In the previous post, we talked about RNN, and how performing Backpropagation through time (BPTT) on an unrolled RNN with many time steps can lead to the problems of vanishing / exploding gradients, and difficulties in learning long term dependencies.

In this post, we’re going to look at a the LSTM (Long Short Term Memory) model that is a variant of an RNN, but is designed specifically to combat the 2 issues.

LSTM Structure

Lets visually inspect the difference between a normal RNN cell and an LSTM cell.

An unrolled RNN cell
An unrolled LSTM cell

The A on each of the cells represent the Activation in the cell. In the RNN, this can either be a Sigmoid, tanh, ReLU, or other activation functions. In the LSTM however, its a combination of 3 Sigmoids and a tanh function.

The biggest difference, aside from the more complex internal structure of the LSTM, is that it has two connecting data pipelines from cell to cell.

Cell State

The top line of an LSTM cell represents the cell state

The LSTM has the ability to modify the cell state by removing information (through the multiplicative forget gate), or adding information (through the additive input gate)

This data flow from cell to cell is modified by two operators: The multiplication operator, and the addition operator denoted by the two pink nodes

LSTM Gates

The LSTM has 3 gates in the cell:

  1. Forget Gate
  2. Input gate
  3. Output Gate

These gates modify the data that is flowing in and out of the LSTM cell

The Common Sigmoid Layer

In all the 3 gates, there exists the common Sigmoid layer. This layer outputs a value from 0 to 1 for each state in the cell state.

The common Sigmoid layer in all 3 gates

The Sigmoid layer outputs a value from 0 to 1. This value corresponds to a value in the cell state, and this would mean different things for different gates.

In the Forget gate, 0 would mean forget the value in the cell state, and 1 would mean remember the value entirely.

In the Input gate, 0 would mean do not update this value at all, and 1 would mean update the value entirely.

In the Output gate, 0 would mean do not output this cell state value, and 1 would mean output this value entirely.

Forget Gate

The first gate is the forget gate. This gate decides what information to discard from the cell state.

This gate has the Sigmoid activation function. It takes in the previous time step’s output, and the current time step input.

The two inputs are concatenated, and passed through the Sigmoid layer.

Recall that a Sigmoid activation function outputs a value from 0 to 1. A value of 0 means completely forget this input value, while 1 means to remember the value entirely.

Input Gate

The next gate is the input gate, which is a combination of both the Sigmoid and tanh activation function. This gate decides what new information to add to the cell state.

There are two steps in the input gate phase

In the first step, the output of the previous time step and the input of the current time step are concatenated together, and passed into a Sigmoid. This layer decides which cell state values to update. (0 means do not update, 1 means update entirely)

The inputs are also passed into a tanh activation function, which tells the model what to update the cells states with.

The multiplicative combination of these two outputs tells us which cell state to update (from the sigmoid layer), and what to update it with (tanh layer)

Output Gate

The last gate, output gate, decides what value the cell would output. The output is derived from multiplying the outputs from the Sigmoid layer and tanh layer

The Sigmoid layer takes in the previous and current time step values, and outputs values 0 to 1 for each value in the cell. A value of 0 means do not output this cell state value at all, and 1 means to output the entire value.

The tanh layer takes in the current cell state, which scales the values to be from -1 to 1. This value is then multiplied by the output of the Sigmoid layer to get the final output value.

Modification of the Cell State

The cell states in each LSTM cell are modified either by the forget gate, or the input gate.

The forget gate outputs values 0-1, and is multiplied by the cell state. Cell states multiplied by 0 will be completely forgotten, while those multiplied by value > 0 will be remembered by varying degrees.

The input gate then updates each value in the cell state by a candidate amount (from the tanh layer), scaled by a factor (decided from the Sigmoid layer)


In each LSTM cell, there contains a cell state.

Forget gate decides what to drop from the cell state

Input gate creates candidate values to update the cell state

Output gate decides what values to output from the cell state

Final Notes

I usually end with the conclusion, but all the information above was all the technical aspects of an LSTM. Here are some further questions relating to LSTM.

Q: What are you actually training when you do your Backpropagation through time on an LSTM?

A: Recall that the gates have to control what to forget, input and output from each LSTM cell. What exactly to forget, input and output are the variables being trained.

Q: So… how does it combat vanishing/exploding gradients?

Gradients explode when their values are greater than 1, and vanish when their values are lesser than 1, and are backpropagated for too large a time step.

The key to LSTM preventing the vanexplgrad (I just made that up) is cell state updating step. Below shows the formula for updating the cell

Calculating the current cell state using values from the previous cell state
  • c(t) is the current cell state to compute
  • i is the input gate that decides which cell state to update, g is the actual value changes to be made to the cell state. These two are multiplied together to get final cell state changes.
  • f is the forget gate, and c(t-1) is the previous cell state. These two are multiplied to drop values from the previous cell state.

When performing backpropagation, we find the derivative w.r.t the error. This gives us the formula

Derivative of w.r.t the error

This formula does not have any multiplicative element in it, and so when BPTT occurs, a linear carousel occurs, thus preventing vanexplgrad.

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