tx · AwRrLAxF5aEBEdiTUheEyimrHT9gq5mRd1ki8zFuTWZw

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 11:30 [3082469] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "AwRrLAxF5aEBEdiTUheEyimrHT9gq5mRd1ki8zFuTWZw",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714293015946,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "TT3MU9AZKuxbWrB4YBVdaN9ZdEprtRfc2HrZDWesq5XpEkaoh1H46dQhNuT3pDhT2qsFWcsnLwRdng9DMb6mDBj"
    ],
    "script": "base64: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",
    "height": 3082469,
    "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 = [[600497, 600732], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259050, -635638]
6
+
let layer1Biases = [-259051, -635637]
7
7
 
8
8
 
let layer2Weights = [[832965, -897142]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
12
 
func exp_approximation (x) = {
13
-
    let scale = toBigInt(100000)
14
-
    let e = toBigInt(271828)
15
-
    let factor1 = fraction(x, scale, toBigInt(1))
16
-
    let factor2 = fraction(fraction((x * x), scale, toBigInt(1)), toBigInt((2 * 100000)), toBigInt(1))
17
-
    let factor3 = fraction(fraction(((x * x) * x), scale, toBigInt(1)), toBigInt(((6 * 100000) * 100000)), toBigInt(1))
18
-
    fraction((((toBigInt(1000000) + factor1) + factor2) + factor3), toBigInt(1), toBigInt(1))
13
+
    let scale = 10000
14
+
    let scaled_x = (x / scale)
15
+
    let e = 27183
16
+
    let factor1 = scaled_x
17
+
    let factor2 = ((scaled_x * scaled_x) / (2 * scale))
18
+
    let factor3 = (((scaled_x * scaled_x) * scaled_x) / ((6 * scale) * scale))
19
+
    (((10000 + factor1) + factor2) + factor3)
19
20
 
    }
20
21
 
21
22
 
22
23
 
func sigmoid (z,debugPrefix) = {
23
-
    let base = toBigInt(100000)
24
-
    let positiveZ = toBigInt(if ((0 > z))
24
+
    let base = 10000
25
+
    let positiveZ = if ((0 > z))
25
26
 
        then -(z)
26
-
        else z)
27
+
        else z
27
28
 
    let expValue = exp_approximation(positiveZ)
28
-
    let sigValue = fraction(base, (base + expValue), toBigInt(1))
29
-
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), toInt(positiveZ)), IntegerEntry((debugPrefix + "expValue"), toInt(expValue)), IntegerEntry((debugPrefix + "sigValue"), toInt(sigValue))], toInt(sigValue))
29
+
    let sigValue = ((base * 10000) / (base + expValue))
30
+
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
30
31
 
    }
31
32
 
32
33
 
33
34
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
34
35
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
35
36
 
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
36
-
    let $t017241770 = sigmoid(sum0, "Layer1N0")
37
-
    let debug0 = $t017241770._1
38
-
    let sig0 = $t017241770._2
39
-
    let $t017751821 = sigmoid(sum1, "Layer1N1")
40
-
    let debug1 = $t017751821._1
41
-
    let sig1 = $t017751821._2
37
+
    let $t016721718 = sigmoid(sum0, "Layer1N0")
38
+
    let debug0 = $t016721718._1
39
+
    let sig0 = $t016721718._2
40
+
    let $t017231769 = sigmoid(sum1, "Layer1N1")
41
+
    let debug1 = $t017231769._1
42
+
    let sig1 = $t017231769._2
42
43
 
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
43
44
 
    }
44
45
 
45
46
 
46
47
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
47
48
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
48
-
    let $t020902136 = sigmoid(sum0, "Layer2N0")
49
-
    let debug0 = $t020902136._1
50
-
    let sig0 = $t020902136._2
49
+
    let $t020382084 = sigmoid(sum0, "Layer2N0")
50
+
    let debug0 = $t020382084._1
51
+
    let sig0 = $t020382084._2
51
52
 
    $Tuple2(sig0, debug0)
52
53
 
    }
53
54
 
61
62
 
        then 1000000
62
63
 
        else 0
63
64
 
    let inputs = [scaledInput1, scaledInput2]
64
-
    let $t023872485 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
65
-
    let layer1Output = $t023872485._1
66
-
    let debugLayer1 = $t023872485._2
67
-
    let $t024902594 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
68
-
    let layer2Output = $t024902594._1
69
-
    let debugLayer2 = $t024902594._2
65
+
    let $t023352433 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
66
+
    let layer1Output = $t023352433._1
67
+
    let debugLayer1 = $t023352433._2
68
+
    let $t024382542 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
69
+
    let layer2Output = $t024382542._1
70
+
    let debugLayer2 = $t024382542._2
70
71
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
71
72
 
    }
72
73
 

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 = [[600497, 600732], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259050, -635638]
6
+
let layer1Biases = [-259051, -635637]
7
7
 
8
8
 
let layer2Weights = [[832965, -897142]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
12
 
func exp_approximation (x) = {
13
-
    let scale = toBigInt(100000)
14
-
    let e = toBigInt(271828)
15
-
    let factor1 = fraction(x, scale, toBigInt(1))
16
-
    let factor2 = fraction(fraction((x * x), scale, toBigInt(1)), toBigInt((2 * 100000)), toBigInt(1))
17
-
    let factor3 = fraction(fraction(((x * x) * x), scale, toBigInt(1)), toBigInt(((6 * 100000) * 100000)), toBigInt(1))
18
-
    fraction((((toBigInt(1000000) + factor1) + factor2) + factor3), toBigInt(1), toBigInt(1))
13
+
    let scale = 10000
14
+
    let scaled_x = (x / scale)
15
+
    let e = 27183
16
+
    let factor1 = scaled_x
17
+
    let factor2 = ((scaled_x * scaled_x) / (2 * scale))
18
+
    let factor3 = (((scaled_x * scaled_x) * scaled_x) / ((6 * scale) * scale))
19
+
    (((10000 + factor1) + factor2) + factor3)
19
20
 
    }
20
21
 
21
22
 
22
23
 
func sigmoid (z,debugPrefix) = {
23
-
    let base = toBigInt(100000)
24
-
    let positiveZ = toBigInt(if ((0 > z))
24
+
    let base = 10000
25
+
    let positiveZ = if ((0 > z))
25
26
 
        then -(z)
26
-
        else z)
27
+
        else z
27
28
 
    let expValue = exp_approximation(positiveZ)
28
-
    let sigValue = fraction(base, (base + expValue), toBigInt(1))
29
-
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), toInt(positiveZ)), IntegerEntry((debugPrefix + "expValue"), toInt(expValue)), IntegerEntry((debugPrefix + "sigValue"), toInt(sigValue))], toInt(sigValue))
29
+
    let sigValue = ((base * 10000) / (base + expValue))
30
+
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
30
31
 
    }
31
32
 
32
33
 
33
34
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
34
35
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
35
36
 
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
36
-
    let $t017241770 = sigmoid(sum0, "Layer1N0")
37
-
    let debug0 = $t017241770._1
38
-
    let sig0 = $t017241770._2
39
-
    let $t017751821 = sigmoid(sum1, "Layer1N1")
40
-
    let debug1 = $t017751821._1
41
-
    let sig1 = $t017751821._2
37
+
    let $t016721718 = sigmoid(sum0, "Layer1N0")
38
+
    let debug0 = $t016721718._1
39
+
    let sig0 = $t016721718._2
40
+
    let $t017231769 = sigmoid(sum1, "Layer1N1")
41
+
    let debug1 = $t017231769._1
42
+
    let sig1 = $t017231769._2
42
43
 
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
43
44
 
    }
44
45
 
45
46
 
46
47
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
47
48
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
48
-
    let $t020902136 = sigmoid(sum0, "Layer2N0")
49
-
    let debug0 = $t020902136._1
50
-
    let sig0 = $t020902136._2
49
+
    let $t020382084 = sigmoid(sum0, "Layer2N0")
50
+
    let debug0 = $t020382084._1
51
+
    let sig0 = $t020382084._2
51
52
 
    $Tuple2(sig0, debug0)
52
53
 
    }
53
54
 
54
55
 
55
56
 
@Callable(i)
56
57
 
func predict (input1,input2) = {
57
58
 
    let scaledInput1 = if ((input1 == 1))
58
59
 
        then 1000000
59
60
 
        else 0
60
61
 
    let scaledInput2 = if ((input2 == 1))
61
62
 
        then 1000000
62
63
 
        else 0
63
64
 
    let inputs = [scaledInput1, scaledInput2]
64
-
    let $t023872485 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
65
-
    let layer1Output = $t023872485._1
66
-
    let debugLayer1 = $t023872485._2
67
-
    let $t024902594 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
68
-
    let layer2Output = $t024902594._1
69
-
    let debugLayer2 = $t024902594._2
65
+
    let $t023352433 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
66
+
    let layer1Output = $t023352433._1
67
+
    let debugLayer1 = $t023352433._2
68
+
    let $t024382542 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
69
+
    let layer2Output = $t024382542._1
70
+
    let debugLayer2 = $t024382542._2
70
71
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
71
72
 
    }
72
73
 
73
74
 

github/deemru/w8io/169f3d6 
41.61 ms ◑