ok no

skin_cancer_classification.py

@@ -29,9 +29,9 @@ 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 pandas numpy matplotlib seaborn pillow scikit-learn tensorflow
-#   !pip install --upgrade kagglehub[pandas-datasets,hf-datasets]
+#  !pip install --upgrade kagglehub[pandas-datasets,hf-datasets]
 
 import kagglehub
 
@@ -282,28 +282,24 @@ data_augmentation = tf.keras.Sequential([
     tf.keras.layers.RandomContrast(0.1),
 ])
 
-with strategy.scope():
+base_model = DenseNet121(
-    base_model = DenseNet121(
+    weights='imagenet',
-        weights='imagenet',
+    include_top=False,
-        include_top=False,
+    input_shape=(224, 224, 3)
-        input_shape=(224, 224, 3)
+)
-    )
 
-with strategy.scope():
+inputs = tf.keras.Input(shape=(224,224,3))
-    inputs = tf.keras.Input(shape=(224,224,3))
 
-    x = data_augmentation(inputs)
+x = data_augmentation(inputs)
 
-with strategy.scope():
+x = base_model.output
-    x = base_model.output
+x = GlobalAveragePooling2D()(x)
-    x = GlobalAveragePooling2D()(x)
+x = Dense(512, activation='relu')(x) # Added another Dense layer
-    x = Dense(512, activation='relu')(x) # Added another Dense layer
+x = Dense(256, activation='relu')(x) # Existing Dense layer
-    x = Dense(256, activation='relu')(x) # Existing Dense layer
+predictions = Dense(1, activation='sigmoid')(x) # Output layer for binary classification
-    predictions = Dense(1, activation='sigmoid')(x) # Output layer for binary classification
 
-with strategy.scope():
+model = Model(inputs=base_model.input, outputs=predictions)
-    model = Model(inputs=base_model.input, outputs=predictions)
+model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
-    model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
 
 """## 4. Data Generators