AI News, Difference between revisions of "Artificial Neural Networks/Recurrent Networks"

Difference between revisions of "Artificial Neural Networks/Recurrent Networks"

In a recurrent network, the weight matrix for each layer l contains input weights from all other neurons in the network, not just neurons from the previous layer.

Recurrent networks, in contrast to feed-forward networks, do have feedback elements that enable signals from one layer to be fed back to a previous layer.

context layer feeds the hidden layer at iteration N with a value computed from the output of the hidden layer at iteration N-1, providing a short memory effect.

RNN or Recurrent Neural Network for Noobs

It also shows a demo implementation of a RNN used for a specific purpose, but you would be able to generalise it for your needs.

In english the word recurrent means: In the case of this type of Neural Network it’s called Recurrent since it does the same operation over and over on sets of sequential input.

In a conversation a sentence means something but the entire flow of the conversation mostly means something completely different.

Also in a time series data like stock market data, a single tick data means the current price, but a full days data will show movement and allow us to take decision whether to buy or sell.

CNNs generally don’t perform well when the input data is interdependent in a sequential pattern.

All the words generated are dependent on the words generated before (in certain cases, it’s dependent on words coming after as well, but we will discuss that later).

Predicting phonetic segments based on input sound waves, thus formulating a word.

This can be used for video search where we do image description of a video frame by frame.

Feed-forward networks channel information through a series of operations which take place in each node of the network.

The objective of the training phase is to reduce the error while the feed-forward network tries to guess the class.

In a feed-forward network whatever image is shown to the classifier during test phase, it doesn’t alter the weights so the second decision is not affected.

Recurrent networks, on the other hand, take as their input not just the current input example they see, but also what they have perceived previously in time.

In simple terms there is a input layer, a hidden layer with certain activations and finally we receive an output.

If we increase the number of layers in the above example, input layer takes the input.

So they start looking somewhat like this: We will provide input to the hidden layer at each step.

A recurrent neuron now stores all the previous step input and merges that information with the current step input.

Thus it also captures some information regarding the correlation between current data step and the previous steps.

The decision at a time step t-1 affects the decision taken at time t.

We combine the present data with recent past to take a call on a particular problem at hand.

This example is excessively rudimentary but in principle it aligns with our decision making capability.

Thus can we digitise our brains once we have a fairly advanced model and systems capable of storing and computing them in reasonable time periods.

So what happens when we have models better and faster than our brains training on data from millions of people?

Let’s come back to the problem at hand and rephrase the above explanation with an example to predict what the next letter is after a sequence of letters.

If we were trying to figure out the 8th letter after 7 letters were fed to the network, what would have happened.

In the above diagram, the hidden layer or the RNN block applies a formula to the current input as well as the previous state.

The formula for the current state can be written like this: ht is the new state and ht-1 is the previous state.

So the formula looks like: The above example takes only the last step as memory and thus merging with the data of last step.

To increase the memory capacity of the network, and hold longer sequences in memory, we have to add more states to the equation, like ht-2, ht-3 etc.

The network learns by back propagating the error via the network to update the weights.

But a standard backpropagation like how used in feed forward networks can’t be used here.

The problem with RNNs is that they are cyclic graphs unlike feed-forward networks which are acyclic directional graphs.

Each time step t layer connects to all possible layers in the time step t+1.

Thus we randomly initialise the weights, unroll the network and then use backpropagation to optimise the weights in the hidden layer.

An outcome of the unrolling is that each layer now starts maintaining different weights and thus end up getting optimised differently.

7 types of Artificial Neural Networks for Natural Language Processing

An artificial neural network (ANN) is a computational nonlinear model based on the neural structure of the brain that is able to learn to perform tasks like classification, prediction, decision-making, visualization, and others just by considering examples.

An artificial neural network consists of artificial neurons or processing elements and is organized in three interconnected layers: input, hidden that may include more than one layer, and output.

Artificial neuron with four inputs The weighted sum of the inputs produces the activation signal that is passed to the activation function to obtain one output from the neuron.

Linear function f(x)=ax Step function Logistic (Sigmoid) Function Tanh Function Rectified linear unit (ReLu) function Training is the weights optimizing process in which the error of predictions is minimized and the network reaches a specified level of accuracy.

Artificial neural networks with multiple hidden layers between the input and output layers are called deep neural networks (DNNs), and they can model complex nonlinear relationships.

convolutional neural network (CNN) contains one or more convolutional layers, pooling or fully connected, and uses a variation of multilayer perceptrons discussed above.

He presents a model built on top of word2vec, conducts a series of experiments with it, and tests it against several benchmarks, demonstrating that the model performs excellent.

In Text Understanding from Scratch, Xiang Zhang and Yann LeCun, demonstrate that CNNs can achieve outstanding performance without the knowledge of words, phrases, sentences and any other syntactic or semantic structures with regards to a human language [2].

recursive neural network (RNN) is a type of deep neural network formed by applying the same set of weights recursively over a structure to make a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topological order [6].

recurrent neural network (RNN), unlike a feedforward neural network, is a variant of a recursive artificial neural network in which connections between neurons make a directed cycle.

These blocks have three or four “gates” (for example, input gate, forget gate, output gate) that control information flow drawing on the logistic function.

In this paper, we described different variants of artificial neural networks, such as deep multilayer perceptron (MLP), convolutional neural network (CNN), recursive neural network (RNN), recurrent neural network (RNN), long short-term memory (LSTM), sequence-to-sequence model, and shallow neural networks including word2vec for word embeddings.

We demonstrated that convolutional neural networks are primarily utilized for text classification tasks while recurrent neural networks are commonly used for natural language generation or machine translation.

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