Model training failing after 1 epoch

Question

I am a noob in python and needed to train a model on a dataset.I found both the notebook and dataset at the same place and made appropriate changes to notebook to run the data from storage.The code fails at the training stage after 1 epoch completes with 'graph execution error'

Here is my jupyter notebook:https://github.com/Megahedron69/wasteSegregationmodel

Here is the dataset:https://www.kaggle.com/datasets/aashidutt3/waste-segregation-image-dataset

Here is the original notebook:https://www.kaggle.com/code/gpiosenka/waste-f1-score-97

exact error location in notebook:

Entire error:

Answer 1

Thanks to @Dr. Snoopy answer.There were corrupt images in my data set so used a simple python script in my root directory to remove truncated images

#pip install pillow
from PIL import Image
import os

def find_truncated_images(dataset_dir):
    truncated_images = []
    for root, _, files in os.walk(dataset_dir):
        for filename in files:
            file_path = os.path.join(root, filename)
            try:
                with Image.open(file_path) as img:
                    img.load()
            except (IOError, OSError) as e:
                # Log the file path if it's a truncated image
                print(f"Truncated image: {file_path}")
                truncated_images.append(file_path)
    return truncated_images

def remove_truncated_images(truncated_images):
    for file_path in truncated_images:
        try:
            os.remove(file_path)
            print(f"Removed: {file_path}")
        except OSError as e:
            print(f"Error removing {file_path}: {e}")

if __name__ == "__main__":
    dataset_dir = "."  # Set the path to your dataset directory
    truncated_images = find_truncated_images(dataset_dir)
    remove_truncated_images(truncated_images)