A Long Short-Term Memory (LSTM) network is a special kind of Recurrent Neural Network (RNN) designed to learn long-term dependencies in sequence data. Introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, LSTMs were created to overcome the limitations of traditional RNNs, particularly the vanishing gradient problem, which makes it difficult for RNNs to learn and retain information over long sequences.
Core Features of LSTM Networks:
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Memory Cells: At the heart of an LSTM is the concept of memory cells. These cells can maintain information in memory for long periods of time. Each cell contains mechanisms called gates that control the flow of information into and out of the cell, making LSTMs capable of remembering and forgetting information deliberately.
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Gates: LSTMs have three types of gates that regulate the information entering the memory cell (input gate), being remembered (forget gate), and being output (output gate):
- Input Gate: Decides how much of the new information to store in the memory cell.
- Forget Gate: Determines what portion of the existing memory to retain or discard.
- Output Gate: Controls the amount of memory to transfer to the output.
How LSTMs Work:
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Forget Gate: First, the forget gate decides which information to discard from the cell state by looking at the current input and the previous output. This gate outputs values between 0 and 1 for each number in the cell state, with 0 meaning "completely forget this" and 1 meaning "completely retain this."
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Input Gate: Next, the input gate decides which new information to update in the cell state. A sigmoid layer decides which values to update, and a tanh layer creates a vector of new candidate values that could be added to the state.
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Update Cell State: The old cell state is updated into the new cell state through the operations of the forget gate and the input gate. The old state is multiplied by the forget gate's output, potentially dropping values it decides to forget, and then adding the new candidate values, scaled by how much each state value should be updated.
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Output Gate: Finally, the output gate decides what the next hidden state should be. The hidden state contains information on previous inputs. The hidden state is used for predictions. The sigmoid layer decides which parts of the cell state will be output, and then the cell state is passed through a tanh function (to normalize the values to be between -1 and 1) and multiplied by the output of the sigmoid gate, so that only the decided parts of the cell state are output.
Applications of LSTMs:
LSTMs are highly versatile and have been used successfully in a wide range of applications, including:
- Language modeling and text generation.
- Speech recognition.
- Machine translation.
- Time series prediction.
- And many other tasks that involve sequential data.
The adaptability and effectiveness of LSTMs in handling long-term dependencies make them a powerful tool in the deep learning toolbox, especially for tasks that involve complex, sequential relationships in data.