Pythia – The Neural Network designer

Pythia – The Neural Network Designer © 2000 by Runtime Software - 4/13/23 - 7:52 PM 1 of 29 pages

Pythia – The Neural Network designer

Introduction

Pythia is a program for the development and design of Neural Networks. Neural Networks are used to detect hidden relations in a set of patterns, e.g. stock market data or weather data.

Pythia features Backpropagation Networks. The network parameters (“Weights”) are initially set to random value. During the “Training phase” the actual output of the network is compared with the desired output and the error propagated back toward the input of the network.

A Neural Network has two phases, commonly referred to as the “Training phase” and the “Reproduction phase”. During the training phase sample data containing both – inputs and desired outputs – are processed to optimize the network’s output, meaning to minimize the deviation

(OUTPUTDATA – OUTPUTNET)2

OUTPUTDATA is the output value in the training data, OUTPUTNET is the output value provided by reproducing the input data with the network.

During the “reproduction phase” the network’s parameters are not changed anymore and the network is used for the reproduction of input data in order to “predict” suitable output data.

Picture 1 shows a typical Backpropagation Network. It has 2 inputs and 1 output. It contains two layers (levels) of neurons, level 1 with 2 neurons and level 2 with 1 neuron.

In Backpropagation Networks each neuron has one output and as many inputs as neurons in the previous level.

Each network input is connected to every neuron in the first level. Each neuron output is connected to every neuron in the next level.

The Network’s output is the output of the last level’s neurons.

Picture 1 – A typical Backpropagation network


Each neurons output is calculated asOn = F(ΣIk * Wkn)

k

O ist the neuron’s output, n is the number of the neuron,Ik are the neurons inputs, k is the number of inputs,Wkn are the neurons weights.F is the Fermi function 1/(1+Exp(-4*(x-0.5)))

The network is processed from the left to the right. Picture 2 shows a sample how the output of a Neural Network is calculated from the input.

Pythia allows you to import data from different file formats or from spreadsheet programs like Microsoft Excel. You can design and train Neural Networks. Both, data and networks can easily be stored on disk.A special feature of Pythia is the Evolutionary Optimizer that automatically generates suitable networks for a given training data set. It uses evolutionary algorithms for the selection and generation of neural networks.

Picture 2 – Sample calculation of a NetworkThe activity of N1 is calculated as

A = (1*0.249733)+(0*-0.233776) = 0.249733The output of N1 is calculated as

O = Fermi(A)= 1/(1+Exp(-4*(0.249733-0.5))) = 0.268731The outputs of N1 and N2 are the inputs for the calculation of N3


In order to get familiar with Pythia we recommend to work through the two examples we discuss below.

Example 1 – XOR problem

A classic example for Neural Networks to learn is the XOR problem. The network is supposed to learn the pattern:

The network we need to design will have 2 inputs and 1 output.

First of all we need to get the training patterns (0,0,1), (0,1,1), (1, 0, 1) and (1,1,0) into Pythia. The easiest way is using a spreadsheet program like Excel.

Enter the pattern as shown in Picture 4.

Select columns A-C and rows 1-4 with the mouse.

Select EDIT→COPY or simply press CTRL-INS.

The data are now in the windows clipboard.

Next step is calling Pythia.Initially Pythia will look as shown in Picture 5.

Input 1 Input 2 OutputPattern 1 0 0 0Pattern 2 0 1 1Pattern 3 1 0 1Pattern 4 1 1 0

Picture 3 The XOR problem

Picture 4 – Data entry into an Excel table


Select EDIT→PASTE CELLS or simply press SHIFT-INSERT.

You will get a dialog box as shown in Picture 6.

Leave the Field delimiter (TAB) and the Fields per record (3).

Paste to new Pattern Set is correct too, because we want to create a whole new pattern set.

Notice that No outputs is set to 1 by default. If you network is supposed to have more than just one output, you must specify it here.

Finally press the Ok button.

Picture 5 – Pythia after the program start

Picture 6 – Paste dialog


Picture 7 shows Pythia after pasting the pattern set data.

You see the columns I1, I2 and O1. The columns O1(NET) and (SQ DV) are empty because they are filled later by the Neural Network we will design now.It is a good idea to save the pattern set under the name “XOR.PAT” now.

Picture 7 – The pattern set data have been pasted into a new table


To create a new Neural Network do the following steps:

Select NET→CREATE NET

Enter the number of inputs your net will have (2)

Enter the number of neurons in level 1. We select 2.

Enter the number of neurons in level 2. Since this is the last level we define, it is equal to the output level. Therefore select 1.


Picture 8 – Create a new Neural Network


Now try to reproduce the pattern set with the new network.

Select NET→REPRO PATTERN SET

As you see in Picture 10 the results are quiet poor. The column O1(NET) should be similar to O1.The most right column SQ DV tells you about the square of the deviation and is defined as

SQ DVi = (O1i – O1(NET)i)2

i is the pattern number

Example: (0-0.071094)2 = 0.005054

It is the goal of the training phase to minimize these values. When you look at the desired outputs in column O1 you will see that the output is either 0 or 1. Consequently a

Picture 9 – A new created Neural Network

Picture 10 – Reproduction with the untrained net


O1(NET) value of <0.5 will be sufficient for 0 and a value >0.5 will be sufficient for 1. This means the deviation for each reproduced pattern must be <0.5, meaning the square of this deviation must be < 0.25.

Let’s setup a learn plan.

Select NET→LEARN PATTERN SET

Picture 11 shows the dialog window for setting up the learn plan.

Please leave the default values with the exception of *deviation. Check this item and enter the value 0.25.

Press the Ok button.

If every deviation is below 0.25 the net is trained sufficiently. This is probably not the case the first time you try it. The reason is that not all starting value for the neuron’s weights cause this network to learn the pattern set.

Please set new random weights before you try the training again. You can do this either by checking the box Set weights randomly or by selecting the appropriate menu point from the main menu bar (NET→SET RANDOM WEIGHTS).

Do the training again. Repeat until any deviation is below 0.25.

After achieving this goal the network is trained sufficiently in order to predict the correct outcome of the possible inputs (0,0), (0,1), (1,0) and (1,1).

You can save the network on disk now.

It should be mentioned that the pattern set XOR.PAT would be learned by a larger Neural Network on the first trial. However, there are several reasons to prefer a network as small as possible. One reason is the performance of the network. A small one is calculated faster. The second, even more important reason, is the networks ability to abstraction. A large network might be able to learn the training patterns, but might fail on slightly different data during the reproduction phase.

Picture 11 – Set up a learn plan


Example 2 – Stock Market Prediction

A nice application for Neural Network is the prediction of the stock market. We keep our following example very simple and merely show how to prepare the stock market data for input into a Neural Network. Results should always be subject to human interpretation and it is always the investor’s risk to bet any real money on it.It must be stated clearly: No Neural Network can ever predict a market crash or any point gain or loss due to actual economical or political events you don’t know in advance. But a Neural Network might be able discover relations between stock market data that are not obvious.Our example will examine the Dow Jones Industrial Index in 1999.

Picture 12 shows some DJI data for 1999.

Our idea is that there might be a relation between today’s open, high, low, close and volume and tomorrow’s close.

Using the values directly as input into our network is not advisable because our interest is merely the percent drop and gain. We therefore derive our input data from the original data in the following way:

ΔOpen(t) = % change day t-1 → day t = (Open(t)-Open(t-1))/Open(t-1)*100ΔHigh(t) = % rel. to Open = (High(t)-Open(t))/Open(t)*100ΔLow(t) = % rel. to Open = (Low(t)-Open(t))/Open(t)*100ΔClose(t) = % change day t-1 → day t = (Close(t)-Close(t-1))/Close(t-1)*100ΔVolume(t) = % change day t-1 → day t = (Volume(t)-Volume(t-1))/Volume(t-1)*100

The output we define as

ΔClose(t+1) = % change day t → day t+1 = (Close(t+1)-Close(t))/Close(t)*100

The formulas can be entered easily into a spreadsheet table and will look as shown in picture 13:

Date Open High Low Close Volume1/4/99 9184.01 9350.33 9122.47 9184.27 8831/5/99 9184.78 9338.74 9182.98 9311.19 7791/6/99 9315.42 9562.22 9315.42 9544.97 9861/7/99 9542.14 9542.14 9426.02 9537.76 8571/8/99 9538.28 9647.96 9525.41 9643.32 9401/11/99 9643.32 9643.32 9532.61 9619.89 816

::Picture 12 – DJI open, high, low, close and volume in 1999

Date Open High Low Close Volume ΔOpen ΔHigh ΔLow ΔClose ΔVolume ΔClose+1

12/31/98 9274.12 9287.77 9181.43 9181.43 755 -0.50274 0.147184 -0.99945 -1.005 27.10438 0.030932

1/4/99 9184.01 9350.33 9122.47 9184.27 883 -0.97163 1.810974 -0.67008 0.030932 16.95364 1.381928

1/5/99 9184.78 9338.74 9182.98 9311.19 779 0.008384 1.676251 -0.0196 1.381928 -11.778 2.510742

1/6/99 9315.42 9562.22 9315.42 9544.97 986 1.422353 2.649371 0 2.510742 26.57253 -0.07554

1/7/99 9542.14 9542.14 9426.02 9537.76 857 2.433814 0 -1.21692 -0.07554 -13.0832 1.106759

1/8/99 9538.28 9647.96 9525.41 9643.32 940 -0.04045 1.149893 -0.13493 1.106759 9.684947 -0.24297

1/11/99 9643.32 9643.32 9532.61 9619.89 816 1.101247 0 -1.14805 -0.24297 -13.1915 -1.50948

1/12/99 9618.86 9620.15 9451.77 9474.68 794 -0.25365 0.013411 -1.73711 -1.50948 -2.69608 -1.32057

1/13/99 9471.34 9471.34 9213.1 9349.56 935 -1.53365 0 -2.72654 -1.32057 17.75819 -2.44536

1/14/99 9349.56 9359.08 9087.72 9120.93 798 -1.28577 0.101823 -2.80056 -2.44536 -14.6524 2.407868

1/15/99 9127.16 9342.61 9127.16 9340.55 921 -2.37872 2.360537 0 2.407868 15.41353 0.157057

Picture 13 – DJI data modified for input into a Neural Network


Now you can select and copy the right 6 columns and paste them into Pythia. You also can simply load the sample file DOW99I.PAT that comes with Pythia.Our approach will be to train a network with the data of the 1st half year 1999. With the then trained network we will examine the output for the reproduction of the 2nd half of 1999.

Our Neural Network expects any input and output value to be between 0 and 1. Therefore the pattern sets must be normalized before being processed by the network.

The normalization is calculated:

N(i) = (i - low) / (high - low)i is the input (or output value)low is the minimum possible valuehigh is the maximum possible value

This is usually done automatically, but we should take the time to examine normalization parameters now.

Select PATTERN→OPTIONS. For our current example you see the options as shown in Picture 14.

Input1 for example has possible values between –2.378722 and 2.846741.

Suppose you have to normalize the value I1=0.5. You would calculate:

N(0.5) = (0.5 – (-2.378722)) / (2.846741- (–2.378722)) = 0.55090276


Once you create a new pattern set Pythia calculates the lowest and the highest value for each column.

You can change these values in the option menu if you like.

If you train a network with a certain pattern set you must choose the same normalization parameters for a pattern set you are going to reproduce with this network.

The display format parameters only specifies how the number are displayed in the table.

Now let’s design an appropriate network.

Create a new network with the values (5,5,7,1). This network has the required 5 inputs and 1 output.

Select NET → LEARN PATTERN SET. Leave the default values except the repetition count, which we set to 3000. Press the Ok button

The training pattern set will now be processed 3000 times. Finally the trained network automatically reproduces the inputs of the pattern set.

The results can be examined in Picture 15. Have a look at the columns O1 and NET(O1). As you remember, NET(O1) is the output generated by the Neural Network, O1 is the output we would like to get. The values describe the percentage drop or gain between two day’s close values.

Our network at least has learned some of the patterns. Especially if our network makes a significant statement (let’s say it predicts a gain or drop of 0.6% or more) it is usually right with the direction the DJI moves.

Picture 14 – Options for pattern sets


Now let’s test the same network with the 2nd half year data. The network has not been trained with these. So they are completely unknown to the network.

Please load dow99ii.pat into Pythia. Note that the normalization options are the same for dow99i.pat and dow99ii.pat.

Reproduce this pattern set.

You see that this time the outcome is very poor.

Picture 15 – The network after the training


Next step would be to modify the model, usually by adding input parameters like interest rates, overseas market indices, currency ratio etc.

Picture 16 – Reproducing the 2nd half 1999 data generates poor results


Evolutionary Optimizer

The Evolutionary Optimizer is a tool for generating Neural Networks. The only thing you must provide is the training pattern set. Let’s see how the Evolutionary Optimizer generates a network for the XOR sample:

Load the pattern set XOR.PAT into Pythia.

Select NET→ EVOLUTIONARY OPTIMIZER

You will see a dialog window as shown in Picture 17.

Uncheck the Ø deviation² field Change the *deviation² field to

0.25 Change the # neurons field to 3 Press the Ok button

The Evolutionary Optimization will start processing now. It will stop if the Goals to achieve are true, meaning

*deviation² < 0.25 and # neurons <= 3

However, if will perform max. 1000 evolution steps.

You can find further explanations of the options in the reference part of this manual.

Picture 17 – The Evolutionary Optimizer


After a couple of generations the optimizer finally found a suitable network in line 11. This network has 3 neurons and the max. deviation for any pattern is 0.078666.Click the checkbox on the left of this line and press the Ok button. You now have a network that is sufficiently trained for the XOR problem.

Picture 18 – The Evolutionary Optimizer generated a network


Reference

The reference part of this manual will describe in detail the menu items and the settings in the various dialog boxes.

The Main Window

Pythia’s main windows contains three areas:

The pattern set area on the left side The network area on the right side A log window on the bottom

The pattern and the network area can contain any number of pattern sets or networks. Any operation a user selects will process the currently selected pattern set or network.

The log windows displays the last transactions.

FILE → EXIT

This terminates Pythia.

PATTERN → READ PATTERN SET

This menu item reads a previously saved pattern set into pythia. Pattern set files have the extension “.PAT”

PATTERN → IMPORT PATTERN SET

This menu item imports a data file as a pattern set into pythia. The data file’s format is assumed to be ASCII.

After choosing a file name a dialog box as shown in Picture 19 will pop up. A preview window displays the first lines of the file.

You can further specify a field delimiter, the number of fields per record and the number of outputs.

Picture 19 – Import pattern file dialog


Pythia tries to determine the field delimiter and the field per record value automatically. Usually you won’t have to change these.

The number of outputs field default to 1. You must change this if your data contain more than one output.

Finally press the Ok button. Your new pattern set will be display on the left side of Pythia’s main windows.

Note: During import some values will set automatically. These settings are the Normalization parameters and the Display format.

Normalization:

The High of each input or output is set to the maximum value of the referred column.The Low of each input or output is set to the minimum value of the referred column.

Display format:

The display for each column is set to (9,6), meaning the values will be displayed by a 9 character string with 6 characters after the decimal point.

PATTERN → SAVE PATTERN SET

This menu item allows you to save the currently selected pattern set. Any special settings like Normalization and Display format parameters are stored along with the pattern set.

PATTERN → CLOSE PATTERN SET

This menu item closes the currently selected pattern set. The user will be prompted to save this pattern set if it was modified.


PATTERN → OPTIONS

This menu item allows you to change certain settings for the pattern set.

Normalization:

A Neural Network expects any input and output value to be between 0 and 1. Therefore the pattern sets must be normalized before being processed by the network.

The normalization is calculated:

N(i) = (i - low) / (high - low)i is the input (or output value)low is the minimum possible valuehigh is the maximum possible value

The dialog box shown in Picture 20 allows you change the normalization parameters.

The initial parameters are calculated when the pattern set was first created, either by importing or by pasting. They are set to the lowest and highest value found in a column.

The low..high range should span any possible value. However, if your data set contain any “runaways” you should either exclude these from you data or ignore them by manually adjusting the normalization parameters.

You can change values in the option window by simply entering the new value into the cell.

Alternatively, you can make use of the following operations:

Push this button to specify a new low value for all columns.Push this button to specify a new high value for all columns.Restore the default values (new calculation as if the pattern set was just created).Discard changes.

Note: If you train a network with a certain pattern set you must choose the same normalization parameters for another pattern set you are going to reproduce with this network.

The display format parameters only specifies how the number are displayed in the table.

Picture 20 – Options for pattern sets


Display format:

The display format parameters specify how the numbers are displayed in the table.

By default the display for each column is set to (9,6), meaning the values will be displayed by a 9 character string with 6 characters after the decimal point.

Use the following operations to change the look of the numbers:

Push this button to specify a new width for all columns.Push this button to specify a new decimal value for all columns.Restore the default values (9,6).Discard changes.

PATTERN → TOGGLE VIEW

Select this command to toggle between the original and the normalized form of the pattern set.

You recognize the currently select view on the tab of the pattern set. The notation N(xxx.pat) means normalized, xxx.pat indicates native (original).


NET → CREATE NET

This menu item is used to create a new Neural Network.

Enter the new network’s desired topology into the dialog box display in Picture 21.

Begin with the number of inputs the new network is supposed to have.

Continue with the levels (usually 2 or 3 levels).

Keep in mind that the last described level is the output level, meaning the number of neurons in the last level must match the number of neurons in the output level.

Pressing the Sample button automatically creates a sample topology that is able to process the currently selected pattern set (if there is one).

Finally press the Ok button. The new created Neural Net is now displayed in the network area of Pythia’s main window.

Picture 22 shows how this will look like.

Picture 21 – Create a new Neural Network


NET → READ NET

This menu item reads a previously saved network into pythia. Network files have the extension “.NN”

NET → CLOSE NET

This menu item closes the currently selected network. The user will be prompted to save this network if it was modified.

NET → SAVE NET

This menu item allows you to save the currently selected network. The network topology and weights are stored to a file with the extension “.NN”.

Picture 22 – A new created Neural Network


NET → EVOLUTIONARY OPTIMIZER

The Evolutionary Optimizer is a tool for generating Neural Networks. The only thing you must provide is the training pattern set. Selecting this menu item pops up a dialog box as displayed in Picture 23.

How the Evolutionary Optimizer works

(the following brief description assumes parameters to be set as shown in Picture 23)

The Evolutionary Optimizer initially creates a generation containing 50 randomly created networks.

Each network within this generation will be trained shortly and its fitness determined according to the parameters in Goals to achieve.

Then a new generation of networks will be created from the old one according to the following procedure:

Two “parent” networks will be chosen out of the old generation. The selection algorithm will choose networks with a high fitness by a higher probability.

Two “children” networks will be created from the two “parent” networks. With the probability of 0.2 the two “children” networks will be crossed over. This

means they will swap level with each other.Example:

Child 1: (2,2,1) → (2,2 │1) Child 2: (2,6,6,1) → (2,6 │6,1)

Compose the 1st part of child 1 with the 2nd part of child 2 to (2,2,6,1)Compose the 1st part of child 2 with the 2nd part of child 1 to (2,6,1)The “children” have been crossed over to (2,2,6,1) and (2,6,1).

The “children” now will be mutated with a probability of 0.04. Mutation means insertion or deletion of a level, insertion or deletion of a neuron into a level or change of weights.

The two “parent” network will now be checked if they belong to the 10 “fittest” of the old generation. If they do they will be mutated and rolled over into the new generation.

The selection continues until the new generation has 50 members too. After completion the new generation will be evaluated.

The Evolutionary Optimizer is considered ready as soon as it found a network with a fitness of 100.

Otherwise it continues for 1000 generations, what might take hours or days to compute. You always can stop the process if you do not see any progress.


Options

There are lots of options you can fine-tune the optimizer’s work with.

1 st ancestor Neural Net:

Specify a template network the first generation is created from or allow to generate a new random one with create new.

Pattern set to learn:

Specify the pattern set you want to generate a network for. This pattern set must be loaded previously.

Check the option Mix pattern randomly if you want to overcome a given but unwanted sort order in the pattern set. Note that a pattern set’s sort order influences the training phase of the network.

Goals to achieve:

Here you can specify what the network should be optimized for. There are three goals possible:

Optimize for medium deviation (Ø deviation²) Optimize for max. deviation within the pattern set (*deviation²) Optimize for size (# neurons)

Tag or untag the checkboxes on the left side to determine if the certain goal shall play a role at all.

If a goal is tagged specify the value you want to push the network below.

Specify the contribution a goal will make to the overall fitness.

The example in Picture 23 does not care about the medium deviation, but wants the max. deviation to be below 0.25 and the network size below or equal 3. Both checked goals will contribute 50% to the overall fitness of an evolutionary created network (1:1 = 50:50). If you would want the network size to have an importance of only 20% you

Picture 23 – The Evolutionary Optimizer


would have to enter the value 4 and 1 into the contribution fields for max. deviation and size.

Evolutionary algorithms settings:

Different options influence the optimization process

Population size: number of networks created per generation.

Evolution steps: max. number of generations before stop.

Mutation rate: probability for a network to be modified during rollover to a new generation.

Cross over rate: probability for a child network to be crossed over with another child network

# fittest/Generation: number of networks that are rolled over to a new generation as they are. Any other networks of one generation are discarded. However, discarded network still might become parent network.

Modify fittest: if checked, the rolled over networks are modified with the given mutation rate.

Pressing the Ok button starts the evolutionary process. You can watch the progress in the progress window shown in Picture 24

During optimization you always can stop the process by pressing the Stop button.

The colored circle right of the checkbox show you the quality of a network. Red means no goal achieved at all, yellow means some goals achieved and green means all goals achieved. If there is any green network the optimization terminates.

The column Topology describes the levels of the network. The column Neurons describes the number of neurons the network owns. The column Ø dev² shows the network’s medium square deviation on pattern set

reproduction. The column *dev² shows the network’s max square deviation on pattern set

reproduction. The column Fitness describes the network’s “fitness” with a number between 0

and 100.


When the optimization stops (by either finding a network with a “fitness” of 100 or stopping manually) you can choose one or more network to be move into the network area of Pythia’s main window.

The moved networks are ready for use.

NET → REPRO PATTERN SET

This menu item causes the currently selected network to reproduce the currently selected pattern set. The net output columns and the deviation column will be refreshed.

Note: You can only choose this command if the currently selected network is compatible to the currently selected pattern set. Compatibility means same number of inputs and same number of outputs.

Picture 24 – The Evolutionary Optimizer generated a network


NET → LEARN PATTERN SET

Choosing this menu item pops up a dialog box (Picture 25) that allows you to specify a learn plan for the training of the currently selected network with the currently selected pattern set.

Options

Neuronal Network to train:

Specify the network you want to train. This network must be compatible with the pattern set and must have been loaded into the network area of the main window.

Pattern set to learn:

Specify the pattern set you want to train the network with. This pattern set must be loaded previously.

Check the option Mix pattern randomly if you want to overcome a given but unwanted sort order in the pattern set. Note that a pattern set’s sort order influences the training of the network.

Set weights randomly:

Check this box to set the network’s weights to initial random values. This might be necessary because sometimes the given weight values do not lead to a suitable network even if this is possible.

Train until:

Declare the cancel criteria of the learn plan here. You can connect the criteria with each other by the AND or the OR operator.

The criteria for exiting the learn plan are:

Repetition Medium deviation (Ø deviation²) Max. deviation within the pattern set (*deviation²) Time passed

Picture 25 – Set up a learn plan


Tag or untag the checkboxes on the left side to determine if the certain criterion shall play a role at all.

In the example in Picture 25 the learn plan will stop as soon as either the repetition count reaches 1000 or the max deviation is below 0.25 or 300 seconds have passed.

Learn rate:

The learn rate specifies how fast an error is propagated backwards. A large value accelerates the learning, but might cause the network to overshoot the mark. Choose a value between 0.1 and 0.5.

Tag Automatically adjust if you want Pythia to adjust the learn rate during the training automatically. (Note: this feature is not implemented yet).

Finally:

Tag Reproduce pattern set if you want to get the pattern set reproduced after the training. This is equivalent with the command NET → REPRO PATTERN SET.

Check show results in native form to get the pattern set shown in their original form instead of the normalized form. This is equivalent with the command PATTERN → TOGGLE VIEW.

NET → REPRODUCE SINGLE

This menu item causes the currently selected network to reproduce a single pattern.

Manually enter the inputs, delimited by a , .

The number of data you need to enter equals the number of inputs of the network.

Furthermore, you need to check if the data entered will be interpreted normalized or original.

Picture 26 – Reproduce a single pattern


The result of this operation will be displayed in Pythia’s log window on the bottom of the main window.

NET → LEARN SINGLE

This menu item causes the currently selected network to perform one learn step for a single pattern.

Manually enter the inputs and outputs, delimited by a , .

The number of data you need to enter equals the number of inputs plus the number of outputs of the network.

Furthermore, you need to check if the data entered will be interpreted normalized or original.

The result of this operation will be displayed in Pythia’s log window on the bottom of the main window.

NET → SET RANDOM WEIGHTS

Choose this menu item to reset a network’s weights to random values. This might be necessary because sometimes the given weight values do not lead to a suitable network even if this is possible.

You need to enter the low..high range for the new random weights. These are usually between –1 and 1.

Picture 27 – Result of the single pattern reproduction

Picture 28 – Perform one learn step for a single pattern

Picture 29 – Result of a single learn step

Picture 30 – Set new random weights


NET → SET LEARN RATE

Choose this menu item to change a network’s learn rate.

The learn rate specifies how fast an error is propagated backwards. A large value accelerates the learning, but might cause the network to overshoot the mark. Choose a value between 0.1 and 0.5.

EDIT → COPY CELLS

This command copies a pattern set’s selected cells into the windows clipboard. From there you can paste the data into other applications, e.g. text editors or MS Excel.

EDIT → PASTE CELLS

This command pastes cells from the Windows clipboard into Pythia.

You will get a dialog box as shown in Picture 31.

Field delimiter:

This specifies the delimiter between single fields.

Fields per record:

Specifies the number of fields in each record.

Paste to new Pattern Set:

Select this options if you intend to paste the clipboard into a whole new pattern set. You must specify the number of inputs too.

Paste to selected Pattern Set:

Select this option if you want to paste the clipboard into an existing pattern set. You can either paste at the current position or append.


Picture 31 – Paste dialog

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Pythia – The Neural Network designer