All about Long Short-Term Memory

AI Maverick
7 min readMay 18


All about Long Short-Term Memory or LSTM in Machine learning


Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture designed to address the challenges of capturing long-term dependencies and mitigating the vanishing gradient problem in sequential data processing. By incorporating specialized memory cells and gating mechanisms, LSTM networks are capable of selectively retaining and forgetting information over extended time intervals, enabling them to model and learn from complex temporal patterns.


(Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture that is widely used in machine learning and deep learning for handling sequential data. It is particularly effective in capturing long-term dependencies and managing the vanishing gradient problem commonly encountered in traditional RNNs.

The primary advantage of LSTM over traditional RNNs lies in its ability to selectively retain and forget information over extended time intervals. This is accomplished through the use of specialized memory cells, which are capable of storing information for long periods without degradation. As a result, LSTM networks are better equipped to capture dependencies that exist across time steps, making them suitable for tasks such as natural language processing, speech recognition, time series analysis, and more.

Key components

The key components of an LSTM network are as follows:

  1. Memory cell: The memory cell forms the core of the LSTM architecture. It stores information over time by incorporating input, previous memory, and output from the previous time step. It consists of a cell state (the memory) and various gating mechanisms that control the flow of information.
  2. Forget gate: This gate determines which information should be discarded from the cell state. It takes as input the previous output and the current input and produces a value between 0 and 1 for each element of the cell state. A value of 0 indicates that the corresponding information should be forgotten, while a value of 1 means it should be retained.
  3. Input gate: The input gate regulates how much of the new input should be added to the cell state. It uses a combination of the previous output and the current input to produce an update value between 0 and 1 for each element of the cell state.
  4. Output gate: The output gate determines the amount of information to be output from the memory cell. It considers the current input and the previous output to compute an output value that is passed to the next time step and potentially to the final model output.

These gates work together to control the flow of information within the LSTM network, allowing it to capture long-term dependencies and mitigate the vanishing gradient problem. By selectively retaining and forgetting information, the LSTM network can effectively model and learn from sequential data.


Training an LSTM network involves optimizing its parameters using a variant of backpropagation called backpropagation through time (BPTT). BPTT calculates gradients by unfolding the network over time and propagating errors backward. This process enables the LSTM to learn the patterns and relationships within the sequential data and make predictions or classifications based on them.

Overall, LSTM networks have proven to be a powerful tool for processing sequential data, offering improved performance over traditional RNNs in many applications. Their ability to capture long-term dependencies makes them well-suited for tasks that involve processing sequences of data points over extended periods.

step-by-step overview of the training phase of an LSTM

  1. Data Preparation: Before training an LSTM network, the sequential data needs to be prepared. This involves preprocessing the data, such as normalizing or scaling and dividing it into appropriate input sequences and corresponding target sequences. Each input sequence represents a chunk of sequential data, and the corresponding target sequence represents the desired output or prediction.
  2. Initialization: The parameters of the LSTM network, including the weights and biases of the memory cells and gates, are initialized randomly or with pre-trained values. These parameters will be adjusted during the training phase to minimize the error between the network’s predictions and the target values.
  3. Forward Pass: The training process begins by feeding an input sequence into the LSTM network. The network processes the input sequence one-time step at a time, passing the information through the memory cells and gates. At each time step, the network computes the output based on the input, the previous output, and the previous memory cell state.
  4. Loss Calculation: After the forward pass, the network’s output is compared to the corresponding target sequence. A loss function is used to quantify the discrepancy between the predicted values and the true values. Common loss functions for different types of tasks include mean squared error (MSE) for regression and categorical cross-entropy for classification.
  5. Backpropagation: Once the loss is calculated, the gradients of the parameters with respect to the loss are computed using the chain rule of calculus. The BPTT algorithm unfolds the LSTM network over time, treating it as a deep feedforward neural network, and propagates the gradients backward through the unfolded network.
  6. Gradient Update: The gradients computed in the previous step are used to update the parameters of the LSTM network. This is typically done using an optimization algorithm such as stochastic gradient descent (SGD) or one of its variants. The optimization algorithm adjusts the parameters in a way that minimizes the loss function.
  7. Iteration: Steps 3 to 6 are repeated for multiple iterations or epochs, where each iteration involves feeding a new input sequence, computing the loss, performing backpropagation, and updating the parameters. The number of iterations and the batch size (number of input sequences processed simultaneously) are hyperparameters that need to be tuned based on the specific problem and available computational resources.
  8. Validation and Testing: During training, it is common to monitor the network’s performance on a validation set, which is separate from the training set. This helps in assessing the network’s generalization ability and detecting overfitting. Once training is complete, the final performance of the LSTM network is evaluated on a separate testing set.

By repeating the forward pass, loss calculation, backpropagation, and gradient update steps over multiple iterations, the LSTM network gradually learns to capture the patterns and dependencies within the sequential data. The goal is to minimize the loss function and improve the network’s ability to make accurate predictions or classifications.

It’s worth noting that the training phase of a LSTM network can be computationally intensive and may require significant computational resources, especially for large-scale datasets and complex architectures. However, once the LSTM network is trained, it can be used for efficient predictions or classifications of new sequential data.

LSTM layers

An LSTM network is composed of multiple layers, which work together to process sequential data and capture dependencies over time. Let’s explore the different layers commonly found in an LSTM network:

  1. Input Layer: The input layer of an LSTM network receives the sequential data as input. Each time step of the sequential data is typically represented as a vector or a sequence of feature values. The input layer processes the input data and passes it to the subsequent layers.
  2. LSTM Layers: The LSTM layers form the core of the LSTM network. They consist of memory cells and gating mechanisms that control the flow of information. The number of LSTM layers in an LSTM network can vary based on the complexity of the task and the depth of the network. Each LSTM layer can have multiple memory cells, allowing the network to capture different levels of abstraction and temporal dependencies.
  3. Hidden Layers: In addition to the LSTM layers, an LSTM network can include one or more hidden layers composed of fully connected (dense) layers or other types of neural network layers. These hidden layers can be inserted between the LSTM layers or stacked on top of them. They provide additional capacity for the network to learn complex representations and transformations of the sequential data.
  4. Output Layer: The output layer produces the final output of the LSTM network. Its structure depends on the specific task the network is designed for. For example, in regression tasks, the output layer might consist of a single neuron that produces a continuous value as the prediction. In classification tasks, the output layer might involve multiple neurons with softmax activation to produce class probabilities.
  5. Activation Functions: Activation functions are applied at various stages of an LSTM network to introduce non-linearities. Common activation functions used in LSTM layers and hidden layers include sigmoid, tanh, and ReLU. These activation functions enable the network to model complex relationships and capture non-linear dependencies in the sequential data.

The number and configuration of layers in an LSTM network depend on the complexity of the task, the amount of available data, and the computational resources. Deeper networks with multiple LSTM layers and hidden layers can capture more intricate patterns and relationships in the sequential data, but they may require more training time and computational power.


STM (Long Short-Term Memory) networks have emerged as a powerful architecture for processing sequential data in machine learning and deep learning. They address the challenges of capturing long-term dependencies and mitigating the vanishing gradient problem that traditional recurrent neural networks face.

The key strength of LSTM lies in its ability to selectively retain and forget information over extended time intervals. Through specialized memory cells and gating mechanisms, LSTM networks can store and update information in the memory state while controlling the flow of information through forget, input, and output gates. This enables them to capture complex patterns and dependencies in sequential data.