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Neural Network Language NNL

Language Project. Neural Network Language NNL. Neural Network. Neural networks have a mass appeal Simulates brain characteristics Weighted connection to nodes. Neural Network cont. A neural network consists of four main parts: 1. Processing units.

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Neural Network Language NNL

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  1. Language Project Neural Network LanguageNNL

  2. Neural Network • Neural networks have a mass appeal • Simulates brain characteristics • Weighted connection to nodes

  3. Neural Network cont. • A neural network consists of four main parts: • 1. Processing units. • 2. Weighted interconnections between the various processing units which determine how the activation of one unit leads to input for another unit. • 3. An activation rule which acts on the set of input signals at a unit to produce a new output signal, or activation. • 4. Optionally, a learning rule that specifies how to adjust the weights for a given input/output pair.

  4. Learning and predicting • Learning method is back propagation • Error is calculated at result nodes • This error is then fed backward through the net • Adjustments to weight reduce error • We look at our result nodes and Output node which is the last node, and • The difference we get is the error

  5. Predicting • Predicting is summation of weights • Dendrites have input value • Input times weight gives value • All value summed give total activation

  6. Parameters • Internal parameters • Learning rate • Threshold • Dendrite initial value • Value • Rand • Activation formula • Triangle • Logistic • linear

  7. Elements • sense- • Gets input from the file • Input is in sequence • dendrite- • Connects neurons • Has weight • Weight is a floating value from 0-1

  8. Elements cont • soma – • body of a neuron • synapse – • It is used for connection. It determines which dendrite will go to which neuron • result – • results are supplied to this node

  9. Parts of Language • The derived NN Engine consists of three parts • Framework initialization • Link necessary files • Topology implementation • NNL specific code • Processor • Process input using topology

  10. Properties • Its imperative • Keywords are not case sensitive • Id’s are case sensitive • Last neuron of layer is one to one relationship with result • We can implement two neural networks, take the same sense values and get different results to compare • Readable • Writable • We are using functions without declarations • Functions can take any values • Error will be caught in semantics

  11. Input and Output • Network input • Format in in in … in # out out …out • Error checking for input file • Network output • The output file is same format • Populate empty output with predictions

  12. Grammar Used • A-> O; A  | D; A | N ; A | Y; A | S ; A | R;A| e • O-> soma I F     --------body of a neuron • E->dendrite E • I-> id • Y->synapse II     --------- a connection • F->function P • P->( P’) • P’->Z,P’| Z • D-> dendrite I F    -------- input to neuron • N -> neuron E   --- a neuron composed of  soma and dendrite • S->sense Z I   ---- information is supplied to this node • R-> result Z id ---- results are supplied to this node • Z->number

  13. Sample Code • // create first neuron (Logistic and Triangle are functions) soma s1 Logistic(10, 2, 5); dendrite d1 Value(1);dendrite d2 Rand(1,2);neuron n1 s1 d1 d2 ;// create second neuronsoma s2 Triangle(3);dendrite d3 Value(1);dendrite d4 Rand(1,2);neuron n2 s2 d3 d4 ;

  14. // create second neuronsoma s3 Triangle(3);dendrite d5 value(4);dendrite d6 Rand(1,2);neuron n3 s3 d5 d6;// connect neuronssynapse n2 d2;synapse n3 d1;// input sense 1 d1;sense 2 d3;sense 3 d4;// out result 1 n1;

  15. Engine • Performs analysis on neurons • Detects layer of neuron • order neurons in a list by layer • Processes neuron • Cascades to get predictions • Performs back propagation • Input data • Streams data from input file • Check data for errors • Output data • Writes results

  16. Traversing neurons

  17. Compiler • Lexical Analyzer • Removes comments • Conditions code • Tokenizes • Parser • Recursive descent • Does not do static semantic

  18. Compiler cont. • Semantics • checks ids declarations • Checks how ids are assembled • Code generation • Transforms NNL code into java • Adds the engine

  19. System Interactions • Creating network • Using network

  20. Example • Detecting letters • detect L and T in a 5 by 5 grid • Compare L and T – no false positive • Compare random results (noise rejection) • Topology • One layer network • One output • Twenty five inputs

  21. Example cont.

  22. Future Enhancements • Persistent data for each NNL program • Easier training methods • Keyword to generate redundant declarations • Visual connection tool • Include parameter choices • High level semantics

  23. Questions

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