giild
commented
1 year ago Some additional information, in case someone else runs into the same issue. If I move backend setting to after the imports, load throws this error. Here is the log from console.
Using TensorFlow backend
2.12.0
Traceback (most recent call last):
File "/Users/peterlin/kaggle/ATD-keras/keras/convert_keras_to_json.py", line 98, in
'tuple' object has no attribute 'rank'
Call arguments received by layer 'res_net_backbone' (type ResNetBackbone): • inputs=<KerasTensor shape=(160, 256, 256, 3), dtype=float32, name=input_layer> • training=None • mask=None
SuryanarayanaY
I was testing a sample Kaggle competition notebook found here https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/code
I wrote a simple script to load the model and ran into an issue. It took me a few days to figure out why.
if I set KERAS_BACKEND after importing keras libraries, models.load_model fails with a strange error. Once I moved line to the top, it got rid of the error. It would be good to update the documentation so that users can resolve this quickly without wasting a bunch of time. I tend to import all the libraries first and then set any environment variables after the imports. I can make a documentation pull request if that helps.
import os
backend must be set before loading keras_core to avoid load errors
os.environ["KERAS_BACKEND"] = "tensorflow" import sys import keras_cv import keras_core as keras from keras_core import layers
import numpy as np import pandas as pd import tensorflow as tf
class Config: SEED = 61 IMAGE_SIZE = [256, 256] BATCH_SIZE = 160 EPOCHS = 10 TARGET_COLS = [ "bowel_injury", "extravasation_injury", "kidney_healthy", "kidney_low", "kidney_high", "liver_healthy", "liver_low", "liver_high", "spleen_healthy", "spleen_low", "spleen_high", ] BASE_PATH = "./input/rsna-2023-abdominal-trauma-detection" AUTOTUNE = tf.data.AUTOTUNE
config = Config()
def main(): args = sys.argv[0:] if len(args) == 1: print('Example usage:') print(' python train_model.py checkpoint_path') else: print(args) filename = args[1] print("try to load the model") loadmodel = keras.models.load_model(filename) print(loadmodel.summary())
if name == "main": main()