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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License").
Image Captioning with Attention
<table class="tfo-notebook-buttons" align="left"><td>
<a target="_blank"  href="https
Step1:  Download and prepare the MS-COCO dataset
We will use the MS-COCO dataset to train our model. This dataset contains >82,000 images, each of which has been annotated with at least 5 different captions. The code code below will download and extract the dataset automatically.  
Caution
Step2:  Optionally, limit the size of the training set for faster training
For this example, we'll select a subset of 30,000 captions and use these and the corresponding images to train our model. As always, captioning quality will improve if you choose to use more data.
Step3:  Preprocess the images using InceptionV3
Next, we will use InceptionV3 (pretrained on Imagenet) to classify each image. We will extract features from the last convolutional layer. 
First, we will need to convert the images into the format inceptionV3 expects by
Step4:  Initialize InceptionV3 and load the pretrained Imagenet weights
To do so, we'll create a tf.keras model where the output layer is the last convolutional layer in the InceptionV3 architecture. 
* Each image is forwarded through the network and the vector that we get at the end is stored in a dictionary (image_name --> feature_vector). 
* We use the last convolutional layer because we are using attention in this example. The shape of the output of this layer is 8x8x2048. 
* We avoid doing this during training so it does not become a bottleneck. 
* After all the images are passed through the network, we pickle the dictionary and save it to disk.
Step5:  Caching the features extracted from InceptionV3
We will pre-process each image with InceptionV3 and cache the output to disk. Caching the output in RAM would be faster but memory intensive, requiring 8 * 8 * 2048 floats per image. At the time of writing, this would exceed the memory limitations of Colab (although these may change, an instance appears to have about 12GB of memory currently). 
Performance could be improved with a more sophisticated caching strategy (e.g., by sharding the images to reduce random access disk I/O) at the cost of more code.
This will take about 10 minutes to run in Colab with a GPU. If you'd like to see a progress bar, you could
Step6:  Preprocess and tokenize the captions
First, we'll tokenize the captions (e.g., by splitting on spaces). This will give us a  vocabulary of all the unique words in the data (e.g., "surfing", "football", etc).
Next, we'll limit the vocabulary size to the top 5,000 words to save memory. We'll replace all other words with the token "UNK" (for unknown).
Finally, we create a word --> index mapping and vice-versa.
We will then pad all sequences to the be same length as the longest one.
Step7:  Split the data into training and testing
Step8:  Our images and captions are ready! Next, let's create a tf.data dataset to use for training our model.
Step9:  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, we extract the features from the lower convolutional layer of InceptionV3 giving us a vector of shape (8, 8, 2048). 
We 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.
Step10:  Training
We 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.
Step11:  Caption!
The evaluate function is similar to the training loop, except we 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.
Step12:  Try it on your own images
For fun, below we've provided a method you can use to caption your own images with the model we'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!)
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Python Code:
# Import TensorFlow and enable eager execution
# This code requires TensorFlow version >=1.9
import tensorflow as tf
tf.enable_eager_execution()
# We'll generate plots of attention in order to see which parts of an image
# our model focuses on during captioning
import matplotlib.pyplot as plt
# Scikit-learn includes many helpful utilities
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import re
import numpy as np
import os
import time
import json
from glob import glob
from PIL import Image
import pickle
Explanation: Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License").
Image Captioning with Attention
<table class="tfo-notebook-buttons" align="left"><td>
<a target="_blank"  href="https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb">
    <img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>  
</td><td>
<a target="_blank"  href="https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/generative_examples/image_captioning_with_attention.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a></td></table>
Image captioning is the task of generating a caption for an image. Given an image like this:
 
Image Source, License: Public Domain
Our goal is generate a caption, such as "a surfer riding on a wave". Here, we'll use an attention based model. This enables us to see which parts of the image the model focuses on as it generates a caption.
This model architecture below is similar to Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. 
The code uses tf.keras and eager execution, which you can learn more about in the linked guides.
This notebook is an end-to-end example. If you run it, it will download the  MS-COCO dataset, preprocess and cache a subset of the images using Inception V3, train an encoder-decoder model, and use it to generate captions on new images.
The code requires TensorFlow version >=1.9. If you're running this in Colab
In this example, we're training on a relatively small amount of data as an example. On a single P100 GPU, this example will take about ~2 hours to train. We train on the first 30,000 captions (corresponding to about ~20,000 images depending on shuffling, as there are multiple captions per image in the dataset)
End of explanation
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'
name_of_zip = 'train2014.zip'
if not os.path.exists(os.path.abspath('.') + '/' + name_of_zip):
  image_zip = tf.keras.utils.get_file(name_of_zip, 
                                      cache_subdir=os.path.abspath('.'),
                                      origin = 'http://images.cocodataset.org/zips/train2014.zip',
                                      extract = True)
  PATH = os.path.dirname(image_zip)+'/train2014/'
else:
  PATH = os.path.abspath('.')+'/train2014/'
Explanation: Download and prepare the MS-COCO dataset
We will use the MS-COCO dataset to train our model. This dataset contains >82,000 images, each of which has been annotated with at least 5 different captions. The code code below will download and extract the dataset automatically.  
Caution: large download ahead. We'll use the training set, it's a 13GB file.
End of explanation
# read the json file
with open(annotation_file, 'r') as f:
    annotations = json.load(f)
# storing the captions and the image name in vectors
all_captions = []
all_img_name_vector = []
for annot in annotations['annotations']:
    caption = '<start> ' + annot['caption'] + ' <end>'
    image_id = annot['image_id']
    full_coco_image_path = PATH + 'COCO_train2014_' + '%012d.jpg' % (image_id)
    
    all_img_name_vector.append(full_coco_image_path)
    all_captions.append(caption)
# shuffling the captions and image_names together
# setting a random state
train_captions, img_name_vector = shuffle(all_captions,
                                          all_img_name_vector,
                                          random_state=1)
# selecting the first 30000 captions from the shuffled set
num_examples = 30000
train_captions = train_captions[:num_examples]
img_name_vector = img_name_vector[:num_examples]
len(train_captions), len(all_captions)
Explanation: Optionally, limit the size of the training set for faster training
For this example, we'll select a subset of 30,000 captions and use these and the corresponding images to train our model. As always, captioning quality will improve if you choose to use more data.
End of explanation
def load_image(image_path):
    img = tf.read_file(image_path)
    img = tf.image.decode_jpeg(img, channels=3)
    img = tf.image.resize_images(img, (299, 299))
    img = tf.keras.applications.inception_v3.preprocess_input(img)
    return img, image_path
Explanation: Preprocess the images using InceptionV3
Next, we will use InceptionV3 (pretrained on Imagenet) to classify each image. We will extract features from the last convolutional layer. 
First, we will need to convert the images into the format inceptionV3 expects by:
* Resizing the image to (299, 299)
* Using the preprocess_input method to place the pixels in the range of -1 to 1 (to match the format of the images used to train InceptionV3).
End of explanation
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)
Explanation: Initialize InceptionV3 and load the pretrained Imagenet weights
To do so, we'll create a tf.keras model where the output layer is the last convolutional layer in the InceptionV3 architecture. 
* Each image is forwarded through the network and the vector that we get at the end is stored in a dictionary (image_name --> feature_vector). 
* We use the last convolutional layer because we are using attention in this example. The shape of the output of this layer is 8x8x2048. 
* We avoid doing this during training so it does not become a bottleneck. 
* After all the images are passed through the network, we pickle the dictionary and save it to disk.
End of explanation
# getting the unique images
encode_train = sorted(set(img_name_vector))
# feel free to change the batch_size according to your system configuration
image_dataset = tf.data.Dataset.from_tensor_slices(
                                encode_train).map(load_image).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())
Explanation: Caching the features extracted from InceptionV3
We will pre-process each image with InceptionV3 and cache the output to disk. Caching the output in RAM would be faster but memory intensive, requiring 8 * 8 * 2048 floats per image. At the time of writing, this would exceed the memory limitations of Colab (although these may change, an instance appears to have about 12GB of memory currently). 
Performance could be improved with a more sophisticated caching strategy (e.g., by sharding the images to reduce random access disk I/O) at the cost of more code.
This will take about 10 minutes to run in Colab with a GPU. If you'd like to see a progress bar, you could: install tqdm (!pip install tqdm), then change this line: 
for img, path in image_dataset: 
to:
for img, path in tqdm(image_dataset):.
End of explanation
# This will find the maximum length of any caption in our dataset
def calc_max_length(tensor):
    return max(len(t) for t in tensor)
# The steps above is a general process of dealing with text processing
# choosing the top 5000 words from the vocabulary
top_k = 5000
tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=top_k, 
                                                  oov_token="<unk>", 
                                                  filters='!"#$%&()*+.,-/:;=?@[\]^_`{|}~ ')
tokenizer.fit_on_texts(train_captions)
train_seqs = tokenizer.texts_to_sequences(train_captions)
tokenizer.word_index = {key:value for key, value in tokenizer.word_index.items() if value <= top_k}
# putting <unk> token in the word2idx dictionary
tokenizer.word_index[tokenizer.oov_token] = top_k + 1
tokenizer.word_index['<pad>'] = 0
# creating the tokenized vectors
train_seqs = tokenizer.texts_to_sequences(train_captions)
# creating a reverse mapping (index -> word)
index_word = {value:key for key, value in tokenizer.word_index.items()}
# padding each vector to the max_length of the captions
# if the max_length parameter is not provided, pad_sequences calculates that automatically
cap_vector = tf.keras.preprocessing.sequence.pad_sequences(train_seqs, padding='post')
# calculating the max_length 
# used to store the attention weights
max_length = calc_max_length(train_seqs)
Explanation: Preprocess and tokenize the captions
First, we'll tokenize the captions (e.g., by splitting on spaces). This will give us a  vocabulary of all the unique words in the data (e.g., "surfing", "football", etc).
Next, we'll limit the vocabulary size to the top 5,000 words to save memory. We'll replace all other words with the token "UNK" (for unknown).
Finally, we create a word --> index mapping and vice-versa.
We will then pad all sequences to the be same length as the longest one.
End of explanation
# Create training and validation sets using 80-20 split
img_name_train, img_name_val, cap_train, cap_val = train_test_split(img_name_vector, 
                                                                    cap_vector, 
                                                                    test_size=0.2, 
                                                                    random_state=0)
len(img_name_train), len(cap_train), len(img_name_val), len(cap_val)
Explanation: Split the data into training and testing
End of explanation
# feel free to change these parameters according to your system's configuration
BATCH_SIZE = 64
BUFFER_SIZE = 1000
embedding_dim = 256
units = 512
vocab_size = len(tokenizer.word_index)
# shape of the vector extracted from InceptionV3 is (64, 2048)
# these two variables represent that
features_shape = 2048
attention_features_shape = 64
# loading 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))
# using map to load the numpy files in parallel
# NOTE: Be sure to set num_parallel_calls to the number of CPU cores you have
# https://www.tensorflow.org/api_docs/python/tf/py_func
dataset = dataset.map(lambda item1, item2: tf.py_func(
          map_func, [item1, item2], [tf.float32, tf.int32]), num_parallel_calls=8)
# shuffling and batching
dataset = dataset.shuffle(BUFFER_SIZE)
# https://www.tensorflow.org/api_docs/python/tf/contrib/data/batch_and_drop_remainder
dataset = dataset.batch(BATCH_SIZE)
dataset = dataset.prefetch(1)
Explanation: Our images and captions are ready! Next, let's create a tf.data dataset to use for training our model.
End of explanation
def gru(units):
  # If you have a GPU, we recommend using the CuDNNGRU layer (it provides a 
  # significant speedup).
  if tf.test.is_gpu_available():
    return tf.keras.layers.CuDNNGRU(units, 
                                    return_sequences=True, 
                                    return_state=True, 
                                    recurrent_initializer='glorot_uniform')
  else:
    return tf.keras.layers.GRU(units, 
                               return_sequences=True, 
                               return_state=True, 
                               recurrent_activation='sigmoid', 
                               recurrent_initializer='glorot_uniform')
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)
    
    # score shape == (batch_size, 64, hidden_size)
    score = tf.nn.tanh(self.W1(features) + self.W2(hidden_with_time_axis))
    
    # attention_weights shape == (batch_size, 64, 1)
    # we get 1 at the last axis because we are applying score to self.V
    attention_weights = tf.nn.softmax(self.V(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 we have already extracted the features and dumped it using pickle
    # 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 = gru(self.units)
    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, vocab_size)
optimizer = tf.train.AdamOptimizer()
# We are masking the loss calculated for padding
def loss_function(real, pred):
    mask = 1 - np.equal(real, 0)
    loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=real, logits=pred) * mask
    return tf.reduce_mean(loss_)
Explanation: 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, we extract the features from the lower convolutional layer of InceptionV3 giving us a vector of shape (8, 8, 2048). 
We 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.
End of explanation
# adding this in a separate cell because if you run the training cell 
# many times, the loss_plot array will be reset
loss_plot = []
EPOCHS = 20
for epoch in range(EPOCHS):
    start = time.time()
    total_loss = 0
    
    for (batch, (img_tensor, target)) in enumerate(dataset):
        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([tokenizer.word_index['<start>']] * BATCH_SIZE, 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]))
        
        variables = encoder.variables + decoder.variables
        
        gradients = tape.gradient(loss, variables) 
        
        optimizer.apply_gradients(zip(gradients, variables), tf.train.get_or_create_global_step())
        
        if batch % 100 == 0:
            print ('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1, 
                                                          batch, 
                                                          loss.numpy() / int(target.shape[1])))
    # storing the epoch end loss value to plot later
    loss_plot.append(total_loss / len(cap_vector))
    
    print ('Epoch {} Loss {:.6f}'.format(epoch + 1, 
                                         total_loss/len(cap_vector)))
    print ('Time taken for 1 epoch {} sec\n'.format(time.time() - start))
plt.plot(loss_plot)
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.title('Loss Plot')
plt.show()
Explanation: Training
We 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.
End of explanation
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([tokenizer.word_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.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy()
        result.append(index_word[predicted_id])
        if index_word[predicted_id] == '<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 l in range(len_result):
        temp_att = np.resize(attention_plot[l], (8, 8))
        ax = fig.add_subplot(len_result//2, len_result//2, l+1)
        ax.set_title(result[l])
        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([index_word[i] 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)
# opening the image
Image.open(img_name_val[rid])
Explanation: Caption!
The evaluate function is similar to the training loop, except we 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.
End of explanation
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)
Explanation: Try it on your own images
For fun, below we've provided a method you can use to caption your own images with the model we'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!)
End of explanation
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