Using the above-given information, the context vector will be more responsible for performing more accurately by reducing the bugs on the transformed data. Using the attention mechanism in a network, a context vector can have the following information: We can introduce an attention mechanism to create a shortcut between the entire input and the context vector where the weights of the shortcut connection can be changeable for every output.īecause of the connection between input and context vector, the context vector can have access to the entire input, and the problem of forgetting long sequences can be resolved to an extent. As we have discussed in the above section, the encoder compresses the sequential input and processes the input in the form of a context vector. So providing a proper attention mechanism to the network, we can resolve the issue.Ī mechanism that can help a neural network to memorize long sequences of the information or data can be considered as the attention mechanism and broadly it is used in the case of Neural machine translation(NMT). We can often face the problem of forgetting the starting part of the sequence after processing the whole sequence of information or we can consider it as the sentence. A critical disadvantage with the context vector of fixed length design is that the network becomes incapable of remembering the large sentences. When we talk about the work of the encoder, we can say that it modifies the sequential information into an embedding which can also be called a context vector of a fixed length. So we can say in the architecture of this network, we have an encoder and a decoder which can also be a neural network. The above image is a representation of a seq2seq model where LSTM encode and LSTM decoder are used to translate the sentences from the English language into French. A simple example of the task given to the seq2seq model can be a translation of text or audio information into other languages. More formally we can say that the seq2seq models are designed to perform the transformation of sequential information into sequential information and both of the information can be of arbitrary form. Let’s talk about the seq2seq models which are also a kind of neural network and are well known for language modelling. In many of the cases, we see that the traditional neural networks are not capable of holding and working on long and large information. The major points that we will discuss here are listed below. In this article, we are going to discuss the attention layer in neural networks and we understand its significance and how it can be added to the network practically. We can use the attention layer in its architecture to improve its performance. Neural networks built using different layers can easily incorporate this feature through one of the layers. This can be achieved by adding an additional attention feature to the models. Paying attention to important information is necessary and it can improve the performance of the model. If we are providing a huge dataset to the model to learn, it is possible that a few important parts of the data might be ignored by the models.
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