Multilayer artificial neuron networks are an essential part of deep learning. This lesson will help you understand what multilayer ANNs are and overfitting and underfitting. Not only that, by the end of the lesson you will also learn:
- Analyze how to regularize and minimize the cost function of neural networks
- Perform backpropagation to adjust neural network weights
- Examine the convergence of a multilayer ANN
- Explore multilayer ANNs
- Implement forward propagation with a multilayer perceptron (MLP)
- Understand how model capacity is affected by underfitting and overfitting
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About single-layer ANN
I trained a single layer neural network using perceptron rules and Adaline rules.
The weights are updated based on a unit function for perceptron rules or a linear function for Adaline rules.
History of multilayer ANN
Deep learning deals with training multilayer artificial neural networks, also known as deep neural networks. After Rosenblatt developed his perceptron in the 1950s, his neural networks continued until his 1986, when Dr. Hinton and his colleagues developed his backpropagation algorithm for training multilayer neural networks. Interest in it was waning. This topic is currently gaining traction as many major companies such as Google, Facebook, and Microsoft are investing heavily in applications using deep neural networks.
Multilayer ANN
A fully connected multilayer neural network is called a multilayer perceptron (MLP).
There are three layers, including one hidden layer. If there are multiple hidden layers, it is called a deep ANN. MLP is a classic example of a feedforward artificial neural network. In this figure, the ith activation unit of the lth layer is denoted as ai(l).
The number of layers and neurons are called hyperparameters of a neural network, and these need to be tuned. To find these ideal values, you should use cross-validation techniques.
Weight conditioning training is done through backpropagation. Deeper neural networks are better at processing data. However, deeper layers may experience vanishing gradient problems. A special algorithm is required to solve this problem.
notation
In the following expression:
- ai(in) refers to the i-th value in the input layer
- ai(h) refers to the i-th unit of the hidden layer
- ai(out) refers to the i-th unit of the output layer
- ao(in) is just a bias unit and is equal to 1. The corresponding weight will be w0.
- The weighting coefficient from layer l to layer l+1 is denoted by wk,j(l).
Here is a simplified diagram of the multilayer. This image shows a fully connected three-layer neural network with three input neurons and three output neurons. A bias term is added to the input vector.
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Forward propagation
Let's take a closer look at forward propagation in the next topic.
MLP learning procedure
The steps to learn MLP are as follows.
- Starting from the input layer, propagate the data to the output layer. This step is forward propagation.
- Based on the output, calculate the error (the difference between the predicted and known results). Errors should be kept to a minimum.
- Backpropagate the error. Find the derivative with respect to each weight in the network and update the model.
Repeat the above three steps over multiple epochs to learn the ideal weights.
Finally, the output is taken through a threshold function to get the predicted class labels.
Forward propagation in MLP
The first step is to compute the hidden layer activation unit al(h).
The activation unit is the result of applying the activation function φ to the z-value. To be able to learn the weights using gradient descent, they must be differentiable. The activation function φ is often a sigmoid (logistic) function.
It enables the nonlinearity needed to solve complex problems such as image processing.
sigmoid curve
A sigmoid curve is an S-shaped curve.
Activate hidden layers
The hidden layer activation is expressed as:
z(h) = a(in) W(h)
a(h) =
For the output layer:
Z(out) = A(h) W(out)
A(out) =
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conclusion
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In this tutorial, we learned all about multilayer artificial neural networks. However, if you want to master AI and machine learning, the Artificial Intelligence graduate program affiliated with IBM should be your next stop. Become an AI and machine learning pro like never before with masterclasses from top Caltech faculty and online bootcamps from Simplilearn.
