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This notebook is configured to use keras_nlp and standard tf.keras. It works perfectly.
If reconfigured to use keras_nlp with Keras Core the model stops working (failing version here):
it displays non-sensical accuracies > 1 during training
eval accuracy the same as if the mode was doing random predictions
.predict returns predictions of the wrong shape (shape=(n,) instead of (n,3)
therefore the final np.argmax(predictions, axis=1) fails.
there is nan in the predictions
The behavior does not change when pip installing form GitHub master (latest version) rather than PyPi (latest published package) for both Keras Core and KerasNLP
The config I used to make the model fail:
!pip install keras-core
import os
os.environ['KERAS_BACKEND'] = 'tensorflow'
import keras_core as keras
The text was updated successfully, but these errors were encountered:
Repro notebook: https://www.kaggle.com/code/alexia/kerasnlp-starter-notebook-contradictory-dearwatson
This notebook is configured to use keras_nlp and standard tf.keras. It works perfectly.
If reconfigured to use keras_nlp with Keras Core the model stops working (failing version here):
np.argmax(predictions, axis=1)
fails.The behavior does not change when pip installing form GitHub master (latest version) rather than PyPi (latest published package) for both Keras Core and KerasNLP
The config I used to make the model fail:
The text was updated successfully, but these errors were encountered: