From ccaa2da8f36eadb52c95be1e99d7c1c4edca5dbc Mon Sep 17 00:00:00 2001
From: Dennis Noll <dennis.noll@rwth-aachen.de>
Date: Wed, 2 Sep 2020 10:17:03 +0200
Subject: [PATCH] [keras] black
---
keras.py | 37 +++++++++++++++++++++++++++++++------
1 file changed, 31 insertions(+), 6 deletions(-)
diff --git a/keras.py b/keras.py
index 1911c2c..5248cbe 100644
--- a/keras.py
+++ b/keras.py
@@ -473,7 +473,10 @@ class PlotMulticlass(TFSummaryCallback):
imgs["roc_curve"] = figure_to_image(
figure_roc_curve(
- truth, prediction, class_names=self.class_names, sample_weight=self.sample_weight
+ truth,
+ prediction,
+ class_names=self.class_names,
+ sample_weight=self.sample_weight,
)
)
imgs["roc_curve_log"] = figure_to_image(
@@ -511,7 +514,10 @@ class PlotMulticlass(TFSummaryCallback):
)
imgs["node_activation"] = figure_to_image(
figure_node_activations(
- prediction, truth, class_names=self.class_names, sample_weight=self.sample_weight
+ prediction,
+ truth,
+ class_names=self.class_names,
+ sample_weight=self.sample_weight,
)
)
imgs["node_activation_disjoint_unweighted"] = figure_to_image(
@@ -601,7 +607,12 @@ class CheckpointModel(tf.keras.callbacks.Callback):
class BestTracker(tf.keras.callbacks.Callback):
def __init__(
- self, monitor="val_loss", mode="auto", min_delta=0, min_delta_rel=0, baseline=None
+ self,
+ monitor="val_loss",
+ mode="auto",
+ min_delta=0,
+ min_delta_rel=0,
+ baseline=None,
):
pin(locals())
self.reset()
@@ -684,7 +695,15 @@ class PatientTracker(BestTracker):
class ScaleOnPlateau(PatientTracker):
def __init__(
- self, target, factor, min=None, max=None, verbose=0, log_key=None, linearly=False, **kwargs
+ self,
+ target,
+ factor,
+ min=None,
+ max=None,
+ verbose=0,
+ log_key=None,
+ linearly=False,
+ **kwargs,
):
pin(locals(), kwargs)
super(ScaleOnPlateau, self).__init__(**kwargs)
@@ -722,7 +741,12 @@ class ScaleOnPlateau(PatientTracker):
if self.verbose > 0:
print(
"\nEpoch %05d: %s scaling %s to %s."
- % (epoch + 1, self.__class__.__name__, self.log_key or self.target, new)
+ % (
+ epoch + 1,
+ self.__class__.__name__,
+ self.log_key or self.target,
+ new,
+ )
)
@@ -1186,6 +1210,7 @@ def feature_importance(*args, method="grad", columns=[], **kwargs):
return {
k: v
for k, v in sorted(
- dict(zip(columns, importance.astype(float))).items(), key=lambda item: item[1]
+ dict(zip(columns, importance.astype(float))).items(),
+ key=lambda item: item[1],
)
}
--
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