Multi-layer Perceptron Forward Pass Backpropagation. Lecture 11: Aykut Erdem November 2016 Hacettepe University

Transcription

1 Multi-layer Perceptron Forward Pass Backpropagation Lecture 11: Aykut Erdem November 2016 Hacettepe University

2 Administrative Assignment 2 due Nov. 10, 2016! Midterm exam on Monday, Nov. 14, 2016 You are responsible from the beginning till the end of this week You can prepare and bring a full-page copy sheet (A4-paper, both sides) to the exam. Assignment 3 will be out soon! It is due December 1, 2016 You will implement a 2-layer Neural Network 2

3 Last time Linear classification slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 3

4 Last time Linear classification slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 4

5 Last time Linear classification slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 5

6 Interactive web demo time. slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 6

7 Last time Perceptron x 1 x 2 x 3... x n w 1 w n synaptic weights output slide by Alex Smola f(x) = X i w i x i = hw, xi 7

8 This Week Multi-layer perceptron Forward Pass Backward Pass 8

9 Introduction 9

10 A brief history of computers 1970s 1980s 1990s 2000s 2010s Data RAM? 1MB 100MB 10GB 1TB CPU? 10MF 1GF 100GF 1PF GPU deep kernel deep Data grows nets methods nets at higher exponent Moore s law (silicon) vs. Kryder s law (disks) slide by Alex Smola Early algorithms data bound, now CPU/RAM bound 10

11 Not linearly separable data Some datasets are not linearly separable! - e.g. XOR problem slide by Alex Smola Nonlinear separation is trivial 11

12 Addressing non-linearly separable data Two options: - Option 1: Non-linear functions - Option 2: Non-linear classifiers slide by Dhruv Batra 12

13 Option 1 Non-linear features Choose non-linear features, e.g., - Typical linear features: w0 + Σi wi xi - Example of non-linear features: Degree 2 polynomials, w0 + Σi wi xi + Σij wij xi xj Classifier hw(x) still linear in parameters w - As easy to learn - Data is linearly separable in higher dimensional spaces - Express via kernels slide by Dhruv Batra 13

14 Option 2 Non-linear classifiers Choose a classifier hw(x) that is non-linear in parameters w, e.g., - Decision trees, neural networks, More general than linear classifiers But, can often be harder to learn (non-convex optimization required) Often very useful (outperforms linear classifiers) In a way, both ideas are related slide by Dhruv Batra 14

15 Biological Neurons Soma (CPU) Cell body - combines signals Dendrite (input bus) Combines the inputs from several other nerve cells Synapse (interface) Interface and parameter store between neurons slide by Alex Smola Axon (cable) May be up to 1m long and will transport the activation signal to neurons at different locations 15

16 Recall: The Neuron Metaphor Neurons - accept information from multiple inputs, - transmit information to other neurons. Multiply inputs by weights along edges Apply some function to the set of inputs at each node slide by Dhruv Batra 16

17 Types of Neuron 1 2 D 1 0 f(~x, ) y = 0 + X i x i i 1 z = 0 + X i 1 0 y = 1 x i i 1+e z Linear Neuron 2 f(~x, ) slide by Dhruv Batra 1 2 D Perceptron 1 0 z = 0 + X i y = f(~x, ) x i i 1 if z 0 0 otherwise D Logistic Neuron Potentially more. Requires a convex loss function for gradient descent training. 17

18 Limitation A single neuron is still a linear decision boundary What to do? Idea: Stack a bunch of them together! slide by Dhruv Batra 18

19 Nonlinearities via Layers Cascade neurons together The output from one layer is the input to the next Each layer has its own sets of weights y 1i = k(x i,x) y 1i (x) = (hw 1i,xi) Kernels slide by Alex Smola y 2 (x) = (hw 2,y 1 i) Deep Nets optimize all weights 19

20 Nonlinearities via Layers slide by Alex Smola y 1i (x) = (hw 1i,xi) y 2i (x) = (hw 2i,y 1 i) y 3 (x) = (hw 3,y 2 i) 20

21 Representational Power Neural network with at least one hidden layer is a universal approximator (can represent any function). Proof in: Approximation by Superpositions of Sigmoidal Function, Cybenko, paper slide by Raquel Urtasun, Richard Zemel, Sanja Fidler The capacity of the network increases with more hidden units and more hidden layers 21

22 A simple example Consider a neural network with two layers of neurons neurons in the top layer represent known shapes. - neurons in the bottom layer represent pixel intensities. A pixel gets to vote if it has ink on it. - Each inked pixel can vote for several different shapes. x slide by Geoffrey Hinton The shape that gets the most votes wins. x x x f( w x ) 22

23 How to display the weights The input image slide by Geoffrey Hinton Give each output unit its own map of the input image and display the weight coming from each pixel in the location of that pixel in the map. Use a black or white blob with the area representing the magnitude of the weight and the color representing the sign. 23

24 How to learn the weights The image slide by Geoffrey Hinton Show the network an image and increment the weights from active pixels to the correct class. Then decrement the weights from active pixels to whatever class the network guesses. 24

25 The image slide by Geoffrey Hinton 25

26 The image slide by Geoffrey Hinton 26

27 The image slide by Geoffrey Hinton 27

28 The image slide by Geoffrey Hinton 28

29 The image slide by Geoffrey Hinton 29

30 The learned weights The details of the learning algorithm will be explained later. The image slide by Geoffrey Hinton 30

31 Why insufficient A two layer network with a single winner in the top layer is equivalent to having a rigid template for each shape. - The winner is the template that has the biggest overlap with the ink. slide by Geoffrey Hinton The ways in which hand-written digits vary are much too complicated to be captured by simple template matches of whole shapes. - To capture all the allowable variations of a digit we need to learn the features that it is composed of. 31

32 Multilayer Perceptron slide by Alex Smola Layer Representation y i = W i x i x i+1 = (y i ) (typically) iterate between linear mapping Wx and nonlinear function Loss function l(y, y i ) to measure quality of estimate so far y W 4 x4 W 3 x3 W 2 x2 W 1 x1 32

33 Forward Pass 33

34 Forward Pass: What does the Network Compute? slide by Raquel Urtasun, Richard Zemel, Sanja Fidler Output of the network can be written as: X XDX h j (x) = f (v j0 + x i v ji ) o k (x) = g(w k0 + (j indexing hidden units, k indexing the output units, D number of inputs) Activation functions f, g : sigmoid/logistic, tanh, or rectified linear (ReLU) (z) = X JX j=1 h j (x)w kj ) 1 exp( z), tanh(z) =exp(z), ReLU(z) =max(0, z) 1 + exp( z) exp(z)+exp( z) i=1 34

35 Forward Pass in Python Example code for a forward pass for a 3-layer network in Python: slide by Raquel Urtasun, Richard Zemel, Sanja Fidler Can be implemented efficiently using matrix operations Example above: W 1 is matrix of size 4 3, W 2 is 4 4. What about biases and W 3? [ 35

36 Special Case What is a single layer (no hiddens) network with a sigmoid act. function? slide by Raquel Urtasun, Richard Zemel, Sanja Fidler Network: Logistic regression! o k (x) = exp( z k ) z k = JX w k0 + x j w kj j=1 36

37 Example Example&applica3on& Consider!trying!to!classify!image!of!handwritten!digit:!32x32! Example Application pixels! Single!output!units!!it!is!a!4!(one!vs.!all)?! Classify image of handwritten digit (32x32 pixels): 4 vs non-4 Use!the!sigmoid!output!function:! Classify image of handwritten digit (32x32 pixels): 4 vs non-4 ok = 1 1+ exp( zk ) J zk = (wk 0 + h j (x)vkj ) j=1 Can!train!the!network,!that!is,!adjust!all!the!parameters!w,!to! optimize!the!training!objective,!but!this!is!a!complicated! function!of!the!parameters!! How would you build your network? How would you build your network? For example, use one hidden layer and the sigmoid activation function: For example, use one hidden layer and the sigmoid activation function: slide by Raquel Urtasun, Richard Zemel, Sanja Fidler ok (x) zk = = exp( zk ) wk0 + J X hj (x)wkj j=1 How wetrain train network, that all is,the adjust all the Howcan can we the the network, that is, adjust parameters w? parameters w? Urtasun, Zemel, Fidler (UofT) CSC 411: 10-Neural Networks I Feb 10, / 62 37

38 Training Neural Networks slide by Raquel Urtasun, Richard Zemel, Sanja Fidler Find weights: NX X w = argmin X loss(o (n), t (n) ) w n=1 where o = f(x;w) is the output of a neural network Define a loss function, e.g.: - Squared loss: - Cross-entropy loss: Pk 1 2 (o(n) k t (n) k ) 2 2 P k k (n) P Pk t(n) k log o (n) k Gradient descent: w t+1 = t where η is the learning rate (and E is error/loss) 38

39 Useful derivatives slide by Raquel Urtasun, Richard Zemel, Sanja Fidler name function derivative Sigmoid (z) = 1 1+exp( z) (z) (1 (z)) exp(z) exp( z) Tanh tanh(z) = exp(z)+exp( z) 1/ cosh 2 (z) ( 1, if z > 0 ReLU ReLU(z) =max(0, z) 0, if z apple 0 39

40 Backpropagation and Neural Networks 40

41 Recap: Loss function/optimization TODO: slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson We defined a (linear) score function: Define a loss function that quantifies our unhappiness with the scores across the training data. 2. Come up with a way of efficiently finding the parameters that minimize the loss function. (optimization) 41

42 Softmax Classifier (Multinomial Logistic Regression) slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 42

43 Softmax Classifier (Multinomial Logistic Regression) slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 43

44 Softmax Classifier (Multinomial Logistic Regression) slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 44

45 Softmax Classifier (Multinomial Logistic Regression) slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 45

46 Softmax Classifier (Multinomial Logistic Regression) slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 46

47 Softmax Classifier (Multinomial Logistic Regression) slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 47

48 Softmax Classifier (Multinomial Logistic Regression) slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 48

49 Softmax Classifier (Multinomial Logistic Regression) slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 49

50 Softmax Classifier (Multinomial Logistic Regression) slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 50

51 Softmax Classifier (Multinomial Logistic Regression) slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 51

52 Optimization 52

53 Gradient Descent slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 53

54 Mini-batch Gradient Descent only use a small portion of the training set to compute the gradient slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 54

55 Mini-batch Gradient Descent only use a small portion of the training set to compute the gradient slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson there are also more fancy update formulas (momentum, Adagrad, RMSProp, Adam, ) 55

56 The effects of different update form formulas slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson (image credits to Alec Radford) 56

57 slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 57

58 slide by Fei-Fei Li & Andrej Karpathy & Justin Johnson 58