tx · 8Tiq9PrpLGFMXBGGABe7MVibryxyyrcnbvTnKbFxbEWk

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 14:08 [3082637] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "8Tiq9PrpLGFMXBGGABe7MVibryxyyrcnbvTnKbFxbEWk",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714302541761,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "4wJ6pTXkDpSVRGqGJThxPwgwSkVwvm69FdSu1Z4KZG4TNQWj3EF4idHh3kSP7evTNx8zvt9rcAPbFA4UF7oqrTJP"
    ],
    "script": "base64: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",
    "height": 3082637,
    "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], [414197, 414252]]
4
+
let layer1Weights = [[600496, 600733], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259050, -635637]
6
+
let layer1Biases = [-259051, -635637]
7
7
 
8
-
let layer2Weights = [[832965, -897142]]
8
+
let layer2Weights = [[832966, -897142]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
17
17
 
18
18
 
19
19
 
func exp_approx (x) = {
20
-
    let maxExp = 100000
21
-
    if ((-(maxExp) > x))
22
-
        then 1
23
-
        else if ((x > maxExp))
24
-
            then 1000000000
25
-
            else {
26
-
                let scaled_x = (x / 10000)
27
-
                let scaled_x2 = fraction(scaled_x, scaled_x, 10000, DOWN)
28
-
                let exp_result = ((10000 - scaled_x) + (scaled_x2 / 2))
29
-
                (10000 - exp_result)
30
-
                }
20
+
    let abs_x = if ((0 > x))
21
+
        then -(x)
22
+
        else x
23
+
    let adjusted_x = if ((abs_x > 100000))
24
+
        then 100000
25
+
        else abs_x
26
+
    let scaled_x = (adjusted_x / 1000)
27
+
    (100000 - (10 * scaled_x))
31
28
 
    }
32
29
 
33
30
 
34
31
 
func sigmoid (z,debugPrefix) = {
35
32
 
    let clampedZ = clampZ(z, 100000)
36
33
 
    let expValue = exp_approx(-(clampedZ))
37
-
    let sigValue = fraction(10000, (10000 + expValue), 1, DOWN)
38
-
    $Tuple2([IntegerEntry((debugPrefix + "clampedZ"), clampedZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
34
+
    let sigValue = fraction(1000000, (1000000 + expValue), 1, DOWN)
35
+
    $Tuple2([IntegerEntry((debugPrefix + "inputZ"), z), IntegerEntry((debugPrefix + "clampedZ"), clampedZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
39
36
 
    }
40
37
 
41
38
 
42
39
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
43
40
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
44
41
 
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
45
-
    let $t017651818 = sigmoid(sum0, "Layer1N0")
46
-
    let debugEntries0 = $t017651818._1
47
-
    let sig0 = $t017651818._2
48
-
    let $t018231876 = sigmoid(sum1, "Layer1N1")
49
-
    let debugEntries1 = $t018231876._1
50
-
    let sig1 = $t018231876._2
42
+
    let $t018561909 = sigmoid(sum0, "Layer1N0")
43
+
    let debugEntries0 = $t018561909._1
44
+
    let sig0 = $t018561909._2
45
+
    let $t019141967 = sigmoid(sum1, "Layer1N1")
46
+
    let debugEntries1 = $t019141967._1
47
+
    let sig1 = $t019141967._2
51
48
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
52
49
 
    let output = [sig0, sig1]
53
50
 
    $Tuple2(debugInfo, output)
56
53
 
57
54
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
58
55
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
59
-
    let $t021862239 = sigmoid(sum0, "Layer2N0")
60
-
    let debugEntries0 = $t021862239._1
61
-
    let sig0 = $t021862239._2
56
+
    let $t022772330 = sigmoid(sum0, "Layer2N0")
57
+
    let debugEntries0 = $t022772330._1
58
+
    let sig0 = $t022772330._2
62
59
 
    let debugInfo = debugEntries0
63
60
 
    let output = sig0
64
61
 
    $Tuple2(debugInfo, output)
74
71
 
        then 1000000
75
72
 
        else 0
76
73
 
    let inputs = [scaledInput1, scaledInput2]
77
-
    let $t025512649 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
78
-
    let debugLayer1 = $t025512649._1
79
-
    let layer1Output = $t025512649._2
80
-
    let $t026542758 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
81
-
    let debugLayer2 = $t026542758._1
82
-
    let layer2Output = $t026542758._2
74
+
    let $t026422740 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
75
+
    let debugLayer1 = $t026422740._1
76
+
    let layer1Output = $t026422740._2
77
+
    let $t027452849 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
78
+
    let debugLayer2 = $t027452849._1
79
+
    let layer2Output = $t027452849._2
83
80
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
84
81
 
    }
85
82
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let layer1Weights = [[600496, 600733], [414197, 414252]]
4
+
let layer1Weights = [[600496, 600733], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259050, -635637]
6
+
let layer1Biases = [-259051, -635637]
7
7
 
8
-
let layer2Weights = [[832965, -897142]]
8
+
let layer2Weights = [[832966, -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
-
    let maxExp = 100000
21
-
    if ((-(maxExp) > x))
22
-
        then 1
23
-
        else if ((x > maxExp))
24
-
            then 1000000000
25
-
            else {
26
-
                let scaled_x = (x / 10000)
27
-
                let scaled_x2 = fraction(scaled_x, scaled_x, 10000, DOWN)
28
-
                let exp_result = ((10000 - scaled_x) + (scaled_x2 / 2))
29
-
                (10000 - exp_result)
30
-
                }
20
+
    let abs_x = if ((0 > x))
21
+
        then -(x)
22
+
        else x
23
+
    let adjusted_x = if ((abs_x > 100000))
24
+
        then 100000
25
+
        else abs_x
26
+
    let scaled_x = (adjusted_x / 1000)
27
+
    (100000 - (10 * scaled_x))
31
28
 
    }
32
29
 
33
30
 
34
31
 
func sigmoid (z,debugPrefix) = {
35
32
 
    let clampedZ = clampZ(z, 100000)
36
33
 
    let expValue = exp_approx(-(clampedZ))
37
-
    let sigValue = fraction(10000, (10000 + expValue), 1, DOWN)
38
-
    $Tuple2([IntegerEntry((debugPrefix + "clampedZ"), clampedZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
34
+
    let sigValue = fraction(1000000, (1000000 + expValue), 1, DOWN)
35
+
    $Tuple2([IntegerEntry((debugPrefix + "inputZ"), z), IntegerEntry((debugPrefix + "clampedZ"), clampedZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
39
36
 
    }
40
37
 
41
38
 
42
39
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
43
40
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
44
41
 
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
45
-
    let $t017651818 = sigmoid(sum0, "Layer1N0")
46
-
    let debugEntries0 = $t017651818._1
47
-
    let sig0 = $t017651818._2
48
-
    let $t018231876 = sigmoid(sum1, "Layer1N1")
49
-
    let debugEntries1 = $t018231876._1
50
-
    let sig1 = $t018231876._2
42
+
    let $t018561909 = sigmoid(sum0, "Layer1N0")
43
+
    let debugEntries0 = $t018561909._1
44
+
    let sig0 = $t018561909._2
45
+
    let $t019141967 = sigmoid(sum1, "Layer1N1")
46
+
    let debugEntries1 = $t019141967._1
47
+
    let sig1 = $t019141967._2
51
48
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
52
49
 
    let output = [sig0, sig1]
53
50
 
    $Tuple2(debugInfo, output)
54
51
 
    }
55
52
 
56
53
 
57
54
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
58
55
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
59
-
    let $t021862239 = sigmoid(sum0, "Layer2N0")
60
-
    let debugEntries0 = $t021862239._1
61
-
    let sig0 = $t021862239._2
56
+
    let $t022772330 = sigmoid(sum0, "Layer2N0")
57
+
    let debugEntries0 = $t022772330._1
58
+
    let sig0 = $t022772330._2
62
59
 
    let debugInfo = debugEntries0
63
60
 
    let output = sig0
64
61
 
    $Tuple2(debugInfo, output)
65
62
 
    }
66
63
 
67
64
 
68
65
 
@Callable(i)
69
66
 
func predict (input1,input2) = {
70
67
 
    let scaledInput1 = if ((input1 == 1))
71
68
 
        then 1000000
72
69
 
        else 0
73
70
 
    let scaledInput2 = if ((input2 == 1))
74
71
 
        then 1000000
75
72
 
        else 0
76
73
 
    let inputs = [scaledInput1, scaledInput2]
77
-
    let $t025512649 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
78
-
    let debugLayer1 = $t025512649._1
79
-
    let layer1Output = $t025512649._2
80
-
    let $t026542758 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
81
-
    let debugLayer2 = $t026542758._1
82
-
    let layer2Output = $t026542758._2
74
+
    let $t026422740 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
75
+
    let debugLayer1 = $t026422740._1
76
+
    let layer1Output = $t026422740._2
77
+
    let $t027452849 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
78
+
    let debugLayer2 = $t027452849._1
79
+
    let layer2Output = $t027452849._2
83
80
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
84
81
 
    }
85
82
 
86
83
 

github/deemru/w8io/026f985 
34.03 ms ◑