My_Custom_Generator.py

from tensorflow import keras

class My_Custom_Generator(keras.utils.Sequence):
  
  def __init__(self, images, labels, batch_size):
    self.images = images
    self.labels = labels
    self.batch_size = batch_size
    
    
  def __len__(self) :
    return (np.ceil(len(self.images) / float(self.batch_size))).astype(np.int)
  
  
  def __getitem__(self, idx) :
    batch_x = self.images[idx * self.batch_size : (idx+1) * self.batch_size]
    batch_y = self.labels[idx * self.batch_size : (idx+1) * self.batch_size]

    train_image = []
    train_label = []

    for i in range(0, len(batch_x)):
      img_path = batch_x[i]
      label = batch_y[i]
      # read method takes image path and label and returns corresponding matrices
      image, label_matrix = read(img_path, label)
      train_image.append(image)
      train_label.append(label_matrix)
    return np.array(train_image), np.array(train_label)