tx · Yxm27VmnSiXh83CHjo1DgvTCSkak33fbPnBnSF6qgCS

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 13:21 [3082584] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "Yxm27VmnSiXh83CHjo1DgvTCSkak33fbPnBnSF6qgCS",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714299726158,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "2df9WqWxRfZh4TQjuvx9VvWmYxqWAqwgdPkLWskoXicuNVAgNfu5HFBhU3o2gMvVgLM3K12qpni6hGbMaKVskuNX"
    ],
    "script": "base64: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",
    "height": 3082584,
    "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, 600732], [414197, 414253]]
4
+
let layer1Weights = [[600497, 600733], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259051, -635638]
6
+
let layer1Biases = [-259050, -635637]
7
7
 
8
-
let layer2Weights = [[832966, -897142]]
8
+
let layer2Weights = [[832965, -897142]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
18
18
 
19
19
 
func exp_approx (x) = {
20
20
 
    let base = 1000000
21
-
    let scaledX = (x / 10000)
22
-
    if ((0 > x))
23
-
        then (base / (1 + (base * -(scaledX))))
24
-
        else (1 + (base * scaledX))
21
+
    let maxExp = 200000
22
+
    if ((-(maxExp) > x))
23
+
        then 0
24
+
        else if ((x > maxExp))
25
+
            then (base * base)
26
+
            else (base / (1 + (x / 10000)))
25
27
 
    }
26
28
 
27
29
 
39
41
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
40
42
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
41
43
 
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
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
44
+
    let $t015381591 = sigmoid(sum0, "Layer1N0")
45
+
    let debugEntries0 = $t015381591._1
46
+
    let sig0 = $t015381591._2
47
+
    let $t015961649 = sigmoid(sum1, "Layer1N1")
48
+
    let debugEntries1 = $t015961649._1
49
+
    let sig1 = $t015961649._2
48
50
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
49
51
 
    let output = [sig0, sig1]
50
52
 
    $Tuple2(debugInfo, output)
53
55
 
54
56
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
55
57
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
56
-
    let $t019472000 = sigmoid(sum0, "Layer2N0")
57
-
    let debugEntries0 = $t019472000._1
58
-
    let sig0 = $t019472000._2
58
+
    let $t019592012 = sigmoid(sum0, "Layer2N0")
59
+
    let debugEntries0 = $t019592012._1
60
+
    let sig0 = $t019592012._2
59
61
 
    let debugInfo = debugEntries0
60
62
 
    let output = sig0
61
63
 
    $Tuple2(debugInfo, output)
71
73
 
        then 1000000
72
74
 
        else 0
73
75
 
    let inputs = [scaledInput1, scaledInput2]
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
76
+
    let $t023242422 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
77
+
    let debugLayer1 = $t023242422._1
78
+
    let layer1Output = $t023242422._2
79
+
    let $t024272531 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
80
+
    let debugLayer2 = $t024272531._1
81
+
    let layer2Output = $t024272531._2
80
82
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
81
83
 
    }
82
84
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let layer1Weights = [[600497, 600732], [414197, 414253]]
4
+
let layer1Weights = [[600497, 600733], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259051, -635638]
6
+
let layer1Biases = [-259050, -635637]
7
7
 
8
-
let layer2Weights = [[832966, -897142]]
8
+
let layer2Weights = [[832965, -897142]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
12
 
func clampZ (z,limit) = if ((z > limit))
13
13
 
    then limit
14
14
 
    else if ((-(limit) > z))
15
15
 
        then -(limit)
16
16
 
        else z
17
17
 
18
18
 
19
19
 
func exp_approx (x) = {
20
20
 
    let base = 1000000
21
-
    let scaledX = (x / 10000)
22
-
    if ((0 > x))
23
-
        then (base / (1 + (base * -(scaledX))))
24
-
        else (1 + (base * scaledX))
21
+
    let maxExp = 200000
22
+
    if ((-(maxExp) > x))
23
+
        then 0
24
+
        else if ((x > maxExp))
25
+
            then (base * base)
26
+
            else (base / (1 + (x / 10000)))
25
27
 
    }
26
28
 
27
29
 
28
30
 
func sigmoid (z,debugPrefix) = {
29
31
 
    let clampedZ = clampZ(z, 100000)
30
32
 
    let positiveZ = if ((0 > z))
31
33
 
        then -(z)
32
34
 
        else z
33
35
 
    let expValue = exp_approx(-(positiveZ))
34
36
 
    let sigValue = (1000000 / (1000000 + expValue))
35
37
 
    $Tuple2([IntegerEntry((debugPrefix + "clampedZ"), clampedZ), IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
36
38
 
    }
37
39
 
38
40
 
39
41
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
40
42
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
41
43
 
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
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
44
+
    let $t015381591 = sigmoid(sum0, "Layer1N0")
45
+
    let debugEntries0 = $t015381591._1
46
+
    let sig0 = $t015381591._2
47
+
    let $t015961649 = sigmoid(sum1, "Layer1N1")
48
+
    let debugEntries1 = $t015961649._1
49
+
    let sig1 = $t015961649._2
48
50
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
49
51
 
    let output = [sig0, sig1]
50
52
 
    $Tuple2(debugInfo, output)
51
53
 
    }
52
54
 
53
55
 
54
56
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
55
57
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
56
-
    let $t019472000 = sigmoid(sum0, "Layer2N0")
57
-
    let debugEntries0 = $t019472000._1
58
-
    let sig0 = $t019472000._2
58
+
    let $t019592012 = sigmoid(sum0, "Layer2N0")
59
+
    let debugEntries0 = $t019592012._1
60
+
    let sig0 = $t019592012._2
59
61
 
    let debugInfo = debugEntries0
60
62
 
    let output = sig0
61
63
 
    $Tuple2(debugInfo, output)
62
64
 
    }
63
65
 
64
66
 
65
67
 
@Callable(i)
66
68
 
func predict (input1,input2) = {
67
69
 
    let scaledInput1 = if ((input1 == 1))
68
70
 
        then 1000000
69
71
 
        else 0
70
72
 
    let scaledInput2 = if ((input2 == 1))
71
73
 
        then 1000000
72
74
 
        else 0
73
75
 
    let inputs = [scaledInput1, scaledInput2]
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
76
+
    let $t023242422 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
77
+
    let debugLayer1 = $t023242422._1
78
+
    let layer1Output = $t023242422._2
79
+
    let $t024272531 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
80
+
    let debugLayer2 = $t024272531._1
81
+
    let layer2Output = $t024272531._2
80
82
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
81
83
 
    }
82
84
 
83
85
 

github/deemru/w8io/6500d08 
48.22 ms ◑