Unexpected NaN Values in TensorFlow Model Predictions
This might be a silly question, but I'm training a neural network using TensorFlow 2.6.0 and I've run into an scenario where my model occasionally outputs NaN values during inference after training. I've checked my data, and there are no NaNs in my training dataset, which consists of normalized features ranging from 0 to 1. Here's the model architecture I've implemented: ```python import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers model = keras.Sequential([ layers.Input(shape=(10,)), # 10 features layers.Dense(64, activation='relu'), layers.Dense(64, activation='relu'), layers.Dense(1) # Regression task ]) model.compile(optimizer='adam', loss='mean_squared_error') ``` During training, I observe the loss decreasing as expected; however, occasionally, I get the following warning during prediction: ``` UserWarning: The input contains NaN values. ``` I've tried adding gradient clipping and adjusted the learning rate to 0.001, but I still see NaNs popping up in the predictions. Here's the training loop I used: ```python try: model.fit(X_train, y_train, epochs=100, validation_split=0.2) except Exception as e: print(f'behavior during training: {e}') ``` After training, I make predictions like this: ```python predictions = model.predict(X_test) ``` I also checked for any division by zero errors in my custom loss function, but everything seems to be functioning correctly there. I'm at a loss as to why this is occurring. Any insights into why my model might be producing NaN values during inference, or how I can debug this scenario further? I'm working on a application that needs to handle this. Is there a better approach? How would you solve this?