tx · 2pTSvMCyo3YZTF9HXrKDsZxXPjrtSvBivLFN1ejfzWek

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.03.23 15:56 [3030725] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "2pTSvMCyo3YZTF9HXrKDsZxXPjrtSvBivLFN1ejfzWek",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1711198668694,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "2mjXjGB4wmwwYk1PRdDLhzWfZSVe2rqnzSBdmaxnRFPNyZGvASFgqkEM7Hap7kd2VESs1bN66PzzStCY3FHaiu2D"
    ],
    "script": 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    "height": 3030725,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let layer1Weights = [[4721113, -5002107], [6226846, -6353789]]
4
+
let layer1Weights = [[-9275240, 6222139], [-9201827, -6516189], [-1528731, 11450396], [-7524843, -6044814]]
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5
 
6
-
let layer1Biases = [-2521378, 3389498]
6
+
let layer1Biases = [-2569627, 2312524, -4752973, 1895166]
7
7
 
8
-
let layer2Weights = [[8109936, -7559760]]
8
+
let layer2Weights = [[-7575203, 5523326, 6581110, 3773202], [6861028, -5706216, -6035509, -3323542]]
9
9
 
10
-
let layer2Biases = [3490942]
10
+
let layer2Biases = [-3161622, 2945010]
11
11
 
12
-
func sigmoid (z,debugPrefix) = {
12
+
let layer3Weights = [[-8939640, 9517362]]
13
+
14
+
let layer3Biases = [-192349]
15
+
16
+
func sigmoid (z) = {
13
17
 
    let e = 2718281
14
18
 
    let base = 1000000
15
19
 
    let positiveZ = if ((0 > z))
16
20
 
        then -(z)
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21
 
        else z
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22
 
    let expPart = fraction(e, base, positiveZ)
19
-
    let sigValue = fraction(base, base, (base + expPart))
20
-
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
23
+
    fraction(base, base, (base + expPart))
21
24
 
    }
22
25
 
23
26
 
24
-
func dotProduct (a,b) = {
25
-
    let product0 = fraction(a[0], b[0], 1000000)
26
-
    let product1 = fraction(a[1], b[1], 1000000)
27
-
    (product0 + product1)
27
+
func forwardPassLayer1 (input,weights,biases) = {
28
+
    let sum0 = ((((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + fraction(input[2], weights[0][2], 1000000)) + fraction(input[3], weights[0][3], 1000000)) + biases[0])
29
+
    let sum1 = ((((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + fraction(input[2], weights[1][2], 1000000)) + fraction(input[3], weights[1][3], 1000000)) + biases[1])
30
+
    let sum2 = ((((fraction(input[0], weights[2][0], 1000000) + fraction(input[1], weights[2][1], 1000000)) + fraction(input[2], weights[2][2], 1000000)) + fraction(input[3], weights[2][3], 1000000)) + biases[2])
31
+
    let sum3 = ((((fraction(input[0], weights[3][0], 1000000) + fraction(input[1], weights[3][1], 1000000)) + fraction(input[2], weights[3][2], 1000000)) + fraction(input[3], weights[3][3], 1000000)) + biases[3])
32
+
    let sig0 = sigmoid(sum0)
33
+
    let sig1 = sigmoid(sum1)
34
+
    let sig2 = sigmoid(sum2)
35
+
    let sig3 = sigmoid(sum3)
36
+
[sig0, sig1, sig2, sig3]
28
37
 
    }
29
38
 
30
39
 
31
-
func forwardPass (input,weights,biases,layer) = {
32
-
    let sum0 = (dotProduct(input, weights[0]) + biases[0])
33
-
    let sum1 = (dotProduct(input, weights[1]) + biases[1])
34
-
    let $t013311388 = sigmoid(sum0, (layer + "L1N1"))
35
-
    let sigmoidDebug0 = $t013311388._1
36
-
    let sig0 = $t013311388._2
37
-
    let $t013931450 = sigmoid(sum1, (layer + "L1N2"))
38
-
    let sigmoidDebug1 = $t013931450._1
39
-
    let sig1 = $t013931450._2
40
-
    $Tuple2([sig0, sig1, sum0, sum1], (sigmoidDebug0 ++ sigmoidDebug1))
40
+
func forwardPassLayer2 (input,weights,biases) = {
41
+
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
42
+
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
43
+
    let sig0 = sigmoid(sum0)
44
+
    let sig1 = sigmoid(sum1)
45
+
[sig0, sig1]
41
46
 
    }
42
47
 
43
48
 
44
-
func xorNeuralNetwork (input1,input2) = {
45
-
    let input = [input1, input2]
46
-
    let $t016281720 = forwardPass(input, layer1Weights, layer1Biases, "HL")
47
-
    let hiddenLayerOutput = $t016281720._1
48
-
    let hiddenDebug = $t016281720._2
49
-
    let $t017251860 = sigmoid((dotProduct([hiddenLayerOutput[0], hiddenLayerOutput[1]], layer2Weights[0]) + layer2Biases[0]), "OL")
50
-
    let outputDebug = $t017251860._1
51
-
    let output = $t017251860._2
52
-
    $Tuple2([output, (dotProduct([hiddenLayerOutput[0], hiddenLayerOutput[1]], layer2Weights[0]) + layer2Biases[0]), hiddenLayerOutput[2], hiddenLayerOutput[3]], (hiddenDebug ++ outputDebug))
49
+
func forwardPassLayer3 (input,weights,bias) = {
50
+
    let dotProduct = (fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[0], 1000000))
51
+
    let sum = (dotProduct + bias)
52
+
    sigmoid(sum)
53
53
 
    }
54
54
 
55
55
 
56
56
 
@Callable(i)
57
-
func predict (input1,input2) = {
57
+
func predict_three (input1,input2) = {
58
58
 
    let scaledInput1 = if ((input1 == 1))
59
59
 
        then 1000000
60
60
 
        else 0
61
61
 
    let scaledInput2 = if ((input2 == 1))
62
62
 
        then 1000000
63
63
 
        else 0
64
-
    let $t022452326 = xorNeuralNetwork(scaledInput1, scaledInput2)
65
-
    let networkOutputs = $t022452326._1
66
-
    let debugEntries = $t022452326._2
67
-
    ([IntegerEntry("result", networkOutputs[0]), IntegerEntry("outputLayerSum", networkOutputs[1]), IntegerEntry("hiddenLayerOutput1Sum", networkOutputs[2]), IntegerEntry("hiddenLayerOutput2Sum", networkOutputs[3])] ++ debugEntries)
64
+
    let inputs = [scaledInput1, scaledInput2]
65
+
    let layer1Output = forwardPassLayer1(inputs, layer1Weights, layer1Biases)
66
+
    let layer2Output = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases)
67
+
    let output = forwardPassLayer3(layer2Output, [-8939640, 9517362], -192349)
68
+
[IntegerEntry("result", output)]
68
69
 
    }
69
70
 
70
71
 

github/deemru/w8io/6500d08 
27.05 ms ◑