tx · EmzniGRyRBaaKfZ7oDtNFkjbeo6yUeRZvkAi3sevZ1v1

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.03.24 13:51 [3032037] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "EmzniGRyRBaaKfZ7oDtNFkjbeo6yUeRZvkAi3sevZ1v1",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1711277471395,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "zbsWwSt2X1V2a45AM242emMzWBARR4EZc9egm39sumtsjmuQr95UrhWahN3hsBdix46ZxFmZuu4GMYYCP3vZZZy"
    ],
    "script": "base64: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",
    "height": 3032037,
    "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 $t019922048 = sigmoid(sum0, (debugPrefix + "L1N0"))
34
-
    let debug0 = $t019922048._1
35
-
    let sig0 = $t019922048._2
36
-
    let $t020532109 = sigmoid(sum1, (debugPrefix + "L1N1"))
37
-
    let debug1 = $t020532109._1
38
-
    let sig1 = $t020532109._2
39
-
    let $t021142170 = sigmoid(sum2, (debugPrefix + "L1N2"))
40
-
    let debug2 = $t021142170._1
41
-
    let sig2 = $t021142170._2
42
-
    let $t021752231 = sigmoid(sum3, (debugPrefix + "L1N3"))
43
-
    let debug3 = $t021752231._1
44
-
    let sig3 = $t021752231._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 $t011901246 = sigmoid(sum0, (debugPrefix + "L1N0"))
28
+
    let debug0 = $t011901246._1
29
+
    let sig0 = $t011901246._2
30
+
    let $t012511307 = sigmoid(sum1, (debugPrefix + "L1N1"))
31
+
    let debug1 = $t012511307._1
32
+
    let sig1 = $t012511307._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 $t026692725 = sigmoid(sum0, (debugPrefix + "L2N0"))
53
-
    let debug0 = $t026692725._1
54
-
    let sig0 = $t026692725._2
55
-
    let $t027302786 = sigmoid(sum1, (debugPrefix + "L2N1"))
56
-
    let debug1 = $t027302786._1
57
-
    let sig1 = $t027302786._2
58
-
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
59
-
    }
60
-
61
-
62
-
func forwardPassLayer3 (input,weights,biases,debugPrefix) = {
63
38
 
    let sum = ((fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[0], 1000000)) + biases)
64
-
    let $t030553099 = sigmoid(sum, debugPrefix)
65
-
    let debug = $t030553099._1
66
-
    let sig = $t030553099._2
39
+
    let $t015761620 = sigmoid(sum, debugPrefix)
40
+
    let debug = $t015761620._1
41
+
    let sig = $t015761620._2
67
42
 
    $Tuple2(sig, debug)
68
43
 
    }
69
44
 
77
52
 
        then 1000000
78
53
 
        else 0
79
54
 
    let inputs = [scaledInput1, scaledInput2]
80
-
    let $t033613459 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
81
-
    let layer1Output = $t033613459._1
82
-
    let debugLayer1 = $t033613459._2
83
-
    let $t034643568 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
84
-
    let layer2Output = $t034643568._1
85
-
    let debugLayer2 = $t034643568._2
86
-
    let $t035733675 = forwardPassLayer3(layer2Output, [-8939640, 9517362], -192349, "Layer3")
87
-
    let output = $t035733675._1
88
-
    let debugLayerLast = $t035733675._2
55
+
    let $t018821980 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
56
+
    let layer1Output = $t018821980._1
57
+
    let debugLayer1 = $t018821980._2
58
+
    let $t019852088 = forwardPassLayer2(layer1Output, [8329656, -8971418], -3811788, "Layer2")
59
+
    let output = $t019852088._1
60
+
    let debugLayerLast = $t019852088._2
89
61
 
[IntegerEntry("result", output)]
90
62
 
    }
91
63
 

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 $t019922048 = sigmoid(sum0, (debugPrefix + "L1N0"))
34
-
    let debug0 = $t019922048._1
35
-
    let sig0 = $t019922048._2
36
-
    let $t020532109 = sigmoid(sum1, (debugPrefix + "L1N1"))
37
-
    let debug1 = $t020532109._1
38
-
    let sig1 = $t020532109._2
39
-
    let $t021142170 = sigmoid(sum2, (debugPrefix + "L1N2"))
40
-
    let debug2 = $t021142170._1
41
-
    let sig2 = $t021142170._2
42
-
    let $t021752231 = sigmoid(sum3, (debugPrefix + "L1N3"))
43
-
    let debug3 = $t021752231._1
44
-
    let sig3 = $t021752231._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 $t011901246 = sigmoid(sum0, (debugPrefix + "L1N0"))
28
+
    let debug0 = $t011901246._1
29
+
    let sig0 = $t011901246._2
30
+
    let $t012511307 = sigmoid(sum1, (debugPrefix + "L1N1"))
31
+
    let debug1 = $t012511307._1
32
+
    let sig1 = $t012511307._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 $t026692725 = sigmoid(sum0, (debugPrefix + "L2N0"))
53
-
    let debug0 = $t026692725._1
54
-
    let sig0 = $t026692725._2
55
-
    let $t027302786 = sigmoid(sum1, (debugPrefix + "L2N1"))
56
-
    let debug1 = $t027302786._1
57
-
    let sig1 = $t027302786._2
58
-
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
59
-
    }
60
-
61
-
62
-
func forwardPassLayer3 (input,weights,biases,debugPrefix) = {
63
38
 
    let sum = ((fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[0], 1000000)) + biases)
64
-
    let $t030553099 = sigmoid(sum, debugPrefix)
65
-
    let debug = $t030553099._1
66
-
    let sig = $t030553099._2
39
+
    let $t015761620 = sigmoid(sum, debugPrefix)
40
+
    let debug = $t015761620._1
41
+
    let sig = $t015761620._2
67
42
 
    $Tuple2(sig, debug)
68
43
 
    }
69
44
 
70
45
 
71
46
 
@Callable(i)
72
47
 
func predict (input1,input2) = {
73
48
 
    let scaledInput1 = if ((input1 == 1))
74
49
 
        then 1000000
75
50
 
        else 0
76
51
 
    let scaledInput2 = if ((input2 == 1))
77
52
 
        then 1000000
78
53
 
        else 0
79
54
 
    let inputs = [scaledInput1, scaledInput2]
80
-
    let $t033613459 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
81
-
    let layer1Output = $t033613459._1
82
-
    let debugLayer1 = $t033613459._2
83
-
    let $t034643568 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
84
-
    let layer2Output = $t034643568._1
85
-
    let debugLayer2 = $t034643568._2
86
-
    let $t035733675 = forwardPassLayer3(layer2Output, [-8939640, 9517362], -192349, "Layer3")
87
-
    let output = $t035733675._1
88
-
    let debugLayerLast = $t035733675._2
55
+
    let $t018821980 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
56
+
    let layer1Output = $t018821980._1
57
+
    let debugLayer1 = $t018821980._2
58
+
    let $t019852088 = forwardPassLayer2(layer1Output, [8329656, -8971418], -3811788, "Layer2")
59
+
    let output = $t019852088._1
60
+
    let debugLayerLast = $t019852088._2
89
61
 
[IntegerEntry("result", output)]
90
62
 
    }
91
63
 
92
64
 

github/deemru/w8io/6500d08 
24.18 ms ◑