1. Home
  2. Applications of LSTM Networks

Applications of LSTM Networks

LSTM models need to be trained with a training dataset prior to their employment in real-world applications. Some of the most demanding applications are discussed below:

  1. Language modeling or text generation, involves the computation of words when a sequence of words is fed as input. Language models can be operated at the character level, n-gram level, sentence level, or even paragraph level.
  2. Image processing involves performing an analysis of a picture and concluding its result into a sentence. For this, it’s required to have a dataset comprising a good amount of pictures with their corresponding descriptive captions. A model that has already been trained is used to predict features of images present in the dataset. This is photo data. The dataset is then processed in such a way that only the words that are most suggestive are present in it. This is text data. Using these two types of data, we try to fit the model. The work of the model is to generate a descriptive sentence for the picture one word at a time by taking input words that were predicted previously by the model and also the image.
  3. Speech and Handwriting Recognition.
  4. Music generation is quite similar to that of text generation where LSTMs predict musical notes instead of text by analyzing a combination of given notes fed as input.
  5. Language Translation involves mapping a sequence in one language to a sequence in another language. Similar to image processing, a dataset, containing phrases and their translations, is first cleaned and only a part of it is used to train the model. An encoder-decoder LSTM model is used which first converts the input sequence to its vector representation (encoding) and then outputs it to its translated version.

Understanding of LSTM Networks

This article talks about the problems of conventional RNNs, namely, the vanishing and exploding gradients, and provides a convenient solution to these problems in the form of Long Short Term Memory (LSTM). Long Short-Term Memory is an advanced version of recurrent neural network (RNN) architecture that was designed to model chronological sequences and their long-range dependencies more precisely than conventional RNNs. 

Similar Reads

Introduction to LSTM

LSTM networks are an extension of recurrent neural networks (RNNs) mainly introduced to handle situations where RNNs fail....

Structure of LSTM

The basic difference between the architectures of RNNs and LSTMs is that the hidden layer of LSTM is a gated unit or gated cell. It consists of four layers that interact with one another in a way to produce the output of that cell along with the cell state. These two things are then passed onto the next hidden layer. Unlike RNNs which have got only a single neural net layer of tanh, LSTMs comprise three logistic sigmoid gates and one tanh layer. Gates have been introduced in order to limit the information that is passed through the cell. They determine which part of the information will be needed by the next cell and which part is to be discarded. The output is usually in the range of 0-1 where ‘0’ means ‘reject all’ and ‘1’ means ‘include all’....

Variations in LSTM Networks

With the increasing popularity of LSTMs, various alterations have been tried on the conventional LSTM architecture to simplify the internal design of cells to make them work in a more efficient way and to reduce computational complexity. Gers and Schmidhuber introduced peephole connections which allowed gate layers to have knowledge about the cell state at every instant. Some LSTMs also made use of a coupled input and forget gate instead of two separate gates which helped in making both decisions simultaneously. Another variation was the use of the Gated Recurrent Unit(GRU) which improved the design complexity by reducing the number of gates. It uses a combination of the cell state and hidden state and also an update gate which has forgotten and input gates merged into it....

GRU v/s LSTM

In spite of being quite similar to LSTMs, GRUs have never been so popular. But what are GRUs? GRU stands for Gated Recurrent Units. As the name suggests, these recurrent units, proposed by Cho, are also provided with a gated mechanism to effectively and adaptively capture dependencies of different time scales. They have an update gate and a reset gate. The former is responsible for selecting what piece of knowledge is to be carried forward, whereas the latter lies in between two successive recurrent units and decides how much information needs to be forgotten. Activation at time t:...

Applications of LSTM Networks

LSTM models need to be trained with a training dataset prior to their employment in real-world applications. Some of the most demanding applications are discussed below:...

Drawbacks of Using LSTM Networks

As it is said, everything in this world comes with its own advantages and disadvantages, LSTMs too, have a few drawbacks which are discussed below:...

Categories

HTML CSS3 Bootstrap Foundation Sass JavaScript TypeScript JQuery AngularJS React NodeJS Highcharts PHP Python Python3 Django Linux Docker Ruby Java C C ++ Perl Servlet JSP Lua Scala Go ASP C # SQL MySQL SQLite MongoDB Redis Android Swift ionic Kotlin XML tutorial

Contact US