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Least-squares-based Multilayer perceptron training with weighted adaptation -- Software simulation project. EE 690 Design of Embodied Intelligence. Outline. Multilayer Perceptron Least-squares based Learning Algorithm Weighted Adaptation in training

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slide1

Least-squares-based Multilayer perceptron training with weighted adaptation-- Software simulation project

EE 690

Design of Embodied Intelligence

outline
Outline
  • Multilayer Perceptron
  • Least-squares based Learning Algorithm
  • Weighted Adaptation in training
  • Signal-to-Noise Ratio Figure and Overfitting
  • Software simulation project
multilayer perceptron mlp

Inputs x

Outputs z

Multilayer perceptron (MLP)

Feedforward (no recurrent connections) network with units arranged in layers

multilayer perceptron mlp1

MLP

Multilayer perceptron (MLP)
  • Efficient mapping from inputs to outputs
  • Powerful universal function approximation
  • Number of inputs and outputs determined by the data
  • Number of hidden neurons
  • Number of hidden layers

outputs

inputs

multilayer perceptron learning
Back-propagation (BP) training algorithm: how much each weight is responsible for the error signal

BP has two phases:

Forward pass phase: feedforward propagation of input signals through network

Backward pass phase: propagates the error backwards through network

output layer

input layer

hidden layer

Multilayer Perceptron Learning
slide6

Multilayer Perceptron Learning

  • Backward Pass

We want to know how to modify weights in order to decrease E.

  • Use gradient descent:
  • Gradient-based adjustment could go to local minima
  • Time-consuming due to large number of learning steps and the step size needs to be configured
least squares based learning algorithm

Optimized weights

Optimized signals

Least-squares based Learning Algorithm
  • Least-squared fit (LSF): to obtain the minimum sum of squared error
  • For underdetermined problem, LSF finds the solution with the minimum SSE
  • For overdetermined problem, pseudo-inverse finds the solution with minimum norm
  • Can be applied in the optimization for weights or signals on the layers
least squares based learning algorithm i

b1

b2

W1

W2

d

x

z2

y2

y1

z1

Least-squares based Learning Algorithm (I)
  • Start with desired output signal back-propagation
  •  signals optimization
  • Propagation of the desired outputs back through layers
  • Optimization of the weights between layers

(1). y2=f -1(z2), scale y1 to (-1, 1).

(2). Based on W2,b2:W2.z1=y2-b2.

(3). y1=f-1(z1), scale y1 to (-1, 1).

(4). Optimize W1, b1 to satisfy W1.x-b1=y1.

(5). Evaluate z1, y1 using the new W1 and bias b1.

(6). Optimize W2, b2 to satisfy W2.z1+b2=y2.

(7). Evaluate z2, y2 using the new W2 and bias b2.

(8). Evaluate the MSE

least squares based learning algorithm i1

Optimize W1, b1 to satisfy W1.x=y1-b1

Least-squares based Learning Algorithm (I)
  • Weights optimization with weighted LSF

The location of x on the transfer function determines its effect on output signal of this layer

dy/dx weighting term in LSF

Δy

Δy

Δx

Δx

Weighted LSF

least squares based learning algorithm ii

x

Least-squares based Learning Algorithm (II)

II. Weights optimization with iterative fitting

W1 can be further adjusted based on the output error

Each hidden neuron: basis function

Start with the 1st hidden neurons, and continue to other neurons

as long as eout exists

least squares based learning algorithm iii

b1

b2

W1

W2

d

x

z2

y2

y1

z1

Least-squares based Learning Algorithm (III)
  • III. Start with input feedforward  weights optimization
  • Propagation of the inputs forward through layers
  • Optimization of the weights between layers and signals on layers

(1). Evaluate z1, y1 using the initial W1 and bias b1.

(2). y2=f -1(d).

(3). Optimize W2, b2 to satisfy W2.z1+b2=y2.

(4). Based on W2,b2, optimize z1 to satisfy

W2.z1-b2=y2.

(5). y1=f-1(z1).

(6). Optimize W1, b1 to satisfy W1.x+b1=y1.

(7). Evaluate y1, z1, y2, z2 using the new W1,W2

and bias b1,b2.

(8). Evaluate the MSE

least squares based learning algorithm iii1
Least-squares based Learning Algorithm (III)

y

  • Signal optimization with weighted adaptation

The location of x on the transfer function determines how much the signal can be changed

x

overfitting problem
Overfitting problem
  • Learning algorithm can adapt MLP to fit into the training data.
  • For the noisy training data, how well we should learn into the data?
  • Overfitting
  • Number of hidden neurons

Number of layers

 affect the training accuracy, determined by users: critical

  • Optimized Approximation Algorithm –SNRF criterion
signal to noise ratio figure snrf
Signal-to-noise ratio figure (SNRF)
  • Sampled data: function value + noise
  • Error signal:

approximation error component + noise component

Noise part

Should not be learned

Useful signal

Should be reduced

  • Assumption: continuous function & WGN as noise
  • Signal-to-noise ratio figure (SNRF):

signal energy/noise energy

  • Compare SNRFe and SNRFWGN

Learning should stop – ?

If there is useful signal left unlearned

If noise dominates in the error signal

signal to noise ratio figure snrf1

Error signal

Training data and approximating function

Signal-to-noise ratio figure (SNRF)

noise component

approximation error component

+

optimization using snrf
Optimization using SNRF
  • SNRFe< threshold SNRFWGN
  • Start with small network (small # of neurons or layers)
  • Train the MLP  etrain
  • Compare SNRFe & SNRFWGN
  • Add hidden neurons

Noise dominates in the error signal,

Little information left unlearned,

Learning should stop

Stopping criterion:

SNRFe< threshold SNRFWGN

optimization using snrf1
Optimization using SNRF
  • Set the structure of MLP
  • Train the MLP with back-propagation iteration

 etrain

  • Compare SNRFe & SNRFWGN
  • Keep training with more iterations

Applied in optimizing number of iterations in back-propagation training to avoid overfitting (overtraining)

software simulation project

M x N matrix: “Features”

1 x N vector: “Values”

Software simulation project
  • Prepare the data
  • Data sample along the row: N samples
  • Features along the column: M features
  • Desired output in a row vector: N values
  • Save “features” and “values” in a training MAT file
  • How to recall the function
  • Run “main_MLP_LS.m”
  • Specify MAT file path and name and MLP parameters in command window.
software simulation project1
Software simulation project
  • Input the path where data file can be found (C:*): E:\Research\MLP_LSInitial_desired\MLP_LS_package\
  • Input the name of data file (*.mat): mackey_glass_data.mat
  • There are overall 732 samples. How do you like to divide them into training and testing set?

Number of training samples: 500

Number of testing samples: 232

  • How many layers does MLP have? 3:2:7
  • How many neurons there are on each hidden layer ? 3:1:10
  • What kind of tranfer function you like to have on hidden neurons?
  • 0. Linear tranfer function
  • 1. Tangent sigmoid
  • 2. Logrithmic sigmoid
  • 2
software simulation project2

b1

W1

b2

W2

d

x

z2

y2

y1

z1

Software simulation project
  • There are 4 types of training algorithms you can choose from. Which type you like to use?
  • 1. Least-squared based training (I)
  • 2. Least-squared based training with iterative neuron fitting (II)
  • 3. Least-squared based training with weighted signal adaptation (III)
  • 4. Back-propagation training (BP)
  • 1
  • How many iterations you would like to have in the training ? 3
  • How many Monte-Carlo runs you would like to have for the training? 2
software simulation project3
Software simulation project
  • Results:

J_train (num_layer, num_neuron)

J_test (num_layer, num_neuron)

SNRF (num_layer, num_neuron)

  • Present training and testing errors for various configurations of the MLP
  • Present the optimum configuration found by SNRF
  • Present the comparison of the results, including errors, network structure
software simulation project4
Software simulation project
  • Typical database and literature survey
  • Function approximation & classification dataset

“IEEE Neural Networks Council Standards Committee Working Group on Data modeling Benchmarks”

http://neural.cs.nthu.edu.tw/jang/benchmark/#MG

“Neural Network Databases and Learning Data”

http://www.neoxi.com/NNR/Neural_Network_Databases.php

“UCI Machine Learning Repository”

http://www.ics.uci.edu/~mlearn/MLRepository.html

  • Data are normalized
  • Multiple input, with signal output.
  • For multiple output data, use separate MLPs.
  • Compare results from literature which uses the same dataset (*)
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