SparseCategoricalCrossentropy.__init__() got an unexpected keyword argument using Streamlit /Python - streamlit

Question

I am classifying images using Streamlit and Python. I am getting an error: SparseCategoricalCrossentropy.__init__() got an unexpected keyword argument 'ignore_class'.
TypeError: SparseCategoricalCrossentropy.__init__() got an unexpected keyword argument 'ignore_class'
Traceback:
File "C:\Users\DELL\AppData\Local\Programs\Python\Python310\lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 565, in _run_script
exec(code, module.__dict__)
File "C:\Users\DELL\Desktop\Gradio\flask and htm\project-folder\backend\org.py", line 18, in <module>
model = load_model()
File "C:\Users\DELL\AppData\Local\Programs\Python\Python310\lib\site-packages\streamlit\runtime\legacy_caching\caching.py", line 625, in wrapped_func
return get_or_create_cached_value()
File "C:\Users\DELL\AppData\Local\Programs\Python\Python310\lib\site-packages\streamlit\runtime\legacy_caching\caching.py", line 609, in get_or_create_cached_value
return_value = non_optional_func(*args, **kwargs)
File "C:\Users\DELL\Desktop\Gradio\flask and htm\project-folder\backend\org.py", line 8, in load_model
model = tf.keras.models.load_model('model/my_model.h5')
File "C:\Users\DELL\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\utils\traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\DELL\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\losses.py", line 153, in from_config
return cls(**config)
This is my code.
import streamlit as st
import tensorflow as tf
st.set_option('deprecation.showfileUploaderEncoding', False)
#st.cache(allow_output_mutation=True)
def load_model():
model = tf.keras.models.load_model('model/my_model.h5')
return model
model = load_model()
st.write("""
# Image Classification App
"""
)
file = st.file_uploader("Please upload an image", type=["jpg", "png"])
import cv2
from PIL import Image, ImageOps
import numpy as np
def import_and_predict(image_data, model):
size = (180,180)
image =ImageOps.fit(image_data, size, Image.LANCZOS)
img =np.asarray(image)
img_reshape = img[np.newaxis,...]
prediction = model.predict(img_reshape)
return prediction
if file is None:
st.text("Please upload an image file")
else:
image = Image.open(file)
st.image(image, use_column_width=True)
predictions = import_and_predict(image, model)
class_names = ['dog', 'cat', 'horse']
string = "This image most likely is a :" +class_names[np.argmax(predictions)]
st.success(string)

Answer 1

Try loading the model with compile=False. This may also be due to a version mismatch.
Try it out,
def load_model():
model = tf.keras.models.load_model('model/my_model.h5', compile=False)
return model
It will load the model only for inference. If you still face issues then it may be a version conflict. The model was saved in a different version of TensorFlow.