Mathematics Vision Project Module 1 Sequences 1.1 Answer Key
intermediate/seq2seq_translation_tutorial
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NLP From Scratch: Translation with a Sequence to Sequence Network and Attention¶
Author: Sean Robertson
This is the third and final tutorial on doing "NLP From Scratch", where we write our own classes and functions to preprocess the data to do our NLP modeling tasks. We hope after you complete this tutorial that you'll proceed to learn how torchtext can handle much of this preprocessing for you in the three tutorials immediately following this one.
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:
- https://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 NLP From Scratch: Classifying Names with a Character-Level RNN and NLP From Scratch: Generating Names with a Character-Level RNN helpful as those concepts are very similar to the Encoder and Decoder models, respectively.
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 import optim import torch.nn.functional as F device = torch . device ( "cuda" if torch . cuda . is_available () else "cpu" )
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 site https://tatoeba.org/ which has downloads available at https://tatoeba.org/eng/downloads - and better yet, someone did the extra work of splitting language pairs into individual text files here: https://www.manythings.org/anki/
The English to French pairs are too big to include in the repo, so download to data/eng-fra.txt before continuing. The file is a tab separated list of translation pairs:
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 which will be used to replace rare words later.
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 # https://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 the reverse 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 10599 sentence pairs Counting words... Counted words: fra 4345 eng 2803 ['je vais avoir besoin de votre aide .', 'i m going to need your help .']
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 ): super ( EncoderRNN , self ) . __init__ () 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 output , hidden = self . gru ( output , hidden ) return output , hidden def initHidden ( self ): return torch . zeros ( 1 , 1 , self . hidden_size , device = device )
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 <SOS> token, and the first hidden state is the context vector (the encoder's last hidden state).
class DecoderRNN ( nn . Module ): def __init__ ( self , hidden_size , output_size ): super ( DecoderRNN , self ) . __init__ () 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 ( dim = 1 ) def forward ( self , input , hidden ): output = self . embedding ( input ) . view ( 1 , 1 , - 1 ) output = F . relu ( output ) output , hidden = self . gru ( output , hidden ) output = self . softmax ( self . out ( output [ 0 ])) return output , hidden def initHidden ( self ): return torch . zeros ( 1 , 1 , self . hidden_size , device = device )
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 between 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 (called attn_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 , dropout_p = 0.1 , max_length = MAX_LENGTH ): super ( AttnDecoderRNN , self ) . __init__ () self . hidden_size = hidden_size self . output_size = output_size 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_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 )), dim = 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 ) output = F . relu ( output ) output , hidden = self . gru ( output , hidden ) output = F . log_softmax ( self . out ( output [ 0 ]), dim = 1 ) return output , hidden , attn_weights def initHidden ( self ): return torch . zeros ( 1 , 1 , self . hidden_size , device = device )
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 tensorFromSentence ( lang , sentence ): indexes = indexesFromSentence ( lang , sentence ) indexes . append ( EOS_token ) return torch . tensor ( indexes , dtype = torch . long , device = device ) . view ( - 1 , 1 ) def tensorsFromPair ( pair ): input_tensor = tensorFromSentence ( input_lang , pair [ 0 ]) target_tensor = tensorFromSentence ( output_lang , pair [ 1 ]) return ( input_tensor , target_tensor )
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 <SOS> token as its first input, and the last hidden state of the encoder as its first hidden state.
"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_tensor , target_tensor , 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_tensor . size ( 0 ) target_length = target_tensor . size ( 0 ) encoder_outputs = torch . zeros ( max_length , encoder . hidden_size , device = device ) loss = 0 for ei in range ( input_length ): encoder_output , encoder_hidden = encoder ( input_tensor [ ei ], encoder_hidden ) encoder_outputs [ ei ] = encoder_output [ 0 , 0 ] decoder_input = torch . tensor ([[ SOS_token ]], device = device ) 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_outputs ) loss += criterion ( decoder_output , target_tensor [ di ]) decoder_input = target_tensor [ 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_outputs ) topv , topi = decoder_output . topk ( 1 ) decoder_input = topi . squeeze () . detach () # detach from history as input loss += criterion ( decoder_output , target_tensor [ di ]) if decoder_input . item () == EOS_token : break loss . backward () encoder_optimizer . step () decoder_optimizer . step () return loss . item () / 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 ' %d m %d s' % ( 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 = [ tensorsFromPair ( 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_tensor = training_pair [ 0 ] target_tensor = training_pair [ 1 ] loss = train ( input_tensor , target_tensor , 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 plt . switch_backend ( 'agg' ) 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 ): with torch . no_grad (): input_tensor = tensorFromSentence ( input_lang , sentence ) input_length = input_tensor . size ()[ 0 ] encoder_hidden = encoder . initHidden () encoder_outputs = torch . zeros ( max_length , encoder . hidden_size , device = device ) for ei in range ( input_length ): encoder_output , encoder_hidden = encoder ( input_tensor [ ei ], encoder_hidden ) encoder_outputs [ ei ] += encoder_output [ 0 , 0 ] decoder_input = torch . tensor ([[ SOS_token ]], device = device ) # SOS 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_outputs ) decoder_attentions [ di ] = decoder_attention . data topv , topi = decoder_output . data . topk ( 1 ) if topi . item () == EOS_token : decoded_words . append ( '<EOS>' ) break else : decoded_words . append ( output_lang . index2word [ topi . item ()]) decoder_input = topi . squeeze () . detach () 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 makes it easier to run multiple experiments) 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 ) . to ( device ) attn_decoder1 = AttnDecoderRNN ( hidden_size , output_lang . n_words , dropout_p = 0.1 ) . to ( device ) trainIters ( encoder1 , attn_decoder1 , 75000 , print_every = 5000 )
Out:
1m 32s (- 21m 28s) (5000 6%) 2.8472 2m 56s (- 19m 7s) (10000 13%) 2.2774 4m 22s (- 17m 29s) (15000 20%) 1.9672 5m 48s (- 15m 59s) (20000 26%) 1.7093 7m 15s (- 14m 31s) (25000 33%) 1.4992 8m 41s (- 13m 2s) (30000 40%) 1.3725 10m 7s (- 11m 34s) (35000 46%) 1.1886 11m 34s (- 10m 7s) (40000 53%) 1.0893 13m 0s (- 8m 40s) (45000 60%) 0.9779 14m 27s (- 7m 13s) (50000 66%) 0.8445 15m 53s (- 5m 46s) (55000 73%) 0.7866 17m 22s (- 4m 20s) (60000 80%) 0.7059 18m 49s (- 2m 53s) (65000 86%) 0.6520 20m 15s (- 1m 26s) (70000 93%) 0.5844 21m 42s (- 0m 0s) (75000 100%) 0.5491
evaluateRandomly ( encoder1 , attn_decoder1 )
Out:
> c est toi la plus vieille . = you re the oldest . < you re the oldest . <EOS> > je suis vraiment desole de t avoir derange . = i m very sorry to have troubled you . < i m sorry sorry to have troubled you . <EOS> > j ai les glandes . = i m really angry . < i m really angry . <EOS> > je suis tellement confus ! = i m so confused . < i m so confused . <EOS> > il est impatient d y aller . = he is eager to go there . < he is eager to go there . <EOS> > tu plaisantes ! = you re joking ! < you re kidding ! <EOS> > j en ai assez de l anglais . = i m fed up with english . < i m fed up with english . <EOS> > ma patience est a bout . = i am at the end of my patience . < i am at at the office . <EOS> > ils ne sont pas en ville . = they re out of town . < they re out of town . <EOS> > elle n est pas infirmiere mais docteur . = she is not a nurse but a doctor . < she is not a doctor but a doctor . <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 ( ' ' ) + [ '<EOS>' ], 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 is five years younger than me . <EOS> input = elle est trop petit . output = she s 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 talented young director . <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
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Mathematics Vision Project Module 1 Sequences 1.1 Answer Key
Source: https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html
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