Understanding Recurrent Neural Networks

Recurrent Neural Networks (RNN) are a type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, or the spoken word. Unlike traditional neural networks, which process inputs independently, RNNs have loops, allowing information to persist.

Architecture of RNN

The architecture of an RNN consists of three layers: the input layer, the hidden layer, and the output layer.

• Input Layer: This is where the network takes in the sequence of inputs.
• Hidden Layer: This layer contains loops that allow information to be passed from one step in the sequence to the next. This is the "recurrent" part of the RNN, and it gives the network its ability to remember information.
• Output Layer: This layer produces the sequence of outputs.

Time Steps in RNN

In an RNN, each element in the input sequence is associated with a specific time step. The network processes each element one at a time, using information from previous time steps to inform the processing of the current one. This is what allows the RNN to exhibit temporal dynamic behavior and handle variable-length input sequences.

Problems in RNN: Vanishing and Exploding Gradient

RNNs are notoriously difficult to train effectively. The main reason for this is the so-called vanishing and exploding gradient problems. These problems occur when the gradients, which the network uses to update its weights, become either too small (vanish) or too large (explode). This can cause the network to take a long time to learn (in the case of vanishing gradients) or to fail to learn at all (in the case of exploding gradients).

Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU)

To overcome the vanishing and exploding gradient problems, researchers have developed variants of the RNN, such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU).

• LSTM: LSTMs have a similar control flow as a standard RNN, but they also have a way to carry information across many time steps. This helps them remember longer sequences of data, making them more effective for many tasks.
• GRU: GRUs are a simplified version of LSTMs that perform almost as well but are faster to compute. They combine the forget and input gates into a single "update gate."

Building a Basic RNN using Tensorflow

Tensorflow provides built-in functions for creating and training RNNs. You can create an RNN in Tensorflow by first defining the architecture of the network, including the number of hidden layers and the number of units in each layer. Then, you can train the network using one of Tensorflow's optimizers and a suitable loss function for your task.

In conclusion, RNNs are a powerful tool for sequence-based tasks. Despite their challenges, with the right architecture and training techniques, they can achieve impressive results.