How to write a Keras data generator

keras_fit_generator.py

def generator(features, labels, batch_size):

 # Create empty arrays to contain batch of features and labels#

 batch_features = np.zeros((batch_size, 64, 64, 3))
 batch_labels = np.zeros((batch_size,1))

 while True:
   for i in range(batch_size):
     # choose random index in features
     index= random.choice(len(features),1)
     batch_features[i] = some_processing(features[index])
     batch_labels[i] = labels[index]
   yield batch_features, batch_labels

def generate_arrays_from_file(path):
    while 1:
    f = open(path)
    for line in f:
        # create numpy arrays of input data
        # and labels, from each line in the file
        x1, x2, y = process_line(line)
        yield ({'input_1': x1, 'input_2': x2}, {'output': y})
    f.close()

model.fit_generator(generate_arrays_from_file('/my_file.txt'),
        steps_per_epoch=10000, epochs=10)
        
from skimage.io import imread
from skimage.transform import resize
import numpy as np

# Here, `x_set` is list of path to the images and `y_set` are the associated classes.

class CIFAR10Sequence(Sequence):
def __init__(self, x_set, y_set, batch_size):
    self.X,self.y = x_set,y_set
    self.batch_size = batch_size

def __len__(self):
    return len(self.X) // self.batch_size

def __getitem__(self,idx):
    batch_x = self.X[idx*self.batch_size:(idx+1)*self.batch_size]
    batch_y = self.y[idx*self.batch_size:(idx+1)*self.batch_size]

    return np.array([
    resize(imread(file_name), (200,200))
       for file_name in batch_x]), np.array(batch_y)