This script demonstrates how to implement a basic character-level sequence-to-sequence model. We apply it to translating short English sentences into short French sentences, character-by-character. Note that it is fairly unusual to do character-level machine translation, as word-level models are more common in this domain. Keras Documentation. Sequence to sequence example in Keras character-level. Summary of the algorithm We start with input sequences from a domain e.
English sentences and corresponding target sequences from another domain e. French sentences. A decoder LSTM is trained to turn the target sequences into the same sequence but offset by one timestep in the future, a training process called "teacher forcing" in this context. It uses as initial state the state vectors from the encoder. In inference mode, when we want to decode unknown input sequences, we: Encode the input sequence into state vectors Start with a target sequence of size 1 just the start-of-sequence character Feed the state vectors and 1-char target sequence to the decoder to produce predictions for the next character Sample the next character using these predictions we simply use argmax.
Append the sampled character to the target sequence Repeat until we generate the end-of-sequence character or we hit the character limit. Data download English to French sentence pairs. Lots of neat sentence pairs datasets. Path to the data txt file on disk. Define an input sequence and process it. We don't use the return states in the training model, but we will use them in inference.
Here's the drill: 1 encode input and retrieve initial decoder state 2 run one step of decoder with this initial state and a "start of sequence" token as target. Sampling loop for a batch of sequences to simplify, here we assume a batch of size 1.Last Updated on August 14, The Encoder-Decoder LSTM is a recurrent neural network designed to address sequence-to-sequence problems, sometimes called seq2seq.How to Use Tensorflow for Seq2seq Models (LIVE)
Sequence-to-sequence prediction problems are challenging because the number of items in the input and output sequences can vary. For example, text translation and learning to execute programs are examples of seq2seq problems. Sequence prediction often involves forecasting the next value in a real valued sequence or outputting a class label for an input sequence.
This is often framed as a sequence of one input time step to one output time step e. There is a more challenging type of sequence prediction problem that takes a sequence as input and requires a sequence prediction as output.
These are called sequence-to-sequence prediction problems, or seq2seq for short. One modeling concern that makes these problems challenging is that the length of the input and output sequences may vary.
Given that there are multiple input time steps and multiple output time steps, this form of problem is referred to as many-to-many type sequence prediction problem.
This architecture is comprised of two models: one for reading the input sequence and encoding it into a fixed-length vector, and a second for decoding the fixed-length vector and outputting the predicted sequence. The use of the models in concert gives the architecture its name of Encoder-Decoder LSTM designed specifically for seq2seq problems. The encoder maps a variable-length source sequence to a fixed-length vector, and the decoder maps the vector representation back to a variable-length target sequence.
The Encoder-Decoder LSTM was developed for natural language processing problems where it demonstrated state-of-the-art performance, specifically in the area of text translation called statistical machine translation.
The innovation of this architecture is the use of a fixed-sized internal representation in the heart of the model that input sequences are read to and output sequences are read from. For this reason, the method may be referred to as sequence embedding. In one of the first applications of the architecture to English-to-French translation, the internal representation of the encoded English phrases was visualized.
The plots revealed a qualitatively meaningful learned structure of the phrases harnessed for the translation task. On the task of translation, the model was found to be more effective when the input sequence was reversed. Further, the model was shown to be effective even on very long input sequences. We were able to do well on long sentences because we reversed the order of words in the source sentence but not the target sentences in the training and test set. By doing so, we introduced many short term dependencies that made the optimization problem much simpler.
This approach has also been used with image inputs where a Convolutional Neural Network is used as a feature extractor on input images, which is then read by a decoder LSTM. First, the input sequence is shown to the network one encoded character at a time.
Encoder-Decoder Long Short-Term Memory Networks
We need an encoding level to learn the relationship between the steps in the input sequence and develop an internal representation of these relationships.
One or more LSTM layers can be used to implement the encoder model. The output of this model is a fixed-size vector that represents the internal representation of the input sequence. The number of memory cells in this layer defines the length of this fixed-sized vector. The decoder must transform the learned internal representation of the input sequence into the correct output sequence. One or more LSTM layers can also be used to implement the decoder model. This model reads from the fixed sized output from the encoder model.
The same weights can be used to output each time step in the output sequence by wrapping the Dense layer in a TimeDistributed wrapper. That is, the encoder will produce a 2-dimensional matrix of outputs, where the length is defined by the number of memory cells in the layer. The decoder is an LSTM layer that expects a 3D input of [samples, time steps, features] in order to produce a decoded sequence of some different length defined by the problem.
If you try to force these pieces together, you get an error indicating that the output of the decoder is 2D and 3D input to the decoder is required. We can solve this using a RepeatVector layer. This layer simply repeats the provided 2D input multiple times to create a 3D output.In this tutorial, we will answer some common questions about autoencoders, and we will cover code examples of the following models:. Note: all code examples have been updated to the Keras 2. You will need Keras version 2.
Additionally, in almost all contexts where the term "autoencoder" is used, the compression and decompression functions are implemented with neural networks.
An autoencoder trained on pictures of faces would do a rather poor job of compressing pictures of trees, because the features it would learn would be face-specific. This differs from lossless arithmetic compression. It doesn't require any new engineering, just appropriate training data. To build an autoencoder, you need three things: an encoding function, a decoding function, and a distance function between the amount of information loss between the compressed representation of your data and the decompressed representation i.
Sequence to sequence example in Keras (character-level).
And you don't even need to understand any of these words to start using autoencoders in practice. Usually, not really. In picture compression for instance, it is pretty difficult to train an autoencoder that does a better job than a basic algorithm like JPEG, and typically the only way it can be achieved is by restricting yourself to a very specific type of picture e.
The fact that autoencoders are data-specific makes them generally impractical for real-world data compression problems: you can only use them on data that is similar to what they were trained on, and making them more general thus requires lots of training data.
But future advances might change this, who knows. They are rarely used in practical applications.
In they briefly found an application in greedy layer-wise pretraining for deep convolutional neural networks , but this quickly fell out of fashion as we started realizing that better random weight initialization schemes were sufficient for training deep networks from scratch. Inbatch normalization  started allowing for even deeper networks, and from late we could train arbitrarily deep networks from scratch using residual learning .
Today two interesting practical applications of autoencoders are data denoising which we feature later in this postand dimensionality reduction for data visualization. With appropriate dimensionality and sparsity constraints, autoencoders can learn data projections that are more interesting than PCA or other basic techniques.
For 2D visualization specifically, t-SNE pronounced "tee-snee" is probably the best algorithm around, but it typically requires relatively low-dimensional data. So a good strategy for visualizing similarity relationships in high-dimensional data is to start by using an autoencoder to compress your data into a low-dimensional space e.
Otherwise scikit-learn also has a simple and practical implementation. Their main claim to fame comes from being featured in many introductory machine learning classes available online. As a result, a lot of newcomers to the field absolutely love autoencoders and can't get enough of them. This is the reason why this tutorial exists! Otherwise, one reason why they have attracted so much research and attention is because they have long been thought to be a potential avenue for solving the problem of unsupervised learning, i.
Then again, autoencoders are not a true unsupervised learning technique which would imply a different learning process altogetherthey are a self-supervised technique, a specific instance of supervised learning where the targets are generated from the input data. In order to get self-supervised models to learn interesting features, you have to come up with an interesting synthetic target and loss function, and that's where problems arise: merely learning to reconstruct your input in minute detail might not be the right choice here.
At this point there is significant evidence that focusing on the reconstruction of a picture at the pixel level, for instance, is not conductive to learning interesting, abstract features of the kind that label-supervized learning induces where targets are fairly abstract concepts "invented" by humans such as "dog", "car" In fact, one may argue that the best features in this regard are those that are the worst at exact input reconstruction while achieving high performance on the main task that you are interested in classification, localization, etc.
In self-supervized learning applied to vision, a potentially fruitful alternative to autoencoder-style input reconstruction is the use of toy tasks such as jigsaw puzzle solving, or detail-context matching being able to match high-resolution but small patches of pictures with low-resolution versions of the pictures they are extracted from.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. In the code examples herein the section titled "Sequence-to-sequence autoencoder," it reads:. My question is, why are we doing the RepeatVector operation? Instead, they have the following diagram:. What am I missing here?
What exactly is the input sequence to the Decoder portion of the autoencoder? A slightly better method is to use a sequence autoencoder, which uses a RNN to read a long input sequence into a single vector.
This vector will then be used to reconstruct the original sequence. So the example reads everything into a single vector, then uses that vector to reconstruct the original sequence. If you want to iteratively generate something but you only have one input, you can repeat the vector. That means each time step will get the same input but a different hidden state. Do you have a link to some literature where they've used such an architecture?
Almost all frequently cited papers that I found use a different architecture. Similar to the picture in the post, in another popular paper by Srivastava et. It seems they're using the reversed input from the encoder as input here. There's a section as follows:. The decoder can be of two kinds — conditional or unconditioned. A conditional decoder receives the last generated output frame as input, i.
Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am trying to implement a seq2seq encoder-decoder using Keras, with bidirectional lstm on the encoder as follows:.
Although the error pointed to the last line of the block in the question, however it was due to the wrong number of hidden units in the inference decoder. How to remove inference encoder in the above comment. I mean how to solve the error without inference encoder. Learn more. Asked 1 year, 10 months ago. Active 4 months ago.
Viewed 3k times. I am trying to implement a seq2seq encoder-decoder using Keras, with bidirectional lstm on the encoder as follows: from keras.
Any ideas? Your code runs on my machine without any errors though, after creating a Model object and calling compile and fit. Could you post the complete code you are using? Maybe there is a problem in the parts you have not posted.
You were right, the error pointed to the last line of this block, however the real error was propagated from another line relating to the inference decoder! Thanks, it is solved now!
Active Oldest Votes. Full working code: from keras. Hi, now that you have defined your encoder and decoder models, how would you go about training them?
How would you go about combining these two Keras models into a single auto-encoder model which could be trained using mode.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.
I created this post to share a flexible and reusable implementation of a sequence to sequence model using Keras. I strongly recommend visiting Guillaume's repository for some great projects. His post presents an implementation of a seq2seq model for machine translation. This post is also available on my websitefeel free to visit :.
Time series prediction is a widespread problem. Applications range from price and weather forecasting to biological signal prediction. I will focus on the practical aspects of the implementation, rather than the theory underlying neural networks, though I will try to share some of the reasoning behind the ideas I present.
I assume a basic understanding of how RNNs work. A "many to one" recurrent neural net takes as input a sequence and returns one value. For a more detailed description of the difference between many to one, many to many RNNs etc. How can a "many to one" neural network be used for time series prediction? A "many to one" RNN can be seen as a function fthat takes as input n steps of a time series, and outputs a value. An RNN can, for instance, be trained to intake the past 4 values of a time series and output a prediction of the next value.
Let X be a time series and X t the value of that time series at time tthen. The function f is composed of 4 RNN cells and can be represented as following:. If more than one prediction is needed which is often the case then the value predicted can be used as input and a new prediction can be made.
Following is a representation of 3 runs through a RNN model to produce predictions for 3 steps in the future. As you can see, the basis of the prediction model f is a single unit, the RNN cell, that takes as input X t and the state of the network not represented in these graphs for clarity and ouputs a single value discarded unless all the input values have been input to the cell. The function f described above is evaluated by running the cell of the network 4 times, each time with a new input and the state output from the previous step.
There are multiple reasons why this architecture might not be the best for time series prediction, compounding errors is one. However, in my opinion, there is a more important reason as to why it might not be the best method. In a time series prediction problem there are intuitively two distinct tasks. Human beings predicting a time series would proceed by looking at the known values of the past, and use their understanding of what happened in the past to predict the future values.
Subscribe to RSS
These two tasks require two distinct skillsets:. By using a single RNN cell in our model we are asking it to be capable of both memorising important events of the past and using these events to predict future values.
This is the reasoning behind considering the encoder-decoder for time series prediction.
Rather than having a single multi-tasking cell, the model will use two specialised cells. One for memorising important events of the past encoder and one for converting the important events into a prediction of the future decoder.
This idea of having two cells an encoder and a decoder is used in other maching learning tasks, the most prominent being perhaps machine translation. In machine translation, the idea behind having two separate tasks is even clearer. Let's say we're creating a system that translates French to English. First we need an element encoder that is capable of understanding French, its only task is to understand the input sentence and create a representation of what that sentence means.
Then we need a second system decoder that is capable of converting a representation of the meaning of the French sentence to a sentence in English with the same meaning. Instead of having a super intelligent cell that can understand French and speak English, we can create two cells, the encoder understands French but cannot speak English and the decoder knows how to speak English but cannot understand French.
How to implement Seq2Seq LSTM Model in Keras #ShortcutNLP
By working together, these specialised cells outperform the super cell.So Here I will explain complete guide of seq2seq for in Keras. Let's get started! It can be used as a model for machine interaction and machine translation. By learning a large number of sequence pairs, this model generates one from the other. More kindly explained, the definition of Seq2Seq is below:.
And here we have examples of business applications of seq2seq:. For training our seq2seq model, we will use Cornell Movie — Dialogs Corpus Dataset which contains overconversational exchanges between 10, pairs of movie characters. And it involves 9, characters from movies. Here one of the conversations from the data set:.
Then we will input these pairs of conversation into Encoder and Decoder. So that means our Neural Network model has two input layer as you can see below. To make this clear, I will explain how it works with detail. The Layers can be broken down into 5 different parts:. NOTE: Data is word embedded in 50 dimensions. The tricky argument of LSTM layer is these two:. Whether the last output of the output sequence or a complete sequence is returned.
Additional Information:. Before jumping on preprocessing of Seq2Seq, I wanna mention about this:. Preprocessing for Seq2Seq. The whole process could be broken down into 8steps:. I always use this my own function to clean text for Seq2Seq:.