The backpropagation algorithm was used to train the multi layer perception MLP

  

Backpropagation

Backpropagation

  

Learning Algorithm

Learning Algorithm x ₁ x _n

  

The backpropagation algorithm was used to train the

multi layer perception MLP

Architecture of BP Nets

• Multi-layer, feed-forward networks have the following

  characteristics:

• They must have at least one hidden layer
• – Hidden units must be non-linear units (usually with sigmoid activation functions)

Other Feedforward Networks

  Madaline •

• – Multiple adalines (of a sort) as hidden nodes
• – Dynamically change the network size (# of hidden

  Adaptive multi-layer networks

Introduction to Backpropagation

• In 1969 a method for learning in multi-layer network, Backpropagation (or generalized delta rule) , was invented by Bryson and Ho.
• It is best-known example of a training algorithm. Uses training data to adjust weights and thresholds of neurons

• How many hidden layers and hidden units per

  layer?

• – Theoretically, one hidden layer (possibly with many

  hidden units) is sufficient for any L2 functions
• – There is no theoretical results on minimum necessary # of hidden units (either problem dependent or independent)

  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 output nodes

Three Three - - layer back layer back - - propagation neural network propagation neural network

  x ₁ x ₂ ^{1 2} i ^{1 2} y ₁ y ₂ Input signals w ^w _ij

¹

²

_j

  Generalized delta rule

• Delta rule only works for the output layer.

  Description of Training BP Net:

Feedforward Stage

  1. Initialize weights with small, random values

  2. While stopping condition is not true

• – for each training pair (input/output):
• each input unit broadcasts its value to all hidden units

  Training BP Net:

Backpropagation stage

  

3. Each output computes its error term, its own

weight correction term and its bias(threshold) correction term & sends it to layer below

  

Training a Back Prop Net:

Adjusting the Weights

  5. Each output unit updates its weights and bias

How long should you train?

• Goal: balance between correct responses for

  training patterns & correct responses for new

  patterns (memorization v. generalization)

• • In general, network is trained until it reaches an

  Graphical description of of training multi-layer neural network using BP algorithm

  To apply the BP algorithm to the following FNN

• To teach the neural network we need training data set. The training data set consists of input signals

  (x and x ) assigned with corresponding target _{1 2} (desired output) z.

• The network training is an iterative process. In each iteration weights coefficients of nodes are modified

• Propagation of signals through the hidden layer. Symbols w represent weights of

  mn connections between output of neuron m and input of neuron n in the next layer.

• Propagation of signals through the output layer.
• In the next algorithm step the output signal of the network y is compared with the desired output value (the target), which is found in training data set. The difference is called error

  It is impossible to compute error signal for internal neurons directly, •

because output values of these neurons are unknown. For many

years the effective method for training multiplayer networks has been unknown.

  Only in the middle eighties the backpropagation algorithm has • been worked out. The idea is to propagate error signal δ

  (computed in single teaching step) back to all neurons, which output signals were input for discussed neuron. The weights' coefficients w used to propagate errors back are equal to • _mn

this used during computing output value. Only the direction of data flow is

changed (signals are propagated from output to inputs one after the

other). This technique is used for all network layers. If propagated errors

came from few neurons they are added. The illustration is below:

• When the error signal for each neuron is computed, the weights coefficients of each neuron input node may

  Coefficient η affects network teaching speed. There are a few techniques

  Training Algorithm 1

• Step 0: Initialize the weights to small random values

  Training Algorithm 2

• Step 3: Calculate the weight correction

  term for each output unit, for unit k it is:

Training Algorithm 3

• • Step 4: Propagate the delta terms (errors)

  back through the weights of the hidden

  th units where the delta input for the j hidden unit is: _m

  

Training Algorithm 4

• • Step 5: Calculate the weight correction term for

  the hidden units:

  ∆w = ηδ x

  ij j i

Options

• There are a number of options in the design of a backprop system
• – Initial weights – best to set the initial weights (and all other free parameters) to random

  Example

• • The XOR function could not be solved by

  a single layer perceptron network

XOR Architecture

  1 _{v 1 x v 21}

  11

  Σ _{v 31} f f _{1 -.4 -.2} Σ _.3

Feedfoward – 1 st Pass

  x

  ^.21 f Σ ^-.3 _{.15 -.4} ^{1 1 1} y_in ₁ ^{= -.3(1) + .21(0) + .25(0) = -.3} _{f = .43 1} y_in

• _{+.3(.56) = -.318 3} ^{= -.4(1) - .2(.43)}

Backpropagate

• – y
₃ )f’(y_in ₃ ) =(t ₃ – y ₃ )f(y_in ₃ )[1- f(y_in ₃ )]

  ^.21 f Σ ^-.3 _.15 ^{1 1}

  δ ₃ = (t ₃

Update the Weights – First Pass

• – y
₃ )f’(y_in ₃ ) =(t ₃ – y ₃ )f(y_in ₃ )[1- f(y_in ₃ )]

  ^.21 f Σ ^-.3 _.15 ^{1 1}

  δ ₃ = (t ₃ f _{1 -.5 -2} Σ ₁