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Neural Machine Translation with Attention

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This notebook trains a sequence to sequence (seq2seq) model for Spanish to English translation. This is an advanced example that assumes some knowledge of sequence to sequence models.

After training the model in this notebook, you will be able to input a Spanish sentence, such as "¿todavia estan en casa?", and return the English translation: "are you still at home?"

The translation quality is reasonable for a toy example, but the generated attention plot is perhaps more interesting. This shows which parts of the input sentence has the model's attention while translating:

spanish-english attention plot

from __future__ import absolute_import, division, print_function

!pip install -q tensorflow-gpu==2.0.0-alpha0
import tensorflow as tf

import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

import unicodedata
import re
import numpy as np
import os
import io
import time

Download and prepare the dataset

We'll use a language dataset provided by http://www.manythings.org/anki/. This dataset contains language translation pairs in the format:

May I borrow this book? ¿Puedo tomar prestado este libro?

There are a variety of languages available, but we'll use the English-Spanish dataset. For convenience, we've hosted a copy of this dataset on Google Cloud, but you can also download your own copy. After downloading the dataset, here are the steps we'll take to prepare the data:

  1. Add a start and end token to each sentence.
  2. Clean the sentences by removing special characters.
  3. Create a word index and reverse word index (dictionaries mapping from word → id and id → word).
  4. Pad each sentence to a maximum length.
# Download the file
path_to_zip = tf.keras.utils.get_file(
    'spa-eng.zip', origin='http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip', 
    extract=True)

path_to_file = os.path.dirname(path_to_zip)+"/spa-eng/spa.txt"
# Converts the unicode file to ascii
def unicode_to_ascii(s):
    return ''.join(c for c in unicodedata.normalize('NFD', s)
        if unicodedata.category(c) != 'Mn')


def preprocess_sentence(w):
    w = unicode_to_ascii(w.lower().strip())
    
    # creating a space between a word and the punctuation following it
    # eg: "he is a boy." => "he is a boy ." 
    # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation
    w = re.sub(r"([?.!,¿])", r" \1 ", w)
    w = re.sub(r'[" "]+', " ", w)
    
    # replacing everything with space except (a-z, A-Z, ".", "?", "!", ",")
    w = re.sub(r"[^a-zA-Z?.!,¿]+", " ", w)
    
    w = w.rstrip().strip()
    
    # adding a start and an end token to the sentence
    # so that the model know when to start and stop predicting.
    w = '<start> ' + w + ' <end>'
    return w
en_sentence = u"May I borrow this book?"
sp_sentence = u"¿Puedo tomar prestado este libro?"
print(preprocess_sentence(en_sentence))
print(preprocess_sentence(sp_sentence).encode('utf-8'))
<start> may i borrow this book ? <end>
<start> ¿ puedo tomar prestado este libro ? <end>
# 1. Remove the accents
# 2. Clean the sentences
# 3. Return word pairs in the format: [ENGLISH, SPANISH]
def create_dataset(path, num_examples):
    lines = io.open(path, encoding='UTF-8').read().strip().split('\n')
    
    word_pairs = [[preprocess_sentence(w) for w in l.split('\t')]  for l in lines[:num_examples]]
    
    return zip(*word_pairs)
en, sp = create_dataset(path_to_file, None)
print(en[-1])
print(sp[-1])
<start> if you want to sound like a native speaker , you must be willing to practice saying the same sentence over and over in the same way that banjo players practice the same phrase over and over until they can play it correctly and at the desired tempo . <end>
<start> si quieres sonar como un hablante nativo , debes estar dispuesto a practicar diciendo la misma frase una y otra vez de la misma manera en que un musico de banjo practica el mismo fraseo una y otra vez hasta que lo puedan tocar correctamente y en el tiempo esperado . <end>
def max_length(tensor):
    return max(len(t) for t in tensor)
def tokenize(lang):
  lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(
      filters='')
  lang_tokenizer.fit_on_texts(lang)
  
  tensor = lang_tokenizer.texts_to_sequences(lang)
  
  tensor = tf.keras.preprocessing.sequence.pad_sequences(tensor,
                                                         padding='post')
  
  return tensor, lang_tokenizer
def load_dataset(path, num_examples=None):
    # creating cleaned input, output pairs
    targ_lang, inp_lang = create_dataset(path, num_examples)

    input_tensor, inp_lang_tokenizer = tokenize(inp_lang)
    target_tensor, targ_lang_tokenizer = tokenize(targ_lang)

    return input_tensor, target_tensor, inp_lang_tokenizer, targ_lang_tokenizer

Limit the size of the dataset to experiment faster (optional)

Training on the complete dataset of >100,000 sentences will take a long time. To train faster, we can limit the size of the dataset to 30,000 sentences (of course, translation quality degrades with less data):

# Try experimenting with the size of that dataset
num_examples = 30000
input_tensor, target_tensor, inp_lang, targ_lang = load_dataset(path_to_file, num_examples)

# Calculate max_length of the target tensors
max_length_targ, max_length_inp = max_length(target_tensor), max_length(input_tensor)
# Creating training and validation sets using an 80-20 split
input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor, target_tensor, test_size=0.2)

# Show length
len(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val)
(24000, 24000, 6000, 6000)
def convert(lang, tensor):
  for t in tensor:
    if t!=0:
      print ("%d ----> %s" % (t, lang.index_word[t]))
print ("Input Language; index to word mapping")
convert(inp_lang, input_tensor_train[0])
print ()
print ("Target Language; index to word mapping")
convert(targ_lang, target_tensor_train[0])
Input Language; index to word mapping
1 ----> <start>
431 ----> vuestro
92 ----> perro
7 ----> es
36 ----> muy
189 ----> grande
3 ----> .
2 ----> <end>

Target Language; index to word mapping
1 ----> <start>
31 ----> your
104 ----> dog
8 ----> is
48 ----> very
155 ----> big
3 ----> .
2 ----> <end>

Create a tf.data dataset

BUFFER_SIZE = len(input_tensor_train)
BATCH_SIZE = 64
steps_per_epoch = len(input_tensor_train)//BATCH_SIZE
embedding_dim = 256
units = 1024
vocab_inp_size = len(inp_lang.word_index)+1
vocab_tar_size = len(targ_lang.word_index)+1

dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)
dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
example_input_batch, example_target_batch = next(iter(dataset))
example_input_batch.shape, example_target_batch.shape
(TensorShape([64, 16]), TensorShape([64, 11]))

Write the encoder and decoder model

Here, we'll implement an encoder-decoder model with attention which you can read about in the TensorFlow Neural Machine Translation (seq2seq) tutorial. This example uses a more recent set of APIs. This notebook implements the attention equations from the seq2seq tutorial. The following diagram shows that each input words is assigned a weight by the attention mechanism which is then used by the decoder to predict the next word in the sentence.

attention mechanism

The input is put through an encoder model which gives us the encoder output of shape (batch_size, max_length, hidden_size) and the encoder hidden state of shape (batch_size, hidden_size).

Here are the equations that are implemented:

attention equation 0 attention equation 1

We're using Bahdanau attention. Lets decide on notation before writing the simplified form:

  • FC = Fully connected (dense) layer
  • EO = Encoder output
  • H = hidden state
  • X = input to the decoder

And the pseudo-code:

  • score = FC(tanh(FC(EO) + FC(H)))
  • attention weights = softmax(score, axis = 1). Softmax by default is applied on the last axis but here we want to apply it on the 1st axis, since the shape of score is (batch_size, max_length, hidden_size). Max_length is the length of our input. Since we are trying to assign a weight to each input, softmax should be applied on that axis.
  • context vector = sum(attention weights * EO, axis = 1). Same reason as above for choosing axis as 1.
  • embedding output = The input to the decoder X is passed through an embedding layer.
  • merged vector = concat(embedding output, context vector)
  • This merged vector is then given to the GRU

The shapes of all the vectors at each step have been specified in the comments in the code:

class Encoder(tf.keras.Model):
  def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):
    super(Encoder, self).__init__()
    self.batch_sz = batch_sz
    self.enc_units = enc_units
    self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
    self.gru = tf.keras.layers.GRU(self.enc_units, 
                                   return_sequences=True, 
                                   return_state=True, 
                                   recurrent_initializer='glorot_uniform')

  def call(self, x, hidden):
    x = self.embedding(x)
    output, state = self.gru(x, initial_state = hidden)        
    return output, state

  def initialize_hidden_state(self):
    return tf.zeros((self.batch_sz, self.enc_units))
encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)

# sample input
sample_hidden = encoder.initialize_hidden_state()
sample_output, sample_hidden = encoder(example_input_batch, sample_hidden)
print ('Encoder output shape: (batch size, sequence length, units) {}'.format(sample_output.shape))
print ('Encoder Hidden state shape: (batch size, units) {}'.format(sample_hidden.shape))
Encoder output shape: (batch size, sequence length, units) (64, 16, 1024)
Encoder Hidden state shape: (batch size, units) (64, 1024)
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, query, values):
    # hidden shape == (batch_size, hidden size)
    # hidden_with_time_axis shape == (batch_size, 1, hidden size)
    # we are doing this to perform addition to calculate the score
    hidden_with_time_axis = tf.expand_dims(query, 1)

    # score shape == (batch_size, max_length, hidden_size)
    score = self.V(tf.nn.tanh(
        self.W1(values) + self.W2(hidden_with_time_axis)))

    # attention_weights shape == (batch_size, max_length, 1)
    # we get 1 at the last axis because we are applying score to self.V
    attention_weights = tf.nn.softmax(score, axis=1)

    # context_vector shape after sum == (batch_size, hidden_size)
    context_vector = attention_weights * values
    context_vector = tf.reduce_sum(context_vector, axis=1)
    
    return context_vector, attention_weights
attention_layer = BahdanauAttention(10)
attention_result, attention_weights = attention_layer(sample_hidden, sample_output)

print("Attention result shape: (batch size, units) {}".format(attention_result.shape))
print("Attention weights shape: (batch_size, sequence_length, 1) {}".format(attention_weights.shape))
Attention result shape: (batch size, units) (64, 1024)
Attention weights shape: (batch_size, sequence_length, 1) (64, 16, 1)
class Decoder(tf.keras.Model):
  def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):
    super(Decoder, self).__init__()
    self.batch_sz = batch_sz
    self.dec_units = dec_units
    self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
    self.gru = tf.keras.layers.GRU(self.dec_units, 
                                   return_sequences=True, 
                                   return_state=True, 
                                   recurrent_initializer='glorot_uniform')
    self.fc = tf.keras.layers.Dense(vocab_size)

    # used for attention
    self.attention = BahdanauAttention(self.dec_units)

  def call(self, x, hidden, enc_output):
    # enc_output shape == (batch_size, max_length, hidden_size)
    context_vector, attention_weights = self.attention(hidden, enc_output)

    # 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)

    # output shape == (batch_size * 1, hidden_size)
    output = tf.reshape(output, (-1, output.shape[2]))

    # output shape == (batch_size, vocab)
    x = self.fc(output)

    return x, state, attention_weights
decoder = Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE)

sample_decoder_output, _, _ = decoder(tf.random.uniform((64, 1)), 
                                      sample_hidden, sample_output)

print ('Decoder output shape: (batch_size, vocab size) {}'.format(sample_decoder_output.shape))
Decoder output shape: (batch_size, vocab size) (64, 4935)

Define the optimizer and the loss function

optimizer = tf.keras.optimizers.Adam()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

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_)

Checkpoints (Object-based saving)

checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(optimizer=optimizer,
                                 encoder=encoder,
                                 decoder=decoder)

Training

  1. Pass the input through the encoder which return encoder output and the encoder hidden state.
  2. The encoder output, encoder hidden state and the decoder input (which is the start token) is passed to the decoder.
  3. The decoder returns the predictions and the decoder hidden state.
  4. The decoder hidden state is then passed back into the model and the predictions are used to calculate the loss.
  5. Use teacher forcing to decide the next input to the decoder.
  6. Teacher forcing is the technique where the target word is passed as the next input to the decoder.
  7. The final step is to calculate the gradients and apply it to the optimizer and backpropagate.
@tf.function
def train_step(inp, targ, enc_hidden):
  loss = 0
        
  with tf.GradientTape() as tape:
    enc_output, enc_hidden = encoder(inp, enc_hidden)

    dec_hidden = enc_hidden

    dec_input = tf.expand_dims([targ_lang.word_index['<start>']] * BATCH_SIZE, 1)       

    # Teacher forcing - feeding the target as the next input
    for t in range(1, targ.shape[1]):
      # passing enc_output to the decoder
      predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)

      loss += loss_function(targ[:, t], predictions)

      # using teacher forcing
      dec_input = tf.expand_dims(targ[:, t], 1)

  batch_loss = (loss / int(targ.shape[1]))

  variables = encoder.trainable_variables + decoder.trainable_variables

  gradients = tape.gradient(loss, variables)

  optimizer.apply_gradients(zip(gradients, variables))
  
  return batch_loss
EPOCHS = 10

for epoch in range(EPOCHS):
  start = time.time()

  enc_hidden = encoder.initialize_hidden_state()
  total_loss = 0

  for (batch, (inp, targ)) in enumerate(dataset.take(steps_per_epoch)):
    batch_loss = train_step(inp, targ, enc_hidden)
    total_loss += batch_loss

    if batch % 100 == 0:
        print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1,
                                                     batch,
                                                     batch_loss.numpy()))
  # saving (checkpoint) the model every 2 epochs
  if (epoch + 1) % 2 == 0:
    checkpoint.save(file_prefix = checkpoint_prefix)

  print('Epoch {} Loss {:.4f}'.format(epoch + 1,
                                      total_loss / steps_per_epoch))
  print('Time taken for 1 epoch {} sec\n'.format(time.time() - start))

Epoch 1 Batch 0 Loss 4.5657
Epoch 1 Batch 100 Loss 2.1792
Epoch 1 Batch 200 Loss 1.7583
Epoch 1 Batch 300 Loss 1.7384
Epoch 1 Loss 1.9949
Time taken for 1 epoch 49.0501441956 sec

Epoch 2 Batch 0 Loss 1.5181
Epoch 2 Batch 100 Loss 1.4801
Epoch 2 Batch 200 Loss 1.2197
Epoch 2 Batch 300 Loss 1.2561
Epoch 2 Loss 1.3435
Time taken for 1 epoch 34.9393060207 sec

Epoch 3 Batch 0 Loss 1.0709
Epoch 3 Batch 100 Loss 1.0403
Epoch 3 Batch 200 Loss 0.7950
Epoch 3 Batch 300 Loss 0.8709
Epoch 3 Loss 0.9174
Time taken for 1 epoch 34.288944006 sec

Epoch 4 Batch 0 Loss 0.7071
Epoch 4 Batch 100 Loss 0.6888
Epoch 4 Batch 200 Loss 0.4978
Epoch 4 Batch 300 Loss 0.5841
Epoch 4 Loss 0.6134
Time taken for 1 epoch 35.33512187 sec

Epoch 5 Batch 0 Loss 0.4685
Epoch 5 Batch 100 Loss 0.4926
Epoch 5 Batch 200 Loss 0.3000
Epoch 5 Batch 300 Loss 0.3866
Epoch 5 Loss 0.4136
Time taken for 1 epoch 34.1087429523 sec

Epoch 6 Batch 0 Loss 0.3144
Epoch 6 Batch 100 Loss 0.3042
Epoch 6 Batch 200 Loss 0.1984
Epoch 6 Batch 300 Loss 0.2658
Epoch 6 Loss 0.2875
Time taken for 1 epoch 35.1170010567 sec

Epoch 7 Batch 0 Loss 0.2284
Epoch 7 Batch 100 Loss 0.2275
Epoch 7 Batch 200 Loss 0.1428
Epoch 7 Batch 300 Loss 0.1976
Epoch 7 Loss 0.2032
Time taken for 1 epoch 33.9758181572 sec

Epoch 8 Batch 0 Loss 0.1724
Epoch 8 Batch 100 Loss 0.1635
Epoch 8 Batch 200 Loss 0.0983
Epoch 8 Batch 300 Loss 0.1408
Epoch 8 Loss 0.1461
Time taken for 1 epoch 35.9068500996 sec

Epoch 9 Batch 0 Loss 0.1353
Epoch 9 Batch 100 Loss 0.1123
Epoch 9 Batch 200 Loss 0.0888
Epoch 9 Batch 300 Loss 0.0953
Epoch 9 Loss 0.1107
Time taken for 1 epoch 34.0839440823 sec

Epoch 10 Batch 0 Loss 0.1103
Epoch 10 Batch 100 Loss 0.0954
Epoch 10 Batch 200 Loss 0.0654
Epoch 10 Batch 300 Loss 0.0824
Epoch 10 Loss 0.0885
Time taken for 1 epoch 35.4287509918 sec

Translate

  • 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.
def evaluate(sentence):
    attention_plot = np.zeros((max_length_targ, max_length_inp))
    
    sentence = preprocess_sentence(sentence)

    inputs = [inp_lang.word_index[i] for i in sentence.split(' ')]
    inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs], 
                                                           maxlen=max_length_inp, 
                                                           padding='post')
    inputs = tf.convert_to_tensor(inputs)
    
    result = ''

    hidden = [tf.zeros((1, units))]
    enc_out, enc_hidden = encoder(inputs, hidden)

    dec_hidden = enc_hidden
    dec_input = tf.expand_dims([targ_lang.word_index['<start>']], 0)
    
    for t in range(max_length_targ):
        predictions, dec_hidden, attention_weights = decoder(dec_input, 
                                                             dec_hidden, 
                                                             enc_out)
        
        # storing the attention weights to plot later on
        attention_weights = tf.reshape(attention_weights, (-1, ))
        attention_plot[t] = attention_weights.numpy()

        predicted_id = tf.argmax(predictions[0]).numpy()

        result += targ_lang.index_word[predicted_id] + ' '

        if targ_lang.index_word[predicted_id] == '<end>':
            return result, sentence, attention_plot
        
        # the predicted ID is fed back into the model
        dec_input = tf.expand_dims([predicted_id], 0)

    return result, sentence, attention_plot
# function for plotting the attention weights
def plot_attention(attention, sentence, predicted_sentence):
    fig = plt.figure(figsize=(10,10))
    ax = fig.add_subplot(1, 1, 1)
    ax.matshow(attention, cmap='viridis')
    
    fontdict = {'fontsize': 14}
    
    ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)
    ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)

    plt.show()
def translate(sentence):
    result, sentence, attention_plot = evaluate(sentence)
        
    print('Input: %s' % (sentence).encode('utf-8'))
    print('Predicted translation: {}'.format(result))
    
    attention_plot = attention_plot[:len(result.split(' ')), :len(sentence.split(' '))]
    plot_attention(attention_plot, sentence.split(' '), result.split(' '))

Restore the latest checkpoint and test

# restoring the latest checkpoint in checkpoint_dir
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))

translate(u'hace mucho frio aqui.')
Input: <start> hace mucho frio aqui . <end>
Predicted translation: it s very cold here . <end> 

png

translate(u'esta es mi vida.')
Input: <start> esta es mi vida . <end>
Predicted translation: this is my life . <end> 

png

translate(u'¿todavia estan en casa?')
Input: <start> ¿ todavia estan en casa ? <end>
Predicted translation: are you still home ? <end> 

png

# wrong translation
translate(u'trata de averiguarlo.')
Input: <start> trata de averiguarlo . <end>
Predicted translation: try to figure it out . <end> 

png

Next steps

  • Download a different dataset to experiment with translations, for example, English to German, or English to French.
  • Experiment with training on a larger dataset, or using more epochs