Unexpected NaN Values When Using tf.keras.Model for Custom Training Loop in TensorFlow 2.8
Can someone help me understand After trying multiple solutions online, I still can't figure this out... I'm writing unit tests and I tried several approaches but none seem to work. I'm stuck on something that should probably be simple. I'm working with an scenario where the loss values during the training loop become NaN after a few epochs when using a custom training loop with TensorFlow 2.8. I'm using a simple feedforward neural network for a regression task with Mean Squared behavior as the loss function. I've implemented the training loop as follows: ```python import tensorflow as tf import numpy as np # Dummy dataset X_train = np.random.rand(1000, 10) Y_train = np.random.rand(1000, 1) # Build the model model = tf.keras.Sequential([ tf.keras.layers.Dense(64, activation='relu', input_shape=(10,)), tf.keras.layers.Dense(1) ]) optimizer = tf.keras.optimizers.Adam(learning_rate=0.001) loss_fn = tf.keras.losses.MeanSquaredError() # Custom training loop for epoch in range(10): with tf.GradientTape() as tape: predictions = model(X_train, training=True) loss = loss_fn(Y_train, predictions) grads = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) print(f'Epoch {epoch}, Loss: {loss.numpy()}') ``` Initially, everything runs smoothly, but after around the 5th epoch, the printed loss jumps to NaN. I've tried normalizing the input data and setting a lower learning rate, but it hasn't resolved the scenario. The weights do not seem to explode, as I’ve monitored their values, but I'm unsure why the loss would suddenly become undefined. Any insights on debugging NaN values in TensorFlow training loops, specifically related to potential data issues, or gradient updates would be greatly appreciated. I'm working on a application that needs to handle this. Has anyone else encountered this? I'm working on a REST API that needs to handle this. My team is using Python for this mobile app. I'd love to hear your thoughts on this. Any advice would be much appreciated. For reference, this is a production microservice. The stack includes Python and several other technologies.