tx · 6zJ8QRcPZvRhEXXc7Bhy38mZu4RMyVgBXbvcTVw8gMMR

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 13:14 [3082576] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "6zJ8QRcPZvRhEXXc7Bhy38mZu4RMyVgBXbvcTVw8gMMR",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714299254677,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "49dYRZjTq5Cc5H1eUmvwxtLoCZqxupCxbEKuLqtc6qvNDyPGPw5TEUYhBbXGmPeC9SLtxU7fDwT7BvCqb8DhboE7"
    ],
    "script": "base64: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",
    "height": 3082576,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Diff: 
Old
New
Differences
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
4
 
let layer1Weights = [[600497, 600732], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259050, -635637]
6
+
let layer1Biases = [-259051, -635638]
7
7
 
8
-
let layer2Weights = [[832965, -897142]]
8
+
let layer2Weights = [[832966, -897142]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
+
func clampZ (z,limit) = if ((z > limit))
13
+
    then limit
14
+
    else if ((-(limit) > z))
15
+
        then -(limit)
16
+
        else z
17
+
18
+
19
+
func exp_approx (x) = {
20
+
    let base = 1000000
21
+
    let scaledX = (x / 10000)
22
+
    if ((0 > x))
23
+
        then (base / (1 + (base * -(scaledX))))
24
+
        else (1 + (base * scaledX))
25
+
    }
26
+
27
+
12
28
 
func sigmoid (z,debugPrefix) = {
13
-
    let e = 2718281
14
-
    let base = 1000000
15
-
    let scaledZ = (z / 10000)
16
-
    let expPart = fraction(e, base, -(scaledZ))
17
-
    let sigValue = fraction(base, (base + expPart), base)
18
-
    $Tuple2([IntegerEntry((debugPrefix + "z"), z), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
29
+
    let clampedZ = clampZ(z, 100000)
30
+
    let positiveZ = if ((0 > z))
31
+
        then -(z)
32
+
        else z
33
+
    let expValue = exp_approx(-(positiveZ))
34
+
    let sigValue = (1000000 / (1000000 + expValue))
35
+
    $Tuple2([IntegerEntry((debugPrefix + "clampedZ"), clampedZ), IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
19
36
 
    }
20
37
 
21
38
 
22
39
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
23
40
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
24
41
 
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
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
42
+
    let $t015261579 = sigmoid(sum0, "Layer1N0")
43
+
    let debugEntries0 = $t015261579._1
44
+
    let sig0 = $t015261579._2
45
+
    let $t015841637 = sigmoid(sum1, "Layer1N1")
46
+
    let debugEntries1 = $t015841637._1
47
+
    let sig1 = $t015841637._2
31
48
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
32
49
 
    let output = [sig0, sig1]
33
50
 
    $Tuple2(debugInfo, output)
36
53
 
37
54
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
38
55
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
39
-
    let $t014821535 = sigmoid(sum0, "Layer2N0")
40
-
    let debugEntries0 = $t014821535._1
41
-
    let sig0 = $t014821535._2
56
+
    let $t019472000 = sigmoid(sum0, "Layer2N0")
57
+
    let debugEntries0 = $t019472000._1
58
+
    let sig0 = $t019472000._2
42
59
 
    let debugInfo = debugEntries0
43
60
 
    let output = sig0
44
61
 
    $Tuple2(debugInfo, output)
54
71
 
        then 1000000
55
72
 
        else 0
56
73
 
    let inputs = [scaledInput1, scaledInput2]
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
74
+
    let $t023122410 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
75
+
    let debugLayer1 = $t023122410._1
76
+
    let layer1Output = $t023122410._2
77
+
    let $t024152519 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
78
+
    let debugLayer2 = $t024152519._1
79
+
    let layer2Output = $t024152519._2
63
80
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
64
81
 
    }
65
82
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
4
 
let layer1Weights = [[600497, 600732], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259050, -635637]
6
+
let layer1Biases = [-259051, -635638]
7
7
 
8
-
let layer2Weights = [[832965, -897142]]
8
+
let layer2Weights = [[832966, -897142]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
+
func clampZ (z,limit) = if ((z > limit))
13
+
    then limit
14
+
    else if ((-(limit) > z))
15
+
        then -(limit)
16
+
        else z
17
+
18
+
19
+
func exp_approx (x) = {
20
+
    let base = 1000000
21
+
    let scaledX = (x / 10000)
22
+
    if ((0 > x))
23
+
        then (base / (1 + (base * -(scaledX))))
24
+
        else (1 + (base * scaledX))
25
+
    }
26
+
27
+
12
28
 
func sigmoid (z,debugPrefix) = {
13
-
    let e = 2718281
14
-
    let base = 1000000
15
-
    let scaledZ = (z / 10000)
16
-
    let expPart = fraction(e, base, -(scaledZ))
17
-
    let sigValue = fraction(base, (base + expPart), base)
18
-
    $Tuple2([IntegerEntry((debugPrefix + "z"), z), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
29
+
    let clampedZ = clampZ(z, 100000)
30
+
    let positiveZ = if ((0 > z))
31
+
        then -(z)
32
+
        else z
33
+
    let expValue = exp_approx(-(positiveZ))
34
+
    let sigValue = (1000000 / (1000000 + expValue))
35
+
    $Tuple2([IntegerEntry((debugPrefix + "clampedZ"), clampedZ), IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
19
36
 
    }
20
37
 
21
38
 
22
39
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
23
40
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
24
41
 
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
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
42
+
    let $t015261579 = sigmoid(sum0, "Layer1N0")
43
+
    let debugEntries0 = $t015261579._1
44
+
    let sig0 = $t015261579._2
45
+
    let $t015841637 = sigmoid(sum1, "Layer1N1")
46
+
    let debugEntries1 = $t015841637._1
47
+
    let sig1 = $t015841637._2
31
48
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
32
49
 
    let output = [sig0, sig1]
33
50
 
    $Tuple2(debugInfo, output)
34
51
 
    }
35
52
 
36
53
 
37
54
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
38
55
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
39
-
    let $t014821535 = sigmoid(sum0, "Layer2N0")
40
-
    let debugEntries0 = $t014821535._1
41
-
    let sig0 = $t014821535._2
56
+
    let $t019472000 = sigmoid(sum0, "Layer2N0")
57
+
    let debugEntries0 = $t019472000._1
58
+
    let sig0 = $t019472000._2
42
59
 
    let debugInfo = debugEntries0
43
60
 
    let output = sig0
44
61
 
    $Tuple2(debugInfo, output)
45
62
 
    }
46
63
 
47
64
 
48
65
 
@Callable(i)
49
66
 
func predict (input1,input2) = {
50
67
 
    let scaledInput1 = if ((input1 == 1))
51
68
 
        then 1000000
52
69
 
        else 0
53
70
 
    let scaledInput2 = if ((input2 == 1))
54
71
 
        then 1000000
55
72
 
        else 0
56
73
 
    let inputs = [scaledInput1, scaledInput2]
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
74
+
    let $t023122410 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
75
+
    let debugLayer1 = $t023122410._1
76
+
    let layer1Output = $t023122410._2
77
+
    let $t024152519 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
78
+
    let debugLayer2 = $t024152519._1
79
+
    let layer2Output = $t024152519._2
63
80
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
64
81
 
    }
65
82
 
66
83
 

github/deemru/w8io/6500d08 
54.23 ms ◑