Reformatting using format.sh

keras-team · Feb 9, 2023 · ebd6940 · ebd6940
1 parent 71eaa69
commit ebd6940
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Showing 2 changed files with 14 additions and 8 deletions.
diff --git a/keras/applications/applications_test.py b/keras/applications/applications_test.py
@@ -193,7 +193,9 @@ def test_application_pooling(self, app, last_dim):
 
     @parameterized.parameters(MODEL_LIST)
     def test_application_classifier_activation(self, app):
-        model = app(weights=None, include_top=True, classifier_activation="softmax")
+        model = app(
+            weights=None, include_top=True, classifier_activation="softmax"
+        )
         last_layer_act = model.layers[-1].activation.__name__
         self.assertEqual(last_layer_act, "softmax")
 
@@ -225,7 +227,7 @@ def test_application_variable_input_channels(self, app, last_dim):
 
     @parameterized.parameters(*MOBILENET_V3_FOR_WEIGHTS)
     def test_mobilenet_v3_load_weights(
-            self, mobilenet_class, alpha, minimalistic, include_top
+        self, mobilenet_class, alpha, minimalistic, include_top
     ):
         mobilenet_class(
             input_shape=(224, 224, 3),

diff --git a/keras/applications/convnext.py b/keras/applications/convnext.py
@@ -343,9 +343,11 @@ def apply(x):
         x = layers.LayerNormalization(
             epsilon=1e-6, name=name + "_head_layernorm"
         )(x)
-        x = layers.Dense(num_classes,
-                         activation=classifier_activation,
-                         name=name + "_head_dense")(x)
+        x = layers.Dense(
+            num_classes,
+            activation=classifier_activation,
+            name=name + "_head_dense",
+        )(x)
         return x
 
     return apply
@@ -526,9 +528,11 @@ def ConvNeXt(
 
     if include_top:
         imagenet_utils.validate_activation(classifier_activation, weights)
-        x = Head(num_classes=classes,
-                 classifier_activation=classifier_activation,
-                 name=model_name)(x)
+        x = Head(
+            num_classes=classes,
+            classifier_activation=classifier_activation,
+            name=model_name,
+        )(x)
 
     else:
         if pooling == "avg":