introduction to deep neural network

Copyright 2011 Trend Micro Inc. 1

Introduction to Deep Neural Network Liwei Ren, Ph.D

San Jose, California, Nov, 2016

Copyright 2011 Trend Micro Inc.

Agenda

• What a DNN is

• How a DNN works

• Why a DNN works

• Those DNNs in action

• Where the challenges are

• Successful stories

• Security problems

• Summary

• Quiz

• What else

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What is a DNN?

• DNN and AI in the secular world

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What is a DNN?


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What is a DNN?


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What is a DNN?

• DNN in the technical world

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What is a DNN?


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What is a DNN?


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What is a DNN?

• Categorizing the DNNs :

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What is a DNN?

• Three technical elements • Architecture: the graph, weights/biases, activation functions

• Activity Rule: weights/biases, activation functions

• Learning Rule: a typical one is backpropagation algorithm

• Three masters in this area:

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What is a DNN?

• Given a practical problem , we have two approaches to solve it.

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What is a DNN?

• An example: image recognition

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What is a DNN?

• An example: image recognition

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What is a DNN?

• In the mathematical world – A DNN is a mathematical function f: D S, where D ⊆ Rn and S ⊆ Rm,

which is constructed by a directed graph based architecture.

– A DNN is also a composition of functions from a network of primitive functions.

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What is a DNN?

• We denote the a feed-forward DNN function by O= f(I) which is determined by a few parameters G, Φ ,W,B

• Hyper-parameters:

– G is the directed graph which presents the structure

– Φ presents one or multiple activation functions for activating the nodes

• Parameters:

– W is the vector of weights relevant to the edges

– B is the vector of biases relevant to the nodes

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What is a DNN?

• Activation at a node:

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What is a DNN?

• Activation function:

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What is a DNN?

• G=(V,E) is a graph and Φ is a set of activation functions.

• <G,Φ> constructs a family of functions F: – F(G,Φ) = { f | f is a function constructed by <G, Φ ,W> where WϵRN }

• N= total number of weights at all nodes of output layer and hidden layers.

• Each f(I) can be denoted by f(I ,W).

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What is a DNN?

• Mathematically, a DNN based supervised machine learning technology can be described as follows : – Given g ϵ { h | h:D S where D ⊆ Rn and S ⊆ Rm} and δ>0 , find f ϵ

F(G,Φ) such that 𝑓 − 𝑔 < δ.

• Essentially, it is to identify a W ϵ RN such that 𝑓(∗,𝑊) − 𝑔 < δ

• However, in practice, g is not explicitly expressed . It usually appears in a sequence of samples:

– { <I(j),T(j)> | T(j) =g(I(j)), j=1, 2, …,M}

• where I(j) is an input vector and T(j) is its corresponding target vector.

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How Does a DNN work ?

• The function g is not explicitly expressed, we are not able to calculate g − f(∗,W)

• Instead, we evaluate the error function E(W)= 1

2𝑀∑||T(j) -

f(I(j),W)||2

• We expect to determine W such that E(W) < δ

• How to identify W ϵ RN so that E(W) < δ ? Lets solve the nonlinear optimization problem min{E(W)| W ϵ RN} , i.e.:

min{1

2𝑀 ∑|| T(j) - f(I(j),W) ||2 | W ϵ RN } (P1)

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• (P1) is for batch mode training, however ,it is too expensive.

• In order to reduce the computational cost, a sequential mode is introduced.

• Picking <I,T> ϵ {<I(1),T(1) >, <I(2),T(2)> ,…, <I(M),T(M)>}

sequentially, let the output of the network as O= f(I,W) for any W:

• Error function E(W)= ||T- f(I,W)||2 /2 = ∑(Tj-Oj)2 /2

• Each Oj can be considered as a function of W. We denote it as Oj(W).

• We have the optimization problem for training with sequential mode:

– min{ ∑(Tj-Oj(W) )2 /2 | W ϵ RN} (P2)

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• One may ask whether we get the same solution for both batch mode and sequential mode ?

• BTW

– batch mode = offline mode

– sequential mode = online mode

• We focus on online mode in this talk

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• How to solve the unconstrained nonlinear optimization problem (P2)?

• The general approach of unconstrained nonlinear optimization is to find local minima of E(W) by using the iterative process of Gradient Descent.

•∂E = (∂E/∂W1, ∂E/∂W2, …, ∂E/∂WT)

• The iterations: – ΔWj = - γ ∂E/∂Wj for j=1, …,T

– Updating W in each step by

• Wj (k+1) = Wj

(k) - γ ∂E(W (k))/∂Wj for j=1, …,T (A1)

• until E(W (k+1)) < δ or E(W (k+1)) can not be reduced anymore

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• The algorithm of Gradient Descent:

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• From the perspective of mathematics, the process of Gradient Descent is straightforward.

• However, from the perspective of scientific computing, it is quite challenging to calculate the values of all ∂E/∂Wj for j=1, …,N:

– The complexity of presenting each ∂E/∂Wj where j=1, …,N.

– There are (k+1)-layer function compositions for a DNN of k hidden layers.

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• For example, we have a very simple network as follows with the activation function φ(v)=1/(1 + 𝑒−𝑣).

• E(W) = [ T - f(I,W) ]2 /2= [T – φ(w1φ(w3I+ w2) + w0)]2 /2, we

have:

– ∂E/∂w0 = -[T – φ(w1φ(w3I + w2) + w0)] φ’(w1φ(w3I+w2) + w0)

– ∂E/∂w1 = -[T – φ(w1φ(w3I + w2) + w0)] φ’(w1φ(w3I+w2) + w0) φ(w3I+w2)

– ∂E/∂w2 = - w1 [T – φ(w1φ(w3I + w2) + w0)] φ’(w1φ(w3I+w2) + w0) φ’(w3I+w2)

– ∂E/∂w3 = - I w1 [T – φ(w1φ(w3I + w2) + w0)] φ’(w1φ(w3I+w2) + w0) φ’(w3I+w2)

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• Lets imagine a network of N inputs, M outputs and K hidden layers each of which has L nodes.

– It is a daunting task to express ∂E/∂wj explicitly. Last simple example already shows this.

• The backpropagation (BP) algorithm was proposed as a rescue:

– Main idea : the weights of (k-1)-th hidden layer can be expressed by the k-th layer recursively.

– We can start with the output layer which is considered as (L+1)-layer.

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• BP algorithm has the following major steps:

1. Feed-forward computation

2. Back-propagation to the output layer

3. Back-propagation to the hidden layers

4. Weight updates

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• A general DNN can be drawn as follows

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• How to express the weights of (k-1)-th hidden layer by the weights of k-th layer recursively?

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• Let us experience the BP with our small network.

– E(W) = [ T - f(I,W) ]2 /2= [T – φ(w1φ(w3I+ w2) + w0)]2 /2.

• ∂E/∂w0 = - φ’(O) (T – O)

• ∂E/∂w1 = -φ’(O) (T – O) φ(O)

• ∂E/∂w2 = - φ’(O) (T – O) φ’(H) w1 * 1

• ∂E/∂w3 = - φ’(O) (T – O) φ’(H) w1 * I

– Let H0(1)= 1, H1

(1) = H = φ(w3I+ w2), H1(0) = I, we verify the follows:

• δ1(2)= φ’(O) (T – O)

• w0+

= w0 + γ δ1(2) H0

(1) , w1+

= w1 + γ δ1(2) H1

(1)

• δ1(1)= φ’(H1

(1)) δ1(2) w1

• w2+

= w2 + γ δ1(1) H0

(0) , w3+

= w3 + γ δ1(1) H1

(0)

• where w0 = w0,1(2) , w1 = w1,1

(2), w2 = w0,1(1) , w2 = w1,1

(1)

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Why Does a DNN Work?

• It is amazing ! However, why does it work?

• For a FNN, it is to ask whether the following approximation problem has a solution: – Given g ϵ { h | h:D S where D ⊆ Rn and S ⊆ Rm} and δ>0 , find a W ϵ

RN such that 𝑓(∗,𝑊) − 𝑔 < δ.

• Universal approximation theorem (S): – Let φ(.) be a bounded and monotonically-increasing continuous function. Let

Im denote the m-dimensional unit hypercube [0,1]m . The space of continuous functions on Im is denoted by C(Im) . Then, given any function f ϵ C(Im) and ε>0 , there exists an integer N , real constants vi, bi ϵ R and real vectors wi ϵ Rm, where i=1, …, N , such that

|F(x)-f(x)| < ε

for all x ϵ Im , where F(x) = vi φ(wi T x + bi) 𝑵

𝒊=𝟏 is an approximation to the function f which is independent of φ .

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Im



• Its corresponding network with only one hidden layer – NOTE : this is not even a general case for one hidden layer. It is a

special case. WHY?

– However, it is powerful and encouraging from the mathematical perspective.

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Im



The general networks have a general version of Universal Approximation Theorem accordingly:

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Im



• Universal approximation theorem (G): – Let φ(.) be a bounded and monotonically-increasing continuous function. Let

S be a compact space in Rm . Let C(S ) = {g | g:S ⊂ Rm Rn is continuous}. Then, given any function f ϵ C(S) and ε>0 , there exists a FNN as shown above which constructs the network function F such that

|| F(x)-f(x) || < ε

where F is an approximation to the function f which is independent of φ .

• It seems both shallow and deep neural networks can construct an approximation to a given function. – Which is better?

– Or which is more efficient in terms of using less nodes ?

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Im

Rm



• Mathematical foundation of neural networks:

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Im

Rm


Those DNNs in action

• DNN has three elements

• Architecture: the graph , weights/biases, activation functions

• Activity Rule: weights/biases, activation functions

• Learning Rule: a typical one is backpropagation algorithm

• The architecture basically determines the capability of a specific DNN

– Different architectures are suitable for different applications.

– The most general architecture of an ANN is a DAG ( directed acyclic graph).

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Those DNNs in action

• There are a few well-known categories of DNNs.

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What Are the Challenges?

• Given a specific problem, there are a few questions before one starts the journey with DNNs:

– Do you understand the problem that you need to solve?

– Do you really want to solve this problem with DNN, why?

• Do you have an alternative yet effective solution?

– Do you know how to describe the problem in DNN mathematically ?

– Do you know how to implement a DNN , beyond a few APIs and sizzling hype?

– How to collect sufficient data for training?

– How to solve the problem efficiently and cost-effectively?

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• 1st Challenge:

– a full mesh network has the curse of dimensionality.

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• Many tasks of FNN do not need a full mesh network.

• For example, if we can present the input vector as a grid, the nearest-neighborhood models can be used when constructing an effective FNN which can reduce connections

– Image recognition

– GO (圍棋) : a game that two players play on a 19x19 grid of lines.

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• The 2nd challenge is how to describe a technical problem in terms of DNN, i.e., mathematical modeling. There are generally two approaches:

– Applying a well-learned DNN architecture to describe the problem. Deep understanding of the specific network is usually required!

• Two general DNN architectures are well-known

– FNN: feedforward neural network. Its special architecture CNN (convolutional neural network) is widely used in many applications such as image recognition, GO, and etc.

– RNN: recurrent neural network. Its special architecture is LSTM (long short-term memory) which has been applied successfully in speech recognition, language translation, and etc.

• For example, if we want to try a FNN, how to describe the problem in terms of <Input vector, Output vector> with fixed dimension ?

– Creating a novel DNN architecture from ground if none of the existing

models fits your problem. Deep understanding of DNN theory / algorithms is required.

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• Handwriting digit recognition: – Modeling this problem is straightforward

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• Image Recognition is also straightforward

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• However, due to the curse of dimensionality, we can use a special FFN: – Convolutional neural network (CNN)

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• How to construct a DNN to describe language translation ?

– They use LSTM networks

• How to construct a DNN to describe the problem of malware classification?

• How to construct a DNN to describe the network traffic for security purpose?

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• The 3rd challenge is how to collect sufficient training data. To achieve required accuracy, sufficient training data is necessary. WHY?

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• The 4th challenge is how to identify various talents for providing a DNN solution to solve specific problems.

– Who knows how to use existing DL APIs such as TensorFlow

– Who understands various DNN architectures in depth so that he/she knows how to evaluate and identify a suitable DNN architecture to solve the problem.

– Who understands the theory and algorithms of the DNN in depth so that he/she can create and design a novel DNN from ground.

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Successful Stories

• ImageNet : 1M+ images, 1000+ categories, CNN

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Successful Stories

• Unsupervised learning neural networks… YouTube and the Cat .

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Successful Stories

• AlphaGo, a significant milestone in AI history

– More significant than DeepBlue

• Both Policy Network and Value Network are CNNs.

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Successful Stories

• Google Machine Neural Translation… LSTM (Long Short Term Memory) network

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Successful Stories

• Microsoft Speech Recognition … LSTM and TDNN (Time Delay Neural Networks )

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Security Problems

• Not disclosed for the public version.

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Summary

• What a DNN is

• How a DNN works

• Why a DNN works

• The categories of DNNs

• Some challenges

• Well-known stories

• Security problems

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Quiz

• Why do we choose the activation function as a nonlinear function?

• Why Deep? Why deep networks are better than shallow networks?

• What is the difference between online and batch mode training?

• Will online and batch mode training converge to the same solution?

• Why do we need the backpropagation algorithm?

• Why do we apply convolutional neural networks to image recognition?

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Quiz

• If we solve a problem with a FNN, – how many deep layers should we go?

– How many nodes are good for each layer?

– How to estimate and optimize the cost?

• Is it guaranteed that the backpropagation algorithm converge to a solution?

• Why do we need sufficient data for training in order to achieve certain accuracy?

• Can a DNN do some tasks more than extending human’s capabilities or automating extensive manual tasks ? – To prove a mathematical theorem ... or to introduce an interesting

concept… or to appreciate a poem… or to love…

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Quiz

• AlphaGo is trained for 19x19 lattice. If we play GO game on 20x20 board, can AlphaGo handle it?

• ImageNet is trained for 1000 categories. If we add the 1001-th category, what should we do?

• People do consider a special DNN as a black box. Why?

• More questions from you …

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What Else?

• What to share next from me? Why do you care? – Various DNNs: principles, examples, analysis and

experiments…

• ImageNet, AlphaGO, GNMT and etc..

– My Ph.D work and its relevance to DNN

– Little History of AI and Artificial Neural Network

– Various Schools of the AI Discipline

– Strong AI vs. Weak AI

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What Else?

• What to share next from me? Why do you care? – Questions when thinking about AI:

• Are we able to understand how we learn?

• Are we going the right directions mathematically and scientifically?

• Are there simple principles for cognition like what Newton and Einstein established for understanding our universe?

• What are we lack between now and the coming of so called Strong AI?

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What Else?

• What to share next from me? Why do you care? •Questions about who we are.

– Are we created?

– Are we the AI of the creator?

• My little theory about the Universe

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introduction to deep neural network

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