Neural Networks

1. Neural Networks Teacher: Elena Marchiori R4.47 elena@cs.vu.nl Assistant: Kees Jong S2.22 cjong@cs.vu.nl

2. Neural Net types Basics of neural network theory and practice for supervised and unsupervised learning. Most popular Neural Network models: • architectures • learning algorithms • applications

3. Neural Networks • A NN is a machine learning approach inspired by the way in which the brain performs a particular learning task: • Knowledge about the learning task is given in the form of examples. • Inter neuron connection strengths (weights) are used to store the acquired information (the training examples). • During the learning process the weights are modified in order to model the particular learning task correctly on the training examples.

4. Connectionism • Connectionist techniques (a.k.a. neural networks) are inspired by the strong interconnectedness of the human brain. • Neural networks are loosely modeled after the biological processes involved in cognition: 1. Information processing involves many simple elements called neurons. 2. Signals are transmitted between neurons using connecting links. 3. Each link has a weight that controls the strength of its signal. 4. Each neuron applies an activation function to the input that it receives from other neurons. This function determines its output. • Links with positive weights are called excitatory links. • Links with negative weights are called inhibitory links.

5. What is a neural network • A NN is a machine learning approach inspired by the way in which the brain performs a particular learning task: • Knowledge about the learning task is given in the form of examples. • Inter neuron connection strengths (weights) are used to store the acquired information (the training examples). • During the learning process the weights are modified in order to model the particular learning task correctly on the training examples.

6. What is a Neural Network • A neural network is characterized by three things: 1. Its architecture: the pattern of nodes and connections between them. 2. Its learning algorithm, or training method: the method for determining the weights of the connections. 3. Its activation function: the function that produces an output based on the input values received by a node.

7. Learning • Supervised Learning • Recognizing hand-written digits, pattern recognition, regression. • Labeled examples (input , desired output) • Neural Network models: perceptron, feed-forward, radial basis function, support vector machine. • Unsupervised Learning • Find similar groups of documents in the web, content addressable memory, clustering. • Unlabeled examples (different realizations of the input alone) • Neural Network models: self organizing maps, Hopfield networks.

8. Network architectures • Three different classes of network architectures • single-layer feed-forward neurons are organized • multi-layer feed-forward in acyclic layers • recurrent • The architecture of a neural network is linked with the learning algorithm used to train

9. Single Layer Feed-forward Input layer of source nodes Output layer of neurons

10. Multi layer feed-forward 3-4-2 Network Output layer Input layer Hidden Layer

11. z-1 z-1 z-1 Recurrent network Recurrent Network with hidden neuron(s): unit delay operator z-1 implies dynamic system input hidden output

12. Neural Network Architectures

13. The Neuron • The neuron is the basic information processing unit of a NN. It consists of: • A set of synapses or connecting links, each link characterized by a weight: W1, W2, …, Wm • An adder function (linear combiner) which computes the weighted sum of the inputs: • Activation function (squashing function) for limiting the amplitude of the output of the neuron.

14. Bias b x1 w1 Activation function Local Field v Output y Input signal x2 w2 Summing function xm wm Synaptic weights The Neuron

15. Bias as extra input w0 x0 = +1 x1 w1 Activation function Local Field v Input signal Output y x2 w2 Summing function Synaptic weights xm wm • Bias is an external parameter of the neuron. Can be modeled by adding an extra input.

16. v u Bias of a Neuron • Bias b has the effect of applying an affine transformation to u v = u + b • vis the induced field of the neuron

17. Dimensions of a Neural Network • Various types of neurons • Various network architectures • Various learning algorithms • Various applications

18. Face Recognition 90% accurate learning head pose, and recognizing 1-of-20 faces

19. Handwritten digit recognition

20. A Multilayer Net for XOR

21. The XOR problem A single-layer neural network cannot solve the XOR problem. Input Output 00 -> 0 01 -> 1 10 -> 1 11 -> 0 To see why this is true, we can try to express the problem as a linear equation: aX + bY = Z a0 + b0 = 0 a0 + b1 = 1 -> b = 1 a1 + b0 = 1 -> a = 1 a1 + b1 = 0 -> a = -b

22. But adding a third bit makes it doable. Input Output 000 -> 0 010 -> 1 100 -> 1 111 -> 0 We can try to express the problem as a linear equation: aX + bY + cZ = W a0 + b0 + c0 = 0 a0 + b1 + c0 = 1 -> b=1 a1 + b0 + c0 = 1 -> a=1 a1 + b1 + c1 = 0 -> a + b + c = 0 -> 1 + 1 + c = 0 -> c = -2 So the equation: X + Y - 2Z = W will solve the problem.

23. Hidden Units • Hidden units are a layer of nodes that are situated between the input nodes and the output nodes. • Hidden units allow a network to learn non-linear functions. • The hidden units allow the net to represent combinations of the input features. • Given too many hidden units, however, a net will simply memorize the input patterns. • Given too few hidden units, the network may not be able to represent all of the necessary generalizations.

24. Backpropagation Nets • Backpropagation networks are among the most popular and widely used neural networks because they are relatively simple and powerful. • Backpropagation was one of the first general techniques developed to train multilayer networks, which do not have many of the inherent limitations of the earlier, single-layer neural nets criticized by Minsky and Papert. • Backpropagation networks use a gradient descent method to minimize the total squared error of the output. • A backpropagation net is a multilayer, feedforward network that is trained by backpropagating the errors using the generalized delta rule.

25. Training a backpropagation net Feedforward training of input patterns Each input node receives a signal, which is broadcast to all of the hidden units. Each hidden unit computes its activation, which is broadcast to all of the output nodes. Backpropagation of errors Each output node compares its activation with the desired output. Based on this difference, the error is propagated back to all previous nodes. Adjustment of weights The weights of all links are computed simultaneously based on the errors that were propagated backwards.

26. Terminology Input vector: X = (x1 , x2 , ..., xn ) Target vector: Y = (y1 , y2 , ..., ym ) Input unit: X i Hidden unit: Z i Output unit: Y i v ij : weight on link from X i to Z j w ij : weight on link from Z i to Y j v0j : bias on Z j w0j : bias on Y j i : error correction term for output unit Y i  : learning rate

27. The Feedforward Stage 1. Initialize the weights with small, random values. 2. While the stopping condition is not true For each training pair (input/output) Each input unit broadcasts its value to all of the hidden units. Each hidden unit sums its input signals and applies its activation function to compute its output signal. Each hidden unit sends its signal to the output units. Each output unit sums its input signals and applies its activation function to compute its output signal.

28. Backpropagation

29. Adjusting the Weights 1. Each output unit updates its weights and bias: w ij (new) = w ij (old) + w ij 2. Each hidden unit updates its weights and bias: v ij (new) = v ij (old) +  v ij Check stopping conditions. Each training cycle is called an epoch. Typically, many epochs are needed (often thousands). The weights are updated in each cycle.

30. The learning rate w ij (new) = j z i + w ij (old) • The learning rate, ff, controls how big the weight changes are for each iteration. • Ideally, the learning rate should be infinitesimally small, but then learning is very slow. • If the learning rate is too high then the system can suffer from severe oscillations. • You want the learning rate to be as large as possible (for fast learning) without resulting in oscillations. (0.02 is common)

31. An Example: One Layer

32. Multi-Layer

33. The numbers I1 1 I2 1 W13 .1 W14 -.2 W23 .3 W24 .4 W35 .5 W45 -.4 3 .2 4 -.3 5 .4

34. Output! Where N= output of node O= Activation function  = threshold value of node

35. Backpropagating!

36. How long should you train the net? • The goal is to achieve a balance between correct responses for the training patterns and correct responses for new patterns. (That is, a balance between memorization and generalization.) • If you train the net for too long, then you run the risk of overfitting to the training data. • In general, the network is trained until it reaches an acceptable error rate (e.g., 95%). • One approach to avoid overfitting is to break up the data into a training set and a training test set. The weight adjustments are based on the training set. However, at regular intervals the test set is evaluated to see if the error is still decreasing. When the error begins to increase on the test set, training is terminated.