👀 Views: 321 💬 Answers: 1 📅 Created: 2025-06-17

tensorflow keras neural-network Python

I'm working through a tutorial and I've searched everywhere and can't find a clear answer. I'm not sure how to approach I'm relatively new to this, so bear with me. I'm currently working on a neural network model using TensorFlow 2.12, and I've encountered an scenario where the training loss becomes NaN after a few epochs when I apply a learning rate scheduler. I am using the `tf.keras.callbacks.LearningRateScheduler` to adjust the learning rate based on the epoch number. Here’s a snippet of the relevant code: ```python import tensorflow as tf # Define a simple model model = tf.keras.Sequential([ tf.keras.layers.Dense(64, activation='relu', input_shape=(32,)), tf.keras.layers.Dense(1) ]) model.compile(optimizer='adam', loss='mse') # Define a learning rate schedule function def scheduler(epoch, lr): if epoch > 10: return lr * tf.math.exp(-0.1) return lr # Create the LearningRateScheduler callback lr_callback = tf.keras.callbacks.LearningRateScheduler(scheduler) # Generate dummy data import numpy as np x_train = np.random.rand(1000, 32) y_train = np.random.rand(1000, 1) # Fit the model with the learning rate scheduler model.fit(x_train, y_train, epochs=20, callbacks=[lr_callback]) ``` Despite the initial training proceeding without issues, I start noticing that around epoch 5, the training loss spikes and eventually turns into NaN values. I have tried several things to troubleshoot this: 1. I reduced the model complexity by using a single dense layer instead of a deeper architecture, but the scenario continues. 2. I printed the learning rates at each epoch to ensure they are within a reasonable range, and they seem fine initially. 3. I also tried using a different optimizer, such as SGD instead of Adam, but the NaN scenario still arises. The behavior I receive in the logs is `InvalidArgumentError: Nan in the input tensor`. I suspect it might be related to the learning rate getting too low during the training, but I can’t pinpoint the exact cause. Any advice on how to fix this or further debug the NaN scenario would be greatly appreciated! This is part of a larger web app I'm building. What am I doing wrong? I'm coming from a different tech stack and learning Python. Thanks for any help you can provide! I'm open to any suggestions. Any feedback is welcome!