def get_vectorize_layer(texts, vocab_size, max_seq, special_tokens=["[MASK]"]):
"""Build Text vectorization layer
Args:
texts (list): List of string i.e input texts
vocab_size (int): vocab size
max_seq (int): Maximum sequence lenght.
special_tokens (list, optional): List of special tokens. Defaults to ['[MASK]'].
Returns:
layers.Layer: Return TextVectorization Keras Layer
"""
vectorize_layer = TextVectorization(
max_tokens=vocab_size,
output_mode="int",
standardize=custom_standardization,
output_sequence_length=max_seq,
)
vectorize_layer.adapt(texts)
# Insert mask token in vocabulary
vocab = vectorize_layer.get_vocabulary()
vocab = vocab[2:vocab_size - len(special_tokens)] + ["[mask]"]
vectorize_layer.set_vocabulary(vocab)
return vectorize_layer
# model won't support ragged sequences.
vectorize_layer = TextVectorization(
standardize=custom_standardization,
max_tokens=max_features,
output_mode="int",
output_sequence_length=sequence_length,
)
# Now that the vocab layer has been created, call `adapt` on a text-only
# dataset to create the vocabulary. You don't have to batch, but for very large
# datasets this means you're not keeping spare copies of the dataset in memory.
# Let's make a text-only dataset (no labels):
text_ds = raw_train_ds.map(lambda x, y: x)
# Let's call `adapt`:
vectorize_layer.adapt(text_ds)
"""
## Two options to vectorize the data
There are 2 ways we can use our text vectorization layer:
**Option 1: Make it part of the model**, so as to obtain a model that processes raw
strings, like this:
"""
"""
```python
text_input = tf.keras.Input(shape=(1,), dtype=tf.string, name='text')
x = vectorize_layer(text_input)
x = layers.Embedding(max_features + 1, embedding_dim)(x)
...
lowercase = tf.strings.lower(input_string)
return tf.strings.regex_replace(lowercase, "[%s]" % re.escape(strip_chars),
"")
strip_chars = "!\"#$%&'()*+,-./:;<=>?@[\]^_`{|}~"
strip_chars = strip_chars.replace("<", "")
strip_chars = strip_chars.replace(">", "")
vectorization = TextVectorization(
max_tokens=VOCAB_SIZE,
output_mode="int",
output_sequence_length=SEQ_LENGTH,
standardize=custom_standardization,
)
vectorization.adapt(text_data)
"""
## Building a `tf.data.Dataset` pipeline for training
We will generate pairs of images and corresponding captions using a `tf.data.Dataset` object.
The pipeline consists of two steps:
1. Read the image from the disk
2. Tokenize all the five captions corresponding to the image
"""
def read_image(img_path, size=IMAGE_SIZE):
img = tf.io.read_file(img_path)
img = tf.image.decode_jpeg(img, channels=3)
img = tf.image.resize(img, IMAGE_SIZE)
""" from tensorflow.keras.layers import TextVectorization # Example training data, of dtype `string`. training_data = np.array([["This is the 1st sample."], ["And here's the 2nd sample."]]) # Create a TextVectorization layer instance. It can be configured to either # return integer token indices, or a dense token representation (e.g. multi-hot # or TF-IDF). The text standardization and text splitting algorithms are fully # configurable. vectorizer = TextVectorization(output_mode="int") # Calling `adapt` on an array or dataset makes the layer generate a vocabulary # index for the data, which can then be reused when seeing new data. vectorizer.adapt(training_data) # After calling adapt, the layer is able to encode any n-gram it has seen before # in the `adapt()` data. Unknown n-grams are encoded via an "out-of-vocabulary" # token. integer_data = vectorizer(training_data) print(integer_data) """ **Example: turning strings into sequences of one-hot encoded bigrams** """ from tensorflow.keras.layers import TextVectorization # Example training data, of dtype `string`. training_data = np.array([["This is the 1st sample."], ["And here's the 2nd sample."]])
def main():
# Download the dataset
# Using the Flickr8K dataset for this tutorial. This dataset
# comprises over 8,000 images, that are each paired with five
# different captions.
#!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 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
# Pre-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):
# Load captions (text) data and maps them to corresponding
# images.
# @param: filename, path to the text file containing caption
# data.
# @return: caption_mapping, dictionary mapping image names and
# the corresponding captions.
# @return: 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())
# Remove captions that are either too short or 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:
# Add a start and 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.
# @param: caption_data (dict), dictionary containing the mapped
# data.
# @param: train_size (float), fraction of all the full dataset
# to use as training data.
# @param: shuffle (bool), whether to shuffle the dataset before
# splitting.
# @return: training 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.
train_size = int(len(caption_data) * train_size)
training_data = {
img_name: caption_data[img_name]
for img_name in all_images[:train_size]
}
validation_data = {
img_name: caption_data[img_name]
for img_name in all_images[train_size:]
}
# 4) Return the splits.
return training_data, validation_data
# Load the dataset.
captions_mapping, text_data = load_captions_data("Flickr8k.token.txt")
# Split the dataset into training and validation sets.
train_data, valid_data = train_val_split(captions_mapping)
print("Number of training samples: ", len(train_data))
print("Number of validation samples: ", len(valid_data))
# Vectorizing the text data
# Use the TextVectorization layer to vectorize the text data, that
# is to say, to turn the original strings into integer sequences
# where each integer represents the index of a word in a
# vocabulary. Use a custom string standardization scheme (in this
# case, strip punctuation characters except < and >) and the
# default splitting scheme (split on whitespace).
def custom_standardization(input_string):
lowercase = tf.strings.lower(input_string)
return tf.strings.regex_replace(lowercase,
"[%s]" % re.escape(strip_chars), "")
strip_chars = "!\"#$%&'()*+,-./;<=>?@[\]^_`{|}~"
strip_chars = strip_chars.replace("<", "")
strip_chars = strip_chars.replace(">", "")
vectorization = TextVectorization(
max_tokens=VOCAB_SIZE,
output_mode="int",
output_sequence_length=SEQ_LENGTH,
standardize=custom_standardization,
)
vectorization.adapt(text_data)
# Data augmentation for image data.
image_augmentation = keras.Sequential([
layers.RandomFlip("horizontal"),
layers.RandomRotation(0.2),
layers.RandomContrast(0.3),
])
# Building a tf.data.Dataset pipeline for training
# Generate pairs of images and corresponding captions using a
# tf.data.Dataset object. The pipeline consists of two steps:
# 1) Read the image from the disk.
# 2) Tokenize all the five captions corresponding to the image.
def decode_and_resize(img_path):
img = tf.io.read_file(img_path)
img = tf.image.decode_jpeg(img, channels=3)
img = tf.image.resize(img, IMAGE_SIZE)
img = tf.image.convert_image_dtype(img, tf.float32)
return img
def process_input(img_path, captions):
return decode_and_resize(img_path), vectorization(captions)
def make_dataset(images, captions):
'''
if split == "train":
img_dataset = tf.data.Dataset.from_tensor_slices(images).map(
read_train_image, num_parallel_calls=AUTOTUNE
)
else:
img_dataset = tf.data.Dataset.from_tensor_slices(images).map(
read_valid_image, num_parallel_calls=AUTOTUNE
)
cap_dataset = tf.data.Dataset.from_tensor_slices(captions).map(
vectorization, num_parallel_calls=AUTOTUNE
)
dataset = tf.data.Dataset.zip((img_dataset, cap_dataset))
dataset = dataset.batch(BATCH_SIZE).shuffle(256).prefetch(AUTOTUNE)
return dataset
'''
dataset = tf.data.Dataset.from_tensor_slices((images, captions))
dataset = dataset.shuffle(len(images))
dataset = dataset.map(process_input, num_parallel_calls=AUTOTUNE)
dataset = dataset.batch(BATCH_SIZE).prefetch(AUTOTUNE)
return dataset
# Pass the list of images and the list of corresponding captions.
train_dataset = make_dataset(list(train_data.keys()),
list(train_data.values()))
valid_dataset = make_dataset(list(valid_data.keys()),
list(valid_data.values()))
# Building the model
# The image captioning architecture consists of three models:
# 1) A CNN: Used to extract the image features.
# 2) A TransformerEncoder: The extracted image features are then
# passed to a Transformer based encoder that generates a new
# representation of the inputs.
# 3) A TransformerDecoder: This model takes the encoder output and
# the text data (sequences) as inputs and tries to learn to
# generate the caption.
def get_cnn_model():
base_model = efficientnet.EfficientNetB0(
input_shape=(*IMAGE_SIZE, 3),
include_top=False,
weights="imagenet",
)
# Freeze the feature extractor.
base_model.trainable = False
base_model_out = base_model.output
base_model_out = layers.Reshape(
(-1, base_model_out.shape[-1]))(base_model_out)
cnn_model = keras.models.Model(base_model.input, base_model_out)
return cnn_model
class TransformerEncoderBlock(layers.Layer):
def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):
super().__init__(**kwargs)
self.embed_dim = embed_dim
self.dense_dim = dense_dim
self.num_heads = num_heads
self.attention_1 = layers.MultiHeadAttention(num_heads=num_heads,
key_dim=embed_dim,
dropout=0.0)
self.layernorm1 = layers.LayerNormalization()
self.layernorm2 = layers.LayerNormalization()
self.dense_1 = layers.Dense(embed_dim, activation="relu")
def call(self, inputs, training, mask=None):
inputs = self.layernorm1(inputs)
inputs = self.dense_1(inputs)
attention_output_1 = self.attention_1(
query=inputs,
value=inputs,
key=inputs,
attention_mask=None,
training=training,
)
out_1 = self.layernorm2(inputs + attention_output_1)
return out_1
class PositionalEmbedding(layers.Layer):
def __init__(self, sequence_length, vocab_size, embed_dim, **kwargs):
super().__init__(**kwargs)
self.token_embeddings = layers.Embedding(input_dim=vocab_size,
output_dim=embed_dim)
self.position_embeddings = layers.Embedding(
input_dim=sequence_length, output_dim=embed_dim)
self.sequence_length = sequence_length
self.vocab_size = vocab_size
self.embed_dim = embed_dim
self.embed_scale = tf.math.sqrt(tf.cast(embed_dim, tf.float32))
def call(self, inputs):
length = tf.shape(inputs)[-1]
positions = tf.range(start=0, limit=length, delta=1)
embedded_tokens = self.token_embeddings(inputs)
embedded_tokens = embedded_tokens * self.embed_scale
embedded_positions = self.position_embeddings(positions)
return embedded_tokens + embedded_positions
def compute_mask(self, inputs, mask=None):
return tf.math.not_equal(inputs, 0)
class TransformerDecoderBlock(layers.Layer):
def __init__(self, embed_dim, ff_dim, num_heads, **kwargs):
super().__init__(**kwargs)
self.embed_dim = embed_dim
self.ff_dim = ff_dim
self.num_heads = num_heads
self.attention_1 = layers.MultiHeadAttention(num_heads=num_heads,
key_dim=embed_dim,
dropout=0.1)
self.attention_2 = layers.MultiHeadAttention(num_heads=num_heads,
key_dim=embed_dim,
dropout=0.1)
self.ffn_layer_1 = layers.Dense(ff_dim, activation="relu")
self.ffn_layer_2 = layers.Dense(embed_dim)
self.layernorm_1 = layers.LayerNormalization()
self.layernorm_2 = layers.LayerNormalization()
self.layernorm_3 = layers.LayerNormalization()
self.embedding = PositionalEmbedding(embed_dim=EMBED_DIM,
sequence_length=SEQ_LENGTH,
vocab_size=VOCAB_SIZE)
self.out = layers.Dense(VOCAB_SIZE, activation="softmax")
self.dropout_1 = layers.Dropout(0.3)
self.dropout_2 = layers.Dropout(0.5)
self.supports_masking = True
def call(self, inputs, encoder_outputs, training, mask=None):
inputs = self.embedding(inputs)
causal_mask = self.get_causal_attention_mask(inputs)
if mask is not None:
padding_mask = tf.cast(mask[:, :, tf.newaxis], dtype=tf.int32)
combined_mask = tf.cast(mask[:, tf.newaxis, :], dtype=tf.int32)
combined_mask = tf.minimum(combined_mask, causal_mask)
attention_output_1 = self.attention_1(
query=inputs,
value=inputs,
key=inputs,
attention_mask=combined_mask,
training=training,
)
out_1 = self.layernorm_1(inputs + attention_output_1)
attention_output_2 = self.attention_2(
query=out_1,
value=encoder_outputs,
key=encoder_outputs,
attention_mask=padding_mask,
training=training,
)
out_2 = self.layernorm_2(out_1 + attention_output_2)
ffn_out = self.ffn_layer_1(out_2)
ffn_out = self.dropout_1(ffn_out, training=training)
ffn_out = self.ffn_layer_2(ffn_out)
ffn_out = self.layernorm_3(ffn_out + out_2, training=training)
ffn_out = self.dropout_2(ffn_out, training=training)
preds = self.out(ffn_out)
return preds
def get_causal_attention_mask(self, inputs):
input_shape = tf.shape(inputs)
batch_size, sequence_length = input_shape[0], input_shape[1]
i = tf.range(sequence_length)[:, tf.newaxis]
j = tf.range(sequence_length)
mask = tf.cast(i >= j, dtype="int32")
mask = tf.reshape(mask, (1, input_shape[1], input_shape[1]))
mult = tf.concat(
[
tf.expand_dims(batch_size, -1),
tf.constant([1, 1], dtype=tf.int32)
],
axis=0,
)
return tf.tile(mask, mult)
class ImageCaptioningModel(keras.Model):
def __init__(self,
cnn_model,
encoder,
decoder,
num_captions_per_image=5,
image_aug=None):
super().__init__()
self.cnn_model = cnn_model
self.encoder = encoder
self.decoder = decoder
self.loss_tracker = keras.metrics.Mean(name="loss")
self.acc_tracker = keras.metrics.Mean(name="accuracy")
self.num_captions_per_image = num_captions_per_image
self.image_aug = image_aug
def calculate_loss(self, y_true, y_pred, mask):
loss = self.loss(y_true, y_pred)
mask = tf.cast(mask, dtype=loss.dtype)
loss *= mask
return tf.reduce_sum(loss) / tf.reduce_sum(mask)
def calculate_accuracy(self, y_true, y_pred, mask):
accuracy = tf.equal(y_true, tf.argmax(y_pred, axis=2))
accuracy = tf.math.logical_and(mask, accuracy)
accuracy = tf.cast(accuracy, dtype=tf.float32)
mask = tf.cast(mask, dtype=tf.float32)
return tf.reduce_sum(accuracy) / tf.reduce_sum(mask)
def _compute_caption_loss_and_acc(self,
img_embed,
batch_seq,
training=True):
encoder_out = self.encoder(img_embed, training=training)
batch_seq_inp = batch_seq[:, :-1]
batch_seq_true = batch_seq[:, 1:]
mask = tf.math.not_equal(batch_seq_true, 0)
batch_seq_pred = self.decoder(batch_seq_inp,
encoder_out,
training=training,
mask=mask)
loss = self.calculate_loss(batch_seq_true, batch_seq_pred, mask)
acc = self.calculate_accuracy(batch_seq_true, batch_seq_pred, mask)
return loss, acc
def train_step(self, batch_data):
batch_img, batch_seq = batch_data
batch_loss = 0
batch_acc = 0
if self.image_aug:
batch_img = self.image_aug(batch_img)
# 1) Get image embeddings.
img_embed = self.cnn_model(batch_img)
# 2) Pass each of the five captions one by one to the
# decoder along with the encoder outputs and compute the
# loss as well as accuracy for each caption.
for i in range(self.num_captions_per_image):
with tf.GradientTape() as tape:
loss, acc = self._compute_caption_loss_and_acc(
img_embed, batch_seq[:, i, :], training=True)
# 3) Update loss and accuracy.
batch_loss += loss
batch_acc += acc
# 4) Get the list of all the trainable weights.
train_vars = (self.encoder.trainable_variables +
self.decoder.trainable_variables)
# 5) Get the gradients.
grads = tape.gradient(loss, train_vars)
# 6) Update the trainable weights.
self.optimizer.apply_gradients(zip(grads, train_vars))
# 7) Update the trackers.
batch_acc /= float(self.num_captions_per_image)
self.loss_tracker.update_state(batch_loss)
self.acc_tracker.update_state(batch_acc)
# 8) Return the loss and accuracy values.
return {
"loss": self.loss_tracker.result(),
"acc": self.acc_tracker.result()
}
def test_step(self, batch_data):
batch_img, batch_seq = batch_data
batch_loss = 0
batch_acc = 0
# 1) Get image embeddings.
img_embed = self.cnn_model(batch_img)
# 2) Pass each of the five captions one by one to the
# decoder along with the encoder outputs and compute the
# loss as well as accuracy for each caption.
for i in range(self.num_captions_per_image):
loss, acc = self._compute_caption_loss_and_acc(img_embed,
batch_seq[:,
i, :],
training=False)
# 3) Update loss and accuracy.
batch_loss += loss
batch_acc += acc
batch_acc /= float(self.num_captions_per_image)
# 4) Update the trackers.
self.loss_tracker.update_state(batch_loss)
self.acc_tracker.update_state(batch_acc)
# 8) Return the loss and accuracy values.
return {
"loss": self.loss_tracker.result(),
"acc": self.acc_tracker.result()
}
@property
def metrics(self):
# List the metrics here so the 'reset_states()' can be
# called automatically.
return [self.loss_tracker, self.acc_tracker]
cnn_model = get_cnn_model()
encoder = TransformerEncoderBlock(embed_dim=EMBED_DIM,
dense_dim=FF_DIM,
num_heads=1)
decoder = TransformerDecoderBlock(embed_dim=EMBED_DIM,
ff_dim=FF_DIM,
num_heads=2)
caption_model = ImageCaptioningModel(
cnn_model=cnn_model,
encoder=encoder,
decoder=decoder,
image_aug=image_augmentation,
)
# Model training
# Define the loss function.
cross_entropy = keras.losses.SparseCategoricalCrossentropy(
from_logits=False, reduction="none")
# Early stopping criteria.
early_stopping = keras.callbacks.EarlyStopping(patience=3,
restore_best_weights=True)
# Learning Rate Scheduler for the optimizer.
class LRSchedule(keras.optimizers.schedules.LearningRateSchedule):
def __init__(self, post_warmup_learning_rate, warmup_steps):
super().__init__()
self.post_warmup_learning_rate = post_warmup_learning_rate
self.warmup_steps = warmup_steps
def __call__(self, step):
global_step = tf.cast(step, tf.float32)
warmup_steps = tf.cast(self.warmup_steps, tf.float32)
warmup_progress = global_step / warmup_steps
warmup_learning_rate = self.post_warmup_learning_rate * warmup_progress
return tf.cond(
global_step < warmup_steps,
lambda: warmup_learning_rate,
lambda: self.post_warmup_learning_rate,
)
# Create a learning rate schedule.
num_train_steps = len(train_dataset) * EPOCHS
num_warmup_steps = num_train_steps // 15
lr_schedule = LRSchedule(post_warmup_learning_rate=1e-4,
warmup_steps=num_warmup_steps)
# Compile the model.
caption_model.compile(optimizer=keras.optimizers.Adam(lr_schedule),
loss=cross_entropy)
# Fit the model.
caption_model.fit(
train_dataset,
epochs=EPOCHS,
validation_data=valid_dataset,
callbacks=[early_stopping],
)
# Check sample predictions
'''
vocab = vectorization.get_vocabulary()
index_lookup = dict(zip(range(len(vocab)), vocab))
max_decoded_sentence_length = SEQ_LENGTH - 1
valid_images = list(valid_data.keys())
def generate_caption():
# Select a random image from the validation dataset.
sample_img = np.random.choice(valid_images)
# Read the image from the disk.
sample_img = decode_and_resize(sample_img)
img = sample_img.numpy().clip(0, 255).astype(np.uint8)
plt.imshow(img)
plt.show()
# Pass the image to the CNN.
img = tf.expand_dims(sample_img, 0)
img = caption_model.cnn_model(img)
# Pass the image features to the Transformer encoder.
encoded_img = caption_model.encoder(img, training=False)
# Generate the caption using the Transformer decoder.
decoded_caption = "<start>"
for i in range(max_decoded_sentence_length):
tokenized_caption = vectorization([decoded_caption])[:, :-1]
mask = tf.math.not_equal(tokenized_caption, 0)
predictions = caption_model.decoder(
tokenized_caption, encoded_img, training=False, mask=mask
)
sampled_token_index = np.argmax(predictions[0, i, :])
sampled_token = index_lookup[sampled_token_index]
if sampled_token == " <end>":
break
decoded_caption += " " + sampled_token
decoded_caption = decoded_caption.replace("<start>", "")
decoded_caption = decoded_caption.replace("<end>", "").strip()
print("Predicted Caption: ", decoded_caption)
# Check predictions for a few samples.
generate_caption()
generate_caption()
generate_caption()
'''
# End Notes
# Notice that the model starts to generate reasonable captions
# after a few epochs. To keep this example easily runnable, it has
# been trained with a few constraints, like a minimal number of
# attention heads. To improve predictions, try changing these
# training settings and find a good model for your use case.
# Exit the program.
exit(0)
eng_vectorization = TextVectorization(
max_tokens=vocab_size,
output_mode="int",
output_sequence_length=sequence_length,
)
spa_vectorization = TextVectorization(
max_tokens=vocab_size,
output_mode="int",
output_sequence_length=sequence_length + 1,
standardize=custom_standardization,
)
train_eng_texts = [pair[0] for pair in train_pairs]
train_spa_texts = [pair[1] for pair in train_pairs]
eng_vectorization.adapt(train_eng_texts)
spa_vectorization.adapt(train_spa_texts)
"""
Next, we'll format our datasets.
At each training step, the model will seek to predict target words N+1 (and beyond)
using the source sentence and the target words 0 to N.
As such, the training dataset will yield a tuple `(inputs, targets)`, where:
- `inputs` is a dictionary with the keys `encoder_inputs` and `decoder_inputs`.
`encoder_inputs` is the vectorized source sentence and `encoder_inputs` is the target sentence "so far",
that is to say, the words 0 to N used to predict word N+1 (and beyond) in the target sentence.
- `target` is the target sentence offset by one step:
it provides the next words in the target sentence -- what the model will try to predict.
"""
val_labels = labels[-num_validation_samples:] """ ## Create a vocabulary index Let's use the `TextVectorization` to index the vocabulary found in the dataset. Later, we'll use the same layer instance to vectorize the samples. Our layer will only consider the top 20,000 words, and will truncate or pad sequences to be actually 200 tokens long. """ from tensorflow.keras.layers import TextVectorization vectorizer = TextVectorization(max_tokens=20000, output_sequence_length=200) text_ds = tf.data.Dataset.from_tensor_slices(train_samples).batch(128) vectorizer.adapt(text_ds) """ You can retrieve the computed vocabulary used via `vectorizer.get_vocabulary()`. Let's print the top 5 words: """ vectorizer.get_vocabulary()[:5] """ Let's vectorize a test sentence: """ output = vectorizer([["the cat sat on the mat"]]) output.numpy()[0, :6] """ As you can see, "the" gets represented as "2". Why not 0, given that "the" was the first word in the vocabulary? That's because index 0 is reserved for padding and index 1 is