Think about when we are listening to a story or someone is communicating with us. Do we consider their every word individually and process every word they speak, or do we connect one word with the next and so on to understand their conversation? Suppose our brain worked in a way that it processes every single word individually. It would be very difficult to understand each other. However, traditional artificial networks weren’t as advanced and had to process each piece of data individually. Similarly, suppose you are watching a movie, and your mind had to process every scene individually. It would take a lot of time to understand the plot.
LSTM helps the system to carry the data for a long time. Artificial neural networks also work the same way. To understand the concept of LSTM, you need first to understand what Recurrent Neural Networks are and how they function.
Artificial Neural Networks
Artificial neural networks are an artificial network that performs activities similar to our brains. The human brain and its process inspired the model of artificial neural networks. We have neurons in our brains that connect and help transmit the message and learning.
The artificial neural network performs the same function and has the same nature as our brain’s networks. Data is transferred into the neuron through input, and the data is sent as output after processing. Artificial neural networks help perform tasks such as classification of the data and recognition of the pattern.
These artificial networks are the layers of different neuron units. These units consist of three layers. An input receives the data, the hidden layer uses weight to calculate the outcome, and then the result moves to the higher level of the neuron through the output layer. This algorithm helps the system in the learning process.
Recurrent Neural Networks
The concept of recurrent neural networks is to follow the sequence of the information. In the traditional method, we were unable to consider different inputs and outputs collectively. Even if the information was connected, we considered it an individual. This created various challenges for many tasks. It is obvious that you have to know the first word to predict the next word as both are interconnected.
The reason this neural network is recurrent because it can process the same task in the same way, keeping the data in a sequence. The output in recurrent neural networks is according to the previous computation. You can also consider recurrent neural networks as a memory that gathers and stores information about what the system has calculated till now. A recurrent neural network system can look back at a few steps to use previous information for current findings.
Long Short Term Memory (LSTM)
LSTM is useful for deep machine learning. In LSTM, you will find the connection of the feedback. LSTM can process single data as well as a sequence, such as a complete video. This application is for speech recognition and handwriting recognition. It helps in avoiding problems related to long term dependency. Their most common use is developing the learning process of huge problems.
Long and short term memory is also a recurrent neural network, but it is different from other networks. Other networks repeat the module every time the input receives new information. However, LSTM will remember the problem for a longer time and has a chain-like structure to repeat the module. They interact in a special method and contain four neural network layers.
The Working Mechanism of LSTM
The process of data transfer is the same as standard recurrent neural networks. However, the operation to propagate the information is different. When the information passes through, the operation decides which to information process further and which information it should let go of. The main operation consists of cells and gates. The cell state works as a pathway to transfer the information. You can consider cells as memory.
There are various gates in the LSTM process. When the cell state is carrying the information, these gates help the new information flow. The gates will indicate which data is useful to keep and which data is not useful, making it okay to throw. So only the relevant data passes through the chain of sequence for easy prediction.
The gates contain various activations called sigmoid, which contain some values. These values range from zeros to one. These values help in forgetting and keeping the information. If the data multiply by one, the value of that data remains the same. However, if the data multiplies by zero, the value becomes zero and disappears. We can learn more if we closely understand these gates. There are three types of gates:
· Forget Gate
The first gate that we will understand is the forget gate. The function of this gate is to decide to keep or forget the information. Only information coming from previously hidden layers and the current input stays with the sigmoid function. Any value that is closer to one will stay, and any value closer to zero will disappear.
· Input Gate
The input gate helps in updating the cell state. The current input and previous state information pass through the sigmoid function, which will update the value by multiplying with zero and one. Similarly, for regulating the network, the data also passes through the tanh function. Now, the output of sigmoid multiplies by the output of tanh. The output of sigmoid will identify valuable information to keep from the tanh output.
· Cell State
Now, the information we have will help us calculate the cell state. The cell state’s value may drop if the multiplied value is near zero after multiplying the forget vector and the previous cell state. Now, we can find the new cell state by adding the output of the input gate pointwise.
· Output Gate
The next hidden state is defined in the output gate. To find the hidden state’s information, we need to multiply the sigmoid output with the tanh output. You can predict the following information from a hidden state. Now, the new hidden and new cell state will travel to the next step.
Now you know how information travels through the LSTM recurrent neural networks. While recurrent neural networks perform tasks similar to the human brain, they are still different. That is why you have to input a large range of data so that the system can properly develop a good learning process.