tx · 5hqRJoKMtNcDRPoU3dMpZhfLar5vJieTpCzaPJWSNuV7

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.27 17:19 [3081365] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "5hqRJoKMtNcDRPoU3dMpZhfLar5vJieTpCzaPJWSNuV7",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714227581671,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "3kU8Ygwj2TyKfp5xFDBEEynrqQZGGkoYQRqWJdLsQoxrAkg9GNvZVBJ8n9i4t3qE227931bkPaEEWr3yBM32tznE"
    ],
    "script": "base64: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",
    "height": 3081365,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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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, 600733], [414197, 414253]]
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let layer1Biases = [-259050, -635637]
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let layer2Weights = [[832965, -897142]]
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let layer2Biases = [-381179]
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func sigmoid (z,debugPrefix) = {
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    let e = 2718281
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    let base = 1000000
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    let positiveZ = if ((0 > z))
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        then -(z)
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        else z
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    let scaledZ = (positiveZ / 10000)
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    let expPart = fraction(e, base, scaledZ)
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    let sigValue = fraction(base, (base + expPart), base)
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    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), 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 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
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    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
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    let $t011451191 = sigmoid(sum0, "Layer1N0")
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    let debug0 = $t011451191._1
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    let sig0 = $t011451191._2
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    let $t011961242 = sigmoid(sum1, "Layer1N1")
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    let debug1 = $t011961242._1
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    let sig1 = $t011961242._2
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    $Tuple2([sig0, sig1], (debug0 ++ debug1))
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    }
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func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
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    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
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    let $t015111557 = sigmoid(sum0, "Layer2N0")
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    let debug0 = $t015111557._1
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    let sig0 = $t015111557._2
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    $Tuple2(sig0, debug0)
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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 $t018081906 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
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    let layer1Output = $t018081906._1
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    let debugLayer1 = $t018081906._2
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    let $t019112015 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
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    let layer2Output = $t019112015._1
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    let debugLayer2 = $t019112015._2
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    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
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    }
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github/deemru/w8io/169f3d6 
26.34 ms ◑