Update docs

qubvel · Aug 7, 2019 · 9a5280b · 9a5280b
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Showing 3 changed files with 29 additions and 17 deletions.
diff --git a/segmentation_models/losses.py b/segmentation_models/losses.py
@@ -56,9 +56,9 @@ class DiceLoss(Loss):
     .. math:: L(tp, fp, fn) = \frac{(1 + \beta^2) \cdot tp} {(1 + \beta^2) \cdot fp + \beta^2 \cdot fn + fp}
 
     where:
-        tp - true positives;
-        fp - false positives;
-        fn - false negatives;
+         - tp - true positives;
+         - fp - false positives;
+         - fn - false negatives;
 
     Args:
         beta: Float or integer coefficient for precision and recall balance.

diff --git a/segmentation_models/metrics.py b/segmentation_models/metrics.py
@@ -20,10 +20,16 @@ class IOUScore(Metric):
         threshold: value to round predictions (use ``>`` comparison), if ``None`` prediction will not be round
 
     Returns:
-        A callable ``iou_score`` instance. Can be passed to ``model.compile(..., metrics=[iou_score])``.
+       A callable ``iou_score`` instance. Can be used in ``model.compile(...)`` function.
 
     .. _`Jaccard index`: https://en.wikipedia.org/wiki/Jaccard_index
 
+    Example:
+
+    .. code:: python
+
+        metric = IOUScore()
+        model.compile('SGD', loss=loss, metrics=[metric])
     """
 
     def __init__(self, class_weights=None, threshold=None, per_image=True, smooth=SMOOTH):
@@ -56,12 +62,12 @@ class FScore(Metric):
 
     The formula in terms of *Type I* and *Type II* errors:
 
-    .. math:: F_\beta(A, B) = \frac{(1 + \beta^2) TP} {(1 + \beta^2) TP + \beta^2 FN + FP}
+    .. math:: L(tp, fp, fn) = \frac{(1 + \beta^2) \cdot tp} {(1 + \beta^2) \cdot fp + \beta^2 \cdot fn + fp}
 
     where:
-        TP - true positive;
-        FP - false positive;
-        FN - false negative;
+         - tp - true positives;
+         - fp - false positives;
+         - fn - false negatives;
 
     Args:
         beta: f-score coefficient
@@ -72,8 +78,14 @@ class FScore(Metric):
         threshold: value to round predictions (use ``>`` comparison), if ``None`` prediction will not be round
 
     Returns:
-        callable: f_score
+        A callable ``f_score`` instance. Can be used in ``model.compile(...)`` function.
+
+    Example:
+
+    .. code:: python
 
+        metric = FScore()
+        model.compile('SGD', loss=loss, metrics=[metric])
     """
 
     def __init__(self, beta=1, class_weights=None, threshold=None, per_image=True, smooth=SMOOTH):

diff --git a/segmentation_models/utils.py b/segmentation_models/utils.py
@@ -10,14 +10,14 @@ def set_trainable(model, recompile=True, **kwargs):
     Note:
         Model is recompiled using same optimizer, loss and metrics::
 
-        model.compile(
-            model.optimizer,
-            loss=model.loss,
-            metrics=model.metrics,
-            loss_weights=model.loss_weights,
-            sample_weight_mode=model.sample_weight_mode,
-            weighted_metrics=model.weighted_metrics,
-        )
+            model.compile(
+                model.optimizer,
+                loss=model.loss,
+                metrics=model.metrics,
+                loss_weights=model.loss_weights,
+                sample_weight_mode=model.sample_weight_mode,
+                weighted_metrics=model.weighted_metrics,
+            )
 
     Args:
         model (``keras.models.Model``): instance of keras model