tx · JCkBbNxDPwS9TmpCQ9srGadxFoSNr8BhMnsUqjfEDxmf

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 12:38 [3082536] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "JCkBbNxDPwS9TmpCQ9srGadxFoSNr8BhMnsUqjfEDxmf",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714297104684,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "2TP622WAknAZtppM1mDrE3x4GagsULA4kg3riju4Dt74ByM3HruBsU6midTfp6iFK5sEUwao8vdbd19JEEifH547"
    ],
    "script": 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    "height": 3082536,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Diff: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let layer1Weights = [[600497, 600732], [414197, 414252]]
4
+
let layer1Weights = [[600497, 600733], [414197, 414252]]
5
5
 
6
-
let layer1Biases = [-259050, -635637]
6
+
let layer1Biases = [-259050, -635638]
7
7
 
8
-
let layer2Weights = [[832966, -897142]]
8
+
let layer2Weights = [[832966, -897141]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let layer1Weights = [[600497, 600732], [414197, 414252]]
4
+
let layer1Weights = [[600497, 600733], [414197, 414252]]
5
5
 
6
-
let layer1Biases = [-259050, -635637]
6
+
let layer1Biases = [-259050, -635638]
7
7
 
8
-
let layer2Weights = [[832966, -897142]]
8
+
let layer2Weights = [[832966, -897141]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
12
 
func exp_approx (x) = {
13
13
 
    let scale = 100000
14
14
 
    if (((-6 * scale) > x))
15
15
 
        then 1
16
16
 
        else if ((x > (6 * scale)))
17
17
 
            then scale
18
18
 
            else {
19
19
 
                let coefficients = [$Tuple2(60000, (scale - 1)), $Tuple2(50000, (scale - 2)), $Tuple2(40000, (scale - 3)), $Tuple2(30000, (scale - 10)), $Tuple2(20000, (scale - 20)), $Tuple2(10000, (scale - 30)), $Tuple2(0, scale), $Tuple2(-10000, (scale + 30)), $Tuple2(-20000, (scale + 20)), $Tuple2(-30000, (scale + 10)), $Tuple2(-40000, (scale + 3)), $Tuple2(-50000, (scale + 2)), $Tuple2(-60000, (scale + 1))]
20
20
 
                let index = ((x + 60000) / 10000)
21
21
 
                let $t0926968 = coefficients[index]
22
22
 
                let coefficient = $t0926968._1
23
23
 
                let y = $t0926968._2
24
24
 
                y
25
25
 
                }
26
26
 
    }
27
27
 
28
28
 
29
29
 
func sigmoid (z,debugPrefix) = {
30
30
 
    let base = 100000
31
31
 
    let positiveZ = if ((0 > z))
32
32
 
        then -(z)
33
33
 
        else z
34
34
 
    let expValue = exp_approx(positiveZ)
35
35
 
    let sigValue = (base - ((base * base) / (base + expValue)))
36
36
 
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
37
37
 
    }
38
38
 
39
39
 
40
40
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
41
41
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
42
42
 
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
43
43
 
    let $t018331886 = sigmoid(sum0, "Layer1N0")
44
44
 
    let debugEntries0 = $t018331886._1
45
45
 
    let sig0 = $t018331886._2
46
46
 
    let $t018911944 = sigmoid(sum1, "Layer1N1")
47
47
 
    let debugEntries1 = $t018911944._1
48
48
 
    let sig1 = $t018911944._2
49
49
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
50
50
 
    let output = [sig0, sig1]
51
51
 
    $Tuple2(debugInfo, output)
52
52
 
    }
53
53
 
54
54
 
55
55
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
56
56
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
57
57
 
    let $t022542307 = sigmoid(sum0, "Layer2N0")
58
58
 
    let debugEntries0 = $t022542307._1
59
59
 
    let sig0 = $t022542307._2
60
60
 
    let debugInfo = debugEntries0
61
61
 
    let output = sig0
62
62
 
    $Tuple2(debugInfo, output)
63
63
 
    }
64
64
 
65
65
 
66
66
 
@Callable(i)
67
67
 
func predict (input1,input2) = {
68
68
 
    let scaledInput1 = if ((input1 == 1))
69
69
 
        then 1000000
70
70
 
        else 0
71
71
 
    let scaledInput2 = if ((input2 == 1))
72
72
 
        then 1000000
73
73
 
        else 0
74
74
 
    let inputs = [scaledInput1, scaledInput2]
75
75
 
    let $t026192717 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
76
76
 
    let debugLayer1 = $t026192717._1
77
77
 
    let layer1Output = $t026192717._2
78
78
 
    let $t027222826 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
79
79
 
    let debugLayer2 = $t027222826._1
80
80
 
    let layer2Output = $t027222826._2
81
81
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
82
82
 
    }
83
83
 
84
84
 

github/deemru/w8io/6500d08 
51.34 ms ◑