Parellization ok!

skin_cancer_classification.py

@@ -29,7 +29,7 @@ Original file is located at
 import sys
 IN_COLAB = 'google.colab' in sys.modules
 
-#if IN_COLAB:
+# if IN_COLAB:
 #   !pip install pandas numpy matplotlib seaborn pillow scikit-learn tensorflow
 #   !pip install --upgrade kagglehub[pandas-datasets,hf-datasets]
 
@@ -282,24 +282,28 @@ data_augmentation = tf.keras.Sequential([
     tf.keras.layers.RandomContrast(0.1),
 ])
 
-base_model = DenseNet121(
+with strategy.scope():
+    base_model = DenseNet121(
         weights='imagenet',
         include_top=False,
         input_shape=(224, 224, 3)
-)
+    )
 
-inputs = tf.keras.Input(shape=(224,224,3))
+with strategy.scope():
+    inputs = tf.keras.Input(shape=(224,224,3))
 
-x = data_augmentation(inputs)
+    x = data_augmentation(inputs)
 
-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():
+    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
 
-model = Model(inputs=base_model.input, outputs=predictions)
-model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
+with strategy.scope():
+    model = Model(inputs=base_model.input, outputs=predictions)
+    model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
 
 """## 4. Data Generators