[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

explain.py

@@ -46,7 +46,9 @@ def update_sample(samples, N, sample):
 )
 
 # Load the trained model
-model = tf.keras.models.load_model(os.path.join(args.input_dir, "nn_model_sherlock.keras"))
+model = tf.keras.models.load_model(
+    os.path.join(args.input_dir, "nn_model_sherlock.keras")
+)
 
 # Produce a randomly sample of background from the training data
 background = []

train.py

@@ -51,31 +51,31 @@
 regex_model1 = BatchNormalization(axis=1)(regex_model_input)
 regex_model2 = Dense(
     1000,
-    activation='relu',
+    activation="relu",
     kernel_regularizer=tf.keras.regularizers.l2(0.0001),
 )(regex_model1)
 regex_model3 = Dropout(0.35)(regex_model2)
 regex_model4 = Dense(
     1000,
-    activation='relu',
+    activation="relu",
     kernel_regularizer=tf.keras.regularizers.l2(0.0001),
 )(regex_model3)
 
 merged_model2 = BatchNormalization(axis=1)(regex_model4)
 merged_model3 = Dense(
     500,
-    activation='relu',
+    activation="relu",
     kernel_regularizer=tf.keras.regularizers.l2(0.0001),
 )(merged_model2)
 merged_model4 = Dropout(0.35)(merged_model3)
 merged_model5 = Dense(
     500,
-    activation='relu',
+    activation="relu",
     kernel_regularizer=tf.keras.regularizers.l2(0.0001),
 )(merged_model4)
 merged_model_output = Dense(
     len(le.classes_),
-    activation='softmax',
+    activation="softmax",
     kernel_regularizer=tf.keras.regularizers.l2(0.0001),
 )(merged_model5)