tx · J63xWbm4vHe8K69nUjTn84N9HuR4NGt6i3fVHent4GN

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.03.24 15:20 [3032125] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "J63xWbm4vHe8K69nUjTn84N9HuR4NGt6i3fVHent4GN",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1711282876011,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "4D5o2fzNPBDAd9yb35XUdBKSVrVofF3FdzoybCHdQGw7UetsVAQjxLo3N4ct1bPV9z5vQNDrwQTXAYdkPxKwE6Lb"
    ],
    "script": "base64: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",
    "height": 3032125,
    "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 = [[-9275240, 6222139], [-9201827, -6516189], [-1528731, 11450396], [-7524843, -6044814]]
4
+
let layer1Weights = [[6004965, 6007324], [4141966, 4142525]]
5
5
 
6
-
let layer1Biases = [-2569627, 2312524, -4752973, 1895166]
6
+
let layer1Biases = [-2590503, -6356371]
7
7
 
8
-
let layer2Weights = [[-7575203, 5523326, 6581110, 3773202], [6861028, -5706216, -6035509, -3323542]]
8
+
let layer2Weights = [[8329656, -8971418]]
9
9
 
10
-
let layer2Biases = [-3161622, 2945010]
11
-
12
-
let layer3Weights = [[-8939640, 9517362]]
13
-
14
-
let layer3Biases = [-192349]
10
+
let layer2Biases = [-3811788]
15
11
 
16
12
 
func sigmoid (z,debugPrefix) = {
17
13
 
    let e = 2718281
26
22
 
27
23
 
28
24
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
29
-
    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])
30
-
    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])
31
-
    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])
32
-
    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])
33
-
    let $t019832039 = sigmoid(sum0, (debugPrefix + "L1N0"))
34
-
    let debug0 = $t019832039._1
35
-
    let sig0 = $t019832039._2
36
-
    let $t020442100 = sigmoid(sum1, (debugPrefix + "L1N1"))
37
-
    let debug1 = $t020442100._1
38
-
    let sig1 = $t020442100._2
39
-
    let $t021052161 = sigmoid(sum2, (debugPrefix + "L1N2"))
40
-
    let debug2 = $t021052161._1
41
-
    let sig2 = $t021052161._2
42
-
    let $t021662222 = sigmoid(sum3, (debugPrefix + "L1N3"))
43
-
    let debug3 = $t021662222._1
44
-
    let sig3 = $t021662222._2
45
-
    $Tuple2([sig0, sig1, sig2, sig3], (((debug0 ++ debug1) ++ debug2) ++ debug3))
25
+
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
26
+
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
27
+
    let $t011811237 = sigmoid(sum0, (debugPrefix + "L1N0"))
28
+
    let debug0 = $t011811237._1
29
+
    let sig0 = $t011811237._2
30
+
    let $t012421298 = sigmoid(sum1, (debugPrefix + "L1N1"))
31
+
    let debug1 = $t012421298._1
32
+
    let sig1 = $t012421298._2
33
+
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
46
34
 
    }
47
35
 
48
36
 
49
37
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
50
-
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
51
-
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
52
-
    let $t026382694 = sigmoid(sum0, (debugPrefix + "L2N0"))
53
-
    let debug0 = $t026382694._1
54
-
    let sig0 = $t026382694._2
55
-
    let $t026992755 = sigmoid(sum1, (debugPrefix + "L2N1"))
56
-
    let debug1 = $t026992755._1
57
-
    let sig1 = $t026992755._2
58
-
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
59
-
    }
60
-
61
-
62
-
func forwardPassLayer3 (input,weights,biases,debugPrefix) = {
63
38
 
    let sum0 = ((fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[1], 1000000)) + biases)
64
39
 
    let sum1 = ((fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[1], 1000000)) + biases)
65
-
    let $t031093165 = sigmoid(sum0, (debugPrefix + "L3N0"))
66
-
    let debug0 = $t031093165._1
67
-
    let sig0 = $t031093165._2
68
-
    let $t031703226 = sigmoid(sum1, (debugPrefix + "L3N1"))
69
-
    let debug1 = $t031703226._1
70
-
    let sig1 = $t031703226._2
40
+
    let $t016521708 = sigmoid(sum0, (debugPrefix + "L2N0"))
41
+
    let debug0 = $t016521708._1
42
+
    let sig0 = $t016521708._2
43
+
    let $t017131769 = sigmoid(sum1, (debugPrefix + "L2N1"))
44
+
    let debug1 = $t017131769._1
45
+
    let sig1 = $t017131769._2
71
46
 
    $Tuple2(sig0, (debug0 ++ debug1))
72
47
 
    }
73
48
 
81
56
 
        then 1000000
82
57
 
        else 0
83
58
 
    let inputs = [scaledInput1, scaledInput2]
84
-
    let $t034873585 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
85
-
    let layer1Output = $t034873585._1
86
-
    let debugLayer1 = $t034873585._2
87
-
    let $t035903694 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
88
-
    let layer2Output = $t035903694._1
89
-
    let debugLayer2 = $t035903694._2
90
-
    let $t036993809 = forwardPassLayer3(layer2Output, layer3Weights[0], layer3Biases[0], "Layer3")
91
-
    let layer3Output = $t036993809._1
92
-
    let debugLayer3 = $t036993809._2
93
-
    ((([IntegerEntry("result", layer3Output)] ++ debugLayer1) ++ debugLayer2) ++ debugLayer3)
59
+
    let $t020302128 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
60
+
    let layer1Output = $t020302128._1
61
+
    let debugLayer1 = $t020302128._2
62
+
    let $t021332243 = forwardPassLayer2(layer1Output, layer2Weights[0], layer2Biases[0], "Layer2")
63
+
    let layer2Output = $t021332243._1
64
+
    let debugLayer2 = $t021332243._2
65
+
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
94
66
 
    }
95
67
 
96
68
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let layer1Weights = [[-9275240, 6222139], [-9201827, -6516189], [-1528731, 11450396], [-7524843, -6044814]]
4
+
let layer1Weights = [[6004965, 6007324], [4141966, 4142525]]
5
5
 
6
-
let layer1Biases = [-2569627, 2312524, -4752973, 1895166]
6
+
let layer1Biases = [-2590503, -6356371]
7
7
 
8
-
let layer2Weights = [[-7575203, 5523326, 6581110, 3773202], [6861028, -5706216, -6035509, -3323542]]
8
+
let layer2Weights = [[8329656, -8971418]]
9
9
 
10
-
let layer2Biases = [-3161622, 2945010]
11
-
12
-
let layer3Weights = [[-8939640, 9517362]]
13
-
14
-
let layer3Biases = [-192349]
10
+
let layer2Biases = [-3811788]
15
11
 
16
12
 
func sigmoid (z,debugPrefix) = {
17
13
 
    let e = 2718281
18
14
 
    let base = 1000000
19
15
 
    let positiveZ = if ((0 > z))
20
16
 
        then -(z)
21
17
 
        else z
22
18
 
    let expPart = fraction(e, base, positiveZ)
23
19
 
    let sigValue = fraction(base, base, (base + expPart))
24
20
 
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
25
21
 
    }
26
22
 
27
23
 
28
24
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
29
-
    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])
30
-
    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])
31
-
    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])
32
-
    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])
33
-
    let $t019832039 = sigmoid(sum0, (debugPrefix + "L1N0"))
34
-
    let debug0 = $t019832039._1
35
-
    let sig0 = $t019832039._2
36
-
    let $t020442100 = sigmoid(sum1, (debugPrefix + "L1N1"))
37
-
    let debug1 = $t020442100._1
38
-
    let sig1 = $t020442100._2
39
-
    let $t021052161 = sigmoid(sum2, (debugPrefix + "L1N2"))
40
-
    let debug2 = $t021052161._1
41
-
    let sig2 = $t021052161._2
42
-
    let $t021662222 = sigmoid(sum3, (debugPrefix + "L1N3"))
43
-
    let debug3 = $t021662222._1
44
-
    let sig3 = $t021662222._2
45
-
    $Tuple2([sig0, sig1, sig2, sig3], (((debug0 ++ debug1) ++ debug2) ++ debug3))
25
+
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
26
+
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
27
+
    let $t011811237 = sigmoid(sum0, (debugPrefix + "L1N0"))
28
+
    let debug0 = $t011811237._1
29
+
    let sig0 = $t011811237._2
30
+
    let $t012421298 = sigmoid(sum1, (debugPrefix + "L1N1"))
31
+
    let debug1 = $t012421298._1
32
+
    let sig1 = $t012421298._2
33
+
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
46
34
 
    }
47
35
 
48
36
 
49
37
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
50
-
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
51
-
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
52
-
    let $t026382694 = sigmoid(sum0, (debugPrefix + "L2N0"))
53
-
    let debug0 = $t026382694._1
54
-
    let sig0 = $t026382694._2
55
-
    let $t026992755 = sigmoid(sum1, (debugPrefix + "L2N1"))
56
-
    let debug1 = $t026992755._1
57
-
    let sig1 = $t026992755._2
58
-
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
59
-
    }
60
-
61
-
62
-
func forwardPassLayer3 (input,weights,biases,debugPrefix) = {
63
38
 
    let sum0 = ((fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[1], 1000000)) + biases)
64
39
 
    let sum1 = ((fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[1], 1000000)) + biases)
65
-
    let $t031093165 = sigmoid(sum0, (debugPrefix + "L3N0"))
66
-
    let debug0 = $t031093165._1
67
-
    let sig0 = $t031093165._2
68
-
    let $t031703226 = sigmoid(sum1, (debugPrefix + "L3N1"))
69
-
    let debug1 = $t031703226._1
70
-
    let sig1 = $t031703226._2
40
+
    let $t016521708 = sigmoid(sum0, (debugPrefix + "L2N0"))
41
+
    let debug0 = $t016521708._1
42
+
    let sig0 = $t016521708._2
43
+
    let $t017131769 = sigmoid(sum1, (debugPrefix + "L2N1"))
44
+
    let debug1 = $t017131769._1
45
+
    let sig1 = $t017131769._2
71
46
 
    $Tuple2(sig0, (debug0 ++ debug1))
72
47
 
    }
73
48
 
74
49
 
75
50
 
@Callable(i)
76
51
 
func predict (input1,input2) = {
77
52
 
    let scaledInput1 = if ((input1 == 1))
78
53
 
        then 1000000
79
54
 
        else 0
80
55
 
    let scaledInput2 = if ((input2 == 1))
81
56
 
        then 1000000
82
57
 
        else 0
83
58
 
    let inputs = [scaledInput1, scaledInput2]
84
-
    let $t034873585 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
85
-
    let layer1Output = $t034873585._1
86
-
    let debugLayer1 = $t034873585._2
87
-
    let $t035903694 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
88
-
    let layer2Output = $t035903694._1
89
-
    let debugLayer2 = $t035903694._2
90
-
    let $t036993809 = forwardPassLayer3(layer2Output, layer3Weights[0], layer3Biases[0], "Layer3")
91
-
    let layer3Output = $t036993809._1
92
-
    let debugLayer3 = $t036993809._2
93
-
    ((([IntegerEntry("result", layer3Output)] ++ debugLayer1) ++ debugLayer2) ++ debugLayer3)
59
+
    let $t020302128 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
60
+
    let layer1Output = $t020302128._1
61
+
    let debugLayer1 = $t020302128._2
62
+
    let $t021332243 = forwardPassLayer2(layer1Output, layer2Weights[0], layer2Biases[0], "Layer2")
63
+
    let layer2Output = $t021332243._1
64
+
    let debugLayer2 = $t021332243._2
65
+
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
94
66
 
    }
95
67
 
96
68
 

github/deemru/w8io/6500d08 
23.90 ms ◑