tx · 2EAsscTWTcXLbZe4WsBMSSeTj11Qf1Abr2c6XurS2kqL

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.27 17:15 [3081359] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "2EAsscTWTcXLbZe4WsBMSSeTj11Qf1Abr2c6XurS2kqL",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714227234026,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "2q1e3UguMMubQmyAwRhfjfj7Qzrb1gJ7QxKMUsqSyqeJWUUo49ThpfxB3qnt1L6gqQktgt5mEX4dTMkTAAiunpho"
    ],
    "script": "base64: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",
    "height": 3081359,
    "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 = [[-927524, 622214], [-920182, -651619], [-152874, 1145040], [-752484, -604481]]
4
+
let layer1Weights = [[600497, 600733], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-256962, 231253, -475298, 189517]
6
+
let layer1Biases = [-259050, -635637]
7
7
 
8
-
let layer2Weights = [[-757521, 552333, 658111, 377320], [686102, -570621, -603550, -332354]]
8
+
let layer2Weights = [[832965, -897142]]
9
9
 
10
-
let layer2Biases = [-316162, 294501]
11
-
12
-
let layer3Weights = [[-893964, 951736]]
13
-
14
-
let layer3Biases = [-19235]
10
+
let layer2Biases = [-381179]
15
11
 
16
12
 
func sigmoid (z,debugPrefix) = {
17
13
 
    let e = 2718281
19
15
 
    let positiveZ = if ((0 > z))
20
16
 
        then -(z)
21
17
 
        else z
22
-
    let expPart = fraction(e, base, positiveZ)
18
+
    let scaledZ = (positiveZ / 10000)
19
+
    let expPart = fraction(e, base, scaledZ)
23
20
 
    let sigValue = fraction(base, (base + expPart), base)
24
21
 
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
25
22
 
    }
28
25
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
29
26
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
30
27
 
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
31
-
    let sum2 = ((fraction(input[0], weights[2][0], 1000000) + fraction(input[1], weights[2][1], 1000000)) + biases[2])
32
-
    let sum3 = ((fraction(input[0], weights[3][0], 1000000) + fraction(input[1], weights[3][1], 1000000)) + biases[3])
33
-
    let $t014931539 = sigmoid(sum0, "Layer1N0")
34
-
    let debug0 = $t014931539._1
35
-
    let sig0 = $t014931539._2
36
-
    let $t015441590 = sigmoid(sum1, "Layer1N1")
37
-
    let debug1 = $t015441590._1
38
-
    let sig1 = $t015441590._2
39
-
    let $t015951641 = sigmoid(sum2, "Layer1N2")
40
-
    let debug2 = $t015951641._1
41
-
    let sig2 = $t015951641._2
42
-
    let $t016461692 = sigmoid(sum3, "Layer1N3")
43
-
    let debug3 = $t016461692._1
44
-
    let sig3 = $t016461692._2
45
-
    $Tuple2([sig0, sig1, sig2, sig3], (((debug0 ++ debug1) ++ debug2) ++ debug3))
28
+
    let $t011451191 = sigmoid(sum0, "Layer1N0")
29
+
    let debug0 = $t011451191._1
30
+
    let sig0 = $t011451191._2
31
+
    let $t011961242 = sigmoid(sum1, "Layer1N1")
32
+
    let debug1 = $t011961242._1
33
+
    let sig1 = $t011961242._2
34
+
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
46
35
 
    }
47
36
 
48
37
 
49
38
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
50
-
    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])
51
-
    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])
52
-
    let $t022882334 = sigmoid(sum0, "Layer2N0")
53
-
    let debug0 = $t022882334._1
54
-
    let sig0 = $t022882334._2
55
-
    let $t023392385 = sigmoid(sum1, "Layer2N1")
56
-
    let debug1 = $t023392385._1
57
-
    let sig1 = $t023392385._2
58
-
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
59
-
    }
60
-
61
-
62
-
func forwardPassLayer3 (input,weights,biases,debugPrefix) = {
63
39
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
64
-
    let $t026542700 = sigmoid(sum0, "Layer3N0")
65
-
    let debug0 = $t026542700._1
66
-
    let sig0 = $t026542700._2
40
+
    let $t015111557 = sigmoid(sum0, "Layer2N0")
41
+
    let debug0 = $t015111557._1
42
+
    let sig0 = $t015111557._2
67
43
 
    $Tuple2(sig0, debug0)
68
44
 
    }
69
45
 
77
53
 
        then 1000000
78
54
 
        else 0
79
55
 
    let inputs = [scaledInput1, scaledInput2]
80
-
    let $t029513049 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
81
-
    let layer1Output = $t029513049._1
82
-
    let debugLayer1 = $t029513049._2
83
-
    let $t030543158 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
84
-
    let layer2Output = $t030543158._1
85
-
    let debugLayer2 = $t030543158._2
86
-
    let $t031633267 = forwardPassLayer3(layer2Output, layer3Weights, layer3Biases, "Layer3")
87
-
    let layer3Output = $t031633267._1
88
-
    let debugLayer3 = $t031633267._2
89
-
    ((([IntegerEntry("result", layer3Output)] ++ debugLayer1) ++ debugLayer2) ++ debugLayer3)
56
+
    let $t018081906 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
57
+
    let layer1Output = $t018081906._1
58
+
    let debugLayer1 = $t018081906._2
59
+
    let $t019112015 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
60
+
    let layer2Output = $t019112015._1
61
+
    let debugLayer2 = $t019112015._2
62
+
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
90
63
 
    }
91
64
 
92
65
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let layer1Weights = [[-927524, 622214], [-920182, -651619], [-152874, 1145040], [-752484, -604481]]
4
+
let layer1Weights = [[600497, 600733], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-256962, 231253, -475298, 189517]
6
+
let layer1Biases = [-259050, -635637]
7
7
 
8
-
let layer2Weights = [[-757521, 552333, 658111, 377320], [686102, -570621, -603550, -332354]]
8
+
let layer2Weights = [[832965, -897142]]
9
9
 
10
-
let layer2Biases = [-316162, 294501]
11
-
12
-
let layer3Weights = [[-893964, 951736]]
13
-
14
-
let layer3Biases = [-19235]
10
+
let layer2Biases = [-381179]
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
-
    let expPart = fraction(e, base, positiveZ)
18
+
    let scaledZ = (positiveZ / 10000)
19
+
    let expPart = fraction(e, base, scaledZ)
23
20
 
    let sigValue = fraction(base, (base + expPart), base)
24
21
 
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
25
22
 
    }
26
23
 
27
24
 
28
25
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
29
26
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
30
27
 
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
31
-
    let sum2 = ((fraction(input[0], weights[2][0], 1000000) + fraction(input[1], weights[2][1], 1000000)) + biases[2])
32
-
    let sum3 = ((fraction(input[0], weights[3][0], 1000000) + fraction(input[1], weights[3][1], 1000000)) + biases[3])
33
-
    let $t014931539 = sigmoid(sum0, "Layer1N0")
34
-
    let debug0 = $t014931539._1
35
-
    let sig0 = $t014931539._2
36
-
    let $t015441590 = sigmoid(sum1, "Layer1N1")
37
-
    let debug1 = $t015441590._1
38
-
    let sig1 = $t015441590._2
39
-
    let $t015951641 = sigmoid(sum2, "Layer1N2")
40
-
    let debug2 = $t015951641._1
41
-
    let sig2 = $t015951641._2
42
-
    let $t016461692 = sigmoid(sum3, "Layer1N3")
43
-
    let debug3 = $t016461692._1
44
-
    let sig3 = $t016461692._2
45
-
    $Tuple2([sig0, sig1, sig2, sig3], (((debug0 ++ debug1) ++ debug2) ++ debug3))
28
+
    let $t011451191 = sigmoid(sum0, "Layer1N0")
29
+
    let debug0 = $t011451191._1
30
+
    let sig0 = $t011451191._2
31
+
    let $t011961242 = sigmoid(sum1, "Layer1N1")
32
+
    let debug1 = $t011961242._1
33
+
    let sig1 = $t011961242._2
34
+
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
46
35
 
    }
47
36
 
48
37
 
49
38
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
50
-
    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])
51
-
    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])
52
-
    let $t022882334 = sigmoid(sum0, "Layer2N0")
53
-
    let debug0 = $t022882334._1
54
-
    let sig0 = $t022882334._2
55
-
    let $t023392385 = sigmoid(sum1, "Layer2N1")
56
-
    let debug1 = $t023392385._1
57
-
    let sig1 = $t023392385._2
58
-
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
59
-
    }
60
-
61
-
62
-
func forwardPassLayer3 (input,weights,biases,debugPrefix) = {
63
39
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
64
-
    let $t026542700 = sigmoid(sum0, "Layer3N0")
65
-
    let debug0 = $t026542700._1
66
-
    let sig0 = $t026542700._2
40
+
    let $t015111557 = sigmoid(sum0, "Layer2N0")
41
+
    let debug0 = $t015111557._1
42
+
    let sig0 = $t015111557._2
67
43
 
    $Tuple2(sig0, debug0)
68
44
 
    }
69
45
 
70
46
 
71
47
 
@Callable(i)
72
48
 
func predict (input1,input2) = {
73
49
 
    let scaledInput1 = if ((input1 == 1))
74
50
 
        then 1000000
75
51
 
        else 0
76
52
 
    let scaledInput2 = if ((input2 == 1))
77
53
 
        then 1000000
78
54
 
        else 0
79
55
 
    let inputs = [scaledInput1, scaledInput2]
80
-
    let $t029513049 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
81
-
    let layer1Output = $t029513049._1
82
-
    let debugLayer1 = $t029513049._2
83
-
    let $t030543158 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
84
-
    let layer2Output = $t030543158._1
85
-
    let debugLayer2 = $t030543158._2
86
-
    let $t031633267 = forwardPassLayer3(layer2Output, layer3Weights, layer3Biases, "Layer3")
87
-
    let layer3Output = $t031633267._1
88
-
    let debugLayer3 = $t031633267._2
89
-
    ((([IntegerEntry("result", layer3Output)] ++ debugLayer1) ++ debugLayer2) ++ debugLayer3)
56
+
    let $t018081906 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
57
+
    let layer1Output = $t018081906._1
58
+
    let debugLayer1 = $t018081906._2
59
+
    let $t019112015 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
60
+
    let layer2Output = $t019112015._1
61
+
    let debugLayer2 = $t019112015._2
62
+
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
90
63
 
    }
91
64
 
92
65
 

github/deemru/w8io/6500d08 
48.56 ms ◑