👀 Views: 0 💬 Answers: 1 📅 Created: 2025-06-17

tensorflow keras callbacks Python

I'm relatively new to this, so bear with me... I'm trying to implement I'm working with a frustrating scenario with model training using TensorFlow 2.12 and custom callbacks. My model seems to stop responding during training, and I need to figure out why. The training process starts normally, but after a few epochs, it seems to freeze at a specific loss value and doesn't update anymore, even though the epochs continue incrementing. Here’s a snippet of my training setup: ```python import tensorflow as tf from tensorflow.keras import layers, models # Sample model model = models.Sequential([ layers.Dense(64, activation='relu', input_shape=(32,)), layers.Dense(1) ]) model.compile(optimizer='adam', loss='mean_squared_error') # Custom callback to monitor loss class CustomCallback(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs=None): if logs['loss'] < 0.1: print('Stopping early due to low loss.') self.model.stop_training = True # Dummy dataset import numpy as np X_train = np.random.rand(1000, 32) Y_train = np.random.rand(1000, 1) # Fit model model.fit(X_train, Y_train, epochs=100, callbacks=[CustomCallback()]) ``` I’ve tried adjusting the learning rate and disabling the callback to see if it might be causing the freeze, but the scenario continues. The training log shows that the loss value plateaus around 0.05 after a few epochs, but then it stops updating, and I receive no behavior messages or warnings. I also checked the model summary and the input data shapes; everything seems to be configured correctly. Has anyone encountered a similar scenario or have suggestions on debugging this type of behavior? Is there a way to forcefully get feedback on what the model is doing during training? Am I missing something obvious? I'm working with Python in a Docker container on Ubuntu 20.04. What would be the recommended way to handle this?