generator yielded an element of shape (0,) where an element of shape (None, None, None, None, None) was expected

Question

I was training a network and I decided to add more data for training. input size

but when I train my net with this data, I got this error:

InvalidArgumentError                      Traceback (most recent call last)
File c:\Users\YATO.DESKTOP-PCAG66Q\Desktop\New folder (3)\braindl\train.py:84
     81 print(model.inputshape)
     82 print(model.output_shape)
---> 84 history=model.fit(train_img_datagen,
     85           steps_per_epoch=steps_per_epoch,
     86           epochs=100,
     87           verbose=1,
     88           validation_data=val_img_datagen,
     89           validation_steps=val_steps_per_epoch,
     90           )
     92 model.save('tbrainseg_3d.hdf5')

File c:\Users\YATO.DESKTOP-PCAG66Q\anaconda3\Lib\site-packages\keras\src\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     67     filtered_tb = process_traceback_frames(e.__traceback)
     68     # To get the full stack trace, call:
     69     # tf.debugging.disable_traceback_filtering()
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:
     72     del filtered_tb

File c:\Users\YATO.DESKTOP-PCAG66Q\anaconda3\Lib\site-packages\tensorflow\python\eager\execute.py:53, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     51 try:
     52   ctx.ensure_initialized()
...
TypeError: generator yielded an element of shape (0,) where an element of shape (None, None, None, None, None) was expected.


     [[{{node PyFunc}}]]
     [[IteratorGetNext]] [Op:__inference_train_function_7443]

My data generator: my batch size is = 2

import os
import numpy as np


def load_img(img_dir, img_list):
    images=[]
    for i, image_name in enumerate(img_list):    
        if (image_name.split('.')[1] == 'npy'):
            
            image = np.load(img_dir+image_name)
                      
            images.append(image)
    images = np.array(images)
    
    return(images)




def imageLoader(img_dir, img_list, mask_dir, mask_list, batch_size):

    L = len(img_list)

    #keras needs the generator infinite, so we will use while true  
    while True:

        batch_start = 0
        batch_end = batch_size

        while batch_start < L:
            limit = min(batch_end, L)
                       
            X = load_img(img_dir, img_list[batch_start:limit])
            Y = load_img(mask_dir, mask_list[batch_start:limit])

            yield (X,Y) #a tuple with two numpy arrays with batch_size samples     

            batch_start += batch_size   
            batch_end += batch_size`

I would mention that before adding the new data, it is working well.