TLDR: It depends on your function and whether you are in production or development. Don’t use tf.function if you want to be able to debug your function easily, or if it falls under the limitations of AutoGraph or tf.v1 code compatibility. I would highly recommend watching the Inside TensorFlow talks about AutoGraph and Functions, not … Read more
Update: TF2.2 forced me to clean up all implementations – so now they can be used as a reference for TF best practices. Also added a section below on _get_hyper vs. _set_hyper. I’ve implemented Keras AdamW in all major TF & Keras versions – I invite you to examine optimizers_v2.py. Several points: You should inherit … Read more
As you are trying to use function decorator in TF 2.0, please enable run function eagerly by using below line after importing TensorFlow: tf.config.experimental_run_functions_eagerly(True) Since the above is deprecated(no longer experimental?), please use the following instead: tf.config.run_functions_eagerly(True) If you want to know more do refer to this link.
Please try: from tensorflow.keras.models import Sequential instead of from keras.models import Sequential
This seems like a bogus message. I get the same warning message after upgrading to TensorFlow 2.1, but I do not use any class weights or sample weights at all. I do use a generator that returns a tuple like this: return inputs, targets And now I just changed it to the following to make … Read more
Use the keras module from tensorflow like this: import tensorflow as tf Import classes from tensorflow.python.keras.layers import Input, Dense or use directly dense = tf.keras.layers.Dense(…) EDIT Tensorflow 2 from tensorflow.keras.layers import Input, Dense and the rest stays the same.