Attention mechanism deep learning

Follow me:. Powered by jekyll and codinfox-lanyon. So far, we reviewed and implemented the Seq2Seq model with alignment proposed by Bahdahanu et al. This part is identical to what we did for mini-batch training of the vanilla Seq2Seq. So, I will let you refer to the posting to save space. Seting parameters are also identical. For the purpose of sanity check, the parameters can be set to as below.

Similarly, we define the encoder and decoder separately and merge them in the Seq2Seq model. The encoder is defined similarly to the original model, so the emphasis is on the decoder here. Note how the training data is sliced to fit into the decoder model that processes mini-batch inputs. Now we merge the encoder and decoder to create a Seq2Seq model. Since we have already defined the encoder and decoder in detail, implementing the Seq2Seq model is straightforward. Training is also done in a similar fashion.

Just be aware of calculating the negative log likelihood loss. The output has one more dimension, i. The loss continually decreases up to the 10th epoch. Please try training over 10 epochs for more effective training. In mini-batch implementation of alignment models, learned weights need to be permuted as there is another dimension here as well. And each weight can be visualized using a matplotlib heatmap.

Below is an example of visualizing the learned weights of the second test instance according to their saliency.

attention mechanism deep learning

In this posting, we implemented mini-batch alignment Seq2Seq proposed by Bahdahanu et al. Thank you for reading. Buomsoo Kim. Attention in Neural Networks - Alignment Models 4 So far, we reviewed and implemented the Seq2Seq model with alignment proposed by Bahdahanu et al.

Setting parameters Seting parameters are also identical. Adam seq2seq.Attention mechanisms are very important in the sense that they are ubiquitous and are a necessary component of neural machine learning systems.

Attention Mechanisms in Deep Learning — Not So Special

However, for that very same reason, they are not so special. Instead, they are a component of essentially all deep learning models. Giving them an explicit name and identity is certainly useful, but has misrepresented them as more special than they are. Attention is inherent to neural networks, and is furthermore a defining characteristic of such systems.

To learn is to pay attention. I in fact placed it on my list of most important papers of the decade — Yet, the more I think about attentionthe more I realize that contrary to how the community has portrayed itit is far from a radical shift from existing models and paradigms. Furthermore it is not new at all.

attention mechanism deep learning

It has always been an integral part of neural systems. In spite of my newfound insights, Attention is All you Need remains on my list of top ML papers of the decade:. Attention mechanisms are essentially a way to non-uniformly weight the contributions of input feature vectors so as to optimize the process of learning some target.

Sure it is also possible that some target requires equal attention be paid to all inputs i. The neural network, however constructed, self learns to place higher weights on the connections from certain parts of the input data and lesser weights on connections from other parts.

As I stated above, attention mechanisms are a way to non-uniformly weight the contributions of various input features so as to optimize the learning of some target. One way to do this is to linearly transform all the inputs at a given level.

attention mechanism deep learning

The transformed inputs are called keys. Then the current input which we are looking to interpret is also transformed into something called a query. The linear transformation is into a space such that similarity between keys and queries carries information about the relevance of those keys during the translation of a given query. We determine similarity between keys and queries and use that similarity as weights for a linear convex combination of values.

The values are the features at the level of the query including itself. This summation is called a context vector and is fed into the corresponding cell in the next layer in the model. In the above schematic and equations, we see how the similarity weights are generated in Equation 2 and the context vector in Equation 3.Image-level weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years.

Most of advanced solutions exploit class activation map CAM. However, CAMs can hardly serve as the object mask due to the gap between full and weak supervisions. In this paper, we propose a self-supervised equivariant attention mechanism SEAM to discover additional supervision and narrow the gap. Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation, whose pixel-level labels take the same spatial transformation as the input images during data augmentation.

However, this constraint is lost on the CAMs trained by image-level supervision. Therefore, we propose consistency regularization on predicted CAMs from various transformed images to provide self-supervision for network learning. Moreover, we propose a pixel correlation module PCMwhich exploits context appearance information and refines the prediction of current pixel by its similar neighbors, leading to further improvement on CAMs consistency.

The code is released online. Yude Wang. Jie Zhang. Meina Kan. Shiguang Shan. Xilin Chen. Weakly supervised semantic segmentation has attracted much research inte Weakly supervised semantic segmentation WSSS using only image-level la Semantic segmentation is the task of assigning a class-label to each pix Deep learning stands at the forefront in many computer vision tasks.

Weakly-supervised learning under image-level labels supervision has been We propose the first qualitative hypothesis characterizing the behavior Palet al. We propose an approach for learning category-level semantic segmentation Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. Semantic segmentation is a fundamental computer vision task, which aims to predict pixel-wise classification results on images. Thanks to the booming of deep learning researches in recent years, the performance of semantic segmentation model has achieved great progress.A recent trend in Deep Learning are Attention Mechanisms.

In an interviewIlya Sutskever, now the research director of OpenAI, mentioned that Attention Mechanisms are one of the most exciting advancements, and that they are here to stay.

That sounds exciting. But what are Attention Mechanisms? Attention Mechanisms in Neural Networks are very loosely based on the visual attention mechanism found in humans. Attention in Neural Networks has a long history, particularly in image recognition.

Examples include Learning to combine foveal glimpses with a third-order Boltzmann machine or Learning where to Attend with Deep Architectures for Image Tracking. But only recently have attention mechanisms made their way into recurrent neural networks architectures that are typically used in NLP and increasingly also in vision. Traditional Machine Translation systems typically rely on sophisticated feature engineering based on the statistical properties of text.

In short, these systems are complex, and a lot of engineering effort goes into building them. Neural Machine Translation systems work a bit differently. In NMT, we map the meaning of a sentence into a fixed-length vector representation and then generate a translation based on that vector. By not relying on things like n-gram counts and instead trying to capture the higher-level meaning of a text, NMT systems generalize to new sentences better than many other approaches.

In fact, a simple implementation in Tensorflow is no more than a few hundred lines of code. Most NMT systems work by encoding the source sentence e. The decoder keeps generating words until a special end of sentence token is produced. Here, the vectors represent the internal state of the encoder.

If you look closely, you can see that the decoder is supposed to generate a translation solely based on the last hidden state above from the encoder. This vector must encode everything we need to know about the source sentence. It must fully capture its meaning. In more technical terms, that vector is a sentence embedding.

In fact, if you plot the embeddings of different sentences in a low dimensional space using PCA or t-SNE for dimensionality reduction, you can see that semantically similar phrases end up close to each other. Still, it seems somewhat unreasonable to assume that we can encode all information about a potentially very long sentence into a single vector and then have the decoder produce a good translation based on only that.

The first word of the English translation is probably highly correlated with the first word of the source sentence.

But that means decoder has to consider information from 50 steps ago, and that information needs to be somehow encoded in the vector. Recurrent Neural Networks are known to have problems dealing with such long-range dependencies.

In theory, architectures like LSTMs should be able to deal with this, but in practice long-range dependencies are still problematic. For example, researchers have found that reversing the source sequence feeding it backwards into the encoder produces significantly better results because it shortens the path from the decoder to the relevant parts of the encoder.

Similarly, feeding an input sequence twice also seems to help a network to better memorize things. Most translation benchmarks are done on languages like French and German, which are quite similar to English even Chinese word order is quite similar to English.

But there are languages like Japanese where the last word of a sentence could be highly predictive of the first word in an English translation. In that case, reversing the input would make things worse. Attention Mechanisms. With an attention mechanism we no longer try encode the full source sentence into a fixed-length vector.

Importantly, we let the model learn what to attend to based on the input sentence and what it has produced so far.

Attention and Memory in Deep Learning and NLP

So, in languages that are pretty well aligned like English and German the decoder would probably choose to attend to things sequentially.Last Updated on August 7, Attention is a mechanism that was developed to improve the performance of the Encoder-Decoder RNN on machine translation.

Discover how to develop deep learning models for text classification, translation, photo captioning and more in my new bookwith 30 step-by-step tutorials and full source code.

Transformer Neural Networks - EXPLAINED! (Attention is all you need)

Both developed the technique to address the sequence-to-sequence nature of machine translation where input sequences differ in length from output sequences. Ilya Sutskever, et al. Kyunghyun Cho, et al. This work, and some of the same authors Bahdanau, Cho and Bengio developed their specific model later to develop an attention model. Therefore we will take a quick look at the Encoder-Decoder model as described in this paper.

Key to the model is that the entire model, including encoder and decoder, is trained end-to-end, as opposed to training the elements separately. The model is described generically such that different specific RNN models could be used as the encoder and decoder.

Further, unlike the Sutskever, et al. You can see this in the image above where the output y2 uses the context vector Cthe hidden state passed from decoding y1 as well as the output y1. Attention was presented by Dzmitry Bahdanau, et al. Attention is proposed as a solution to the limitation of the Encoder-Decoder model encoding the input sequence to one fixed length vector from which to decode each output time step.

This issue is believed to be more of a problem when decoding long sequences. A potential issue with this encoder—decoder approach is that a neural network needs to be able to compress all the necessary information of a source sentence into a fixed-length vector. This may make it difficult for the neural network to cope with long sentences, especially those that are longer than the sentences in the training corpus. Alignment is the problem in machine translation that identifies which parts of the input sequence are relevant to each word in the output, whereas translation is the process of using the relevant information to select the appropriate output.

Each time the proposed model generates a word in a translation, it soft- searches for a set of positions in a source sentence where the most relevant information is concentrated. The model then predicts a target word based on the context vectors associated with these source positions and all the previous generated target words. Instead of encoding the input sequence into a single fixed context vector, the attention model develops a context vector that is filtered specifically for each output time step.

In this case, a bidirectional input is used where the input sequences are provided both forward and backward, which are then concatenated before being passed on to the decoder.

Rather than re-iterate the equations for calculating attention, we will look at a worked example. In this section, we will make attention concrete with a small worked example. Specifically, we will step through the calculations with un-vectorized terms. This will give you a sufficiently detailed understanding that you could add attention to your own encoder-decoder implementation.

In this example, we will ignore the type of RNN being used in the encoder and decoder and ignore the use of a bidirectional input layer.This is a slightly advanced tutorial and requires basic understanding of sequence to sequence models using RNNs.

Please refer my earlier blog here wherein I have explained in detail the concept of Seq2Seq models. Attention is one of the most influential ideas in the Deep Learning community. Even though this mechanism is now used in various problems like image captioning and others,it was initially designed in the context of Neural Machine Translation using Seq2Seq Models. In this blog post I will consider the same problem as the running example to illustrate the concept.

We would be using attention to design a system which translates a given English sentence to Marathi, the exact same example I considered in my earlier blog. This representation is expected to be a good summary of the entire input sequence. The decoder is then initialized with this context vector, using which it starts generating the transformed output. A critical and apparent disadvantage of this fixed-length context vector design is the incapability of the system to remember longer sequences.

Often is has forgotten the earlier parts of the sequence once it has processed the entire the sequence. The attention mechanism was born to resolve this problem.

Since I have already explained most of the basic concepts required to understand Attention in my previous bloghere I will directly jump into the meat of the issue without any further adieu.

For the illustrative purposes, I will borrow the same example that I used to explain Seq2Seq models in my previous blog. This will help simplify the the concept and explanation. Recall the below diagram in which I summarized the entire process procedure of Seq2Seq modelling.

In the traditional Seq2Seq model, we discard all the intermediate states of the encoder and use only its final states vector to initialize the decoder. This technique works good for smaller sequences, however as the length of the sequence increases, a single vector becomes a bottleneck and it gets very difficult to summarize long sequences into a single vector. This observation was made empirically as it was noted that the performance of the system decreases drastically as the size of the sequence increases.

The central idea behind Attention is not to throw away those intermediate encoder states but to utilize all the states in order to construct the context vectors required by the decoder to generate the output sequence. Notice that since we are using a GRU instead of an LSTM, we only have a single state at each time step and not two states, which thus helps to simplify the illustration. Also note that attention is useful specially in case of longer sequences but for the sake of simplicity we will consider the same above example for illustration.

Recall that these states h1 to h5 are nothing but vectors of fixed length. To develop some intuition think of these states as vectors which store local information within the sequence. For example. Lets represent our Encoder GRU with the below simplified diagram:. Now the idea is to utilize all of these local information collectively in order to decide the next sequence while decoding the target sentence.

Ask yourself, how do you do it in your mind? And so on. As human beings we are quickly able to understand these mappings between different parts of the input sequence and corresponding parts of the output sequence. However its not that straight forward for artificial neural network to automatically detect these mappings.

At time step 1, we can break the entire process into five steps as below:. Before we start decoding, we first need to encode the input sequence into a set of internal states in our case h1, h2, h3, h4 and h5.

Now the hypothesis is that, the next word in the output sequence is dependent on the current state of the decoder decoder is also a GRU as well as on the hidden states of the encoder.Can I have your Attention please! The introduction of the Attention Mechanism in deep learning has improved the success of various models in recent years, and continues to be an omnipresent component in state-of-the-art models.

Therefore, it is vital that we pay Attention to Attention and how it goes about achieving its effectiveness. In this article, I will be covering the main concepts behind Attention, including an implementation of a sequence-to-sequence Attention model, followed by the application of Attention in Transformers and how they can be used for state-of-the-art results. It is advised that you have some knowledge of Recurrent Neural Networks RNNs and their variants, or an understanding of how sequence-to-sequence models work.

The Attention mechanism in Deep Learning is based off this concept of directing your focus, and it pays greater attention to certain factors when processing the data.

attention mechanism deep learning

Let me give you an example of how Attention works in a translation task. What the Attention component of the network will do for each word in the output sentence is map the important and relevant words from the input sentence and assign higher weights to these words, enhancing the accuracy of the output prediction.


The above explanation of Attention is very broad and vague due to the various types of Attention mechanisms available. As the Attention mechanism has undergone multiple adaptations over the years to suit various tasks, there are many different versions of Attention that are applied.

We will only cover the more popular adaptations here, which are its usage in sequence-to-sequence models and the more recent Self-Attention. While Attention does have its application in other fields of deep learning such as Computer Vision, its main breakthrough and success comes from its application in Natural Language Processing NLP tasks.

This is due to the fact that Attention was introduced to address the problem of long sequences in Machine Translationwhich is also a problem for most other NLP tasks as well. Most articles on the Attention Mechanism will use the example of sequence-to-sequence seq2seq models to explain how it works.

This is because Attention was originally introduced as a solution to address the main issue surrounding seq2seq models, and to great success. If you are unfamiliar with seq2seq models, also known as the Encoder-Decoder model, I recommend having a read through this article to get you up to speed.

The standard seq2seq model is generally unable to accurately process long input sequences, since only the last hidden state of the encoder RNN is used as the context vector for the decoder.

On the other hand, the Attention Mechanism directly addresses this issue as it retains and utilises all the hidden states of the input sequence during the decoding process. It does this by creating a unique mapping between each time step of the decoder output to all the encoder hidden states. This means that for each output that the decoder makes, it has access to the entire input sequence and can selectively pick out specific elements from that sequence to produce the output. Before we delve into the specific mechanics behind Attention, we must note that there are 2 different major types of Attention:.

While the underlying principles of Attention are the same in these 2 types, their differences lie mainly in their architectures and computations. The first type of Attention, commonly referred to as Additive Attention, came from a paper by Dzmitry Bahdanauwhich explains the less-descriptive original name.

The paper aimed to improve the sequence-to-sequence model in machine translation by aligning the decoder with the relevant input sentences and implementing Attention. The encoder over here is exactly the same as a normal encoder-decoder structure without Attention.

For these next 3 steps, we will be going through the processes that happen in the Attention Decoder and discuss how the Attention mechanism is utilised.

After obtaining all of our encoder outputs, we can start using the decoder to produce outputs. At each time step of the decoder, we have to calculate the alignment score of each encoder output with respect to the decoder input and hidden state at that time step. The alignment scores for Bahdanau Attention are calculated using the hidden state produced by the decoder in the previous time step and the encoder outputs with the following equation:.

The decoder hidden state and encoder outputs will be passed through their individual Linear layer and have their own individual trainable weights.