tx · G1kcT9nY3LG3KisZWKpcB8Yt76ixtTVHpy1AaMFAazRY

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 14:33 [3082661] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "G1kcT9nY3LG3KisZWKpcB8Yt76ixtTVHpy1AaMFAazRY",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714303957362,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "2c917ZXhRC86GFiK7ENU997FciEzhQfcs86WeqVmg1ZambU7uQHV3VeifeqZsa5W1P9MgjkMo5DjQg5DTBksBbCz"
    ],
    "script": "base64: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",
    "height": 3082661,
    "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 = [[600497, 600732], [414197, 414253]]
4
+
let layer1Weights = [[600497, 600733], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259051, -635637]
6
+
let layer1Biases = [-259050, -635638]
7
7
 
8
8
 
let layer2Weights = [[832966, -897142]]
9
9
 
27
27
 
28
28
 
29
29
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
30
-
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
31
-
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
32
-
    let $t013271380 = sigmoid(sum0, "Layer1N0")
33
-
    let debugEntries0 = $t013271380._1
34
-
    let sig0 = $t013271380._2
35
-
    let $t013851438 = sigmoid(sum1, "Layer1N1")
36
-
    let debugEntries1 = $t013851438._1
37
-
    let sig1 = $t013851438._2
30
+
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 1000000))
31
+
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 1000000))
32
+
    let $t013291382 = sigmoid(sum0, "Layer1N0")
33
+
    let debugEntries0 = $t013291382._1
34
+
    let sig0 = $t013291382._2
35
+
    let $t013871440 = sigmoid(sum1, "Layer1N1")
36
+
    let debugEntries1 = $t013871440._1
37
+
    let sig1 = $t013871440._2
38
38
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
39
39
 
    let output = [sig0, sig1]
40
40
 
    $Tuple2(debugInfo, output)
42
42
 
43
43
 
44
44
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
45
-
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
46
-
    let $t017481801 = sigmoid(sum0, "Layer2N0")
47
-
    let debugEntries0 = $t017481801._1
48
-
    let sig0 = $t017481801._2
45
+
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 1000000))
46
+
    let $t017511804 = sigmoid(sum0, "Layer2N0")
47
+
    let debugEntries0 = $t017511804._1
48
+
    let sig0 = $t017511804._2
49
49
 
    let debugInfo = debugEntries0
50
50
 
    let output = sig0
51
51
 
    $Tuple2(debugInfo, output)
61
61
 
        then 1000000
62
62
 
        else 0
63
63
 
    let inputs = [scaledInput1, scaledInput2]
64
-
    let $t021132211 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
65
-
    let debugLayer1 = $t021132211._1
66
-
    let layer1Output = $t021132211._2
67
-
    let $t022162320 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
68
-
    let debugLayer2 = $t022162320._1
69
-
    let layer2Output = $t022162320._2
64
+
    let $t021162214 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
65
+
    let debugLayer1 = $t021162214._1
66
+
    let layer1Output = $t021162214._2
67
+
    let $t022192323 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
68
+
    let debugLayer2 = $t022192323._1
69
+
    let layer2Output = $t022192323._2
70
70
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
71
71
 
    }
72
72
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let layer1Weights = [[600497, 600732], [414197, 414253]]
4
+
let layer1Weights = [[600497, 600733], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259051, -635637]
6
+
let layer1Biases = [-259050, -635638]
7
7
 
8
8
 
let layer2Weights = [[832966, -897142]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
12
 
func expApprox (x) = {
13
13
 
    let scaledX = fraction(x, 1, 1000000)
14
14
 
    let scaledX2 = fraction(scaledX, scaledX, 1000000)
15
15
 
    let term1 = (1000000 - scaledX)
16
16
 
    let term2 = fraction(scaledX2, 500000, 1)
17
17
 
    (term1 + term2)
18
18
 
    }
19
19
 
20
20
 
21
21
 
func sigmoid (z,debugPrefix) = {
22
22
 
    let expNegZ = expApprox(-(z))
23
23
 
    let onePlusExpNegZ = (1000000 + expNegZ)
24
24
 
    let sigValue = fraction(1000000000000, onePlusExpNegZ, 1)
25
25
 
    $Tuple2([IntegerEntry((debugPrefix + "inputZ"), z), IntegerEntry((debugPrefix + "expNegZ"), expNegZ), IntegerEntry((debugPrefix + "onePlusExpNegZ"), onePlusExpNegZ), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
26
26
 
    }
27
27
 
28
28
 
29
29
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
30
-
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
31
-
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
32
-
    let $t013271380 = sigmoid(sum0, "Layer1N0")
33
-
    let debugEntries0 = $t013271380._1
34
-
    let sig0 = $t013271380._2
35
-
    let $t013851438 = sigmoid(sum1, "Layer1N1")
36
-
    let debugEntries1 = $t013851438._1
37
-
    let sig1 = $t013851438._2
30
+
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 1000000))
31
+
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 1000000))
32
+
    let $t013291382 = sigmoid(sum0, "Layer1N0")
33
+
    let debugEntries0 = $t013291382._1
34
+
    let sig0 = $t013291382._2
35
+
    let $t013871440 = sigmoid(sum1, "Layer1N1")
36
+
    let debugEntries1 = $t013871440._1
37
+
    let sig1 = $t013871440._2
38
38
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
39
39
 
    let output = [sig0, sig1]
40
40
 
    $Tuple2(debugInfo, output)
41
41
 
    }
42
42
 
43
43
 
44
44
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
45
-
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
46
-
    let $t017481801 = sigmoid(sum0, "Layer2N0")
47
-
    let debugEntries0 = $t017481801._1
48
-
    let sig0 = $t017481801._2
45
+
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 1000000))
46
+
    let $t017511804 = sigmoid(sum0, "Layer2N0")
47
+
    let debugEntries0 = $t017511804._1
48
+
    let sig0 = $t017511804._2
49
49
 
    let debugInfo = debugEntries0
50
50
 
    let output = sig0
51
51
 
    $Tuple2(debugInfo, output)
52
52
 
    }
53
53
 
54
54
 
55
55
 
@Callable(i)
56
56
 
func predict (input1,input2) = {
57
57
 
    let scaledInput1 = if ((input1 == 1))
58
58
 
        then 1000000
59
59
 
        else 0
60
60
 
    let scaledInput2 = if ((input2 == 1))
61
61
 
        then 1000000
62
62
 
        else 0
63
63
 
    let inputs = [scaledInput1, scaledInput2]
64
-
    let $t021132211 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
65
-
    let debugLayer1 = $t021132211._1
66
-
    let layer1Output = $t021132211._2
67
-
    let $t022162320 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
68
-
    let debugLayer2 = $t022162320._1
69
-
    let layer2Output = $t022162320._2
64
+
    let $t021162214 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
65
+
    let debugLayer1 = $t021162214._1
66
+
    let layer1Output = $t021162214._2
67
+
    let $t022192323 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
68
+
    let debugLayer2 = $t022192323._1
69
+
    let layer2Output = $t022192323._2
70
70
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
71
71
 
    }
72
72
 
73
73
 

github/deemru/w8io/6500d08 
37.76 ms ◑