allcode.py

from __future__ import absolute_import, division, print_function

# Import TensorFlow >= 1.10 and enable eager execution
import tensorflow as tf

tf.enable_eager_execution()

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

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

print(tf.__version__)

# Download the file
path_to_zip = tf.keras.utils.get_file(
    'spa-eng.zip', origin='http://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


# 1. Remove the accents
# 2. Clean the sentences
# 3. Return word pairs in the format: [ENGLISH, SPANISH]
def create_dataset(path, num_examples):
    lines = 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 word_pairs


# This class creates a word -> index mapping (e.g,. "dad" -> 5) and vice-versa
# (e.g., 5 -> "dad") for each language,
class LanguageIndex():
    def __init__(self, lang):
        self.lang = lang
        self.word2idx = {}
        self.idx2word = {}
        self.vocab = set()

        self.create_index()

    def create_index(self):
        for phrase in self.lang:
            self.vocab.update(phrase.split(' '))

        self.vocab = sorted(self.vocab)

        self.word2idx['<pad>'] = 0
        for index, word in enumerate(self.vocab):
            self.word2idx[word] = index + 1

        for word, index in self.word2idx.items():
            self.idx2word[index] = word


def max_length(tensor):
    return max(len(t) for t in tensor)


def load_dataset(path, num_examples):
    # creating cleaned input, output pairs
    pairs = create_dataset(path, num_examples)

    # index language using the class defined above
    inp_lang = LanguageIndex(sp for en, sp in pairs)
    targ_lang = LanguageIndex(en for en, sp in pairs)

    # Vectorize the input and target languages

    # Spanish sentences
    input_tensor = [[inp_lang.word2idx[s] for s in sp.split(' ')] for en, sp in pairs]

    # English sentences
    target_tensor = [[targ_lang.word2idx[s] for s in en.split(' ')] for en, sp in pairs]

    # Calculate max_length of input and output tensor
    # Here, we'll set those to the longest sentence in the dataset
    max_length_inp, max_length_tar = max_length(input_tensor), max_length(target_tensor)

    # Padding the input and output tensor to the maximum length
    input_tensor = tf.keras.preprocessing.sequence.pad_sequences(input_tensor,
                                                                 maxlen=max_length_inp,
                                                                 padding='post')

    target_tensor = tf.keras.preprocessing.sequence.pad_sequences(target_tensor,
                                                                  maxlen=max_length_tar,
                                                                  padding='post')

    return input_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_tar

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

# 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)

BUFFER_SIZE = len(input_tensor_train)
BATCH_SIZE = 64
N_BATCH = BUFFER_SIZE//BATCH_SIZE
embedding_dim = 256
units = 1024
vocab_inp_size = len(inp_lang.word2idx)
vocab_tar_size = len(targ_lang.word2idx)

dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)
dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)

def gru(units):
  # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)
  # the code automatically does that.
  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 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 = gru(self.enc_units)

    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))


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 = gru(self.dec_units)
        self.fc = tf.keras.layers.Dense(vocab_size)

        # used for attention
        self.W1 = tf.keras.layers.Dense(self.dec_units)
        self.W2 = tf.keras.layers.Dense(self.dec_units)
        self.V = tf.keras.layers.Dense(1)

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

        # 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(hidden, 1)

        # score shape == (batch_size, max_length, hidden_size)
        score = tf.nn.tanh(self.W1(enc_output) + 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(self.V(score), axis=1)

        # context_vector shape after sum == (batch_size, hidden_size)
        context_vector = attention_weights * enc_output
        context_vector = tf.reduce_sum(context_vector, axis=1)

        # 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 * 1, vocab)
        x = self.fc(output)

        return x, state, attention_weights

    def initialize_hidden_state(self):
        return tf.zeros((self.batch_sz, self.dec_units))

encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)
decoder = Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE)

optimizer = tf.train.AdamOptimizer()


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_)

checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(optimizer=optimizer,
                                 encoder=encoder,
                                 decoder=decoder)
EPOCHS = 10

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

    hidden = encoder.initialize_hidden_state()
    total_loss = 0

    for (batch, (inp, targ)) in enumerate(dataset):
        loss = 0

        with tf.GradientTape() as tape:
            enc_output, enc_hidden = encoder(inp, hidden)

            dec_hidden = enc_hidden

            dec_input = tf.expand_dims([targ_lang.word2idx['<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]))

        total_loss += batch_loss

        variables = encoder.variables + decoder.variables

        gradients = tape.gradient(loss, variables)

        optimizer.apply_gradients(zip(gradients, variables))

        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 / N_BATCH))
    print('Time taken for 1 epoch {} sec\n'.format(time.time() - start))


def evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):
    attention_plot = np.zeros((max_length_targ, max_length_inp))

    sentence = preprocess_sentence(sentence)

    inputs = [inp_lang.word2idx[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.word2idx['<start>']], 0)

    for t in range(max_length_targ):
        predictions, dec_hidden, attention_weights = decoder(dec_input, dec_hidden, enc_out)

        # storing the attention weigths to plot later on
        attention_weights = tf.reshape(attention_weights, (-1,))
        attention_plot[t] = attention_weights.numpy()

        predicted_id = tf.multinomial(predictions, num_samples=1)[0][0].numpy()

        result += targ_lang.idx2word[predicted_id] + ' '

        if targ_lang.idx2word[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, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):
    result, sentence, attention_plot = evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp,
                                                max_length_targ)

    print('Input: {}'.format(sentence))
    print('Predicted translation: {}'.format(result))

    attention_plot = attention_plot[:len(result.split(' ')), :len(sentence.split(' '))]
    plot_attention(attention_plot, sentence.split(' '), result.split(' '))

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