Parellization ok!

skin_cancer_classification.py

@@ -282,22 +282,26 @@ data_augmentation = tf.keras.Sequential([
     tf.keras.layers.RandomContrast(0.1),
 ])
 
+with strategy.scope():
     base_model = DenseNet121(
         weights='imagenet',
         include_top=False,
         input_shape=(224, 224, 3)
     )
 
+with strategy.scope():
     inputs = tf.keras.Input(shape=(224,224,3))
 
     x = data_augmentation(inputs)
 
+with strategy.scope():
     x = base_model.output
     x = GlobalAveragePooling2D()(x)
     x = Dense(512, activation='relu')(x) # Added another Dense layer
     x = Dense(256, activation='relu')(x) # Existing Dense layer
     predictions = Dense(1, activation='sigmoid')(x) # Output layer for binary classification
 
+with strategy.scope():
     model = Model(inputs=base_model.input, outputs=predictions)
     model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])