Datasets:
HenryAI
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KerasCodeExamples.txt

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!wget -q https://github.com/jbrownlee/Datasets/releases/download/Flickr8k/Flickr8k_Dataset.zip
!wget -q https://github.com/jbrownlee/Datasets/releases/download/Flickr8k/Flickr8k_text.zip
!unzip -qq Flickr8k_Dataset.zip
!unzip -qq Flickr8k_text.zip
!rm Flickr8k_Dataset.zip Flickr8k_text.zip
# Path to the images
IMAGES_PATH = \"Flicker8k_Dataset\"
# Desired image dimensions
IMAGE_SIZE = (299, 299)
# Vocabulary size
VOCAB_SIZE = 10000
# Fixed length allowed for any sequence
SEQ_LENGTH = 25
# Dimension for the image embeddings and token embeddings
EMBED_DIM = 512
# Per-layer units in the feed-forward network
FF_DIM = 512
# Other training parameters
BATCH_SIZE = 64
EPOCHS = 30
AUTOTUNE = tf.data.AUTOTUNE
Preparing the dataset
def load_captions_data(filename):
\"\"\"Loads captions (text) data and maps them to corresponding images.
Args:
filename: Path to the text file containing caption data.
Returns:
caption_mapping: Dictionary mapping image names and the corresponding captions
text_data: List containing all the available captions
\"\"\"
with open(filename) as caption_file:
caption_data = caption_file.readlines()
caption_mapping = {}
text_data = []
images_to_skip = set()
for line in caption_data:
line = line.rstrip(\"\n\")
# Image name and captions are separated using a tab
img_name, caption = line.split(\"\t\")
# Each image is repeated five times for the five different captions.
# Each image name has a suffix `#(caption_number)`
img_name = img_name.split(\"#\")[0]
img_name = os.path.join(IMAGES_PATH, img_name.strip())
# We will remove caption that are either too short to too long
tokens = caption.strip().split()
if len(tokens) < 5 or len(tokens) > SEQ_LENGTH:
images_to_skip.add(img_name)
continue
if img_name.endswith(\"jpg\") and img_name not in images_to_skip:
# We will add a start and an end token to each caption
caption = \"<start> \" + caption.strip() + \" <end>\"
text_data.append(caption)
if img_name in caption_mapping:
caption_mapping[img_name].append(caption)
else:
caption_mapping[img_name] = [caption]
for img_name in images_to_skip:
if img_name in caption_mapping:
del caption_mapping[img_name]
return caption_mapping, text_data
def train_val_split(caption_data, train_size=0.8, shuffle=True):
\"\"\"Split the captioning dataset into train and validation sets.
Args:
caption_data (dict): Dictionary containing the mapped caption data
train_size (float): Fraction of all the full dataset to use as training data
shuffle (bool): Whether to shuffle the dataset before splitting
Returns:
Traning and validation datasets as two separated dicts
\"\"\"
# 1. Get the list of all image names
all_images = list(caption_data.keys())
# 2. Shuffle if necessary
if shuffle:
np.random.shuffle(all_images)
# 3. Split into training and validation sets