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, you will discover the Stacked LSTM model architecture.

After completing this tutorial, you will know:

  • The benefit of deep neural network architectures.
  • The Stacked LSTM recurrent neural network architecture.
  • How to implement stacked LSTMs in Python with Keras.

Let’s get started.

Gentle Introduction to Stacked Long Short-Term Memory Networks

Gentle Introduction to Stacked Long Short-Term Memory Networks
Photo by Joost Markerink, some rights reserved.


This post is divided into 3 parts, they are:

  1. Why Increase Depth?
  2. Stacked LSTM Architecture
  3. Implement Stacked LSTMs in Keras

Why Increase Depth?

Stacking LSTM hidden layers makes the model deeper, more accurately earning the description as a deep learning technique.

It is the depth of neural networks that is generally attributed to the success of the approach on a wide range of challenging prediction problems.

[the success of deep neural networks] is commonly attributed to the hierarchy that is introduced due to the several layers. Each layer processes some part of the task we wish to solve, and passes it on to the next. In this sense, the DNN can be seen as a processing pipeline, in which each layer solves a part of the task before passing it on to the next, until finally the last layer provides the output.

Training and Analyzing Deep Recurrent Neural Networks, 2013

Additional hidden layers can be added to a Multilayer Perceptron neural network to make it deeper. The additional hidden layers are understood to recombine the learned representation from prior layers and create new representations at high levels of abstraction. For example, from lines to shapes to objects.

A sufficiently large single hidden layer Multilayer Perceptron can be used to approximate most functions. Increasing the depth of the network provides an alternate solution that requires fewer neurons and trains faster. Ultimately, adding depth it is a type of representational optimization.

Deep learning is built around a hypothesis that a deep, hierarchical model can be exponentially more efficient at representing some functions than a shallow one.

How to Construct Deep Recurrent Neural Networks, 2013.

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Stacked LSTM Architecture

The same benefits can be harnessed with LSTMs.

Given that LSTMs operate on sequence data, it means that the addition of layers adds levels of abstraction of input observations over time. In effect, chunking observations over time or representing the problem at different time scales.

… building a deep RNN by stacking multiple recurrent hidden states on top of each other. This approach potentially allows the hidden state at each level to operate at different timescale

How to Construct Deep Recurrent Neural Networks, 2013

Stacked LSTMs or Deep LSTMs were introduced by Graves, et al. in their application of LSTMs to speech recognition, beating a benchmark on a challenging standard problem.

RNNs are inherently deep in time, since their hidden state is a function of all previous hidden states. The question that inspired this paper was whether RNNs could also benefit from depth in space; that is from stacking multiple recurrent hidden layers on top of each other, just as feedforward layers are stacked in conventional deep networks.

Speech Recognition With Deep Recurrent Neural Networks, 2013

In the same work, they found that the depth of the network was more important than the number of memory cells in a given layer to model skill.

Stacked LSTMs are now a stable technique for challenging sequence prediction problems. A Stacked LSTM architecture can be defined as an LSTM model comprised of multiple LSTM layers. An LSTM layer above provides a sequence output rather than a single value output to the LSTM layer below. Specifically, one output per input time step, rather than one output time step for all input time steps.

Stacked Long Short-Term Memory Archiecture

Stacked Long Short-Term Memory Archiecture

Implement Stacked LSTMs in Keras

We can easily create Stacked LSTM models in Keras Python deep learning library

Each LSTMs memory cell requires a 3D input. When an LSTM processes one input sequence of time steps, each memory cell will output a single value for the whole sequence as a 2D array.

We can demonstrate this below with a model that has a single hidden LSTM layer that is also the output layer.

The input sequence has 3 values. Running the example outputs a single value for the input sequence as a 2D array.

To stack LSTM layers, we need to change the configuration of the prior LSTM layer to output a 3D array as input for the subsequent layer.

We can do this by setting the return_sequences argument on the layer to True (defaults to False). This will return one output for each input time step and provide a 3D array.
Below is the same example as above with return_sequences=True.

Running the example outputs a single value for each time step in the input sequence.

Below is an example of defining a two hidden layer Stacked LSTM:

We can continue to add hidden LSTM layers as long as the prior LSTM layer provides a 3D output as input for the subsequent layer; for example, below is a Stacked LSTM with 4 hidden layers.

Further Reading

This section provides more resources on the topic if you are looking go deeper.


In this post, you discovered the Stacked Long Short-Term Memory network architecture.

Specifically, you learned:

  • The benefit of deep neural network architectures.
  • The Stacked LSTM recurrent neural network architecture.
  • How to implement stacked LSTMs in Python with Keras.

Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.

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Long Short-Term Memory Networks with Python

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14 Responses to Stacked Long Short-Term Memory Networks

  1. Thabet August 18, 2017 at 11:58 am #

    Thanks alot Jason !
    Your blog is wonderful
    Please keep up the great work
    Best regards/ Thabet

  2. Devakar Kumar Verma August 23, 2017 at 10:48 pm #

    Hi Jason,
    After first stack of LSTM layer, Don’t we need ‘input_shape’ or ‘batch_input_shape’? Need your expert comment.

    • Jason Brownlee August 24, 2017 at 6:43 am #

      No, the input specification is only needed on the first hidden layer.

      • Devakar Kumar Verma August 24, 2017 at 2:14 pm #

        Thanks for your response

  3. Alessandro October 12, 2017 at 3:20 am #

    Can you specify when this approach is needed?

    Wonderful work, thanks!

    • Jason Brownlee October 12, 2017 at 5:35 am #

      Hard question, nice.

      Perhaps generally when you think there may be a hierarchical structure in your sequence data. You can try some stacked LSTMs and see how it impacts model skill.

      Stacked LSTMS will likely need more epochs to complete training, use normal model diagnostics.

  4. DEHUA TANG October 12, 2017 at 7:27 pm #

    Hi Jason, thanks for your work!
    If I have a large size of data, I want to train a Stacked LSTMS about 30 layers.
    Can you tell me if I train a 30 layers Stacked LSTMS, what do I need to pay attention to?
    Why 3 or 4 layers Stacked LSTMS are common?
    would the 30 layers Stacked LSTMS work?

    • Jason Brownlee October 13, 2017 at 5:46 am #

      That is a lot of layers, I have not developed LSTMs that deep myself. I cannot give you good advice.

      Generally, there are diminishing returns beyond 4 layers.

      • Jaime October 18, 2017 at 9:54 am #

        Thank you I think you for your answer, I think that probably there are to much layers and to try to summarize:

        Its necessary and a Dropout and/or Dense(1 LSTM,1Drop, 1 Dense) layer for every LSTM layer in a model or that is almost the same that for example (2 LSTM, 1Drop, 1Dense)

        Thank you in advance

  5. mitillo October 16, 2017 at 9:52 pm #

    Hello Jason,

    Like always, very useful article,

    I have a question for you

    I am get use to add LSTM labels in this way:

    layers=shape = [4, seq_len, 1] # feature, window, output
    # neuros=neurons = [128, 128, 32, 1]

    LSTM, Dropout and Dense.

    model.add(LSTM(250, input_shape=(layers[1], layers[0]), return_sequences=True))

    model.add(LSTM(neurons[1], input_shape=(layers[1], layers[0]), return_sequences=True))

    model.add(LSTM(neurons[2], input_shape=(layers[1], layers[0]), return_sequences=False))




    model.compile(loss=’mse’,optimizer=optimizador, metrics=[‘accuracy’])

    There is any difference if I add only LSTM I mean something like that

    model.add(LSTM(250, input_shape=(layers[1], layers[0]), return_sequences=True))

    model.add(LSTM(neurons[1], input_shape=(layers[1], layers[0]), return_sequences=True))

    model.add(LSTM(neurons[2], input_shape=(layers[1], layers[0]), return_sequences=False))


    model.compile(loss=’mse’,optimizer=optimizador, metrics=[‘accuracy’])

    Thank you in advance

  6. Ioudarene November 14, 2017 at 8:36 pm #

    Hello Jason,

    First of all thank you for all your work on your website it is very useful.

    I am implementing in keras a stacked lstm network for solving a many to many sequence problem, I would like to know if for that kind of problem you would still put the parameter return_sequences at the value False for the last lstm layer ?

    Thank you in advance.

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