Simple Explanation of LSTM | Deep Learning Tutorial 36 (Tensorflow, Keras & Python)

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Introduction

In this tutorial, we will explore LSTM (Long Short-Term Memory) networks, a specialized version of Recurrent Neural Networks (RNNs) designed to overcome the short-term memory limitations of traditional RNNs. To illustrate this, we will use relatable real-life examples, drawing comparisons with popular media like the movies "Memento" and "Ghazni," in which the main characters suffer from short-term memory issues.

Understanding the Problem

RNNs are commonly used for Natural Language Processing (NLP) tasks like sentence completion. The completion of a sentence often relies on the context built by the words appearing earlier. For instance, in the sentence:

  • "I need to take a loan…"
  • "I had to take a loan…"

The words "need" and "had" signal different contexts, but traditional RNNs struggle to maintain context over long sentences due to the vanishing gradient problem. This means they only have a short-term memory of a few words immediately preceding the current input.

Traditional RNN Architecture

The architecture of classic RNNs can be visualized as a single layer of neurons repeated over time (unrolling the network in time). When processing sentences, RNNs face problems retaining informative earlier words due to their limited memory.

The Need for LSTM

To better manage memory, we can introduce LSTM networks that incorporate two states: a short-term memory (hidden state) and a long-term memory (cell state).

Key Features of LSTM

In LSTM networks, key mechanisms are introduced to manage both memory types:

  1. Forget Gate: This decides which information to discard from the cell state. It analyzes the previous hidden state and the current input to ensure irrelevant data is forgotten, typically yielding a zero vector if that memory is deemed unimportant.

  2. Input Gate: This gate decides what new information should be added to the cell state. It combines the previous hidden state and the current input to generate a new cell memory, incorporating meaningful words while ignoring trivial words.

  3. Output Gate: This gate determines what should be output from the cell state. It assesses the current cell state and generates the new hidden state.

Example: Autocompleting a Sentence

Let’s apply this understanding to a practical sentence completion example. Consider the sentence: “I love eating samosa…”

From the word "samosa," we can conclude that the cuisine is likely Indian. Traditional RNNs struggle here as they often forget the context by retaining only the last few words. A trained LSTM, however, stores key words like "samosa" to maintain awareness of the context leading to “Indian cuisine.”

As we incorporate more complex sentences—for example, "While I love Indian cuisine, my brother Bhavin loves…"—LSTM recognizes and retains key details like "pasta" and "cheese," allowing it to accurately infer that the cuisine being discussed is Italian.

Conclusion

The clever design of LSTM networks effectively addresses the shortcomings of traditional RNNs by allowing them to remember meaningful context over longer sequences of input data. In subsequent tutorials, we will delve into coding examples using LSTM in Tensorflow and Keras.

For detailed mathematical representations and formulas of LSTM, I recommend referring to external resources that specialize in deep learning mathematics.

If you found this article helpful, please share it with your friends! Stay tuned for more tutorials on GRU (Gated Recurrent Units) and practical implementations.

Keywords

LSTM, RNN, deep learning, neural networks, long-term memory, short-term memory, forget gate, input gate, output gate, TensorFlow, Keras, Python, Natural Language Processing (NLP).

FAQ

1. What is LSTM?
LSTM stands for Long Short-Term Memory, a type of neural network architecture specifically designed to overcome the limitations of traditional RNNs regarding short-term memory.

2. How does LSTM work?
LSTM uses a combination of forget gates, input gates, and output gates to manage both short-term and long-term memory effectively, enabling better context retention over long sequences of data.

3. What are some applications of LSTM?
LSTMs are widely used in Natural Language Processing tasks, such as language modeling, sentence completion, and time-series prediction.

4. How are LSTM networks trained?
LSTM networks are trained using large datasets where various sequences of data help them learn to discern which information should be stored or forgotten across different contexts.

5. How does LSTM improve upon traditional RNNs?
LSTM networks excel in remembering longer sequences and context, effectively addressing the vanishing gradient problem encountered in traditional RNNs, thus providing better performance in tasks like language processing.