tx · 3VfJiTQQ4gm1w42zeiKmk1fzLaxCoRabH3w4e7NAmWgj

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 15:05 [3082687] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "3VfJiTQQ4gm1w42zeiKmk1fzLaxCoRabH3w4e7NAmWgj",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714305924824,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
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    ],
    "script": "base64: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",
    "height": 3082687,
    "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, 600732], [414197, 414253]]
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let layer1Biases = [-259050, -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, 1000)
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    let scaledX2 = fraction(scaledX, scaledX, 1000)
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    let term1 = (1000 - scaledX)
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    let term2 = fraction(scaledX2, 500, 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 = (1000 + expNegZ)
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    let sigValue = fraction(1000, 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])
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    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + biases[1])
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    let $t012951348 = sigmoid(sum0, "Layer1N0")
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    let debugEntries0 = $t012951348._1
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    let sig0 = $t012951348._2
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    let $t013531406 = sigmoid(sum1, "Layer1N1")
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    let debugEntries1 = $t013531406._1
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    let sig1 = $t013531406._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])
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    let $t017071760 = sigmoid(sum0, "Layer2N0")
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    let debugEntries0 = $t017071760._1
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    let sig0 = $t017071760._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 1
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        else 0
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    let scaledInput2 = if ((input2 == 1))
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        then 1
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        else 0
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    let inputs = [scaledInput1, scaledInput2]
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    let $t020602158 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
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    let debugLayer1 = $t020602158._1
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    let layer1Output = $t020602158._2
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    let $t021632267 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
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    let debugLayer2 = $t021632267._1
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    let layer2Output = $t021632267._2
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    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
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    }
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github/deemru/w8io/6500d08 
39.24 ms ◑