tx · HwyC8YbVJFQ1rUZWdjFea9Ep2RPaYKUJEWTMEip3U51S

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 11:00 [3082438] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "HwyC8YbVJFQ1rUZWdjFea9Ep2RPaYKUJEWTMEip3U51S",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714291228015,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "4maBEyLzhchsce2W2zeUVGkxuTcy7Q5nkXGnUfkfafZtf5aFMNBEhTdFuTLRUjnKEk4u4cD8rJHWxerPji1J89Xs"
    ],
    "script": "base64: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",
    "height": 3082438,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

View: original | compacted
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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, 414253]]
4
+
let layer1Weights = [[600497, 600732], [414197, 414252]]
5
5
 
6
-
let layer1Biases = [-259050, -635637]
6
+
let layer1Biases = [-259051, -635637]
7
7
 
8
-
let layer2Weights = [[832965, -897141]]
8
+
let layer2Weights = [[832966, -897141]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
+
func exp_approximation (x) = {
13
+
    let e = 2718281
14
+
    let factor1 = x
15
+
    let factor2 = fraction((x * x), (2 * 1000000), 1000000)
16
+
    let factor3 = fraction(((x * x) * x), ((6 * 1000000) * 1000000), 1000000)
17
+
    let exp_approx = (((1000000 + factor1) + factor2) + factor3)
18
+
    exp_approx
19
+
    }
20
+
21
+
12
22
 
func sigmoid (z,debugPrefix) = {
13
-
    let e = 2718281
14
23
 
    let base = 1000000
15
24
 
    let positiveZ = if ((0 > z))
16
25
 
        then -(z)
17
26
 
        else z
18
-
    let expPart = fraction(e, base, positiveZ)
19
-
    let sigValue = fraction(base, (base + expPart), base)
20
-
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
27
+
    let expValue = exp_approximation(positiveZ)
28
+
    let sigValue = fraction(base, (base + expValue), base)
29
+
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
21
30
 
    }
22
31
 
23
32
 
24
33
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
25
34
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
26
35
 
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
27
-
    let $t010791125 = sigmoid(sum0, "Layer1N0")
28
-
    let debug0 = $t010791125._1
29
-
    let sig0 = $t010791125._2
30
-
    let $t011301176 = sigmoid(sum1, "Layer1N1")
31
-
    let debug1 = $t011301176._1
32
-
    let sig1 = $t011301176._2
36
+
    let $t013761422 = sigmoid(sum0, "Layer1N0")
37
+
    let debug0 = $t013761422._1
38
+
    let sig0 = $t013761422._2
39
+
    let $t014271473 = sigmoid(sum1, "Layer1N1")
40
+
    let debug1 = $t014271473._1
41
+
    let sig1 = $t014271473._2
33
42
 
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
34
43
 
    }
35
44
 
36
45
 
37
46
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
38
47
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
39
-
    let $t014451491 = sigmoid(sum0, "Layer2N0")
40
-
    let debug0 = $t014451491._1
41
-
    let sig0 = $t014451491._2
48
+
    let $t017421788 = sigmoid(sum0, "Layer2N0")
49
+
    let debug0 = $t017421788._1
50
+
    let sig0 = $t017421788._2
42
51
 
    $Tuple2(sig0, debug0)
43
52
 
    }
44
53
 
52
61
 
        then 1000000
53
62
 
        else 0
54
63
 
    let inputs = [scaledInput1, scaledInput2]
55
-
    let $t017421840 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
56
-
    let layer1Output = $t017421840._1
57
-
    let debugLayer1 = $t017421840._2
58
-
    let $t018451949 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
59
-
    let layer2Output = $t018451949._1
60
-
    let debugLayer2 = $t018451949._2
64
+
    let $t020392137 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
65
+
    let layer1Output = $t020392137._1
66
+
    let debugLayer1 = $t020392137._2
67
+
    let $t021422246 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
68
+
    let layer2Output = $t021422246._1
69
+
    let debugLayer2 = $t021422246._2
61
70
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
62
71
 
    }
63
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], [414197, 414253]]
4
+
let layer1Weights = [[600497, 600732], [414197, 414252]]
5
5
 
6
-
let layer1Biases = [-259050, -635637]
6
+
let layer1Biases = [-259051, -635637]
7
7
 
8
-
let layer2Weights = [[832965, -897141]]
8
+
let layer2Weights = [[832966, -897141]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
+
func exp_approximation (x) = {
13
+
    let e = 2718281
14
+
    let factor1 = x
15
+
    let factor2 = fraction((x * x), (2 * 1000000), 1000000)
16
+
    let factor3 = fraction(((x * x) * x), ((6 * 1000000) * 1000000), 1000000)
17
+
    let exp_approx = (((1000000 + factor1) + factor2) + factor3)
18
+
    exp_approx
19
+
    }
20
+
21
+
12
22
 
func sigmoid (z,debugPrefix) = {
13
-
    let e = 2718281
14
23
 
    let base = 1000000
15
24
 
    let positiveZ = if ((0 > z))
16
25
 
        then -(z)
17
26
 
        else z
18
-
    let expPart = fraction(e, base, positiveZ)
19
-
    let sigValue = fraction(base, (base + expPart), base)
20
-
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
27
+
    let expValue = exp_approximation(positiveZ)
28
+
    let sigValue = fraction(base, (base + expValue), base)
29
+
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
21
30
 
    }
22
31
 
23
32
 
24
33
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
25
34
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
26
35
 
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
27
-
    let $t010791125 = sigmoid(sum0, "Layer1N0")
28
-
    let debug0 = $t010791125._1
29
-
    let sig0 = $t010791125._2
30
-
    let $t011301176 = sigmoid(sum1, "Layer1N1")
31
-
    let debug1 = $t011301176._1
32
-
    let sig1 = $t011301176._2
36
+
    let $t013761422 = sigmoid(sum0, "Layer1N0")
37
+
    let debug0 = $t013761422._1
38
+
    let sig0 = $t013761422._2
39
+
    let $t014271473 = sigmoid(sum1, "Layer1N1")
40
+
    let debug1 = $t014271473._1
41
+
    let sig1 = $t014271473._2
33
42
 
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
34
43
 
    }
35
44
 
36
45
 
37
46
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
38
47
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
39
-
    let $t014451491 = sigmoid(sum0, "Layer2N0")
40
-
    let debug0 = $t014451491._1
41
-
    let sig0 = $t014451491._2
48
+
    let $t017421788 = sigmoid(sum0, "Layer2N0")
49
+
    let debug0 = $t017421788._1
50
+
    let sig0 = $t017421788._2
42
51
 
    $Tuple2(sig0, debug0)
43
52
 
    }
44
53
 
45
54
 
46
55
 
@Callable(i)
47
56
 
func predict (input1,input2) = {
48
57
 
    let scaledInput1 = if ((input1 == 1))
49
58
 
        then 1000000
50
59
 
        else 0
51
60
 
    let scaledInput2 = if ((input2 == 1))
52
61
 
        then 1000000
53
62
 
        else 0
54
63
 
    let inputs = [scaledInput1, scaledInput2]
55
-
    let $t017421840 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
56
-
    let layer1Output = $t017421840._1
57
-
    let debugLayer1 = $t017421840._2
58
-
    let $t018451949 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
59
-
    let layer2Output = $t018451949._1
60
-
    let debugLayer2 = $t018451949._2
64
+
    let $t020392137 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
65
+
    let layer1Output = $t020392137._1
66
+
    let debugLayer1 = $t020392137._2
67
+
    let $t021422246 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
68
+
    let layer2Output = $t021422246._1
69
+
    let debugLayer2 = $t021422246._2
61
70
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
62
71
 
    }
63
72
 
64
73
 

github/deemru/w8io/169f3d6 
37.17 ms ◑