tx · CXUCY6BFNDgxCV1w6Nf7Fr4orN2Bkr9Wh7ojEq82S228

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.03.24 15:18 [3032123] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "CXUCY6BFNDgxCV1w6Nf7Fr4orN2Bkr9Wh7ojEq82S228",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1711282725855,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "5DhbVuMdnyJ99PcUEbNXonqeomfBPMxcotsLjcNiD4RrLbp9rAdnp1CEzB2jssPj1hNYuh2FAVeHbNHXa46DG3Hf"
    ],
    "script": "base64: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    "height": 3032123,
    "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 = [[6004965, 6007324], [4141966, 4142525]]
4
+
let layer1Weights = [[-9275240, 6222139], [-9201827, -6516189], [-1528731, 11450396], [-7524843, -6044814]]
5
5
 
6
-
let layer1Biases = [-2590503, -6356371]
6
+
let layer1Biases = [-2569627, 2312524, -4752973, 1895166]
7
7
 
8
-
let layer2Weights = [[8329656, -8971418]]
8
+
let layer2Weights = [[-7575203, 5523326, 6581110, 3773202], [6861028, -5706216, -6035509, -3323542]]
9
9
 
10
-
let layer2Biases = [-3811788]
10
+
let layer2Biases = [-3161622, 2945010]
11
+
12
+
let layer3Weights = [[-8939640, 9517362]]
13
+
14
+
let layer3Biases = [-192349]
11
15
 
12
16
 
func sigmoid (z,debugPrefix) = {
13
17
 
    let e = 2718281
22
26
 
23
27
 
24
28
 
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))
46
+
    }
47
+
48
+
49
+
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
25
50
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
26
51
 
    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
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
33
58
 
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
34
59
 
    }
35
60
 
36
61
 
37
-
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
62
+
func forwardPassLayer3 (input,weights,biases,debugPrefix) = {
38
63
 
    let sum0 = ((fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[1], 1000000)) + biases)
39
64
 
    let sum1 = ((fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[1], 1000000)) + biases)
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
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
46
71
 
    $Tuple2(sig0, (debug0 ++ debug1))
47
72
 
    }
48
73
 
56
81
 
        then 1000000
57
82
 
        else 0
58
83
 
    let inputs = [scaledInput1, scaledInput2]
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)
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)
66
94
 
    }
67
95
 
68
96
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let layer1Weights = [[6004965, 6007324], [4141966, 4142525]]
4
+
let layer1Weights = [[-9275240, 6222139], [-9201827, -6516189], [-1528731, 11450396], [-7524843, -6044814]]
5
5
 
6
-
let layer1Biases = [-2590503, -6356371]
6
+
let layer1Biases = [-2569627, 2312524, -4752973, 1895166]
7
7
 
8
-
let layer2Weights = [[8329656, -8971418]]
8
+
let layer2Weights = [[-7575203, 5523326, 6581110, 3773202], [6861028, -5706216, -6035509, -3323542]]
9
9
 
10
-
let layer2Biases = [-3811788]
10
+
let layer2Biases = [-3161622, 2945010]
11
+
12
+
let layer3Weights = [[-8939640, 9517362]]
13
+
14
+
let layer3Biases = [-192349]
11
15
 
12
16
 
func sigmoid (z,debugPrefix) = {
13
17
 
    let e = 2718281
14
18
 
    let base = 1000000
15
19
 
    let positiveZ = if ((0 > z))
16
20
 
        then -(z)
17
21
 
        else z
18
22
 
    let expPart = fraction(e, base, positiveZ)
19
23
 
    let sigValue = fraction(base, base, (base + expPart))
20
24
 
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
21
25
 
    }
22
26
 
23
27
 
24
28
 
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))
46
+
    }
47
+
48
+
49
+
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
25
50
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
26
51
 
    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
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
33
58
 
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
34
59
 
    }
35
60
 
36
61
 
37
-
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
62
+
func forwardPassLayer3 (input,weights,biases,debugPrefix) = {
38
63
 
    let sum0 = ((fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[1], 1000000)) + biases)
39
64
 
    let sum1 = ((fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[1], 1000000)) + biases)
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
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
46
71
 
    $Tuple2(sig0, (debug0 ++ debug1))
47
72
 
    }
48
73
 
49
74
 
50
75
 
@Callable(i)
51
76
 
func predict (input1,input2) = {
52
77
 
    let scaledInput1 = if ((input1 == 1))
53
78
 
        then 1000000
54
79
 
        else 0
55
80
 
    let scaledInput2 = if ((input2 == 1))
56
81
 
        then 1000000
57
82
 
        else 0
58
83
 
    let inputs = [scaledInput1, scaledInput2]
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)
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)
66
94
 
    }
67
95
 
68
96
 

github/deemru/w8io/6500d08 
27.54 ms ◑