tx · 7pAj49vY3uDEe7WV34bD7BY2ayD2n8t87qYQ9jXybgWV

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.03.23 15:34 [3030701] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "7pAj49vY3uDEe7WV34bD7BY2ayD2n8t87qYQ9jXybgWV",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1711197295665,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "4oq6XyUna6Zq2NMKU9hHLkLArGMCbX7bowBeGmUmjrt3B7TjxoLR96aZ2DwKc2woG6fJx1gm1qsWVmHTimkTRs8h"
    ],
    "script": "base64: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",
    "height": 3030701,
    "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 = [[-9275240, 6222139], [-9201827, -6516189], [-1528731, 11450396], [-7524843, -6044814]]
4
+
let layer1Weights = [[6004965, 6007324], [4141966, 4142525]]
5
5
 
6
-
let layer1Biases = [-2569627, 2312524, -4752973, 1895166]
6
+
let layer1Biases = [-2590503, -6356371]
7
7
 
8
-
let layer2Weights = [[-7575203, 5523326, 6581110, 3773202], [6861028, -5706216, -6035509, -3323542]]
8
+
let layer2Weights = [[8329656, -8971418]]
9
9
 
10
-
let layer2Biases = [-3161622, 2945010]
11
-
12
-
let layer3Weights = [[-8939640, 9517362]]
13
-
14
-
let layer3Biases = [-192349]
10
+
let layer2Biases = [-3811788]
15
11
 
16
12
 
func sigmoid (z) = {
17
13
 
    let e = 2718281
25
21
 
26
22
 
27
23
 
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]
37
-
    }
38
-
39
-
40
-
func forwardPassLayer2 (input,weights,biases) = {
41
24
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
42
25
 
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
43
26
 
    let sig0 = sigmoid(sum0)
46
29
 
    }
47
30
 
48
31
 
49
-
func forwardPassLayer3 (input,weights,bias) = {
32
+
func forwardPassLayer2 (input,weights,bias) = {
50
33
 
    let dotProduct = (fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[0], 1000000))
51
34
 
    let sum = (dotProduct + bias)
52
35
 
    sigmoid(sum)
54
37
 
55
38
 
56
39
 
@Callable(i)
57
-
func predict_three_xor (input1,input2) = {
40
+
func predict (input1,input2) = {
58
41
 
    let scaledInput1 = if ((input1 == 1))
59
42
 
        then 1000000
60
43
 
        else 0
63
46
 
        else 0
64
47
 
    let inputs = [scaledInput1, scaledInput2]
65
48
 
    let layer1Output = forwardPassLayer1(inputs, layer1Weights, layer1Biases)
66
-
    let layer2Output = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases)
67
-
    let output = forwardPassLayer3(layer2Output, [-8939640, 9517362], -192349)
49
+
    let output = forwardPassLayer2(layer1Output, [8329656, -8971418], -3811788)
68
50
 
[IntegerEntry("result", output)]
69
51
 
    }
70
52
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let layer1Weights = [[-9275240, 6222139], [-9201827, -6516189], [-1528731, 11450396], [-7524843, -6044814]]
4
+
let layer1Weights = [[6004965, 6007324], [4141966, 4142525]]
5
5
 
6
-
let layer1Biases = [-2569627, 2312524, -4752973, 1895166]
6
+
let layer1Biases = [-2590503, -6356371]
7
7
 
8
-
let layer2Weights = [[-7575203, 5523326, 6581110, 3773202], [6861028, -5706216, -6035509, -3323542]]
8
+
let layer2Weights = [[8329656, -8971418]]
9
9
 
10
-
let layer2Biases = [-3161622, 2945010]
11
-
12
-
let layer3Weights = [[-8939640, 9517362]]
13
-
14
-
let layer3Biases = [-192349]
10
+
let layer2Biases = [-3811788]
15
11
 
16
12
 
func sigmoid (z) = {
17
13
 
    let e = 2718281
18
14
 
    let base = 1000000
19
15
 
    let positiveZ = if ((0 > z))
20
16
 
        then -(z)
21
17
 
        else z
22
18
 
    let expPart = fraction(e, base, positiveZ)
23
19
 
    fraction(base, base, (base + expPart))
24
20
 
    }
25
21
 
26
22
 
27
23
 
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]
37
-
    }
38
-
39
-
40
-
func forwardPassLayer2 (input,weights,biases) = {
41
24
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
42
25
 
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
43
26
 
    let sig0 = sigmoid(sum0)
44
27
 
    let sig1 = sigmoid(sum1)
45
28
 
[sig0, sig1]
46
29
 
    }
47
30
 
48
31
 
49
-
func forwardPassLayer3 (input,weights,bias) = {
32
+
func forwardPassLayer2 (input,weights,bias) = {
50
33
 
    let dotProduct = (fraction(input[0], weights[0], 1000000) + fraction(input[1], weights[0], 1000000))
51
34
 
    let sum = (dotProduct + bias)
52
35
 
    sigmoid(sum)
53
36
 
    }
54
37
 
55
38
 
56
39
 
@Callable(i)
57
-
func predict_three_xor (input1,input2) = {
40
+
func predict (input1,input2) = {
58
41
 
    let scaledInput1 = if ((input1 == 1))
59
42
 
        then 1000000
60
43
 
        else 0
61
44
 
    let scaledInput2 = if ((input2 == 1))
62
45
 
        then 1000000
63
46
 
        else 0
64
47
 
    let inputs = [scaledInput1, scaledInput2]
65
48
 
    let layer1Output = forwardPassLayer1(inputs, layer1Weights, layer1Biases)
66
-
    let layer2Output = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases)
67
-
    let output = forwardPassLayer3(layer2Output, [-8939640, 9517362], -192349)
49
+
    let output = forwardPassLayer2(layer1Output, [8329656, -8971418], -3811788)
68
50
 
[IntegerEntry("result", output)]
69
51
 
    }
70
52
 
71
53
 

github/deemru/w8io/6500d08 
23.54 ms ◑