Unexpected NaN values in TensorFlow with GradientTape during model training
I'm trying to debug I'm working on a project and hit a roadblock. I'm stuck on something that should probably be simple... I'm currently training a TensorFlow model using the `tf.GradientTape` for custom training steps, but I'm working with unexpected `NaN` values during the training process. I'm using TensorFlow version 2.8.0, and my model is relatively straightforward—a few dense layers with ReLU activations. Here's a snippet of the training loop where the scenario occurs: ```python import tensorflow as tf import numpy as np # Simple model definition model = tf.keras.Sequential([ tf.keras.layers.Dense(64, activation='relu', input_shape=(32,)), tf.keras.layers.Dense(1) ]) optimizer = tf.keras.optimizers.Adam(learning_rate=0.001) # Dummy data x_train = np.random.rand(1000, 32).astype(np.float32) y_train = np.random.rand(1000, 1).astype(np.float32) for epoch in range(10): with tf.GradientTape() as tape: predictions = model(x_train, training=True) loss = tf.keras.losses.mean_squared_error(y_train, predictions) print(f'Loss at epoch {epoch}: {loss.numpy()}') grads = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) ``` The training runs for a few epochs without scenario, but then I begin seeing `NaN` values in the loss output and ultimately in the model's predictions. I've tried normalizing my input data but it hasn't resolved the scenario. I've also added checks for `NaN` in the gradients: ```python for grad in grads: if tf.reduce_any(tf.math.is_nan(grad)): print('Found NaN in gradients') ``` This check does trigger, indicating that at least one gradient becomes `NaN` during training. I've also experimented with lowering the learning rate to `0.0001`, but the question continues. What could be causing these `NaN` values in the gradients, and how can I prevent this from happening? Are there specific practices in TensorFlow that can help debug or mitigate this scenario? Is there a better approach? What am I doing wrong? Am I approaching this the right way?