TensorFlow 2.12: implementing Gradient Tape and Mixed Precision in Custom Training Loop
Could someone explain I'm updating my dependencies and I've looked through the documentation and I'm still confused about I'm currently working on a custom training loop in TensorFlow 2.12 and attempting to implement mixed precision training using `tf.keras.mixed_precision`. However, I encounter an scenario when calculating gradients with `tf.GradientTape` that results in inconsistent loss values and NaN gradients for certain batches. Here's a snippet of my training loop: ```python import tensorflow as tf from tensorflow.keras import layers, models # Set mixed precision policy policy = tf.keras.mixed_precision.Policy('mixed_float16') tf.keras.mixed_precision.set_global_policy(policy) # Define a simple model model = models.Sequential([ layers.Dense(128, activation='relu', input_shape=(784,)), layers.Dense(10) ]) optimizer = tf.keras.optimizers.Adam() # Custom training loop for epoch in range(num_epochs): for batch, (x, y) in enumerate(train_dataset): with tf.GradientTape() as tape: predictions = model(x) loss = tf.keras.losses.sparse_categorical_crossentropy(y, predictions) loss = tf.reduce_mean(loss) gradients = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(gradients, model.trainable_variables)) ``` The question arises when I run this code with a batch size greater than 32. The loss starts diverging drastically after a few epochs, and when I print the gradients, I see many `NaN` values appearing. I suspect this might be related to the mixed precision calculations, but I’m not sure how to properly debug this scenario. I've tried using a lower learning rate, but that didn’t help. Additionally, I checked the input data for any anomalies and also normalized the inputs, but the question continues. When I run the model without mixed precision, everything works fine. Any ideas on how to resolve this scenario or best practices to handle mixed precision training? Thanks in advance for your help! What am I doing wrong? Has anyone dealt with something similar? This is part of a larger desktop app I'm building. Am I missing something obvious? The project is a REST API built with Python.