ExplainedVariance
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^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~
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.. autoclass:: pytorch_lightning.metrics.regression.ExplainedVariance
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:noindex:
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******************
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Functional Metrics
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==================
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******************
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The functional metrics follow the simple paradigm input in, output out. This means, they don't provide any advanced mechanisms for syncing across DDP nodes or aggregation over batches. They simply compute the metric value based on the given inputs.
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@ -218,133 +225,133 @@ Classification
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--------------
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accuracy [func]
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^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.accuracy
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:noindex:
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auc [func]
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^^^^^^^^^^
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~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.auc
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:noindex:
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auroc [func]
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^^^^^^^^^^^^
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~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.auroc
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:noindex:
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average_precision [func]
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^^^^^^^^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.average_precision
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:noindex:
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confusion_matrix [func]
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^^^^^^^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.confusion_matrix
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:noindex:
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dice_score [func]
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^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.dice_score
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:noindex:
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f1_score [func]
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^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.f1_score
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:noindex:
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fbeta_score [func]
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^^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.fbeta_score
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:noindex:
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iou [func]
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^^^^^^^^^^
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~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.iou
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:noindex:
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multiclass_roc [func]
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^^^^^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.multiclass_roc
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:noindex:
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precision [func]
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^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.precision
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:noindex:
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precision_recall [func]
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^^^^^^^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.precision_recall
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:noindex:
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precision_recall_curve [func]
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.precision_recall_curve
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:noindex:
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recall [func]
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^^^^^^^^^^^^^
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~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.recall
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:noindex:
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roc [func]
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^^^^^^^^^^
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~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.roc
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:noindex:
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stat_scores [func]
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^^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.stat_scores
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:noindex:
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stat_scores_multiple_classes [func]
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.stat_scores_multiple_classes
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:noindex:
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to_categorical [func]
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^^^^^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.to_categorical
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:noindex:
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to_onehot [func]
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^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.classification.to_onehot
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:noindex:
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@ -354,42 +361,42 @@ Regression
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----------
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mae [func]
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^^^^^^^^^^
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~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.regression.mae
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:noindex:
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mse [func]
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^^^^^^^^^^
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~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.regression.mse
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:noindex:
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psnr [func]
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^^^^^^^^^^^
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~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.regression.psnr
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:noindex:
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rmse [func]
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^^^^^^^^^^^
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~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.regression.rmse
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:noindex:
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rmsle [func]
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^^^^^^^^^^^^
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~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.regression.rmsle
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:noindex:
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ssim [func]
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^^^^^^^^^^^
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~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.regression.mae
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:noindex:
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@ -399,17 +406,17 @@ NLP
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---
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bleu_score [func]
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^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.nlp.bleu_score
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:noindex:
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Self-Supervised
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---------------
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Pairwise
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--------
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embedding_similarity [func]
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^^^^^^^^^^^^^^^^^^^^^^^^^^^
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~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autofunction:: pytorch_lightning.metrics.functional.self_supervised.embedding_similarity
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:noindex:
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