tx · DqCRv1ayUUrGkR5twZhPSWEwMYD341o2LVcrJTNzDuet

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.03.23 15:20 [3030683] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "DqCRv1ayUUrGkR5twZhPSWEwMYD341o2LVcrJTNzDuet",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1711196406610,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "2ter6dFPJ9MtPN7V6CoXQxtdymEobwJU1DQoNPcsXwXqXcDbiwB21vUv8M5G3jqJVJtsEXRez6uXUBbBk76C2pkt"
    ],
    "script": 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    "height": 3030683,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

View: original | compacted
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Diff: 
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New
Differences
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@Callable(i)
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-
func predict (input1,input2) = {
57
+
func predict_three_xor (input1,input2) = {
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    let scaledInput1 = if ((input1 == 1))
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        then 1000000
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        else 0
Full: 
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New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
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{-# SCRIPT_TYPE ACCOUNT #-}
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{-# CONTENT_TYPE DAPP #-}
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let layer1Weights = [[-9275240, 6222139], [-9201827, -6516189], [-1528731, 11450396], [-7524843, -6044814]]
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let layer1Biases = [-2569627, 2312524, -4752973, 1895166]
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let layer2Weights = [[-7575203, 5523326, 6581110, 3773202], [6861028, -5706216, -6035509, -3323542]]
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let layer2Biases = [-3161622, 2945010]
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let layer3Weights = [[-8939640, 9517362]]
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let layer3Biases = [-192349]
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func sigmoid (z) = {
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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 expPart = fraction(e, base, positiveZ)
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    fraction(base, base, (base + expPart))
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    }
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func forwardPassLayer1 (input,weights,biases) = {
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    let sum0 = ((((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + fraction(input[2], weights[0][2], 1000000)) + fraction(input[3], weights[0][3], 1000000)) + biases[0])
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    let sum1 = ((((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + fraction(input[2], weights[1][2], 1000000)) + fraction(input[3], weights[1][3], 1000000)) + biases[1])
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    let sum2 = ((((fraction(input[0], weights[2][0], 1000000) + fraction(input[1], weights[2][1], 1000000)) + fraction(input[2], weights[2][2], 1000000)) + fraction(input[3], weights[2][3], 1000000)) + biases[2])
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    let sum3 = ((((fraction(input[0], weights[3][0], 1000000) + fraction(input[1], weights[3][1], 1000000)) + fraction(input[2], weights[3][2], 1000000)) + fraction(input[3], weights[3][3], 1000000)) + biases[3])
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    let sig0 = sigmoid(sum0)
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    let sig1 = sigmoid(sum1)
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    let sig2 = sigmoid(sum2)
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    let sig3 = sigmoid(sum3)
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[sig0, sig1, sig2, sig3]
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    }
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40
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func forwardPassLayer2 (input,weights,biases) = {
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    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
42
42
 
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
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    let sig0 = sigmoid(sum0)
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    let sig1 = sigmoid(sum1)
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[sig0, sig1]
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    }
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49
49
 
func forwardPassLayer3 (input,weights,bias) = {
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    let dotProduct = (fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[0], 1000000))
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    let sum = (dotProduct + bias)
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    sigmoid(sum)
53
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    }
54
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55
55
 
56
56
 
@Callable(i)
57
-
func predict (input1,input2) = {
57
+
func predict_three_xor (input1,input2) = {
58
58
 
    let scaledInput1 = if ((input1 == 1))
59
59
 
        then 1000000
60
60
 
        else 0
61
61
 
    let scaledInput2 = if ((input2 == 1))
62
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        then 1000000
63
63
 
        else 0
64
64
 
    let inputs = [scaledInput1, scaledInput2]
65
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    let layer1Output = forwardPassLayer1(inputs, layer1Weights, layer1Biases)
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    let layer2Output = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases)
67
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    let output = forwardPassLayer3(layer2Output, [-8939640, 9517362], -192349)
68
68
 
[IntegerEntry("result", output)]
69
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    }
70
70
 
71
71
 

github/deemru/w8io/6500d08 
40.49 ms ◑