Last Updated on August 14, 2019 There is another type of neural network that is dominating difficult machine learning problems that involve sequences of inputs called recurrent neural networks. Recurrent neural networks have connections that have loops, adding feedback and memory to the networks over time. This memory allows this type of network to learn and generalize across sequences of inputs rather than individual patterns. A powerful type of Recurrent Neural Network called the Long Short-Term Memory Network has been […]

Last Updated on August 27, 2020 Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning over long sequences. This differentiates them from regular multilayer neural networks that do not have memory and can only learn a mapping between input and output patterns. It is important to understand the capabilities of complex neural networks like LSTMs on small contrived problems as this understanding will help you scale the network up to large and even very […]

Last Updated on August 14, 2019 Keras uses fast symbolic mathematical libraries as a backend, such as TensorFlow and Theano. A downside of using these libraries is that the shape and size of your data must be defined once up front and held constant regardless of whether you are training your network or making predictions. On sequence prediction problems, it may be desirable to use a large batch size when training the network and a batch size of 1 when […]

Last Updated on August 14, 2019 Long Short-Term Networks or LSTMs are a popular and powerful type of Recurrent Neural Network, or RNN. They can be quite difficult to configure and apply to arbitrary sequence prediction problems, even with well defined and “easy to use” interfaces like those provided in the Keras deep learning library in Python. One reason for this difficulty in Keras is the use of the TimeDistributed wrapper layer and the need for some LSTM layers to […]

Last Updated on August 27, 2020 Long Short-Term Memory (LSTM) networks are a type of Recurrent Neural Network (RNN) that are capable of learning the relationships between elements in an input sequence. A good demonstration of LSTMs is to learn how to combine multiple terms together using a mathematical operation like a sum and outputting the result of the calculation. A common mistake made by beginners is to simply learn the mapping function from input term to the output term. […]

Last Updated on February 20, 2020 Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems. This is a behavior required in complex problem domains like machine translation, speech recognition, and more. LSTMs are a complex area of deep learning. It can be hard to get your hands around what LSTMs are, and how terms like bidirectional and sequence-to-sequence relate to the field. In this post, you will get […]

Last Updated on August 27, 2020 Deep learning neural networks are very easy to create and evaluate in Python with Keras, but you must follow a strict model life-cycle. In this post, you will discover the step-by-step life-cycle for creating, training, and evaluating Long Short-Term Memory (LSTM) Recurrent Neural Networks in Keras and how to make predictions with a trained model. After reading this post, you will know: How to define, compile, fit, and evaluate an LSTM in Keras. How […]

Last Updated on August 27, 2020 Long Short-Term Memory (LSTM) Recurrent Neural Networks are able to learn the order dependence in long sequence data. They are a fundamental technique used in a range of state-of-the-art results, such as image captioning and machine translation. They can also be difficult to understand, specifically how to frame a problem to get the most out of this type of network. In this tutorial, you will discover how to develop a simple LSTM recurrent neural […]

Last Updated on August 27, 2020 A powerful feature of Long Short-Term Memory (LSTM) recurrent neural networks is that they can remember observations over long sequence intervals. This can be demonstrated by contriving a simple sequence echo problem where the entire input sequence or partial contiguous blocks of the input sequence are echoed as an output sequence. Developing LSTM recurrent neural networks to address the sequence echo problem is both a good demonstration of the power of LSTMs and can […]

Last Updated on August 27, 2020 Bidirectional LSTMs are an extension of traditional LSTMs that can improve model performance on sequence classification problems. In problems where all timesteps of the input sequence are available, Bidirectional LSTMs train two instead of one LSTMs on the input sequence. The first on the input sequence as-is and the second on a reversed copy of the input sequence. This can provide additional context to the network and result in faster and even fuller learning […]