Unexpected convergence issues in TensorFlow when training a custom LSTM model
I'm converting an old project and I'm experimenting with I've searched everywhere and can't find a clear answer..... I'm working with unexpected convergence issues while training my custom LSTM model using TensorFlow 2.9.0. The loss seems to plateau very early and does not improve, even with different learning rates. Here’s my model architecture: ```python import tensorflow as tf from tensorflow.keras import layers, models model = models.Sequential() model.add(layers.LSTM(128, input_shape=(timesteps, features), return_sequences=True)) model.add(layers.LSTM(64)) model.add(layers.Dense(1)) model.compile(optimizer='adam', loss='mean_squared_error') ``` I’m using a batch size of 32 and a learning rate of 0.001. However, I’ve tried varying the learning rate down to 0.00001 without much change in the training behavior. After 50 epochs, the loss is around 0.15, and I expect it to decrease further. The training data has been normalized, and I’ve also added dropout layers to reduce overfitting, but it still doesn't seem to help. Here’s how I’m training the model: ```python early_stopping = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=5) model.fit(X_train, y_train, epochs=100, batch_size=32, callbacks=[early_stopping]) ``` What’s puzzling me is that the validation loss is also not improving, which makes me think that my model might be underfitting. I’ve tried increasing the number of LSTM units as well as adding more layers, but the performance remains stagnant. The input data is structured correctly, and I’ve double-checked that there are no NaNs or infinite values. Are there any best practices or configuration tips for optimizing LSTM training in TensorFlow that I might be missing? Additionally, could there be an scenario with my data preprocessing that could lead to this behavior? Any suggestions would be greatly appreciated! My development environment is macOS. Any advice would be much appreciated. This issue appeared after updating to Python 3.11. Any feedback is welcome!