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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. - PowerPoint PPT Presentation
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NEURAL NETWORK LANGUAGE
NNL
Language Project
Neural Network
Neural networks have a mass appeal Simulates brain characteristics
Weighted connection to nodes
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.
Neural Network cont.
Learning method is back propagationError is calculated at result nodesThis error is then fed backward through the netAdjustments to weight reduce errorWe look at our result nodes and Output node
which is the last node, andThe difference we get is the error
Learning and predicting
Predicting Predicting is summation of weights
Dendrites have input valueInput times weight gives valueAll value summed give total activation
Parameters Internal parameters
Learning rate Threshold
Dendrite initial valueValueRand
Activation formulaTriangleLogistic linear
Elements sense-
Gets input from the file Input is in sequence
dendrite- Connects neurons Has weight Weight is a floating value from 0-1
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
Elements cont
Parts of Language The derived NN Engine consists of three parts1. Framework initialization
Link necessary files
2. Topology implementation NNL specific code
3. Processor Process input using topology
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
Input and Output Network input
Format in in in … in # out out …outError checking for input file
Network output The output file is same formatPopulate empty output with predictions
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
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 ;
// 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;
Engine
Performs analysis on neurons Detects layer of neuron order neurons in a list by layer
Processes neuronCascades to get predictionsPerforms back propagation
Input dataStreams data from input fileCheck data for errors
Output dataWrites results
Traversing neurons
Compiler Lexical Analyzer
Removes commentsConditions codeTokenizes
Parser Recursive descent Does not do static semantic
Compiler cont. Semantics
checks ids declarations Checks how ids are assembled
Code generation Transforms NNL code into java Adds the engine
Creating network
System Interactions
Using network
Example Detecting letters
detect L and T in a 5 by 5 gridCompare L and T – no false positiveCompare random results (noise rejection)
TopologyOne layer networkOne output Twenty five inputs
Example cont.
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
Questions