tx · CdzzpSdtWkMdq88oLky7G7s1vveHjZQsJ1rC8WaAeC1X

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 14:25 [3082655] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "CdzzpSdtWkMdq88oLky7G7s1vveHjZQsJ1rC8WaAeC1X",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714303580166,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "4dJXbnXMuRCjDF1TSDJ5fLAYdz6ByjKFJKRWx7R2we2PK1WReLLgs1BNQhDRMNgoMgd3arQFEtJYVDeiBHesYPs2"
    ],
    "script": "base64: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",
    "height": 3082655,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Differences
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{-# STDLIB_VERSION 5 #-}
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{-# SCRIPT_TYPE ACCOUNT #-}
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{-# CONTENT_TYPE DAPP #-}
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let layer1Weights = [[600497, 600732], [414197, 414253]]
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let layer1Biases = [-259051, -635637]
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let layer2Weights = [[832966, -897142]]
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let layer2Biases = [-381179]
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func expApprox (x) = {
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    let scaledX = fraction(x, 1, 1000000)
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    let scaledX2 = fraction(scaledX, scaledX, 1000000)
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    let term1 = (1000000 - scaledX)
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    let term2 = fraction(scaledX2, 500000, 1)
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    (term1 + term2)
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    }
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func sigmoid (z,debugPrefix) = {
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    let expNegZ = expApprox(-(z))
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    let onePlusExpNegZ = (1000000 + expNegZ)
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    let sigValue = fraction(1000000000000, onePlusExpNegZ, 1)
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    $Tuple2([IntegerEntry((debugPrefix + "inputZ"), z), IntegerEntry((debugPrefix + "expNegZ"), expNegZ), IntegerEntry((debugPrefix + "onePlusExpNegZ"), onePlusExpNegZ), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
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    }
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func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
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    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
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    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
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    let $t013271380 = sigmoid(sum0, "Layer1N0")
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    let debugEntries0 = $t013271380._1
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    let sig0 = $t013271380._2
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    let $t013851438 = sigmoid(sum1, "Layer1N1")
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    let debugEntries1 = $t013851438._1
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    let sig1 = $t013851438._2
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    let debugInfo = (debugEntries0 ++ debugEntries1)
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    let output = [sig0, sig1]
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    $Tuple2(debugInfo, output)
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    }
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func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
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    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
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    let $t017481801 = sigmoid(sum0, "Layer2N0")
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    let debugEntries0 = $t017481801._1
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    let sig0 = $t017481801._2
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    let debugInfo = debugEntries0
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    let output = sig0
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    $Tuple2(debugInfo, output)
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    }
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@Callable(i)
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func predict (input1,input2) = {
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    let scaledInput1 = if ((input1 == 1))
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        then 1000000
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        else 0
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    let scaledInput2 = if ((input2 == 1))
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        then 1000000
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        else 0
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    let inputs = [scaledInput1, scaledInput2]
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    let $t021132211 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
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    let debugLayer1 = $t021132211._1
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    let layer1Output = $t021132211._2
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    let $t022162320 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
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    let debugLayer2 = $t022162320._1
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    let layer2Output = $t022162320._2
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    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
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    }
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github/deemru/w8io/6500d08 
2875.35 ms ◑