Unexpected NaN Loss in TensorFlow during Model Training with Custom Loss Function
I'm currently working on a deep learning project using TensorFlow 2.9 and have implemented a custom loss function that combines mean squared behavior (MSE) with a regularization term. However, I'm working with an scenario where the loss suddenly becomes NaN during training after a few epochs. Here's the custom loss function I've implemented: ```python import tensorflow as tf def custom_loss(y_true, y_pred): mse = tf.reduce_mean(tf.square(y_true - y_pred)) reg_lambda = 0.01 reg_loss = reg_lambda * tf.reduce_sum(tf.square(y_pred)) # L2 regularization return mse + reg_loss ``` I compiled my model with this custom loss and started training with the following configuration: ```python model.compile(optimizer='adam', loss=custom_loss) model.fit(train_dataset, epochs=50, validation_data=val_dataset) ``` At first, the training seems to progress well, but after about 10 epochs, I notice that the loss values start to spike and eventually become NaN. I've tried normalizing my input features and adjusting the learning rate (starting with 0.001 and then reducing it to 0.0001), but the question continues. I also checked for any potential issues in my data and found no NaN or infinite values in the training dataset, which consists of 10,000 samples. I'm using TensorFlow's Dataset API to preprocess and batch the data. I've added some debugging print statements to track the loss values during training, and I noticed that after a few epochs, the MSE value becomes extremely large, which seems to trigger the NaN. Could this be related to the regularization term? Is there a best practice for preventing NaN values in custom loss functions? Any suggestions on what I might be overlooking would be greatly appreciated!