Building a homemade Long Short Term Memory with FSMs

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In summary, "Building a homemade Long Short Term Memory with FSMs" discusses the construction of a Long Short Term Memory (LSTM) model using finite state machines (FSMs). The article outlines the fundamental principles of LSTMs, such as their ability to remember long-term dependencies in sequential data, and demonstrates how FSMs can be utilized to replicate these characteristics. It emphasizes the importance of gate mechanisms in LSTMs, explains the architecture of the model, and provides insights into practical implementation, ultimately showcasing a hands-on approach to understanding LSTM functionality.
  • #1
Trollfaz
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I am doing a project to build a Long Short Term Memory algorithm from scratch. LSTMs are capable of retaining memory of the past inputs and carrying them for future operations thanks to Recurring Neural Networks to process a series of inputs such as sounds and text.

One possible way I can think of such methods is Finite State Machines (FSMs) . In the simplest model the FSM at any point in time can be in any state ##s \epsilon S ##. After reading an input at time t, the state of the node transits from ##s_{t-1}## to ##s_t## via a function ##f_{in}(s_{t-1},x_t)## for a valid input ##x\epsilon X##. The node then produces an output ##o_t=f_{out}(s_t)## while it will remain in the transited state for the next iteration. In this way it can retain some memory or information of the past input.

Now in complex modelling such as text, does a large numbers of FSMs build a good LSTM model?
 
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I shall now elaborate on how the network of FSMs work. Allow the system to contain N FSMs for a large value N say ##10^4##. Each FSM has it's output assigned to a random weight and multiplied by it. Hence the aggregate output of the system gives
$$\sum_{i=1}^N w_i o_i= \textbf{w}^T\textbf{o}_t$$
where ##\textbf{w},\textbf{o}_t## is the vector of assigned weights and output of the nodes at t respectively. The weights are free to adjust when we teach the algorithm and are initially set to small random values. During training, we minimize the loss L=##\sum (predicted-actual)^2## by gradient descent with respect to the weights.
 
  • #3
Sounds like what is (or was, see below) normally done with the terminology of 'gates', or 'neurons', being replaced by the words 'finite state machine'. 'Neural network' is another common term that seems to apply to the same general approach.

Disclaimer: I'm Not an expert by any means! I've only dabbled in the field out of curiousity, and that was many years ago.

Cheers,
Tom
 
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FAQ: Building a homemade Long Short Term Memory with FSMs

What is a Long Short Term Memory (LSTM) network?

A Long Short Term Memory (LSTM) network is a type of recurrent neural network (RNN) that is capable of learning long-term dependencies in sequential data. LSTMs are designed to overcome the vanishing gradient problem that traditional RNNs face, allowing them to remember information for extended periods. This makes them particularly effective for tasks involving time series prediction, natural language processing, and other sequential data tasks.

What are Finite State Machines (FSMs) and how do they relate to LSTMs?

Finite State Machines (FSMs) are computational models used to represent and control execution flow based on a finite number of states and transitions between those states. In the context of LSTMs, FSMs can be used to illustrate the internal workings of LSTM units, where the states represent memory cells and transitions correspond to the gates (input, output, and forget gates) that regulate the flow of information. This analogy helps in understanding how LSTMs manage information over time.

How can I build a homemade LSTM using FSM concepts?

To build a homemade LSTM using FSM concepts, start by defining the states that represent the memory cells and the inputs to the network. Then, implement the gating mechanisms (input, forget, and output gates) as transitions that determine how information is processed and stored. You can use programming languages like Python with libraries such as NumPy to create the mathematical operations required for the LSTM. Simulating the FSM behavior will help you visualize how data flows through the network and how long-term dependencies are maintained.

What are the challenges of building an LSTM from scratch?

Building an LSTM from scratch presents several challenges, including understanding the underlying mathematics of the gating mechanisms, ensuring proper initialization of weights, and managing the complexity of backpropagation through time (BPTT) for training. Additionally, optimizing performance and preventing overfitting can be difficult without the use of established frameworks that handle these issues. Debugging and validating the model can also be more time-consuming than using pre-built libraries.

What applications can benefit from using a homemade LSTM?

Homemade LSTMs can be applied to a variety of tasks that involve sequential data, such as time series forecasting, natural language processing (e.g., text generation, sentiment analysis), speech recognition, and even music composition. By customizing the LSTM architecture to fit specific use cases, you can explore unique applications and gain deeper insights into how LSTMs function and perform in different scenarios.

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