tx · 7s1h3jYoYnAwi8pPGmy5HUUL4Ybuoz6K6kVB7GwRyRrV

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 13:00 [3082561] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "7s1h3jYoYnAwi8pPGmy5HUUL4Ybuoz6K6kVB7GwRyRrV",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714298500298,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "49W9Bs84nsFPDTfYxtV57c9nKFgU34mbXiDka2EKtNYw89wd8jw8YkyiLJdU36hna64P1kv8ffNcr5PwyRfi61iY"
    ],
    "script": "base64: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",
    "height": 3082561,
    "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 = [[600496, 600733], [414196, 414253]]
4
+
let layer1Weights = [[600497, 600732], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259051, -635637]
6
+
let layer1Biases = [-259050, -635637]
7
7
 
8
8
 
let layer2Weights = [[832965, -897142]]
9
9
 
12
12
 
func sigmoid (z,debugPrefix) = {
13
13
 
    let e = 2718281
14
14
 
    let base = 1000000
15
-
    let positiveZ = if ((0 > z))
16
-
        then -(z)
17
-
        else z
18
-
    let scaledZ = (positiveZ / 10000)
19
-
    let expPart = fraction(e, base, scaledZ)
15
+
    let scaledZ = (z / 10000)
16
+
    let expPart = fraction(e, base, -(scaledZ))
20
17
 
    let sigValue = fraction(base, (base + expPart), base)
21
-
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
18
+
    $Tuple2([IntegerEntry((debugPrefix + "z"), z), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
22
19
 
    }
23
20
 
24
21
 
25
22
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
26
23
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
27
24
 
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
28
-
    let $t011391192 = sigmoid(sum0, "Layer1N0")
29
-
    let debugEntries0 = $t011391192._1
30
-
    let sig0 = $t011391192._2
31
-
    let $t011971250 = sigmoid(sum1, "Layer1N1")
32
-
    let debugEntries1 = $t011971250._1
33
-
    let sig1 = $t011971250._2
25
+
    let $t010611114 = sigmoid(sum0, "Layer1N0")
26
+
    let debugEntries0 = $t010611114._1
27
+
    let sig0 = $t010611114._2
28
+
    let $t011191172 = sigmoid(sum1, "Layer1N1")
29
+
    let debugEntries1 = $t011191172._1
30
+
    let sig1 = $t011191172._2
34
31
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
35
32
 
    let output = [sig0, sig1]
36
33
 
    $Tuple2(debugInfo, output)
39
36
 
40
37
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
41
38
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
42
-
    let $t015601613 = sigmoid(sum0, "Layer2N0")
43
-
    let debugEntries0 = $t015601613._1
44
-
    let sig0 = $t015601613._2
39
+
    let $t014821535 = sigmoid(sum0, "Layer2N0")
40
+
    let debugEntries0 = $t014821535._1
41
+
    let sig0 = $t014821535._2
45
42
 
    let debugInfo = debugEntries0
46
43
 
    let output = sig0
47
44
 
    $Tuple2(debugInfo, output)
57
54
 
        then 1000000
58
55
 
        else 0
59
56
 
    let inputs = [scaledInput1, scaledInput2]
60
-
    let $t019252023 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
61
-
    let debugLayer1 = $t019252023._1
62
-
    let layer1Output = $t019252023._2
63
-
    let $t020282132 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
64
-
    let debugLayer2 = $t020282132._1
65
-
    let layer2Output = $t020282132._2
57
+
    let $t018471945 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
58
+
    let debugLayer1 = $t018471945._1
59
+
    let layer1Output = $t018471945._2
60
+
    let $t019502054 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
61
+
    let debugLayer2 = $t019502054._1
62
+
    let layer2Output = $t019502054._2
66
63
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
67
64
 
    }
68
65
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let layer1Weights = [[600496, 600733], [414196, 414253]]
4
+
let layer1Weights = [[600497, 600732], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259051, -635637]
6
+
let layer1Biases = [-259050, -635637]
7
7
 
8
8
 
let layer2Weights = [[832965, -897142]]
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
-
    let positiveZ = if ((0 > z))
16
-
        then -(z)
17
-
        else z
18
-
    let scaledZ = (positiveZ / 10000)
19
-
    let expPart = fraction(e, base, scaledZ)
15
+
    let scaledZ = (z / 10000)
16
+
    let expPart = fraction(e, base, -(scaledZ))
20
17
 
    let sigValue = fraction(base, (base + expPart), base)
21
-
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
18
+
    $Tuple2([IntegerEntry((debugPrefix + "z"), z), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
22
19
 
    }
23
20
 
24
21
 
25
22
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
26
23
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
27
24
 
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
28
-
    let $t011391192 = sigmoid(sum0, "Layer1N0")
29
-
    let debugEntries0 = $t011391192._1
30
-
    let sig0 = $t011391192._2
31
-
    let $t011971250 = sigmoid(sum1, "Layer1N1")
32
-
    let debugEntries1 = $t011971250._1
33
-
    let sig1 = $t011971250._2
25
+
    let $t010611114 = sigmoid(sum0, "Layer1N0")
26
+
    let debugEntries0 = $t010611114._1
27
+
    let sig0 = $t010611114._2
28
+
    let $t011191172 = sigmoid(sum1, "Layer1N1")
29
+
    let debugEntries1 = $t011191172._1
30
+
    let sig1 = $t011191172._2
34
31
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
35
32
 
    let output = [sig0, sig1]
36
33
 
    $Tuple2(debugInfo, output)
37
34
 
    }
38
35
 
39
36
 
40
37
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
41
38
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
42
-
    let $t015601613 = sigmoid(sum0, "Layer2N0")
43
-
    let debugEntries0 = $t015601613._1
44
-
    let sig0 = $t015601613._2
39
+
    let $t014821535 = sigmoid(sum0, "Layer2N0")
40
+
    let debugEntries0 = $t014821535._1
41
+
    let sig0 = $t014821535._2
45
42
 
    let debugInfo = debugEntries0
46
43
 
    let output = sig0
47
44
 
    $Tuple2(debugInfo, output)
48
45
 
    }
49
46
 
50
47
 
51
48
 
@Callable(i)
52
49
 
func predict (input1,input2) = {
53
50
 
    let scaledInput1 = if ((input1 == 1))
54
51
 
        then 1000000
55
52
 
        else 0
56
53
 
    let scaledInput2 = if ((input2 == 1))
57
54
 
        then 1000000
58
55
 
        else 0
59
56
 
    let inputs = [scaledInput1, scaledInput2]
60
-
    let $t019252023 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
61
-
    let debugLayer1 = $t019252023._1
62
-
    let layer1Output = $t019252023._2
63
-
    let $t020282132 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
64
-
    let debugLayer2 = $t020282132._1
65
-
    let layer2Output = $t020282132._2
57
+
    let $t018471945 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
58
+
    let debugLayer1 = $t018471945._1
59
+
    let layer1Output = $t018471945._2
60
+
    let $t019502054 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
61
+
    let debugLayer2 = $t019502054._1
62
+
    let layer2Output = $t019502054._2
66
63
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
67
64
 
    }
68
65
 
69
66
 

github/deemru/w8io/6500d08 
25.24 ms ◑