How to implement guide with gradient clipping in tensorflow 2.12 causing instability in lstm training
I've spent hours debugging this and I'm working on a project and hit a roadblock. I've looked through the documentation and I'm still confused about I'm currently training an LSTM model for a time series forecasting task using TensorFlow 2.12, and I’m working with issues with gradient clipping... Even though I've implemented gradient clipping to prevent exploding gradients, my model's training seems quite unstable, and I often see loss values oscillating wildly instead of converging smoothly. I've set up my optimizer like this: ```python optimizer = tf.keras.optimizers.Adam(learning_rate=0.001) ``` And I’m applying gradient clipping in my training loop using the following snippet: ```python with tf.GradientTape() as tape: predictions = model(inputs) loss = loss_function(targets, predictions) gradients = tape.gradient(loss, model.trainable_variables) clipped_gradients = [tf.clip_by_value(g, -1.0, 1.0) for g in gradients] optimizer.apply_gradients(zip(clipped_gradients, model.trainable_variables)) ``` Despite implementing this, I still encounter the following behavior during training: ``` ValueError: Gradient must be finite. Received inf at index 0 ``` This typically happens when the gradients become too large, but I thought clipping would help. I've tried experimenting with different clipping values (e.g., 0.5, 10.0), but it hasn't resolved the instability scenario. Additionally, I've monitored the input data and ensured there are no NaN or infinite values present. Is there anything I might be missing in terms of architecture or gradient handling? Could the architecture of my LSTM model also be contributing to this scenario? Here’s a brief overview of my model: ```python model = tf.keras.Sequential([ tf.keras.layers.LSTM(50, input_shape=(timesteps, features)), tf.keras.layers.Dense(1) ]) ``` Any insights or suggestions on how to stabilize the training process would be greatly appreciated! I'm working on a application that needs to handle this. Is there a better approach? I'm working in a Debian environment.