tx · FiMyqvSHG2XofDqbcQV7uKntJhFJuUqyBaApsxEwfpwT

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.27 17:47 [3081392] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "FiMyqvSHG2XofDqbcQV7uKntJhFJuUqyBaApsxEwfpwT",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714229227495,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "2L8HZncfRAf1aZJAqrqGpv9mQGUsHMHX1o9rWfZUQpNbVjXBX4QgPDgxged89ifvmL9Mz1WM1nYg3YswHfUtoGHE"
    ],
    "script": "base64:AAIFAAAAAAAAAAgIAhIECgIBAQAAAAcAAAAADWxheWVyMVdlaWdodHMJAARMAAAAAgkABEwAAAACAAAAAAAACSmwCQAETAAAAAIAAAAAAAAJKp0FAAAAA25pbAkABEwAAAACCQAETAAAAAIAAAAAAAAGUfUJAARMAAAAAgAAAAAAAAZSLQUAAAADbmlsBQAAAANuaWwAAAAADGxheWVyMUJpYXNlcwkABEwAAAACAP///////AwWCQAETAAAAAIA///////2TQsFAAAAA25pbAAAAAANbGF5ZXIyV2VpZ2h0cwkABEwAAAACCQAETAAAAAIAAAAAAAAMtcUJAARMAAAAAgD///////JPiwUAAAADbmlsBQAAAANuaWwAAAAADGxheWVyMkJpYXNlcwkABEwAAAACAP//////+i8FBQAAAANuaWwBAAAAB3NpZ21vaWQAAAACAAAAAXoAAAALZGVidWdQcmVmaXgEAAAAAWUAAAAAAAApekkEAAAABGJhc2UAAAAAAAAPQkAEAAAACXBvc2l0aXZlWgMJAABmAAAAAgAAAAAAAAAAAAUAAAABegkBAAAAAS0AAAABBQAAAAF6BQAAAAF6BAAAAAdleHBQYXJ0CQAAawAAAAMFAAAAAWUFAAAABGJhc2UFAAAACXBvc2l0aXZlWgQAAAAIc2lnVmFsdWUJAABrAAAAAwUAAAAEYmFzZQkAAGQAAAACBQAAAARiYXNlBQAAAAdleHBQYXJ0BQAAAARiYXNlCQAFFAAAAAIJAARMAAAAAgkBAAAADEludGVnZXJFbnRyeQAAAAIJAAEsAAAAAgUAAAALZGVidWdQcmVmaXgCAAAACXBvc2l0aXZlWgUAAAAJcG9zaXRpdmVaCQAETAAAAAIJAQAAAAxJbnRlZ2VyRW50cnkAAAACCQABLAAAAAIFAAAAC2RlYnVnUHJlZml4AgAAAAdleHBQYXJ0BQAAAAdleHBQYXJ0CQAETAAAAAIJAQAAAAxJbnRlZ2VyRW50cnkAAAACCQABLAAAAAIFAAAAC2RlYnVnUHJlZml4AgAAAAhzaWdWYWx1ZQUAAAAIc2lnVmFsdWUFAAAAA25pbAUAAAAIc2lnVmFsdWUBAAAAEWZvcndhcmRQYXNzTGF5ZXIxAAAABAAAAAVpbnB1dAAAAAd3ZWlnaHRzAAAABmJpYXNlcwAAAAtkZWJ1Z1ByZWZpeAQAAAAEc3VtMAkAAGQAAAACCQAAZAAAAAIJAABrAAAAAwkAAZEAAAACBQAAAAVpbnB1dAAAAAAAAAAAAAkAAZEAAAACCQABkQAAAAIFAAAAB3dlaWdodHMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPQkAJAABrAAAAAwkAAZEAAAACBQAAAAVpbnB1dAAAAAAAAAAAAQkAAZEAAAACCQABkQAAAAIFAAAAB3dlaWdodHMAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAPQkAJAAGRAAAAAgUAAAAGYmlhc2VzAAAAAAAAAAAABAAAAARzdW0xCQAAZAAAAAIJAABkAAAAAgkAAGsAAAADCQABkQAAAAIFAAAABWlucHV0AAAAAAAAAAAACQABkQAAAAIJAAGRAAAAAgUAAAAHd2VpZ2h0cwAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAA9CQAkAAGsAAAADCQABkQAAAAIFAAAABWlucHV0AAAAAAAAAAABCQABkQAAAAIJAAGRAAAAAgUAAAAHd2VpZ2h0cwAAAAAAAAAAAQAAAAAAAAAAAQAAAAAAAA9CQAkAAZEAAAACBQAAAAZiaWFzZXMAAAAAAAAAAAEEAAAACyR0MDEwNzkxMTI1CQEAAAAHc2lnbW9pZAAAAAIFAAAABHN1bTACAAAACExheWVyMU4wBAAAAAZkZWJ1ZzAIBQAAAAskdDAxMDc5MTEyNQAAAAJfMQQAAAAEc2lnMAgFAAAACyR0MDEwNzkxMTI1AAAAAl8yBAAAAAskdDAxMTMwMTE3NgkBAAAAB3NpZ21vaWQAAAACBQAAAARzdW0xAgAAAAhMYXllcjFOMQQAAAAGZGVidWcxCAUAAAALJHQwMTEzMDExNzYAAAACXzEEAAAABHNpZzEIBQAAAAskdDAxMTMwMTE3NgAAAAJfMgkABRQAAAACCQAETAAAAAIFAAAABHNpZzAJAARMAAAAAgUAAAAEc2lnMQUAAAADbmlsCQAETgAAAAIFAAAABmRlYnVnMAUAAAAGZGVidWcxAQAAABFmb3J3YXJkUGFzc0xheWVyMgAAAAQAAAAFaW5wdXQAAAAHd2VpZ2h0cwAAAAZiaWFzZXMAAAALZGVidWdQcmVmaXgEAAAABHN1bTAJAABkAAAAAgkAAGQAAAACCQAAawAAAAMJAAGRAAAAAgUAAAAFaW5wdXQAAAAAAAAAAAAJAAGRAAAAAgkAAZEAAAACBQAAAAd3ZWlnaHRzAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD0JACQAAawAAAAMJAAGRAAAAAgUAAAAFaW5wdXQAAAAAAAAAAAEJAAGRAAAAAgkAAZEAAAACBQAAAAd3ZWlnaHRzAAAAAAAAAAAAAAAAAAAAAAABAAAAAAAAD0JACQABkQAAAAIFAAAABmJpYXNlcwAAAAAAAAAAAAQAAAALJHQwMTQ0NTE0OTEJAQAAAAdzaWdtb2lkAAAAAgUAAAAEc3VtMAIAAAAITGF5ZXIyTjAEAAAABmRlYnVnMAgFAAAACyR0MDE0NDUxNDkxAAAAAl8xBAAAAARzaWcwCAUAAAALJHQwMTQ0NTE0OTEAAAACXzIJAAUUAAAAAgUAAAAEc2lnMAUAAAAGZGVidWcwAAAAAQAAAAFpAQAAAAdwcmVkaWN0AAAAAgAAAAZpbnB1dDEAAAAGaW5wdXQyBAAAAAxzY2FsZWRJbnB1dDEDCQAAAAAAAAIFAAAABmlucHV0MQAAAAAAAAAAAQAAAAAAAA9CQAAAAAAAAAAAAAQAAAAMc2NhbGVkSW5wdXQyAwkAAAAAAAACBQAAAAZpbnB1dDIAAAAAAAAAAAEAAAAAAAAPQkAAAAAAAAAAAAAEAAAABmlucHV0cwkABEwAAAACBQAAAAxzY2FsZWRJbnB1dDEJAARMAAAAAgUAAAAMc2NhbGVkSW5wdXQyBQAAAANuaWwEAAAACyR0MDE3NDIxODQwCQEAAAARZm9yd2FyZFBhc3NMYXllcjEAAAAEBQAAAAZpbnB1dHMFAAAADWxheWVyMVdlaWdodHMFAAAADGxheWVyMUJpYXNlcwIAAAAGTGF5ZXIxBAAAAAxsYXllcjFPdXRwdXQIBQAAAAskdDAxNzQyMTg0MAAAAAJfMQQAAAALZGVidWdMYXllcjEIBQAAAAskdDAxNzQyMTg0MAAAAAJfMgQAAAALJHQwMTg0NTE5NDkJAQAAABFmb3J3YXJkUGFzc0xheWVyMgAAAAQFAAAADGxheWVyMU91dHB1dAUAAAANbGF5ZXIyV2VpZ2h0cwUAAAAMbGF5ZXIyQmlhc2VzAgAAAAZMYXllcjIEAAAADGxheWVyMk91dHB1dAgFAAAACyR0MDE4NDUxOTQ5AAAAAl8xBAAAAAtkZWJ1Z0xheWVyMggFAAAACyR0MDE4NDUxOTQ5AAAAAl8yCQAETgAAAAIJAAROAAAAAgkABEwAAAACCQEAAAAMSW50ZWdlckVudHJ5AAAAAgIAAAAGcmVzdWx0BQAAAAxsYXllcjJPdXRwdXQFAAAAA25pbAUAAAALZGVidWdMYXllcjEFAAAAC2RlYnVnTGF5ZXIyAAAAAIdEw48=",
    "height": 3081392,
    "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, 414253]]
4
+
let layer1Weights = [[600496, 600733], [414197, 414253]]
5
5
 
6
6
 
let layer1Biases = [-259050, -635637]
7
7
 
8
-
let layer2Weights = [[832965, -897142]]
8
+
let layer2Weights = [[832965, -897141]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
15
15
 
    let positiveZ = if ((0 > z))
16
16
 
        then -(z)
17
17
 
        else z
18
-
    let scaledZ = (positiveZ / 10000)
19
-
    let expPart = fraction(e, base, scaledZ)
18
+
    let expPart = fraction(e, base, positiveZ)
20
19
 
    let sigValue = fraction(base, (base + expPart), base)
21
20
 
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
22
21
 
    }
25
24
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
26
25
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
27
26
 
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
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
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
34
33
 
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
35
34
 
    }
36
35
 
37
36
 
38
37
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
39
38
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
40
-
    let $t015111557 = sigmoid(sum0, "Layer2N0")
41
-
    let debug0 = $t015111557._1
42
-
    let sig0 = $t015111557._2
39
+
    let $t014451491 = sigmoid(sum0, "Layer2N0")
40
+
    let debug0 = $t014451491._1
41
+
    let sig0 = $t014451491._2
43
42
 
    $Tuple2(sig0, debug0)
44
43
 
    }
45
44
 
53
52
 
        then 1000000
54
53
 
        else 0
55
54
 
    let inputs = [scaledInput1, scaledInput2]
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
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
62
61
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
63
62
 
    }
64
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, 414253]]
4
+
let layer1Weights = [[600496, 600733], [414197, 414253]]
5
5
 
6
6
 
let layer1Biases = [-259050, -635637]
7
7
 
8
-
let layer2Weights = [[832965, -897142]]
8
+
let layer2Weights = [[832965, -897141]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
12
 
func sigmoid (z,debugPrefix) = {
13
13
 
    let e = 2718281
14
14
 
    let base = 1000000
15
15
 
    let positiveZ = if ((0 > z))
16
16
 
        then -(z)
17
17
 
        else z
18
-
    let scaledZ = (positiveZ / 10000)
19
-
    let expPart = fraction(e, base, scaledZ)
18
+
    let expPart = fraction(e, base, positiveZ)
20
19
 
    let sigValue = fraction(base, (base + expPart), base)
21
20
 
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
22
21
 
    }
23
22
 
24
23
 
25
24
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
26
25
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
27
26
 
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
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
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
34
33
 
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
35
34
 
    }
36
35
 
37
36
 
38
37
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
39
38
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
40
-
    let $t015111557 = sigmoid(sum0, "Layer2N0")
41
-
    let debug0 = $t015111557._1
42
-
    let sig0 = $t015111557._2
39
+
    let $t014451491 = sigmoid(sum0, "Layer2N0")
40
+
    let debug0 = $t014451491._1
41
+
    let sig0 = $t014451491._2
43
42
 
    $Tuple2(sig0, debug0)
44
43
 
    }
45
44
 
46
45
 
47
46
 
@Callable(i)
48
47
 
func predict (input1,input2) = {
49
48
 
    let scaledInput1 = if ((input1 == 1))
50
49
 
        then 1000000
51
50
 
        else 0
52
51
 
    let scaledInput2 = if ((input2 == 1))
53
52
 
        then 1000000
54
53
 
        else 0
55
54
 
    let inputs = [scaledInput1, scaledInput2]
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
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
62
61
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
63
62
 
    }
64
63
 
65
64
 

github/deemru/w8io/169f3d6 
67.17 ms ◑