tx · 6ikdwN6r2UXQdkEA6cxgC4p7TaERghmy8jqXLicqgtRG

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.03.24 18:28 [3032315] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "6ikdwN6r2UXQdkEA6cxgC4p7TaERghmy8jqXLicqgtRG",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1711294174902,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "MSHispBHepyDbX6nr8P37ukoRDNqSUtKTVjMr2jMuyhpjQ9z9TVJGuW5mVpFnBAKTF6QxhMUcbBGExLN6BsfXnJ"
    ],
    "script": "base64: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",
    "height": 3032315,
    "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 = [[4721113, -5002107], [6226846, -6353789]]
4
+
let layer1Weights = [[6004965, 6007324], [4141966, 4142525]]
5
5
 
6
-
let layer1Biases = [-2521378, 3389498]
6
+
let layer1Biases = [-2590503, -6356371]
7
7
 
8
-
let layer2Weights = [[8109936, -7559760]]
8
+
let layer2Weights = [[8329656, -8971418]]
9
9
 
10
-
let layer2Biases = [3490942]
10
+
let layer2Biases = [-3811788]
11
11
 
12
12
 
func sigmoid (z,debugPrefix) = {
13
13
 
    let e = 2718281
21
21
 
    }
22
22
 
23
23
 
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)
24
+
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
25
+
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
26
+
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
27
+
    let $t011651221 = sigmoid(sum0, (debugPrefix + "L1N0"))
28
+
    let debug0 = $t011651221._1
29
+
    let sig0 = $t011651221._2
30
+
    let $t012221278 = sigmoid(sum1, (debugPrefix + "L1N1"))
31
+
    let debug1 = $t012221278._1
32
+
    let sig1 = $t012221278._2
33
+
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
28
34
 
    }
29
35
 
30
36
 
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))
41
-
    }
42
-
43
-
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))
37
+
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
38
+
    let sum0 = ((fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[1], 1000000)) + biases)
39
+
    let sum1 = ((fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[1], 1000000)) + biases)
40
+
    let $t016301686 = sigmoid(sum0, (debugPrefix + "L2N0"))
41
+
    let debug0 = $t016301686._1
42
+
    let sig0 = $t016301686._2
43
+
    let $t016911747 = sigmoid(sum1, (debugPrefix + "L2N1"))
44
+
    let debug1 = $t016911747._1
45
+
    let sig1 = $t016911747._2
46
+
    $Tuple2(sig0, (debug0 ++ debug1))
53
47
 
    }
54
48
 
55
49
 
56
50
 
@Callable(i)
57
-
func predict_original (input1,input2) = {
51
+
func predict (input1,input2) = {
58
52
 
    let scaledInput1 = if ((input1 == 1))
59
53
 
        then 1000000
60
54
 
        else 0
61
55
 
    let scaledInput2 = if ((input2 == 1))
62
56
 
        then 1000000
63
57
 
        else 0
64
-
    let $t022542335 = xorNeuralNetwork(scaledInput1, scaledInput2)
65
-
    let networkOutputs = $t022542335._1
66
-
    let debugEntries = $t022542335._2
67
-
    ([IntegerEntry("result", networkOutputs[0]), IntegerEntry("outputLayerSum", networkOutputs[1]), IntegerEntry("hiddenLayerOutput1Sum", networkOutputs[2]), IntegerEntry("hiddenLayerOutput2Sum", networkOutputs[3])] ++ debugEntries)
58
+
    let inputs = [scaledInput1, scaledInput2]
59
+
    let $t020082106 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
60
+
    let layer1Output = $t020082106._1
61
+
    let debugLayer1 = $t020082106._2
62
+
    let $t021112221 = forwardPassLayer2(layer1Output, layer2Weights[0], layer2Biases[0], "Layer2")
63
+
    let layer2Output = $t021112221._1
64
+
    let debugLayer2 = $t021112221._2
65
+
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
68
66
 
    }
69
67
 
70
68
 

Full: 
Old
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 = [[6004965, 6007324], [4141966, 4142525]]
5
5
 
6
-
let layer1Biases = [-2521378, 3389498]
6
+
let layer1Biases = [-2590503, -6356371]
7
7
 
8
-
let layer2Weights = [[8109936, -7559760]]
8
+
let layer2Weights = [[8329656, -8971418]]
9
9
 
10
-
let layer2Biases = [3490942]
10
+
let layer2Biases = [-3811788]
11
11
 
12
12
 
func sigmoid (z,debugPrefix) = {
13
13
 
    let e = 2718281
14
14
 
    let base = 1000000
15
15
 
    let positiveZ = if ((0 > z))
16
16
 
        then -(z)
17
17
 
        else z
18
18
 
    let expPart = fraction(e, base, positiveZ)
19
19
 
    let sigValue = fraction(base, base, (base + expPart))
20
20
 
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
21
21
 
    }
22
22
 
23
23
 
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)
24
+
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
25
+
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
26
+
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
27
+
    let $t011651221 = sigmoid(sum0, (debugPrefix + "L1N0"))
28
+
    let debug0 = $t011651221._1
29
+
    let sig0 = $t011651221._2
30
+
    let $t012221278 = sigmoid(sum1, (debugPrefix + "L1N1"))
31
+
    let debug1 = $t012221278._1
32
+
    let sig1 = $t012221278._2
33
+
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
28
34
 
    }
29
35
 
30
36
 
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))
41
-
    }
42
-
43
-
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))
37
+
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
38
+
    let sum0 = ((fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[1], 1000000)) + biases)
39
+
    let sum1 = ((fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[1], 1000000)) + biases)
40
+
    let $t016301686 = sigmoid(sum0, (debugPrefix + "L2N0"))
41
+
    let debug0 = $t016301686._1
42
+
    let sig0 = $t016301686._2
43
+
    let $t016911747 = sigmoid(sum1, (debugPrefix + "L2N1"))
44
+
    let debug1 = $t016911747._1
45
+
    let sig1 = $t016911747._2
46
+
    $Tuple2(sig0, (debug0 ++ debug1))
53
47
 
    }
54
48
 
55
49
 
56
50
 
@Callable(i)
57
-
func predict_original (input1,input2) = {
51
+
func predict (input1,input2) = {
58
52
 
    let scaledInput1 = if ((input1 == 1))
59
53
 
        then 1000000
60
54
 
        else 0
61
55
 
    let scaledInput2 = if ((input2 == 1))
62
56
 
        then 1000000
63
57
 
        else 0
64
-
    let $t022542335 = xorNeuralNetwork(scaledInput1, scaledInput2)
65
-
    let networkOutputs = $t022542335._1
66
-
    let debugEntries = $t022542335._2
67
-
    ([IntegerEntry("result", networkOutputs[0]), IntegerEntry("outputLayerSum", networkOutputs[1]), IntegerEntry("hiddenLayerOutput1Sum", networkOutputs[2]), IntegerEntry("hiddenLayerOutput2Sum", networkOutputs[3])] ++ debugEntries)
58
+
    let inputs = [scaledInput1, scaledInput2]
59
+
    let $t020082106 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
60
+
    let layer1Output = $t020082106._1
61
+
    let debugLayer1 = $t020082106._2
62
+
    let $t021112221 = forwardPassLayer2(layer1Output, layer2Weights[0], layer2Biases[0], "Layer2")
63
+
    let layer2Output = $t021112221._1
64
+
    let debugLayer2 = $t021112221._2
65
+
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
68
66
 
    }
69
67
 
70
68
 

github/deemru/w8io/6500d08 
23.55 ms ◑