It can be difficult to determine whether your Long Short-Term Memory model is performing well on your sequence prediction problem. You may be getting a good model skill score, but it is important to know whether your model is a good fit for your data or if it is underfit or overfit and could do […]

# Archive | Long Short-Term Memory Networks

## How to Reshape Input Data for Long Short-Term Memory Networks in Keras

It can be difficult to understand how to prepare your sequence data for input to an LSTM model. Often there is confusion around how to define the input layer for the LSTM model. There is also confusion about how to convert your sequence data that may be a 1D or 2D matrix of numbers to […]

## How to Make Predictions with Long Short-Term Memory Models in Keras

The goal of developing an LSTM model is a final model that you can use on your sequence prediction problem. In this post, you will discover how to finalize your model and use it to make predictions on new data. After completing this post, you will know: How to train a final LSTM model. How […]

## Gentle Introduction to Generative Long Short-Term Memory Networks

The Long Short-Term Memory recurrent neural network was developed for sequence prediction. In addition to sequence prediction problems. LSTMs can also be used as a generative model In this post, you will discover how LSTMs can be used as generative models. After completing this post, you will know: About generative models, with a focus on […]

## Encoder-Decoder Long Short-Term Memory Networks

Gentle introduction to the Encoder-Decoder LSTMs for sequence-to-sequence prediction with example Python code. The Encoder-Decoder LSTM is a recurrent neural network designed to address sequence-to-sequence problems, sometimes called seq2seq. 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 […]

## CNN Long Short-Term Memory Networks

Gentle introduction to CNN LSTM recurrent neural networks with example Python code. Input with spatial structure, like images, cannot be modeled easily with the standard Vanilla LSTM. The CNN Long Short-Term Memory Network or CNN LSTM for short is an LSTM architecture specifically designed for sequence prediction problems with spatial inputs, like images or videos. […]

## Stacked Long Short-Term Memory Networks

Gentle introduction to the Stacked LSTM with example code in Python. The original LSTM model is comprised of a single hidden LSTM layer followed by a standard feedforward output layer. The Stacked LSTM is an extension to this model that has multiple hidden LSTM layers where each layer contains multiple memory cells. In this post, […]

## Mini-Course on Long Short-Term Memory Recurrent Neural Networks with Keras

Long Short-Term Memory (LSTM) recurrent neural networks are one of the most interesting types of deep learning at the moment. They have been used to demonstrate world-class results in complex problem domains such as language translation, automatic image captioning, and text generation. LSTMs are different to multilayer Perceptrons and convolutional neural networks in that they […]

## Get the Most out of LSTMs on Your Sequence Prediction Problem

Long Short-Term Memory (LSTM) Recurrent Neural Networks are a powerful type of deep learning suited for sequence prediction problems. A possible concern when using LSTMs is if the added complexity of the model is improving the skill of your model or is in fact resulting in lower skill than simpler models. In this post, you […]

## 5 Examples of Simple Sequence Prediction Problems for Learning LSTM Recurrent Neural Networks

Sequence prediction is different from traditional classification and regression problems. It requires that you take the order of observations into account and that you use models like Long Short-Term Memory (LSTM) recurrent neural networks that have memory and that can learn any temporal dependence between observations. It is critical to apply LSTMs to learn how […]