Translation with a Sequence to Sequence Network and Attention
http://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html#exercises
Translation with a Sequence to Sequence Network and Attention
Author: Sean Robertson
In this project we will be teaching a neural network to translate from French to English.
[KEY: > input, = target, < output]
> il est en train de peindre un tableau .
= he is painting a picture .
< he is painting a picture .
> pourquoi ne pas essayer ce vin delicieux ?
= why not try that delicious wine ?
< why not try that delicious wine ?
> elle n est pas poete mais romanciere .
= she is not a poet but a novelist .
< she not not a poet but a novelist .
> vous etes trop maigre .
= you re too skinny .
< you re all alone .
... to varying degrees of success.
This is made possible by the simple but powerful idea of the sequence to sequence network, in which two recurrent neural networks work together to transform one sequence to another. An encoder network condenses an input sequence into a vector, and a decoder network unfolds that vector into a new sequence.
To improve upon this model we’ll use an attention mechanism, which lets the decoder learn to focus over a specific range of the input sequence.
Recommended Reading:
I assume you have at least installed PyTorch, know Python, and understand Tensors:
- http://pytorch.org/ For installation instructions
- Deep Learning with PyTorch: A 60 Minute Blitz to get started with PyTorch in general
- Learning PyTorch with Examples for a wide and deep overview
- PyTorch for former Torch users if you are former Lua Torch user
It would also be useful to know about Sequence to Sequence networks and how they work:
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Sequence to Sequence Learning with Neural Networks
- Neural Machine Translation by Jointly Learning to Align and Translate
- A Neural Conversational Model
You will also find the previous tutorials on Classifying Names with a Character-Level RNN andGenerating Names with a Character-Level RNN helpful as those concepts are very similar to the Encoder and Decoder models, respectively.
And for more, read the papers that introduced these topics:
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- Sequence to Sequence Learning with Neural Networks
- Neural Machine Translation by Jointly Learning to Align and Translate
- A Neural Conversational Model
Requirements
from __future__ import unicode_literals, print_function, division
from io import open
import unicodedata
import string
import re
import random
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
use_cuda = torch.cuda.is_available()
Loading data files
The data for this project is a set of many thousands of English to French translation pairs.
This question on Open Data Stack Exchange pointed me to the open translation sitehttp://tatoeba.org/ which has downloads available at http://tatoeba.org/eng/downloads - and better yet, someone did the extra work of splitting language pairs into individual text files here:http://www.manythings.org/anki/
The English to French pairs are too big to include in the repo, so download to data/eng-fra.txtbefore continuing. The file is a tab separated list of translation pairs:
I am cold. Je suis froid.
Note
Download the data from here and extract it to the current directory.
Similar to the character encoding used in the character-level RNN tutorials, we will be representing each word in a language as a one-hot vector, or giant vector of zeros except for a single one (at the index of the word). Compared to the dozens of characters that might exist in a language, there are many many more words, so the encoding vector is much larger. We will however cheat a bit and trim the data to only use a few thousand words per language.
We’ll need a unique index per word to use as the inputs and targets of the networks later. To keep track of all this we will use a helper class called Lang which has word → index (word2index) and index → word (index2word) dictionaries, as well as a count of each word word2count to use to later replace rare words.
SOS_token = 0
EOS_token = 1
class Lang:
def __init__(self, name):
self.name = name
self.word2index = {}
self.word2count = {}
self.index2word = {0: "SOS", 1: "EOS"}
self.n_words = 2 # Count SOS and EOS
def addSentence(self, sentence):
for word in sentence.split(' '):
self.addWord(word)
def addWord(self, word):
if word not in self.word2index:
self.word2index[word] = self.n_words
self.word2count[word] = 1
self.index2word[self.n_words] = word
self.n_words += 1
else:
self.word2count[word] += 1
The files are all in Unicode, to simplify we will turn Unicode characters to ASCII, make everything lowercase, and trim most punctuation.
# Turn a Unicode string to plain ASCII, thanks to
# http://stackoverflow.com/a/518232/2809427
def unicodeToAscii(s):
return ''.join(
c for c in unicodedata.normalize('NFD', s)
if unicodedata.category(c) != 'Mn'
)
# Lowercase, trim, and remove non-letter characters
def normalizeString(s):
s = unicodeToAscii(s.lower().strip())
s = re.sub(r"([.!?])", r" \1", s)
s = re.sub(r"[^a-zA-Z.!?]+", r" ", s)
return s
To read the data file we will split the file into lines, and then split lines into pairs. The files are all English → Other Language, so if we want to translate from Other Language → English I added thereverse flag to reverse the pairs.
def readLangs(lang1, lang2, reverse=False):
print("Reading lines...")
# Read the file and split into lines
lines = open('data/%s-%s.txt' % (lang1, lang2), encoding='utf-8').\
read().strip().split('\n')
# Split every line into pairs and normalize
pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines]
# Reverse pairs, make Lang instances
if reverse:
pairs = [list(reversed(p)) for p in pairs]
input_lang = Lang(lang2)
output_lang = Lang(lang1)
else:
input_lang = Lang(lang1)
output_lang = Lang(lang2)
return input_lang, output_lang, pairs
Since there are a lot of example sentences and we want to train something quickly, we’ll trim the data set to only relatively short and simple sentences. Here the maximum length is 10 words (that includes ending punctuation) and we’re filtering to sentences that translate to the form “I am” or “He is” etc. (accounting for apostrophes replaced earlier).
MAX_LENGTH = 10
eng_prefixes = (
"i am ", "i m ",
"he is", "he s ",
"she is", "she s",
"you are", "you re ",
"we are", "we re ",
"they are", "they re "
)
def filterPair(p):
return len(p[0].split(' ')) < MAX_LENGTH and \
len(p[1].split(' ')) < MAX_LENGTH and \
p[1].startswith(eng_prefixes)
def filterPairs(pairs):
return [pair for pair in pairs if filterPair(pair)]
The full process for preparing the data is:
- Read text file and split into lines, split lines into pairs
- Normalize text, filter by length and content
- Make word lists from sentences in pairs
def prepareData(lang1, lang2, reverse=False):
input_lang, output_lang, pairs = readLangs(lang1, lang2, reverse)
print("Read %s sentence pairs" % len(pairs))
pairs = filterPairs(pairs)
print("Trimmed to %s sentence pairs" % len(pairs))
print("Counting words...")
for pair in pairs:
input_lang.addSentence(pair[0])
output_lang.addSentence(pair[1])
print("Counted words:")
print(input_lang.name, input_lang.n_words)
print(output_lang.name, output_lang.n_words)
return input_lang, output_lang, pairs
input_lang, output_lang, pairs = prepareData('eng', 'fra', True)
print(random.choice(pairs))
Out:
Reading lines...
Read 135842 sentence pairs
Trimmed to 10853 sentence pairs
Counting words...
Counted words:
fra 4489
eng 2925
['il est un peu rude aux encoignures .', 'he s rough around the edges .']
The Seq2Seq Model
A Recurrent Neural Network, or RNN, is a network that operates on a sequence and uses its own output as input for subsequent steps.
A Sequence to Sequence network, or seq2seq network, or Encoder Decoder network, is a model consisting of two RNNs called the encoder and decoder. The encoder reads an input sequence and outputs a single vector, and the decoder reads that vector to produce an output sequence.
Unlike sequence prediction with a single RNN, where every input corresponds to an output, the seq2seq model frees us from sequence length and order, which makes it ideal for translation between two languages.
Consider the sentence “Je ne suis pas le chat noir” → “I am not the black cat”. Most of the words in the input sentence have a direct translation in the output sentence, but are in slightly different orders, e.g. “chat noir” and “black cat”. Because of the “ne/pas” construction there is also one more word in the input sentence. It would be difficult to produce a correct translation directly from the sequence of input words.
With a seq2seq model the encoder creates a single vector which, in the ideal case, encodes the “meaning” of the input sequence into a single vector — a single point in some N dimensional space of sentences.
The Encoder
The encoder of a seq2seq network is a RNN that outputs some value for every word from the input sentence. For every input word the encoder outputs a vector and a hidden state, and uses the hidden state for the next input word.
class EncoderRNN(nn.Module):
def __init__(self, input_size, hidden_size, n_layers=1):
super(EncoderRNN, self).__init__()
self.n_layers = n_layers
self.hidden_size = hidden_size
self.embedding = nn.Embedding(input_size, hidden_size)
self.gru = nn.GRU(hidden_size, hidden_size)
def forward(self, input, hidden):
embedded = self.embedding(input).view(1, 1, -1)
output = embedded
for i in range(self.n_layers):
output, hidden = self.gru(output, hidden)
return output, hidden
def initHidden(self):
result = Variable(torch.zeros(1, 1, self.hidden_size))
if use_cuda:
return result.cuda()
else:
return result
The Decoder
The decoder is another RNN that takes the encoder output vector(s) and outputs a sequence of words to create the translation.
Simple Decoder
In the simplest seq2seq decoder we use only last output of the encoder. This last output is sometimes called the context vector as it encodes context from the entire sequence. This context vector is used as the initial hidden state of the decoder.
At every step of decoding, the decoder is given an input token and hidden state. The initial input token is the start-of-string
class DecoderRNN(nn.Module):
def __init__(self, hidden_size, output_size, n_layers=1):
super(DecoderRNN, self).__init__()
self.n_layers = n_layers
self.hidden_size = hidden_size
self.embedding = nn.Embedding(output_size, hidden_size)
self.gru = nn.GRU(hidden_size, hidden_size)
self.out = nn.Linear(hidden_size, output_size)
self.softmax = nn.LogSoftmax()
def forward(self, input, hidden):
output = self.embedding(input).view(1, 1, -1)
for i in range(self.n_layers):
output = F.relu(output)
output, hidden = self.gru(output, hidden)
output = self.softmax(self.out(output[0]))
return output, hidden
def initHidden(self):
result = Variable(torch.zeros(1, 1, self.hidden_size))
if use_cuda:
return result.cuda()
else:
return result
I encourage you to train and observe the results of this model, but to save space we’ll be going straight for the gold and introducing the Attention Mechanism.
Attention Decoder
If only the context vector is passed betweeen the encoder and decoder, that single vector carries the burden of encoding the entire sentence.
Attention allows the decoder network to “focus” on a different part of the encoder’s outputs for every step of the decoder’s own outputs. First we calculate a set of attention weights. These will be multiplied by the encoder output vectors to create a weighted combination. The result (calledattn_applied in the code) should contain information about that specific part of the input sequence, and thus help the decoder choose the right output words.
Calculating the attention weights is done with another feed-forward layer attn, using the decoder’s input and hidden state as inputs. Because there are sentences of all sizes in the training data, to actually create and train this layer we have to choose a maximum sentence length (input length, for encoder outputs) that it can apply to. Sentences of the maximum length will use all the attention weights, while shorter sentences will only use the first few.
class AttnDecoderRNN(nn.Module):
def __init__(self, hidden_size, output_size, n_layers=1, dropout_p=0.1, max_length=MAX_LENGTH):
super(AttnDecoderRNN, self).__init__()
self.hidden_size = hidden_size
self.output_size = output_size
self.n_layers = n_layers
self.dropout_p = dropout_p
self.max_length = max_length
self.embedding = nn.Embedding(self.output_size, self.hidden_size)
self.attn = nn.Linear(self.hidden_size * 2, self.max_length)
self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size)
self.dropout = nn.Dropout(self.dropout_p)
self.gru = nn.GRU(self.hidden_size, self.hidden_size)
self.out = nn.Linear(self.hidden_size, self.output_size)
def forward(self, input, hidden, encoder_output, encoder_outputs):
embedded = self.embedding(input).view(1, 1, -1)
embedded = self.dropout(embedded)
attn_weights = F.softmax(
self.attn(torch.cat((embedded[0], hidden[0]), 1)))
attn_applied = torch.bmm(attn_weights.unsqueeze(0),
encoder_outputs.unsqueeze(0))
output = torch.cat((embedded[0], attn_applied[0]), 1)
output = self.attn_combine(output).unsqueeze(0)
for i in range(self.n_layers):
output = F.relu(output)
output, hidden = self.gru(output, hidden)
output = F.log_softmax(self.out(output[0]))
return output, hidden, attn_weights
def initHidden(self):
result = Variable(torch.zeros(1, 1, self.hidden_size))
if use_cuda:
return result.cuda()
else:
return result
Note
There are other forms of attention that work around the length limitation by using a relative position approach. Read about “local attention” in Effective Approaches to Attention-based Neural Machine Translation.
Training
Preparing Training Data
To train, for each pair we will need an input tensor (indexes of the words in the input sentence) and target tensor (indexes of the words in the target sentence). While creating these vectors we will append the EOS token to both sequences.
def indexesFromSentence(lang, sentence):
return [lang.word2index[word] for word in sentence.split(' ')]
def variableFromSentence(lang, sentence):
indexes = indexesFromSentence(lang, sentence)
indexes.append(EOS_token)
result = Variable(torch.LongTensor(indexes).view(-1, 1))
if use_cuda:
return result.cuda()
else:
return result
def variablesFromPair(pair):
input_variable = variableFromSentence(input_lang, pair[0])
target_variable = variableFromSentence(output_lang, pair[1])
return (input_variable, target_variable)
Training the Model
To train we run the input sentence through the encoder, and keep track of every output and the latest hidden state. Then the decoder is given the
“Teacher forcing” is the concept of using the real target outputs as each next input, instead of using the decoder’s guess as the next input. Using teacher forcing causes it to converge faster but when the trained network is exploited, it may exhibit instability.
You can observe outputs of teacher-forced networks that read with coherent grammar but wander far from the correct translation - intuitively it has learned to represent the output grammar and can “pick up” the meaning once the teacher tells it the first few words, but it has not properly learned how to create the sentence from the translation in the first place.
Because of the freedom PyTorch’s autograd gives us, we can randomly choose to use teacher forcing or not with a simple if statement. Turn teacher_forcing_ratio up to use more of it.
teacher_forcing_ratio = 0.5
def train(input_variable, target_variable, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length=MAX_LENGTH):
encoder_hidden = encoder.initHidden()
encoder_optimizer.zero_grad()
decoder_optimizer.zero_grad()
input_length = input_variable.size()[0]
target_length = target_variable.size()[0]
encoder_outputs = Variable(torch.zeros(max_length, encoder.hidden_size))
encoder_outputs = encoder_outputs.cuda() if use_cuda else encoder_outputs
loss = 0
for ei in range(input_length):
encoder_output, encoder_hidden = encoder(
input_variable[ei], encoder_hidden)
encoder_outputs[ei] = encoder_output[0][0]
decoder_input = Variable(torch.LongTensor([[SOS_token]]))
decoder_input = decoder_input.cuda() if use_cuda else decoder_input
decoder_hidden = encoder_hidden
use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False
if use_teacher_forcing:
# Teacher forcing: Feed the target as the next input
for di in range(target_length):
decoder_output, decoder_hidden, decoder_attention = decoder(
decoder_input, decoder_hidden, encoder_output, encoder_outputs)
loss += criterion(decoder_output, target_variable[di])
decoder_input = target_variable[di] # Teacher forcing
else:
# Without teacher forcing: use its own predictions as the next input
for di in range(target_length):
decoder_output, decoder_hidden, decoder_attention = decoder(
decoder_input, decoder_hidden, encoder_output, encoder_outputs)
topv, topi = decoder_output.data.topk(1)
ni = topi[0][0]
decoder_input = Variable(torch.LongTensor([[ni]]))
decoder_input = decoder_input.cuda() if use_cuda else decoder_input
loss += criterion(decoder_output, target_variable[di])
if ni == EOS_token:
break
loss.backward()
encoder_optimizer.step()
decoder_optimizer.step()
return loss.data[0] / target_length
This is a helper function to print time elapsed and estimated time remaining given the current time and progress %.
import time
import math
def asMinutes(s):
m = math.floor(s / 60)
s -= m * 60
return '%dm %ds' % (m, s)
def timeSince(since, percent):
now = time.time()
s = now - since
es = s / (percent)
rs = es - s
return '%s (- %s)' % (asMinutes(s), asMinutes(rs))
The whole training process looks like this:
- Start a timer
- Initialize optimizers and criterion
- Create set of training pairs
- Start empty losses array for plotting
Then we call train many times and occasionally print the progress (% of examples, time so far, estimated time) and average loss.
def trainIters(encoder, decoder, n_iters, print_every=1000, plot_every=100, learning_rate=0.01):
start = time.time()
plot_losses = []
print_loss_total = 0 # Reset every print_every
plot_loss_total = 0 # Reset every plot_every
encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)
training_pairs = [variablesFromPair(random.choice(pairs))
for i in range(n_iters)]
criterion = nn.NLLLoss()
for iter in range(1, n_iters + 1):
training_pair = training_pairs[iter - 1]
input_variable = training_pair[0]
target_variable = training_pair[1]
loss = train(input_variable, target_variable, encoder,
decoder, encoder_optimizer, decoder_optimizer, criterion)
print_loss_total += loss
plot_loss_total += loss
if iter % print_every == 0:
print_loss_avg = print_loss_total / print_every
print_loss_total = 0
print('%s (%d %d%%) %.4f' % (timeSince(start, iter / n_iters),
iter, iter / n_iters * 100, print_loss_avg))
if iter % plot_every == 0:
plot_loss_avg = plot_loss_total / plot_every
plot_losses.append(plot_loss_avg)
plot_loss_total = 0
showPlot(plot_losses)
Plotting results
Plotting is done with matplotlib, using the array of loss values plot_losses saved while training.
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
def showPlot(points):
plt.figure()
fig, ax = plt.subplots()
# this locator puts ticks at regular intervals
loc = ticker.MultipleLocator(base=0.2)
ax.yaxis.set_major_locator(loc)
plt.plot(points)
Evaluation
Evaluation is mostly the same as training, but there are no targets so we simply feed the decoder’s predictions back to itself for each step. Every time it predicts a word we add it to the output string, and if it predicts the EOS token we stop there. We also store the decoder’s attention outputs for display later.
def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):
input_variable = variableFromSentence(input_lang, sentence)
input_length = input_variable.size()[0]
encoder_hidden = encoder.initHidden()
encoder_outputs = Variable(torch.zeros(max_length, encoder.hidden_size))
encoder_outputs = encoder_outputs.cuda() if use_cuda else encoder_outputs
for ei in range(input_length):
encoder_output, encoder_hidden = encoder(input_variable[ei],
encoder_hidden)
encoder_outputs[ei] = encoder_outputs[ei] + encoder_output[0][0]
decoder_input = Variable(torch.LongTensor([[SOS_token]])) # SOS
decoder_input = decoder_input.cuda() if use_cuda else decoder_input
decoder_hidden = encoder_hidden
decoded_words = []
decoder_attentions = torch.zeros(max_length, max_length)
for di in range(max_length):
decoder_output, decoder_hidden, decoder_attention = decoder(
decoder_input, decoder_hidden, encoder_output, encoder_outputs)
decoder_attentions[di] = decoder_attention.data
topv, topi = decoder_output.data.topk(1)
ni = topi[0][0]
if ni == EOS_token:
decoded_words.append('' )
break
else:
decoded_words.append(output_lang.index2word[ni])
decoder_input = Variable(torch.LongTensor([[ni]]))
decoder_input = decoder_input.cuda() if use_cuda else decoder_input
return decoded_words, decoder_attentions[:di + 1]
We can evaluate random sentences from the training set and print out the input, target, and output to make some subjective quality judgements:
def evaluateRandomly(encoder, decoder, n=10):
for i in range(n):
pair = random.choice(pairs)
print('>', pair[0])
print('=', pair[1])
output_words, attentions = evaluate(encoder, decoder, pair[0])
output_sentence = ' '.join(output_words)
print('<', output_sentence)
print('')
Training and Evaluating
With all these helper functions in place (it looks like extra work, but it’s easier to run multiple experiments easier) we can actually initialize a network and start training.
Remember that the input sentences were heavily filtered. For this small dataset we can use relatively small networks of 256 hidden nodes and a single GRU layer. After about 40 minutes on a MacBook CPU we’ll get some reasonable results.
Note
If you run this notebook you can train, interrupt the kernel, evaluate, and continue training later. Comment out the lines where the encoder and decoder are initialized and run trainIters again.
hidden_size = 256
encoder1 = EncoderRNN(input_lang.n_words, hidden_size)
attn_decoder1 = AttnDecoderRNN(hidden_size, output_lang.n_words,
1, dropout_p=0.1)
if use_cuda:
encoder1 = encoder1.cuda()
attn_decoder1 = attn_decoder1.cuda()
trainIters(encoder1, attn_decoder1, 75000, print_every=5000)
Out:
3m 23s (- 47m 22s) (5000 6%) 2.8848
6m 44s (- 43m 48s) (10000 13%) 2.3516
10m 12s (- 40m 51s) (15000 20%) 2.0009
13m 38s (- 37m 31s) (20000 26%) 1.7755
16m 49s (- 33m 38s) (25000 33%) 1.5787
20m 5s (- 30m 7s) (30000 40%) 1.4096
23m 17s (- 26m 37s) (35000 46%) 1.3090
26m 33s (- 23m 14s) (40000 53%) 1.0980
29m 45s (- 19m 50s) (45000 60%) 1.0109
32m 57s (- 16m 28s) (50000 66%) 0.9418
36m 12s (- 13m 10s) (55000 73%) 0.8696
39m 27s (- 9m 51s) (60000 80%) 0.8121
42m 41s (- 6m 34s) (65000 86%) 0.7046
45m 56s (- 3m 16s) (70000 93%) 0.6555
49m 7s (- 0m 0s) (75000 100%) 0.6015
evaluateRandomly(encoder1, attn_decoder1)
Out:
> je suis vannee .
= i am exhausted .
< i m weak . <EOS>
> elle a l air de venir .
= she is likely to come .
< she is used to come . <EOS>
> je ne suis pas occupe du tout .
= i m not at all busy .
< i m not at all busy . <EOS>
> c est un marcheur rapide .
= he s a fast walker .
< he s a fast guy . <EOS>
> il est obsede par la proprete .
= he s obsessed with cleanliness .
< he s obsessed by . <EOS>
> je suis malade .
= i m sick .
< i m ill . <EOS>
> vous etes lunatiques .
= you re temperamental .
< you re temperamental . <EOS>
> je suis fort impressionne .
= i m very impressed .
< i m really impressed . <EOS>
> je viens de bulgarie .
= i m from bulgaria .
< i m from . <EOS>
> je ne suis pas ta servante .
= i m not your maid .
< i m not your maid . <EOS>
Visualizing Attention
A useful property of the attention mechanism is its highly interpretable outputs. Because it is used to weight specific encoder outputs of the input sequence, we can imagine looking where the network is focused most at each time step.
You could simply run plt.matshow(attentions) to see attention output displayed as a matrix, with the columns being input steps and rows being output steps:
output_words, attentions = evaluate(
encoder1, attn_decoder1, "je suis trop froid .")
plt.matshow(attentions.numpy())
For a better viewing experience we will do the extra work of adding axes and labels:
def showAttention(input_sentence, output_words, attentions):
# Set up figure with colorbar
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(attentions.numpy(), cmap='bone')
fig.colorbar(cax)
# Set up axes
ax.set_xticklabels([''] + input_sentence.split(' ') +
['' ], rotation=90)
ax.set_yticklabels([''] + output_words)
# Show label at every tick
ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
plt.show()
def evaluateAndShowAttention(input_sentence):
output_words, attentions = evaluate(
encoder1, attn_decoder1, input_sentence)
print('input =', input_sentence)
print('output =', ' '.join(output_words))
showAttention(input_sentence, output_words, attentions)
evaluateAndShowAttention("elle a cinq ans de moins que moi .")
evaluateAndShowAttention("elle est trop petit .")
evaluateAndShowAttention("je ne crains pas de mourir .")
evaluateAndShowAttention("c est un jeune directeur plein de talent .")
Out:
input = elle a cinq ans de moins que moi .
output = she s five years younger than me . <EOS>
input = elle est trop petit .
output = she is too short . <EOS>
input = je ne crains pas de mourir .
output = i m not scared to die . <EOS>
input = c est un jeune directeur plein de talent .
output = he s a mean young man . <EOS>
Exercises
- Try with a different dataset
- Another language pair
- Human → Machine (e.g. IOT commands)
- Chat → Response
- Question → Answer
- Replace the embeddings with pre-trained word embeddings such as word2vec or GloVe
- Try with more layers, more hidden units, and more sentences. Compare the training time and results.
- If you use a translation file where pairs have two of the same phrase (
I am test \t I am test), you can use this as an autoencoder. Try this:- Train as an autoencoder
- Save only the Encoder network
- Train a new Decoder for translation from there
Total running time of the script: ( 49 minutes 15.772 seconds)