tx · 4Y5WL8dQqwHHtb9Ei4jYd9217YqLbJ62uGymgyngzKKK

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.05.04 11:34 [3091149] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "4Y5WL8dQqwHHtb9Ei4jYd9217YqLbJ62uGymgyngzKKK",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714811692029,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "2J7ZsQc173asjfCRUFbtaS8fW1zLuZBiNCGyf3McxtzistfyN8cWHtHu9yqWkEyDmKNoSeyJb8NNjvgEMxj1mg67"
    ],
    "script": "base64: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",
    "height": 3091149,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Diff: 
Old
New
Differences
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
-
func expApprox (x) = {
13
-
    let scaledX = fraction(x, 1, 10)
14
-
    let scaledX2 = fraction(scaledX, scaledX, 10)
15
-
    let term1 = (10 - scaledX)
16
-
    let term2 = fraction(scaledX2, 5, 1)
17
-
    (term1 + term2)
12
+
func relu (x) = if ((x > 0))
13
+
    then x
14
+
    else 0
15
+
16
+
17
+
func dotProduct (v1,v2) = {
18
+
    let sum1 = ((v1[0] * v2[0]) / 10000)
19
+
    let sum2 = ((v1[1] * v2[1]) / 10000)
20
+
    (sum1 + sum2)
18
21
 
    }
19
22
 
20
23
 
21
-
func sigmoid (z,debugPrefix) = {
22
-
    let cappedZ = if ((z > 200))
23
-
        then 200
24
-
        else if ((-200 > z))
25
-
            then -200
26
-
            else z
27
-
    let expNegZ = expApprox(-(cappedZ))
28
-
    let onePlusExpNegZ = (10 + expNegZ)
29
-
    let sigValue = fraction(10, onePlusExpNegZ, 1)
30
-
    $Tuple2([IntegerEntry((debugPrefix + "inputZ"), cappedZ), IntegerEntry((debugPrefix + "expNegZ"), expNegZ), IntegerEntry((debugPrefix + "onePlusExpNegZ"), onePlusExpNegZ), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
31
-
    }
32
-
33
-
34
-
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
35
-
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + biases[0])
36
-
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + biases[1])
37
-
    let $t014091462 = sigmoid(sum0, "Layer1N0")
38
-
    let debugEntries0 = $t014091462._1
39
-
    let sig0 = $t014091462._2
40
-
    let $t014671520 = sigmoid(sum1, "Layer1N1")
41
-
    let debugEntries1 = $t014671520._1
42
-
    let sig1 = $t014671520._2
43
-
    let debugInfo = (debugEntries0 ++ debugEntries1)
44
-
    let output = [sig0, sig1]
45
-
    $Tuple2(debugInfo, output)
46
-
    }
47
-
48
-
49
-
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
50
-
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + biases[0])
51
-
    let $t018211874 = sigmoid(sum0, "Layer2N0")
52
-
    let debugEntries0 = $t018211874._1
53
-
    let sig0 = $t018211874._2
54
-
    let debugInfo = debugEntries0
55
-
    let output = sig0
56
-
    $Tuple2(debugInfo, output)
24
+
func feedforward (inputs) = {
25
+
    let layer1Result1 = relu((dotProduct(inputs, layer1Weights[0]) + layer1Biases[0]))
26
+
    let layer1Result2 = relu((dotProduct(inputs, layer1Weights[1]) + layer1Biases[1]))
27
+
    let layer2Inputs = [layer1Result1, layer1Result2]
28
+
    (dotProduct(layer2Inputs, layer2Weights[0]) + layer2Biases[0])
57
29
 
    }
58
30
 
59
31
 
60
32
 
@Callable(i)
61
33
 
func predict (input1,input2) = {
62
-
    let scaledInput1 = if ((input1 == 1))
63
-
        then 1
64
-
        else 0
65
-
    let scaledInput2 = if ((input2 == 1))
66
-
        then 1
67
-
        else 0
68
-
    let inputs = [scaledInput1, scaledInput2]
69
-
    let $t021742272 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
70
-
    let debugLayer1 = $t021742272._1
71
-
    let layer1Output = $t021742272._2
72
-
    let $t022772381 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
73
-
    let debugLayer2 = $t022772381._1
74
-
    let layer2Output = $t022772381._2
75
-
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
34
+
    let inputs = [input1, input2]
35
+
    let prediction = feedforward(inputs)
36
+
[IntegerEntry("prediction", prediction)]
76
37
 
    }
77
38
 
78
39
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
4
 
let layer1Weights = [[600496, 600733], [414197, 414253]]
5
5
 
6
6
 
let layer1Biases = [-259050, -635637]
7
7
 
8
8
 
let layer2Weights = [[832966, -897141]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
-
func expApprox (x) = {
13
-
    let scaledX = fraction(x, 1, 10)
14
-
    let scaledX2 = fraction(scaledX, scaledX, 10)
15
-
    let term1 = (10 - scaledX)
16
-
    let term2 = fraction(scaledX2, 5, 1)
17
-
    (term1 + term2)
12
+
func relu (x) = if ((x > 0))
13
+
    then x
14
+
    else 0
15
+
16
+
17
+
func dotProduct (v1,v2) = {
18
+
    let sum1 = ((v1[0] * v2[0]) / 10000)
19
+
    let sum2 = ((v1[1] * v2[1]) / 10000)
20
+
    (sum1 + sum2)
18
21
 
    }
19
22
 
20
23
 
21
-
func sigmoid (z,debugPrefix) = {
22
-
    let cappedZ = if ((z > 200))
23
-
        then 200
24
-
        else if ((-200 > z))
25
-
            then -200
26
-
            else z
27
-
    let expNegZ = expApprox(-(cappedZ))
28
-
    let onePlusExpNegZ = (10 + expNegZ)
29
-
    let sigValue = fraction(10, onePlusExpNegZ, 1)
30
-
    $Tuple2([IntegerEntry((debugPrefix + "inputZ"), cappedZ), IntegerEntry((debugPrefix + "expNegZ"), expNegZ), IntegerEntry((debugPrefix + "onePlusExpNegZ"), onePlusExpNegZ), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
31
-
    }
32
-
33
-
34
-
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
35
-
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + biases[0])
36
-
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + biases[1])
37
-
    let $t014091462 = sigmoid(sum0, "Layer1N0")
38
-
    let debugEntries0 = $t014091462._1
39
-
    let sig0 = $t014091462._2
40
-
    let $t014671520 = sigmoid(sum1, "Layer1N1")
41
-
    let debugEntries1 = $t014671520._1
42
-
    let sig1 = $t014671520._2
43
-
    let debugInfo = (debugEntries0 ++ debugEntries1)
44
-
    let output = [sig0, sig1]
45
-
    $Tuple2(debugInfo, output)
46
-
    }
47
-
48
-
49
-
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
50
-
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + biases[0])
51
-
    let $t018211874 = sigmoid(sum0, "Layer2N0")
52
-
    let debugEntries0 = $t018211874._1
53
-
    let sig0 = $t018211874._2
54
-
    let debugInfo = debugEntries0
55
-
    let output = sig0
56
-
    $Tuple2(debugInfo, output)
24
+
func feedforward (inputs) = {
25
+
    let layer1Result1 = relu((dotProduct(inputs, layer1Weights[0]) + layer1Biases[0]))
26
+
    let layer1Result2 = relu((dotProduct(inputs, layer1Weights[1]) + layer1Biases[1]))
27
+
    let layer2Inputs = [layer1Result1, layer1Result2]
28
+
    (dotProduct(layer2Inputs, layer2Weights[0]) + layer2Biases[0])
57
29
 
    }
58
30
 
59
31
 
60
32
 
@Callable(i)
61
33
 
func predict (input1,input2) = {
62
-
    let scaledInput1 = if ((input1 == 1))
63
-
        then 1
64
-
        else 0
65
-
    let scaledInput2 = if ((input2 == 1))
66
-
        then 1
67
-
        else 0
68
-
    let inputs = [scaledInput1, scaledInput2]
69
-
    let $t021742272 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
70
-
    let debugLayer1 = $t021742272._1
71
-
    let layer1Output = $t021742272._2
72
-
    let $t022772381 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
73
-
    let debugLayer2 = $t022772381._1
74
-
    let layer2Output = $t022772381._2
75
-
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
34
+
    let inputs = [input1, input2]
35
+
    let prediction = feedforward(inputs)
36
+
[IntegerEntry("prediction", prediction)]
76
37
 
    }
77
38
 
78
39
 

github/deemru/w8io/6500d08 
29.30 ms ◑