👀 Views: 99 💬 Answers: 1 📅 Created: 2025-09-06

tensorflow lstm gradient-clipping machine-learning Python

I'm trying to implement I've been struggling with this for a few days now and could really use some help..... After trying multiple solutions online, I still can't figure this out. This might be a silly question, but I'm facing a significant issue with exploding gradients while training a sequence-to-sequence model using LSTM layers in TensorFlow 2.12... Despite implementing gradient clipping, the model's loss function still exhibits unstable behavior, and I keep encountering warnings like 'OverflowError: cannot convert float infinity to integer'. Here's a simplified version of my model setup: ```python import tensorflow as tf from tensorflow.keras import layers, models # Define LSTM model model = models.Sequential() model.add(layers.LSTM(128, return_sequences=True, input_shape=(None, num_features))) model.add(layers.LSTM(64)) model.add(layers.Dense(num_classes, activation='softmax')) optimizer = tf.keras.optimizers.Adam(learning_rate=0.001) model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) ``` I've tried implementing gradient clipping as follows: ```python @tf.function def train_step(x, y): with tf.GradientTape() as tape: predictions = model(x, training=True) loss = tf.keras.losses.sparse_categorical_crossentropy(y, predictions) gradients = tape.gradient(loss, model.trainable_variables) clipped_gradients = [tf.clip_by_value(g, -1., 1.) for g in gradients] optimizer.apply_gradients(zip(clipped_gradients, model.trainable_variables)) ``` Despite these efforts, the loss still diverges, and I notice the training metrics oscillating greatly between epochs. I've experimented with reducing the learning rate and increasing the batch size, yet the issue persists. I've also tried initializing my LSTMs with different weight initializers without success. Is there a better technique or configuration I might be missing in handling exploding gradients with LSTMs? Any insights or suggestions would be greatly appreciated, especially regarding best practices for sequence-to-sequence models in TensorFlow 2.12. For context: I'm using Python on Windows. Thanks in advance! Any help would be greatly appreciated! I recently upgraded to Python 3.11. Any feedback is welcome! I recently upgraded to Python latest. How would you solve this?