tx · 4mYq43JfanRZQyBETXNzpVZnazq9HeDF1xkokwGuKXa5

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 12:46 [3082542] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "4mYq43JfanRZQyBETXNzpVZnazq9HeDF1xkokwGuKXa5",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714297558509,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "5FyYDx64RL6qRhvuBaExaKJCPugv5pq8K7QGTiMYYWZsxV4Dfe3721BYqoNapv66HkVdEQtgBEMbQT7s8KQVun9Z"
    ],
    "script": "base64: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",
    "height": 3082542,
    "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, 600733], [414197, 414252]]
4
+
let layer1Weights = [[600496, 600733], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259050, -635638]
6
+
let layer1Biases = [-259050, -635637]
7
7
 
8
-
let layer2Weights = [[832966, -897141]]
8
+
let layer2Weights = [[832965, -897141]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
-
func exp_approx (x) = {
13
-
    let scale = 100000
14
-
    if (((-6 * scale) > x))
15
-
        then 1
16
-
        else if ((x > (6 * scale)))
17
-
            then scale
18
-
            else {
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
-
                let index = ((x + 60000) / 10000)
21
-
                let $t0926968 = coefficients[index]
22
-
                let coefficient = $t0926968._1
23
-
                let y = $t0926968._2
24
-
                y
25
-
                }
26
-
    }
27
-
28
-
29
12
 
func sigmoid (z,debugPrefix) = {
30
-
    let base = 100000
13
+
    let e = 2718281
14
+
    let base = 1000000
31
15
 
    let positiveZ = if ((0 > z))
32
16
 
        then -(z)
33
17
 
        else z
34
-
    let expValue = exp_approx(positiveZ)
35
-
    let sigValue = (base - ((base * base) / (base + expValue)))
36
-
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
18
+
    let expPart = fraction(e, base, positiveZ)
19
+
    let sigValue = fraction(base, (base + expPart), base)
20
+
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
37
21
 
    }
38
22
 
39
23
 
40
24
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
41
-
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
42
-
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
43
-
    let $t018331886 = sigmoid(sum0, "Layer1N0")
44
-
    let debugEntries0 = $t018331886._1
45
-
    let sig0 = $t018331886._2
46
-
    let $t018911944 = sigmoid(sum1, "Layer1N1")
47
-
    let debugEntries1 = $t018911944._1
48
-
    let sig1 = $t018911944._2
49
-
    let debugInfo = (debugEntries0 ++ debugEntries1)
50
-
    let output = [sig0, sig1]
51
-
    $Tuple2(debugInfo, output)
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 $t010791125 = sigmoid(sum0, "Layer1N0")
28
+
    let debug0 = $t010791125._1
29
+
    let sig0 = $t010791125._2
30
+
    let $t011301176 = sigmoid(sum1, "Layer1N1")
31
+
    let debug1 = $t011301176._1
32
+
    let sig1 = $t011301176._2
33
+
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
52
34
 
    }
53
35
 
54
36
 
55
37
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
56
-
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
57
-
    let $t022542307 = sigmoid(sum0, "Layer2N0")
58
-
    let debugEntries0 = $t022542307._1
59
-
    let sig0 = $t022542307._2
60
-
    let debugInfo = debugEntries0
61
-
    let output = sig0
62
-
    $Tuple2(debugInfo, output)
38
+
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
39
+
    let $t014451491 = sigmoid(sum0, "Layer2N0")
40
+
    let debug0 = $t014451491._1
41
+
    let sig0 = $t014451491._2
42
+
    $Tuple2(sig0, debug0)
63
43
 
    }
64
44
 
65
45
 
72
52
 
        then 1000000
73
53
 
        else 0
74
54
 
    let inputs = [scaledInput1, scaledInput2]
75
-
    let $t026192717 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
76
-
    let debugLayer1 = $t026192717._1
77
-
    let layer1Output = $t026192717._2
78
-
    let $t027222826 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
79
-
    let debugLayer2 = $t027222826._1
80
-
    let layer2Output = $t027222826._2
55
+
    let $t017421840 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
56
+
    let layer1Output = $t017421840._1
57
+
    let debugLayer1 = $t017421840._2
58
+
    let $t018451949 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
59
+
    let layer2Output = $t018451949._1
60
+
    let debugLayer2 = $t018451949._2
81
61
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
82
62
 
    }
83
63
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let layer1Weights = [[600497, 600733], [414197, 414252]]
4
+
let layer1Weights = [[600496, 600733], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259050, -635638]
6
+
let layer1Biases = [-259050, -635637]
7
7
 
8
-
let layer2Weights = [[832966, -897141]]
8
+
let layer2Weights = [[832965, -897141]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
-
func exp_approx (x) = {
13
-
    let scale = 100000
14
-
    if (((-6 * scale) > x))
15
-
        then 1
16
-
        else if ((x > (6 * scale)))
17
-
            then scale
18
-
            else {
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
-
                let index = ((x + 60000) / 10000)
21
-
                let $t0926968 = coefficients[index]
22
-
                let coefficient = $t0926968._1
23
-
                let y = $t0926968._2
24
-
                y
25
-
                }
26
-
    }
27
-
28
-
29
12
 
func sigmoid (z,debugPrefix) = {
30
-
    let base = 100000
13
+
    let e = 2718281
14
+
    let base = 1000000
31
15
 
    let positiveZ = if ((0 > z))
32
16
 
        then -(z)
33
17
 
        else z
34
-
    let expValue = exp_approx(positiveZ)
35
-
    let sigValue = (base - ((base * base) / (base + expValue)))
36
-
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
18
+
    let expPart = fraction(e, base, positiveZ)
19
+
    let sigValue = fraction(base, (base + expPart), base)
20
+
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
37
21
 
    }
38
22
 
39
23
 
40
24
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
41
-
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
42
-
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
43
-
    let $t018331886 = sigmoid(sum0, "Layer1N0")
44
-
    let debugEntries0 = $t018331886._1
45
-
    let sig0 = $t018331886._2
46
-
    let $t018911944 = sigmoid(sum1, "Layer1N1")
47
-
    let debugEntries1 = $t018911944._1
48
-
    let sig1 = $t018911944._2
49
-
    let debugInfo = (debugEntries0 ++ debugEntries1)
50
-
    let output = [sig0, sig1]
51
-
    $Tuple2(debugInfo, output)
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 $t010791125 = sigmoid(sum0, "Layer1N0")
28
+
    let debug0 = $t010791125._1
29
+
    let sig0 = $t010791125._2
30
+
    let $t011301176 = sigmoid(sum1, "Layer1N1")
31
+
    let debug1 = $t011301176._1
32
+
    let sig1 = $t011301176._2
33
+
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
52
34
 
    }
53
35
 
54
36
 
55
37
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
56
-
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
57
-
    let $t022542307 = sigmoid(sum0, "Layer2N0")
58
-
    let debugEntries0 = $t022542307._1
59
-
    let sig0 = $t022542307._2
60
-
    let debugInfo = debugEntries0
61
-
    let output = sig0
62
-
    $Tuple2(debugInfo, output)
38
+
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
39
+
    let $t014451491 = sigmoid(sum0, "Layer2N0")
40
+
    let debug0 = $t014451491._1
41
+
    let sig0 = $t014451491._2
42
+
    $Tuple2(sig0, debug0)
63
43
 
    }
64
44
 
65
45
 
66
46
 
@Callable(i)
67
47
 
func predict (input1,input2) = {
68
48
 
    let scaledInput1 = if ((input1 == 1))
69
49
 
        then 1000000
70
50
 
        else 0
71
51
 
    let scaledInput2 = if ((input2 == 1))
72
52
 
        then 1000000
73
53
 
        else 0
74
54
 
    let inputs = [scaledInput1, scaledInput2]
75
-
    let $t026192717 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
76
-
    let debugLayer1 = $t026192717._1
77
-
    let layer1Output = $t026192717._2
78
-
    let $t027222826 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
79
-
    let debugLayer2 = $t027222826._1
80
-
    let layer2Output = $t027222826._2
55
+
    let $t017421840 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
56
+
    let layer1Output = $t017421840._1
57
+
    let debugLayer1 = $t017421840._2
58
+
    let $t018451949 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
59
+
    let layer2Output = $t018451949._1
60
+
    let debugLayer2 = $t018451949._2
81
61
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
82
62
 
    }
83
63
 
84
64
 

github/deemru/w8io/026f985 
41.12 ms ◑