Multilayer perceptron: Difference between revisions

From Artificial Neural Network for PHP
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At first a cumulative input is calculated by the following equation:
At first a cumulative input is calculated by the following equation:


<math>s = \sum^{n}_{k=1} i_{k} \cdot w_{k}</math>
:<math>s = \sum^{n}_{k=1} i_{k} \cdot w_{k}</math>

Considering the ''BIAS'' value the equation is:

:<math>s = (\sum^{n}_{k=1} i_{k} \cdot w_{k}) + BIAS \cdot w_{k}</math>

:<math>BIAS</math> = 1


=== Sigmoid activation function ===
=== Sigmoid activation function ===


<math>o = \rm{sig}(s) = \frac{1}{1 + \rm e^{-s}}</math>
:<math>o = \rm{sig}(s) = \frac{1}{1 + \rm e^{-s}}</math>


=== Hyperbolic tangent activation function ===
=== Hyperbolic tangent activation function ===


<math>o = tanh(s)</math>
:<math>o = tanh(s)</math>


using output range between -1 and 1, or
using output range between -1 and 1, or


<math>o = \frac{tanh(s) + 1}{2}</math>
:<math>o = \frac{tanh(s) + 1}{2}</math>


using output range between 0 and 1.
using output range between 0 and 1.
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If the neural network is initialized by random weights it has of course not the expected output. Therefore training is necessary. While supervised training known inputs and their corresponded output values are presented to the network. So it is possible to compare the real output with the desired output. The error is described as the following algorithm:
If the neural network is initialized by random weights it has of course not the expected output. Therefore training is necessary. While supervised training known inputs and their corresponded output values are presented to the network. So it is possible to compare the real output with the desired output. The error is described as the following algorithm:


<math>E={1\over2} \sum^{n}_{i=1} (t_{i}-o_{i})^{2}</math>
:<math>E={1\over2} \sum^{n}_{i=1} (t_{i}-o_{i})^{2}</math>


:<math>E</math> network error
:<math>E</math> network error
Line 44: Line 50:
The learning algorithm of a single layer perceptron is easy compared to a multilayer perceptron. The reason is that just the output layer is directly connected to the output, but not the hidden layers. Therefore the calculation of the right weights of the hidden layers is difficult mathematically. To get the right delta value for changing the weights of hidden neuron is described in the following equation:
The learning algorithm of a single layer perceptron is easy compared to a multilayer perceptron. The reason is that just the output layer is directly connected to the output, but not the hidden layers. Therefore the calculation of the right weights of the hidden layers is difficult mathematically. To get the right delta value for changing the weights of hidden neuron is described in the following equation:


<math>\Delta w_{ij}= -\alpha \cdot {\partial E \over \partial w_{ij}} = \alpha \cdot \delta_{j} \cdot x_{i}</math>
:<math>\Delta w_{ij}= -\alpha \cdot {\partial E \over \partial w_{ij}} = \alpha \cdot \delta_{j} \cdot x_{i}</math>


:<math>E</math> network error
:<math>E</math> network error
Line 58: Line 64:
In this PHP implementation of multilayer perceptron the following algorithm is used for weight changes in hidden layers:
In this PHP implementation of multilayer perceptron the following algorithm is used for weight changes in hidden layers:


<math>s_{kl} = \sum^{n}_{l=1} w_{k} \cdot \Delta w_{l} \cdot \beta</math>
:<math>s_{kl} = \sum^{n}_{l=1} w_{k} \cdot \Delta w_{l} \cdot \beta</math>


<math>\Delta w_{k} = o_{k} \cdot (1 - o_{k}) \cdot s_{kl}</math>
:<math>\Delta w_{k} = o_{k} \cdot (1 - o_{k}) \cdot s_{kl}</math>


<math>w_{mk} = w_{mk} + \alpha \cdot i_{m} \cdot \Delta w_{k}</math>
:<math>w_{mk} = w_{mk} + \alpha \cdot i_{m} \cdot \Delta w_{k}</math>


:<math>\alpha</math> learning rate
:<math>\alpha</math> learning rate
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== Choosing learning rate and momentum ==
== Choosing learning rate and momentum ==


The proper choosing of learning rate (<math>\alpha</math>) and momentum (<math>\beta</math>) is done by experience. Both values have a range between 0 and 1. This PHP implementation uses a default value of 0.5 for <math>\alpha</math> and 0.95 for <math>\beta</math>. Theses factors can be change by runtime.
The proper choosing of learning rate (<math>\alpha</math>) and momentum (<math>\beta</math>) is done by experience. Both values have a range between 0 and 1. This PHP implementation uses a default value of 0.5 for <math>\alpha</math> and 0.95 for <math>\beta</math>. <math>\alpha</math> and <math>\beta</math> cannot be zero. Otherwise no weight change will be happen and the network would never reach an errorless level. Theses factors can be changed by runtime.


== Binary and linear input ==
== Binary and linear input ==
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If binary input is used easily the input value is 0 for ''false'' and 1 for ''true''.
If binary input is used easily the input value is 0 for ''false'' and 1 for ''true''.


<math>0 : False</math>
:<math>0 : False</math>


<math>1 : True</math>
:<math>1 : True</math>


Using linear input values normalization is needed:
Using linear input values normalization is needed:


<math>i = \frac{f - f_{min}}{f_{max} - f_{min}}</math>
:<math>i = \frac{f - f_{min}}{f_{max} - f_{min}}</math>


:<math>i</math> input value for neural network
:<math>i</math> input value for neural network
Line 100: Line 106:
The interpretation of output values just makes sense for the output layer. The interpretation is depending on the use of the neural network. If the network is used for classification, so binary output is used. Binary has two states: True or false. The network will produce always linear output values. Therefore these values has to be converted to binary values:
The interpretation of output values just makes sense for the output layer. The interpretation is depending on the use of the neural network. If the network is used for classification, so binary output is used. Binary has two states: True or false. The network will produce always linear output values. Therefore these values has to be converted to binary values:


<math>o >= 0.5 : True</math>
:<math>o < 0.5 : False</math>


<math>o < 0.5 : False</math>
:<math>o >= 0.5 : True</math>


:<math>o</math> output value
:<math>o</math> output value
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If using linear output the output values have to be normalized to a real value the network is trained for:
If using linear output the output values have to be normalized to a real value the network is trained for:


<math>f = o \cdot (f_{max} - f_{min}) + f_{min}</math>
:<math>f = o \cdot (f_{max} - f_{min}) + f_{min}</math>


:<math>f</math> real world value
:<math>f</math> real world value
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The same normalization equation for input values is used for output values while training the network.
The same normalization equation for input values is used for output values while training the network.


<math>o = \frac{f - f_{min}}{f_{max} - f_{min}}</math>
:<math>o = \frac{f - f_{min}}{f_{max} - f_{min}}</math>


:<math>o</math> desired output value for neural network
:<math>o</math> desired output value for neural network

Revision as of 17:00, 13 January 2008

General

A multilayer perceptron is a feedforward artificial neural network. This means the signal inside the neural network flows from input layer passing hidden layers to output layer. While training the error correction of neural weights are done in the opposite direction. This is done by the backpropagation algorithm.

Activation

At first a cumulative input is calculated by the following equation:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle s = \sum^{n}_{k=1} i_{k} \cdot w_{k}}

Considering the BIAS value the equation is:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle s = (\sum^{n}_{k=1} i_{k} \cdot w_{k}) + BIAS \cdot w_{k}}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle BIAS} = 1

Sigmoid activation function

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle o = \rm{sig}(s) = \frac{1}{1 + \rm e^{-s}}}

Hyperbolic tangent activation function

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle o = tanh(s)}

using output range between -1 and 1, or

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle o = \frac{tanh(s) + 1}{2}}

using output range between 0 and 1.

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle s} cumulative input
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle w} weight of input
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle i} value of input
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle n} number of inputs
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle k} number of neuron

Error of neural network

If the neural network is initialized by random weights it has of course not the expected output. Therefore training is necessary. While supervised training known inputs and their corresponded output values are presented to the network. So it is possible to compare the real output with the desired output. The error is described as the following algorithm:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E={1\over2} \sum^{n}_{i=1} (t_{i}-o_{i})^{2}}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E} network error
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle n} count of input patterns
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle t_{i}} desired output
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle o_{i}} calculated output

Backpropagation

The learning algorithm of a single layer perceptron is easy compared to a multilayer perceptron. The reason is that just the output layer is directly connected to the output, but not the hidden layers. Therefore the calculation of the right weights of the hidden layers is difficult mathematically. To get the right delta value for changing the weights of hidden neuron is described in the following equation:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \Delta w_{ij}= -\alpha \cdot {\partial E \over \partial w_{ij}} = \alpha \cdot \delta_{j} \cdot x_{i}}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle E} network error
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \Delta w_{ij}} delta value Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle w_{ij}} of neuron connection Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle i} to Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle j}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \alpha} learning rate
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \delta_{j}} the error of neuron Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle j}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle x_{i}} input of neuron Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle i}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle t_{j}} desired output of output neuron Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle j}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle o_{j}} real output of output neuron Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle j} .

Programming solution of backpropagation

In this PHP implementation of multilayer perceptron the following algorithm is used for weight changes in hidden layers:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle s_{kl} = \sum^{n}_{l=1} w_{k} \cdot \Delta w_{l} \cdot \beta}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \Delta w_{k} = o_{k} \cdot (1 - o_{k}) \cdot s_{kl}}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle w_{mk} = w_{mk} + \alpha \cdot i_{m} \cdot \Delta w_{k}}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \alpha} learning rate
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \beta} momentum
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle k} neuron k
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle l} neuron l
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle m} weight m
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle i} input
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle o} output
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle n} count of neurons

To avoid overfitting of neural network the training procedure is finished if real output value has a fault tolerance of 1 per cent of desired output value.

Choosing learning rate and momentum

The proper choosing of learning rate (Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \alpha} ) and momentum (Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \beta} ) is done by experience. Both values have a range between 0 and 1. This PHP implementation uses a default value of 0.5 for Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \alpha} and 0.95 for Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \beta} . Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \alpha} and Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \beta} cannot be zero. Otherwise no weight change will be happen and the network would never reach an errorless level. Theses factors can be changed by runtime.

Binary and linear input

If binary input is used easily the input value is 0 for false and 1 for true.

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle 0 : False}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle 1 : True}

Using linear input values normalization is needed:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle i = \frac{f - f_{min}}{f_{max} - f_{min}}}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle i} input value for neural network
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f} real world value

This PHP implementation is supporting input normalization.

Binary and linear output

The interpretation of output values just makes sense for the output layer. The interpretation is depending on the use of the neural network. If the network is used for classification, so binary output is used. Binary has two states: True or false. The network will produce always linear output values. Therefore these values has to be converted to binary values:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle o < 0.5 : False}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle o >= 0.5 : True}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle o} output value

If using linear output the output values have to be normalized to a real value the network is trained for:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f = o \cdot (f_{max} - f_{min}) + f_{min}}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f} real world value
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle o} real output value of neural network

The same normalization equation for input values is used for output values while training the network.

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle o = \frac{f - f_{min}}{f_{max} - f_{min}}}
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle o} desired output value for neural network
Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle f} real world value

This PHP implementation is supporting output normalization.

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