tx · CTK5XZL5t8cmyyJ2vqgR2bTBcRpvXGeA2RrB6hGC8GVB

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 11:26 [3082465] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "CTK5XZL5t8cmyyJ2vqgR2bTBcRpvXGeA2RrB6hGC8GVB",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714292777376,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "235Csg3re1txjvvECKHzUCogjAGHmxmCPkkCvxiBfZg6z8jtdYwd7JmpiJziBBDx37XE3EsQcEggWY2NBXU9STco"
    ],
    "script": "base64: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",
    "height": 3082465,
    "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, 600733], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259050, -635637]
6
+
let layer1Biases = [-259050, -635638]
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 e = toBigInt(2718281)
14
-
    let factor1 = x
15
-
    let factor2 = fraction((x * x), toBigInt((2 * 1000000)), toBigInt(1000000))
16
-
    let factor3 = fraction(((x * x) * x), toBigInt(((6 * 1000000) * 1000000)), toBigInt(1000000))
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))
17
18
 
    fraction((((toBigInt(1000000) + factor1) + factor2) + factor3), toBigInt(1), toBigInt(1))
18
19
 
    }
19
20
 
20
21
 
21
22
 
func sigmoid (z,debugPrefix) = {
22
-
    let base = toBigInt(1000000)
23
+
    let base = toBigInt(100000)
23
24
 
    let positiveZ = toBigInt(if ((0 > z))
24
25
 
        then -(z)
25
26
 
        else z)
32
33
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
33
34
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
34
35
 
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
35
-
    let $t015111557 = sigmoid(sum0, "Layer1N0")
36
-
    let debug0 = $t015111557._1
37
-
    let sig0 = $t015111557._2
38
-
    let $t015621608 = sigmoid(sum1, "Layer1N1")
39
-
    let debug1 = $t015621608._1
40
-
    let sig1 = $t015621608._2
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
41
42
 
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
42
43
 
    }
43
44
 
44
45
 
45
46
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
46
47
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
47
-
    let $t018771923 = sigmoid(sum0, "Layer2N0")
48
-
    let debug0 = $t018771923._1
49
-
    let sig0 = $t018771923._2
48
+
    let $t020902136 = sigmoid(sum0, "Layer2N0")
49
+
    let debug0 = $t020902136._1
50
+
    let sig0 = $t020902136._2
50
51
 
    $Tuple2(sig0, debug0)
51
52
 
    }
52
53
 
60
61
 
        then 1000000
61
62
 
        else 0
62
63
 
    let inputs = [scaledInput1, scaledInput2]
63
-
    let $t021742272 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
64
-
    let layer1Output = $t021742272._1
65
-
    let debugLayer1 = $t021742272._2
66
-
    let $t022772381 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
67
-
    let layer2Output = $t022772381._1
68
-
    let debugLayer2 = $t022772381._2
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
69
70
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
70
71
 
    }
71
72
 

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, 600733], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259050, -635637]
6
+
let layer1Biases = [-259050, -635638]
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 e = toBigInt(2718281)
14
-
    let factor1 = x
15
-
    let factor2 = fraction((x * x), toBigInt((2 * 1000000)), toBigInt(1000000))
16
-
    let factor3 = fraction(((x * x) * x), toBigInt(((6 * 1000000) * 1000000)), toBigInt(1000000))
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))
17
18
 
    fraction((((toBigInt(1000000) + factor1) + factor2) + factor3), toBigInt(1), toBigInt(1))
18
19
 
    }
19
20
 
20
21
 
21
22
 
func sigmoid (z,debugPrefix) = {
22
-
    let base = toBigInt(1000000)
23
+
    let base = toBigInt(100000)
23
24
 
    let positiveZ = toBigInt(if ((0 > z))
24
25
 
        then -(z)
25
26
 
        else z)
26
27
 
    let expValue = exp_approximation(positiveZ)
27
28
 
    let sigValue = fraction(base, (base + expValue), toBigInt(1))
28
29
 
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), toInt(positiveZ)), IntegerEntry((debugPrefix + "expValue"), toInt(expValue)), IntegerEntry((debugPrefix + "sigValue"), toInt(sigValue))], toInt(sigValue))
29
30
 
    }
30
31
 
31
32
 
32
33
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
33
34
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
34
35
 
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
35
-
    let $t015111557 = sigmoid(sum0, "Layer1N0")
36
-
    let debug0 = $t015111557._1
37
-
    let sig0 = $t015111557._2
38
-
    let $t015621608 = sigmoid(sum1, "Layer1N1")
39
-
    let debug1 = $t015621608._1
40
-
    let sig1 = $t015621608._2
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
41
42
 
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
42
43
 
    }
43
44
 
44
45
 
45
46
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
46
47
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
47
-
    let $t018771923 = sigmoid(sum0, "Layer2N0")
48
-
    let debug0 = $t018771923._1
49
-
    let sig0 = $t018771923._2
48
+
    let $t020902136 = sigmoid(sum0, "Layer2N0")
49
+
    let debug0 = $t020902136._1
50
+
    let sig0 = $t020902136._2
50
51
 
    $Tuple2(sig0, debug0)
51
52
 
    }
52
53
 
53
54
 
54
55
 
@Callable(i)
55
56
 
func predict (input1,input2) = {
56
57
 
    let scaledInput1 = if ((input1 == 1))
57
58
 
        then 1000000
58
59
 
        else 0
59
60
 
    let scaledInput2 = if ((input2 == 1))
60
61
 
        then 1000000
61
62
 
        else 0
62
63
 
    let inputs = [scaledInput1, scaledInput2]
63
-
    let $t021742272 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
64
-
    let layer1Output = $t021742272._1
65
-
    let debugLayer1 = $t021742272._2
66
-
    let $t022772381 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
67
-
    let layer2Output = $t022772381._1
68
-
    let debugLayer2 = $t022772381._2
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
69
70
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
70
71
 
    }
71
72
 
72
73
 

github/deemru/w8io/6500d08 
57.90 ms ◑