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Given an image like the example below, your goal is to generate a caption such as "a surfer riding on a wave".
Image Source; License: Public Domain
To accomplish this, you'll use an attention-based model, which enables us to see what parts of the image the model focuses on as it generates a caption.
The model architecture is similar to Show, Attend and Tell: Neural Image Caption Generation with Visual Attention.
This notebook is an end-to-end example. When you run the notebook, it downloads the MS-COCO dataset, preprocesses and caches a subset of images using Inception V3, trains an encoder-decoder model, and generates captions on new images using the trained model.
In this example, you will train a model on a relatively small amount of data—the first 30,000 captions for about 20,000 images (because there are multiple captions per image in the dataset).
import tensorflow as tf
# You'll generate plots of attention in order to see which parts of an image
# your model focuses on during captioning
import matplotlib.pyplot as plt
import collections
import random
import numpy as np
import os
import time
import json
from PIL import Image
Download and prepare the MS-COCO dataset
You will use the MS-COCO dataset to train your model. The dataset contains over 82,000 images, each of which has at least 5 different caption annotations. The code below downloads and extracts the dataset automatically.
# Download caption annotation files
annotation_folder = '/annotations/'
if not os.path.exists(os.path.abspath('.') + annotation_folder):
annotation_zip = tf.keras.utils.get_file('captions.zip',
cache_subdir=os.path.abspath('.'),
origin='http://images.cocodataset.org/annotations/annotations_trainval2014.zip',
extract=True)
annotation_file = os.path.dirname(annotation_zip)+'/annotations/captions_train2014.json'
os.remove(annotation_zip)
# Download image files
image_folder = '/train2014/'
if not os.path.exists(os.path.abspath('.') + image_folder):
image_zip = tf.keras.utils.get_file('train2014.zip',
cache_subdir=os.path.abspath('.'),
origin='http://images.cocodataset.org/zips/train2014.zip',
extract=True)
PATH = os.path.dirname(image_zip) + image_folder
os.remove(image_zip)
else:
PATH = os.path.abspath('.') + image_folder
Downloading data from http://images.cocodataset.org/annotations/annotations_trainval2014.zip 252878848/252872794 [==============================] - 16s 0us/step 252887040/252872794 [==============================] - 16s 0us/step Downloading data from http://images.cocodataset.org/zips/train2014.zip 13510574080/13510573713 [==============================] - 784s 0us/step 13510582272/13510573713 [==============================] - 784s 0us/step
Optional: limit the size of the training set
To speed up training for this tutorial, you'll use a subset of 30,000 captions and their corresponding images to train your model. Choosing to use more data would result in improved captioning quality.
with open(annotation_file, 'r') as f:
annotations = json.load(f)
# Group all captions together having the same image ID.
image_path_to_caption = collections.defaultdict(list)
for val in annotations['annotations']:
caption = f"<start> {val['caption']} <end>"
image_path = PATH + 'COCO_train2014_' + '%012d.jpg' % (val['image_id'])
image_path_to_caption[image_path].append(caption)
image_paths = list(image_path_to_caption.keys())
random.shuffle(image_paths)
# Select the first 6000 image_paths from the shuffled set.
# Approximately each image id has 5 captions associated with it, so that will
# lead to 30,000 examples.
train_image_paths = image_paths[:6000]
print(len(train_image_paths))
6000
train_captions = []
img_name_vector = []
for image_path in train_image_paths:
caption_list = image_path_to_caption[image_path]
train_captions.extend(caption_list)
img_name_vector.extend([image_path] * len(caption_list))
print(train_captions[0])
Image.open(img_name_vector[0])
<start> a person trying to get a cat out of a suitcase <end>
Preprocess the images using InceptionV3
Next, you will use InceptionV3 (which is pretrained on Imagenet) to classify each image. You will extract features from the last convolutional layer.
First, you will convert the images into InceptionV3's expected format by:
- Resizing the image to 299px by 299px
- Preprocess the images using the preprocess_input method to normalize the image so that it contains pixels in the range of -1 to 1, which matches the format of the images used to train InceptionV3.
def load_image(image_path):
img = tf.io.read_file(image_path)
img = tf.io.decode_jpeg(img, channels=3)
img = tf.keras.layers.Resizing(299, 299)(img)
img = tf.keras.applications.inception_v3.preprocess_input(img)
return img, image_path
Initialize InceptionV3 and load the pretrained Imagenet weights
Now you'll create a tf.keras model where the output layer is the last convolutional layer in the InceptionV3 architecture. The shape of the output of this layer is 8x8x2048
. You use the last convolutional layer because you are using attention in this example. You don't perform this initialization during training because it could become a bottleneck.
- You forward each image through the network and store the resulting vector in a dictionary (image_name --> feature_vector).
- After all the images are passed through the network, you save the dictionary to disk.
image_model = tf.keras.applications.InceptionV3(include_top=False,
weights='imagenet')
new_input = image_model.input
hidden_layer = image_model.layers[-1].output
image_features_extract_model = tf.keras.Model(new_input, hidden_layer)
Caching the features extracted from InceptionV3
You will pre-process each image with InceptionV3 and cache the output to disk. Caching the output in RAM would be faster but also memory intensive, requiring 8 * 8 * 2048 floats per image. At the time of writing, this exceeds the memory limitations of Colab (currently 12GB of memory).
Performance could be improved with a more sophisticated caching strategy (for example, by sharding the images to reduce random access disk I/O), but that would require more code.
The caching will take about 10 minutes to run in Colab with a GPU. If you'd like to see a progress bar, you can:
Install tqdm:
!pip install tqdm
Import tqdm:
from tqdm import tqdm
Change the following line:
for img, path in image_dataset:
to:
for img, path in tqdm(image_dataset):
# Get unique images
encode_train = sorted(set(img_name_vector))
# Feel free to change batch_size according to your system configuration
image_dataset = tf.data.Dataset.from_tensor_slices(encode_train)
image_dataset = image_dataset.map(
load_image, num_parallel_calls=tf.data.AUTOTUNE).batch(16)
for img, path in image_dataset:
batch_features = image_features_extract_model(img)
batch_features = tf.reshape(batch_features,
(batch_features.shape[0], -1, batch_features.shape[3]))
for bf, p in zip(batch_features, path):
path_of_feature = p.numpy().decode("utf-8")
np.save(path_of_feature, bf.numpy())
Preprocess and tokenize the captions
You will transform the text captions into integer sequences using the TextVectorization layer, with the following steps:
- Use adapt to iterate over all captions, split the captions into words, and compute a vocabulary of the top 5,000 words (to save memory).
- Tokenize all captions by mapping each word to it's index in the vocabulary. All output sequences will be padded to length 50.
- Create word-to-index and index-to-word mappings to display results.
caption_dataset = tf.data.Dataset.from_tensor_slices(train_captions)
# We will override the default standardization of TextVectorization to preserve
# "<>" characters, so we preserve the tokens for the <start> and <end>.
def standardize(inputs):
inputs = tf.strings.lower(inputs)
return tf.strings.regex_replace(inputs,
r"!\"#$%&\(\)\*\+.,-/:;=?@\[\\\]^_`{|}~", "")
# Max word count for a caption.
max_length = 50
# Use the top 5000 words for a vocabulary.
vocabulary_size = 5000
tokenizer = tf.keras.layers.TextVectorization(
max_tokens=vocabulary_size,
standardize=standardize,
output_sequence_length=max_length)
# Learn the vocabulary from the caption data.
tokenizer.adapt(caption_dataset)
# Create the tokenized vectors
cap_vector = caption_dataset.map(lambda x: tokenizer(x))
# Create mappings for words to indices and indicies to words.
word_to_index = tf.keras.layers.StringLookup(
mask_token="",
vocabulary=tokenizer.get_vocabulary())
index_to_word = tf.keras.layers.StringLookup(
mask_token="",
vocabulary=tokenizer.get_vocabulary(),
invert=True)
Split the data into training and testing
img_to_cap_vector = collections.defaultdict(list)
for img, cap in zip(img_name_vector, cap_vector):
img_to_cap_vector[img].append(cap)
# Create training and validation sets using an 80-20 split randomly.
img_keys = list(img_to_cap_vector.keys())
random.shuffle(img_keys)
slice_index = int(len(img_keys)*0.8)
img_name_train_keys, img_name_val_keys = img_keys[:slice_index], img_keys[slice_index:]
img_name_train = []
cap_train = []
for imgt in img_name_train_keys:
capt_len = len(img_to_cap_vector[imgt])
img_name_train.extend([imgt] * capt_len)
cap_train.extend(img_to_cap_vector[imgt])
img_name_val = []
cap_val = []
for imgv in img_name_val_keys:
capv_len = len(img_to_cap_vector[imgv])
img_name_val.extend([imgv] * capv_len)
cap_val.extend(img_to_cap_vector[imgv])
len(img_name_train), len(cap_train), len(img_name_val), len(cap_val)
(24012, 24012, 6004, 6004)
Create a tf.data dataset for training
Your images and captions are ready! Next, let's create a tf.data
dataset to use for training your model.
# Feel free to change these parameters according to your system's configuration
BATCH_SIZE = 64
BUFFER_SIZE = 1000
embedding_dim = 256
units = 512
num_steps = len(img_name_train) // BATCH_SIZE
# Shape of the vector extracted from InceptionV3 is (64, 2048)
# These two variables represent that vector shape
features_shape = 2048
attention_features_shape = 64
# Load the numpy files
def map_func(img_name, cap):
img_tensor = np.load(img_name.decode('utf-8')+'.npy')
return img_tensor, cap
dataset = tf.data.Dataset.from_tensor_slices((img_name_train, cap_train))
# Use map to load the numpy files in parallel
dataset = dataset.map(lambda item1, item2: tf.numpy_function(
map_func, [item1, item2], [tf.float32, tf.int64]),
num_parallel_calls=tf.data.AUTOTUNE)
# Shuffle and batch
dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
dataset = dataset.prefetch(buffer_size=tf.data.AUTOTUNE)
Model
Fun fact: the decoder below is identical to the one in the example for Neural Machine Translation with Attention.
The model architecture is inspired by the Show, Attend and Tell paper.
- In this example, you extract the features from the lower convolutional layer of InceptionV3 giving us a vector of shape (8, 8, 2048).
- You squash that to a shape of (64, 2048).
- This vector is then passed through the CNN Encoder (which consists of a single Fully connected layer).
- The RNN (here GRU) attends over the image to predict the next word.
class BahdanauAttention(tf.keras.Model):
def __init__(self, units):
super(BahdanauAttention, self).__init__()
self.W1 = tf.keras.layers.Dense(units)
self.W2 = tf.keras.layers.Dense(units)
self.V = tf.keras.layers.Dense(1)
def call(self, features, hidden):
# features(CNN_encoder output) shape == (batch_size, 64, embedding_dim)
# hidden shape == (batch_size, hidden_size)
# hidden_with_time_axis shape == (batch_size, 1, hidden_size)
hidden_with_time_axis = tf.expand_dims(hidden, 1)
# attention_hidden_layer shape == (batch_size, 64, units)
attention_hidden_layer = (tf.nn.tanh(self.W1(features) +
self.W2(hidden_with_time_axis)))
# score shape == (batch_size, 64, 1)
# This gives you an unnormalized score for each image feature.
score = self.V(attention_hidden_layer)
# attention_weights shape == (batch_size, 64, 1)
attention_weights = tf.nn.softmax(score, axis=1)
# context_vector shape after sum == (batch_size, hidden_size)
context_vector = attention_weights * features
context_vector = tf.reduce_sum(context_vector, axis=1)
return context_vector, attention_weights
class CNN_Encoder(tf.keras.Model):
# Since you have already extracted the features and dumped it
# This encoder passes those features through a Fully connected layer
def __init__(self, embedding_dim):
super(CNN_Encoder, self).__init__()
# shape after fc == (batch_size, 64, embedding_dim)
self.fc = tf.keras.layers.Dense(embedding_dim)
def call(self, x):
x = self.fc(x)
x = tf.nn.relu(x)
return x
class RNN_Decoder(tf.keras.Model):
def __init__(self, embedding_dim, units, vocab_size):
super(RNN_Decoder, self).__init__()
self.units = units
self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
self.gru = tf.keras.layers.GRU(self.units,
return_sequences=True,
return_state=True,
recurrent_initializer='glorot_uniform')
self.fc1 = tf.keras.layers.Dense(self.units)
self.fc2 = tf.keras.layers.Dense(vocab_size)
self.attention = BahdanauAttention(self.units)
def call(self, x, features, hidden):
# defining attention as a separate model
context_vector, attention_weights = self.attention(features, hidden)
# x shape after passing through embedding == (batch_size, 1, embedding_dim)
x = self.embedding(x)
# x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)
x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)
# passing the concatenated vector to the GRU
output, state = self.gru(x)
# shape == (batch_size, max_length, hidden_size)
x = self.fc1(output)
# x shape == (batch_size * max_length, hidden_size)
x = tf.reshape(x, (-1, x.shape[2]))
# output shape == (batch_size * max_length, vocab)
x = self.fc2(x)
return x, state, attention_weights
def reset_state(self, batch_size):
return tf.zeros((batch_size, self.units))
encoder = CNN_Encoder(embedding_dim)
decoder = RNN_Decoder(embedding_dim, units, tokenizer.vocabulary_size())
optimizer = tf.keras.optimizers.Adam()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction='none')
def loss_function(real, pred):
mask = tf.math.logical_not(tf.math.equal(real, 0))
loss_ = loss_object(real, pred)
mask = tf.cast(mask, dtype=loss_.dtype)
loss_ *= mask
return tf.reduce_mean(loss_)
Checkpoint
checkpoint_path = "./checkpoints/train"
ckpt = tf.train.Checkpoint(encoder=encoder,
decoder=decoder,
optimizer=optimizer)
ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=5)
start_epoch = 0
if ckpt_manager.latest_checkpoint:
start_epoch = int(ckpt_manager.latest_checkpoint.split('-')[-1])
# restoring the latest checkpoint in checkpoint_path
ckpt.restore(ckpt_manager.latest_checkpoint)
Training
- You extract the features stored in the respective
.npy
files and then pass those features through the encoder. - The encoder output, hidden state(initialized to 0) and the decoder input (which is the start token) is passed to the decoder.
- The decoder returns the predictions and the decoder hidden state.
- The decoder hidden state is then passed back into the model and the predictions are used to calculate the loss.
- Use teacher forcing to decide the next input to the decoder.
- Teacher forcing is the technique where the target word is passed as the next input to the decoder.
- The final step is to calculate the gradients and apply it to the optimizer and backpropagate.
# adding this in a separate cell because if you run the training cell
# many times, the loss_plot array will be reset
loss_plot = []
@tf.function
def train_step(img_tensor, target):
loss = 0
# initializing the hidden state for each batch
# because the captions are not related from image to image
hidden = decoder.reset_state(batch_size=target.shape[0])
dec_input = tf.expand_dims([word_to_index('<start>')] * target.shape[0], 1)
with tf.GradientTape() as tape:
features = encoder(img_tensor)
for i in range(1, target.shape[1]):
# passing the features through the decoder
predictions, hidden, _ = decoder(dec_input, features, hidden)
loss += loss_function(target[:, i], predictions)
# using teacher forcing
dec_input = tf.expand_dims(target[:, i], 1)
total_loss = (loss / int(target.shape[1]))
trainable_variables = encoder.trainable_variables + decoder.trainable_variables
gradients = tape.gradient(loss, trainable_variables)
optimizer.apply_gradients(zip(gradients, trainable_variables))
return loss, total_loss
EPOCHS = 20
for epoch in range(start_epoch, EPOCHS):
start = time.time()
total_loss = 0
for (batch, (img_tensor, target)) in enumerate(dataset):
batch_loss, t_loss = train_step(img_tensor, target)
total_loss += t_loss
if batch % 100 == 0:
average_batch_loss = batch_loss.numpy()/int(target.shape[1])
print(f'Epoch {epoch+1} Batch {batch} Loss {average_batch_loss:.4f}')
# storing the epoch end loss value to plot later
loss_plot.append(total_loss / num_steps)
if epoch % 5 == 0:
ckpt_manager.save()
print(f'Epoch {epoch+1} Loss {total_loss/num_steps:.6f}')
print(f'Time taken for 1 epoch {time.time()-start:.2f} sec\n')
Epoch 1 Batch 0 Loss 1.9157 Epoch 1 Batch 100 Loss 1.1384 Epoch 1 Batch 200 Loss 0.9826 Epoch 1 Batch 300 Loss 0.8792 Epoch 1 Loss 1.025084 Time taken for 1 epoch 153.68 sec Epoch 2 Batch 0 Loss 0.8554 Epoch 2 Batch 100 Loss 0.8062 Epoch 2 Batch 200 Loss 0.7998 Epoch 2 Batch 300 Loss 0.6949 Epoch 2 Loss 0.775522 Time taken for 1 epoch 47.44 sec Epoch 3 Batch 0 Loss 0.7251 Epoch 3 Batch 100 Loss 0.6746 Epoch 3 Batch 200 Loss 0.7269 Epoch 3 Batch 300 Loss 0.7025 Epoch 3 Loss 0.699518 Time taken for 1 epoch 47.78 sec Epoch 4 Batch 0 Loss 0.6970 Epoch 4 Batch 100 Loss 0.6150 Epoch 4 Batch 200 Loss 0.6196 Epoch 4 Batch 300 Loss 0.6131 Epoch 4 Loss 0.650994 Time taken for 1 epoch 46.87 sec Epoch 5 Batch 0 Loss 0.6139 Epoch 5 Batch 100 Loss 0.6305 Epoch 5 Batch 200 Loss 0.6493 Epoch 5 Batch 300 Loss 0.5535 Epoch 5 Loss 0.611642 Time taken for 1 epoch 45.06 sec Epoch 6 Batch 0 Loss 0.6755 Epoch 6 Batch 100 Loss 0.5603 Epoch 6 Batch 200 Loss 0.5161 Epoch 6 Batch 300 Loss 0.5671 Epoch 6 Loss 0.578854 Time taken for 1 epoch 45.25 sec Epoch 7 Batch 0 Loss 0.5575 Epoch 7 Batch 100 Loss 0.4937 Epoch 7 Batch 200 Loss 0.5625 Epoch 7 Batch 300 Loss 0.5456 Epoch 7 Loss 0.549154 Time taken for 1 epoch 44.85 sec Epoch 8 Batch 0 Loss 0.5555 Epoch 8 Batch 100 Loss 0.5142 Epoch 8 Batch 200 Loss 0.4842 Epoch 8 Batch 300 Loss 0.5119 Epoch 8 Loss 0.519941 Time taken for 1 epoch 44.78 sec Epoch 9 Batch 0 Loss 0.4790 Epoch 9 Batch 100 Loss 0.4654 Epoch 9 Batch 200 Loss 0.4568 Epoch 9 Batch 300 Loss 0.4468 Epoch 9 Loss 0.494242 Time taken for 1 epoch 44.99 sec Epoch 10 Batch 0 Loss 0.4740 Epoch 10 Batch 100 Loss 0.4592 Epoch 10 Batch 200 Loss 0.4380 Epoch 10 Batch 300 Loss 0.4556 Epoch 10 Loss 0.468823 Time taken for 1 epoch 44.89 sec Epoch 11 Batch 0 Loss 0.4488 Epoch 11 Batch 100 Loss 0.4423 Epoch 11 Batch 200 Loss 0.4317 Epoch 11 Batch 300 Loss 0.4371 Epoch 11 Loss 0.444164 Time taken for 1 epoch 45.02 sec Epoch 12 Batch 0 Loss 0.4335 Epoch 12 Batch 100 Loss 0.4473 Epoch 12 Batch 200 Loss 0.3770 Epoch 12 Batch 300 Loss 0.4506 Epoch 12 Loss 0.421234 Time taken for 1 epoch 44.95 sec Epoch 13 Batch 0 Loss 0.4289 Epoch 13 Batch 100 Loss 0.4215 Epoch 13 Batch 200 Loss 0.3689 Epoch 13 Batch 300 Loss 0.3864 Epoch 13 Loss 0.399234 Time taken for 1 epoch 45.16 sec Epoch 14 Batch 0 Loss 0.4013 Epoch 14 Batch 100 Loss 0.3571 Epoch 14 Batch 200 Loss 0.3847 Epoch 14 Batch 300 Loss 0.3722 Epoch 14 Loss 0.379495 Time taken for 1 epoch 44.99 sec Epoch 15 Batch 0 Loss 0.3879 Epoch 15 Batch 100 Loss 0.3652 Epoch 15 Batch 200 Loss 0.3025 Epoch 15 Batch 300 Loss 0.3522 Epoch 15 Loss 0.360756 Time taken for 1 epoch 44.96 sec Epoch 16 Batch 0 Loss 0.3542 Epoch 16 Batch 100 Loss 0.3199 Epoch 16 Batch 200 Loss 0.3565 Epoch 16 Batch 300 Loss 0.3352 Epoch 16 Loss 0.344851 Time taken for 1 epoch 44.96 sec Epoch 17 Batch 0 Loss 0.3681 Epoch 17 Batch 100 Loss 0.3477 Epoch 17 Batch 200 Loss 0.3025 Epoch 17 Batch 300 Loss 0.3349 Epoch 17 Loss 0.326141 Time taken for 1 epoch 44.89 sec Epoch 18 Batch 0 Loss 0.3286 Epoch 18 Batch 100 Loss 0.3203 Epoch 18 Batch 200 Loss 0.3029 Epoch 18 Batch 300 Loss 0.2952 Epoch 18 Loss 0.309969 Time taken for 1 epoch 44.89 sec Epoch 19 Batch 0 Loss 0.2942 Epoch 19 Batch 100 Loss 0.2920 Epoch 19 Batch 200 Loss 0.2899 Epoch 19 Batch 300 Loss 0.2875 Epoch 19 Loss 0.295664 Time taken for 1 epoch 46.18 sec Epoch 20 Batch 0 Loss 0.2843 Epoch 20 Batch 100 Loss 0.2907 Epoch 20 Batch 200 Loss 0.2813 Epoch 20 Batch 300 Loss 0.2554 Epoch 20 Loss 0.283829 Time taken for 1 epoch 45.51 sec
plt.plot(loss_plot)
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.title('Loss Plot')
plt.show()
Caption!
- The evaluate function is similar to the training loop, except you don't use teacher forcing here. The input to the decoder at each time step is its previous predictions along with the hidden state and the encoder output.
- Stop predicting when the model predicts the end token.
- And store the attention weights for every time step.
def evaluate(image):
attention_plot = np.zeros((max_length, attention_features_shape))
hidden = decoder.reset_state(batch_size=1)
temp_input = tf.expand_dims(load_image(image)[0], 0)
img_tensor_val = image_features_extract_model(temp_input)
img_tensor_val = tf.reshape(img_tensor_val, (img_tensor_val.shape[0],
-1,
img_tensor_val.shape[3]))
features = encoder(img_tensor_val)
dec_input = tf.expand_dims([word_to_index('<start>')], 0)
result = []
for i in range(max_length):
predictions, hidden, attention_weights = decoder(dec_input,
features,
hidden)
attention_plot[i] = tf.reshape(attention_weights, (-1, )).numpy()
predicted_id = tf.random.categorical(predictions, 1)[0][0].numpy()
predicted_word = tf.compat.as_text(index_to_word(predicted_id).numpy())
result.append(predicted_word)
if predicted_word == '<end>':
return result, attention_plot
dec_input = tf.expand_dims([predicted_id], 0)
attention_plot = attention_plot[:len(result), :]
return result, attention_plot
def plot_attention(image, result, attention_plot):
temp_image = np.array(Image.open(image))
fig = plt.figure(figsize=(10, 10))
len_result = len(result)
for i in range(len_result):
temp_att = np.resize(attention_plot[i], (8, 8))
grid_size = max(int(np.ceil(len_result/2)), 2)
ax = fig.add_subplot(grid_size, grid_size, i+1)
ax.set_title(result[i])
img = ax.imshow(temp_image)
ax.imshow(temp_att, cmap='gray', alpha=0.6, extent=img.get_extent())
plt.tight_layout()
plt.show()
# captions on the validation set
rid = np.random.randint(0, len(img_name_val))
image = img_name_val[rid]
real_caption = ' '.join([tf.compat.as_text(index_to_word(i).numpy())
for i in cap_val[rid] if i not in [0]])
result, attention_plot = evaluate(image)
print('Real Caption:', real_caption)
print('Prediction Caption:', ' '.join(result))
plot_attention(image, result, attention_plot)
Real Caption: <start> the bus is driving down the busy street. <end> Prediction Caption: a bus is on the street <end>
Try it on your own images
For fun, below you're provided a method you can use to caption your own images with the model you've just trained. Keep in mind, it was trained on a relatively small amount of data, and your images may be different from the training data (so be prepared for weird results!)
image_url = 'https://tensorflow.org/images/surf.jpg'
image_extension = image_url[-4:]
image_path = tf.keras.utils.get_file('image'+image_extension, origin=image_url)
result, attention_plot = evaluate(image_path)
print('Prediction Caption:', ' '.join(result))
plot_attention(image_path, result, attention_plot)
# opening the image
Image.open(image_path)
Prediction Caption: an image of a man with man standing wearing a [UNK] into the [UNK] <end>
Next steps
Congrats! You've just trained an image captioning model with attention. Next, take a look at this example Neural Machine Translation with Attention. It uses a similar architecture to translate between Spanish and English sentences. You can also experiment with training the code in this notebook on a different dataset.