Unexpected Model Performance Drop with Keras LSTM after Hyperparameter Tuning
I've looked through the documentation and I'm still confused about I'm facing an unexpected drop in performance when I apply hyperparameter tuning to my LSTM model using Keras. Initially, my model was yielding an accuracy of around 85% on the validation set. However, after tuning hyperparameters such as the number of units in the LSTM layer, dropout rates, and batch sizes, the accuracy has dropped to 60%. Here's the code I used for defining my LSTM model: ```python from keras.models import Sequential from keras.layers import LSTM, Dense, Dropout model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(timesteps, features))) model.add(Dropout(0.2)) model.add(LSTM(units=50)) model.add(Dropout(0.2)) model.add(Dense(units=1, activation='sigmoid')) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) ``` I implemented a grid search for hyperparameter tuning using `GridSearchCV` from scikit-learn, specifying a parameter grid like this: ```python param_grid = { 'batch_size': [16, 32], 'epochs': [10, 20], 'units': [50, 100], 'dropout': [0.2, 0.5] } ``` I noticed that while the training loss continues to decrease, the validation loss has become unstable, oscillating between high values. I validated that my data was preprocessed correctly and normalized. Additionally, I'm using TensorFlow version 2.6.0 and Keras version 2.6.0. Could this performance drop be related to overfitting due to the increased complexity of the model after tuning? Or might it be an issue with the way I've set up the `GridSearchCV`? Any advice on how to diagnose or fix this issue would be greatly appreciated! Is there a simpler solution I'm overlooking? I'm developing on Windows 11 with Python. Hoping someone can shed some light on this.