Inconsistent Validation Results with EarlyStopping in TensorFlow 2.12 Using Keras
I've encountered a strange issue with I've been working on this all day and I'm refactoring my project and I'm facing an issue where the validation loss fluctuates significantly during training when I utilize the `EarlyStopping` callback in TensorFlow 2.12. My model is built using `tf.keras` with a relatively simple architecture, and I have implemented early stopping to monitor the validation loss to prevent overfitting. However, despite a consistent training loss decrease, the validation loss seems to oscillate rather than trend downwards. I have configured `EarlyStopping` as follows: ```python from tensorflow.keras.callbacks import EarlyStopping early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True) ``` When I run the model, I get the following log for the validation loss: ``` Epoch 1/50 - 3s - loss: 0.4032 - val_loss: 0.5123 Epoch 2/50 - 3s - loss: 0.3210 - val_loss: 0.4876 Epoch 3/50 - 3s - loss: 0.2845 - val_loss: 0.4950 Epoch 4/50 - 3s - loss: 0.2588 - val_loss: 0.5001 Epoch 5/50 - 3s - loss: 0.2441 - val_loss: 0.4895 Epoch 6/50 - 3s - loss: 0.2254 - val_loss: 0.5150 ``` Despite having set `patience=5`, it seems the model is stopping based on slight increases in validation loss which I suspect is part of the natural training process. I've tried increasing the patience parameter up to 10 epochs, but the issue persists. I've also ensured my training and validation datasets are properly split and normalized. Is there a recommended approach to handle such fluctuations in validation loss, or should I consider additional techniques such as learning rate scheduling or modifying the architecture? Any insights or advice would be greatly appreciated! For reference, this is a production REST API. Thanks for taking the time to read this! Thanks, I really appreciate it!