Implementing a caption generation model

First, let's read the dataset and transform it the way we need. Import the os library and declare the directory in which the dataset is present, as shown in the following code:

import os
annotation_dir = 'Flickr8k_text'

Next, define a function to open a file and return the lines present in the file as a list:

def read_file(file_name):
    with open(os.path.join(annotation_dir, file_name), 'rb') as file_handle:
        file_lines = file_handle.read().splitlines()
    return file_lines

Read the image paths of the training and testing datasets followed by the captions file:

train_image_paths = read_file('Flickr_8k.trainImages.txt')
test_image_paths = read_file('Flickr_8k.testImages.txt')
captions = read_file('Flickr8k.token.txt')

print(len(train_image_paths))
print(len(test_image_paths))
print...