Test

skin_cancer_classification.py

@@ -271,9 +271,9 @@ 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(): # Use all gpus
-  model = Model(inputs=base_model.input, outputs=predictions)
-  model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
+# with strategy.scope(): # Use all gpus
+model = Model(inputs=base_model.input, outputs=predictions)
+model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
 
 """## 4. Data Generators
 
@@ -340,9 +340,10 @@ model_checkpoint = ModelCheckpoint(
 )
 
 # Train the model
+print("Training model...")
 history = model.fit(
     train_generator,
-    epochs=3, # You can adjust the number of epochs
+    epochs=50, # You can adjust the number of epochs
     validation_data=val_generator,
     callbacks=[early_stopping, model_checkpoint],
     class_weight=class_weights # Use class weights to handle imbalance