tx · BM99xHVxdCc9EqbXsCkM7ZWSZhTJvHtirzap69CgHG6L

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 14:36 [3082664] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "BM99xHVxdCc9EqbXsCkM7ZWSZhTJvHtirzap69CgHG6L",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714304197056,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "4EvVPj4SU2LdiokNyue3UHYqYUw44mikgCAXZzTeEwvnkq9XT3HvZhRmRmHYjTWbmZjNWtGZYagnviE1PCKiNWRh"
    ],
    "script": "base64:AAIFAAAAAAAAAAgIAhIECgIBAQAAAAgAAAAADWxheWVyMVdlaWdodHMJAARMAAAAAgkABEwAAAACAAAAAAAACSmxCQAETAAAAAIAAAAAAAAJKp0FAAAAA25pbAkABEwAAAACCQAETAAAAAIAAAAAAAAGUfUJAARMAAAAAgAAAAAAAAZSLQUAAAADbmlsBQAAAANuaWwAAAAADGxheWVyMUJpYXNlcwkABEwAAAACAP///////AwWCQAETAAAAAIA///////2TQoFAAAAA25pbAAAAAANbGF5ZXIyV2VpZ2h0cwkABEwAAAACCQAETAAAAAIAAAAAAAAMtcYJAARMAAAAAgD///////JPigUAAAADbmlsBQAAAANuaWwAAAAADGxheWVyMkJpYXNlcwkABEwAAAACAP//////+i8FBQAAAANuaWwBAAAACWV4cEFwcHJveAAAAAEAAAABeAQAAAAHc2NhbGVkWAkAAGsAAAADBQAAAAF4AAAAAAAAAAABAAAAAAAAAAPoBAAAAAhzY2FsZWRYMgkAAGsAAAADBQAAAAdzY2FsZWRYBQAAAAdzY2FsZWRYAAAAAAAAAAPoBAAAAAV0ZXJtMQkAAGUAAAACAAAAAAAAAAPoBQAAAAdzY2FsZWRYBAAAAAV0ZXJtMgkAAGsAAAADBQAAAAhzY2FsZWRYMgAAAAAAAAAB9AAAAAAAAAAAAQkAAGQAAAACBQAAAAV0ZXJtMQUAAAAFdGVybTIBAAAAB3NpZ21vaWQAAAACAAAAAXoAAAALZGVidWdQcmVmaXgEAAAAB2V4cE5lZ1oJAQAAAAlleHBBcHByb3gAAAABCQEAAAABLQAAAAEFAAAAAXoEAAAADm9uZVBsdXNFeHBOZWdaCQAAZAAAAAIAAAAAAAAAA+gFAAAAB2V4cE5lZ1oEAAAACHNpZ1ZhbHVlCQAAawAAAAMAAAAAAAAAA+gFAAAADm9uZVBsdXNFeHBOZWdaAAAAAAAAAAABCQAFFAAAAAIJAARMAAAAAgkBAAAADEludGVnZXJFbnRyeQAAAAIJAAEsAAAAAgUAAAALZGVidWdQcmVmaXgCAAAABmlucHV0WgUAAAABegkABEwAAAACCQEAAAAMSW50ZWdlckVudHJ5AAAAAgkAASwAAAACBQAAAAtkZWJ1Z1ByZWZpeAIAAAAHZXhwTmVnWgUAAAAHZXhwTmVnWgkABEwAAAACCQEAAAAMSW50ZWdlckVudHJ5AAAAAgkAASwAAAACBQAAAAtkZWJ1Z1ByZWZpeAIAAAAOb25lUGx1c0V4cE5lZ1oFAAAADm9uZVBsdXNFeHBOZWdaCQAETAAAAAIJAQAAAAxJbnRlZ2VyRW50cnkAAAACCQABLAAAAAIFAAAAC2RlYnVnUHJlZml4AgAAAAhzaWdWYWx1ZQUAAAAIc2lnVmFsdWUFAAAAA25pbAUAAAAIc2lnVmFsdWUBAAAAEWZvcndhcmRQYXNzTGF5ZXIxAAAABAAAAAVpbnB1dAAAAAd3ZWlnaHRzAAAABmJpYXNlcwAAAAtkZWJ1Z1ByZWZpeAQAAAAEc3VtMAkAAGQAAAACCQAAZAAAAAIJAABoAAAAAgkAAZEAAAACBQAAAAVpbnB1dAAAAAAAAAAAAAkAAZEAAAACCQABkQAAAAIFAAAAB3dlaWdodHMAAAAAAAAAAAAAAAAAAAAAAAAJAABoAAAAAgkAAZEAAAACBQAAAAVpbnB1dAAAAAAAAAAAAQkAAZEAAAACCQABkQAAAAIFAAAAB3dlaWdodHMAAAAAAAAAAAAAAAAAAAAAAAEJAAGRAAAAAgUAAAAGYmlhc2VzAAAAAAAAAAAABAAAAARzdW0xCQAAZAAAAAIJAABkAAAAAgkAAGgAAAACCQABkQAAAAIFAAAABWlucHV0AAAAAAAAAAAACQABkQAAAAIJAAGRAAAAAgUAAAAHd2VpZ2h0cwAAAAAAAAAAAQAAAAAAAAAAAAkAAGgAAAACCQABkQAAAAIFAAAABWlucHV0AAAAAAAAAAABCQABkQAAAAIJAAGRAAAAAgUAAAAHd2VpZ2h0cwAAAAAAAAAAAQAAAAAAAAAAAQkAAZEAAAACBQAAAAZiaWFzZXMAAAAAAAAAAAEEAAAACyR0MDEyODUxMzM4CQEAAAAHc2lnbW9pZAAAAAIFAAAABHN1bTACAAAACExheWVyMU4wBAAAAA1kZWJ1Z0VudHJpZXMwCAUAAAALJHQwMTI4NTEzMzgAAAACXzEEAAAABHNpZzAIBQAAAAskdDAxMjg1MTMzOAAAAAJfMgQAAAALJHQwMTM0MzEzOTYJAQAAAAdzaWdtb2lkAAAAAgUAAAAEc3VtMQIAAAAITGF5ZXIxTjEEAAAADWRlYnVnRW50cmllczEIBQAAAAskdDAxMzQzMTM5NgAAAAJfMQQAAAAEc2lnMQgFAAAACyR0MDEzNDMxMzk2AAAAAl8yBAAAAAlkZWJ1Z0luZm8JAAROAAAAAgUAAAANZGVidWdFbnRyaWVzMAUAAAANZGVidWdFbnRyaWVzMQQAAAAGb3V0cHV0CQAETAAAAAIFAAAABHNpZzAJAARMAAAAAgUAAAAEc2lnMQUAAAADbmlsCQAFFAAAAAIFAAAACWRlYnVnSW5mbwUAAAAGb3V0cHV0AQAAABFmb3J3YXJkUGFzc0xheWVyMgAAAAQAAAAFaW5wdXQAAAAHd2VpZ2h0cwAAAAZiaWFzZXMAAAALZGVidWdQcmVmaXgEAAAABHN1bTAJAABkAAAAAgkAAGQAAAACCQAAaAAAAAIJAAGRAAAAAgUAAAAFaW5wdXQAAAAAAAAAAAAJAAGRAAAAAgkAAZEAAAACBQAAAAd3ZWlnaHRzAAAAAAAAAAAAAAAAAAAAAAAACQAAaAAAAAIJAAGRAAAAAgUAAAAFaW5wdXQAAAAAAAAAAAEJAAGRAAAAAgkAAZEAAAACBQAAAAd3ZWlnaHRzAAAAAAAAAAAAAAAAAAAAAAABCQABkQAAAAIFAAAABmJpYXNlcwAAAAAAAAAAAAQAAAALJHQwMTY5NzE3NTAJAQAAAAdzaWdtb2lkAAAAAgUAAAAEc3VtMAIAAAAITGF5ZXIyTjAEAAAADWRlYnVnRW50cmllczAIBQAAAAskdDAxNjk3MTc1MAAAAAJfMQQAAAAEc2lnMAgFAAAACyR0MDE2OTcxNzUwAAAAAl8yBAAAAAlkZWJ1Z0luZm8FAAAADWRlYnVnRW50cmllczAEAAAABm91dHB1dAUAAAAEc2lnMAkABRQAAAACBQAAAAlkZWJ1Z0luZm8FAAAABm91dHB1dAAAAAEAAAABaQEAAAAHcHJlZGljdAAAAAIAAAAGaW5wdXQxAAAABmlucHV0MgQAAAAMc2NhbGVkSW5wdXQxAwkAAAAAAAACBQAAAAZpbnB1dDEAAAAAAAAAAAEAAAAAAAAAAAEAAAAAAAAAAAAEAAAADHNjYWxlZElucHV0MgMJAAAAAAAAAgUAAAAGaW5wdXQyAAAAAAAAAAABAAAAAAAAAAABAAAAAAAAAAAABAAAAAZpbnB1dHMJAARMAAAAAgUAAAAMc2NhbGVkSW5wdXQxCQAETAAAAAIFAAAADHNjYWxlZElucHV0MgUAAAADbmlsBAAAAAskdDAyMDUwMjE0OAkBAAAAEWZvcndhcmRQYXNzTGF5ZXIxAAAABAUAAAAGaW5wdXRzBQAAAA1sYXllcjFXZWlnaHRzBQAAAAxsYXllcjFCaWFzZXMCAAAABkxheWVyMQQAAAALZGVidWdMYXllcjEIBQAAAAskdDAyMDUwMjE0OAAAAAJfMQQAAAAMbGF5ZXIxT3V0cHV0CAUAAAALJHQwMjA1MDIxNDgAAAACXzIEAAAACyR0MDIxNTMyMjU3CQEAAAARZm9yd2FyZFBhc3NMYXllcjIAAAAEBQAAAAxsYXllcjFPdXRwdXQFAAAADWxheWVyMldlaWdodHMFAAAADGxheWVyMkJpYXNlcwIAAAAGTGF5ZXIyBAAAAAtkZWJ1Z0xheWVyMggFAAAACyR0MDIxNTMyMjU3AAAAAl8xBAAAAAxsYXllcjJPdXRwdXQIBQAAAAskdDAyMTUzMjI1NwAAAAJfMgkABE4AAAACCQAETgAAAAIJAARMAAAAAgkBAAAADEludGVnZXJFbnRyeQAAAAICAAAABnJlc3VsdAUAAAAMbGF5ZXIyT3V0cHV0BQAAAANuaWwFAAAAC2RlYnVnTGF5ZXIxBQAAAAtkZWJ1Z0xheWVyMgAAAACQWE7L",
    "height": 3082664,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Diff: 
Old
New
Differences
10
10
 
let layer2Biases = [-381179]
11
11
 
12
12
 
func expApprox (x) = {
13
-
    let scaledX = fraction(x, 1, 1000000)
14
-
    let scaledX2 = fraction(scaledX, scaledX, 1000000)
15
-
    let term1 = (1000000 - scaledX)
16
-
    let term2 = fraction(scaledX2, 500000, 1)
13
+
    let scaledX = fraction(x, 1, 1000)
14
+
    let scaledX2 = fraction(scaledX, scaledX, 1000)
15
+
    let term1 = (1000 - scaledX)
16
+
    let term2 = fraction(scaledX2, 500, 1)
17
17
 
    (term1 + term2)
18
18
 
    }
19
19
 
20
20
 
21
21
 
func sigmoid (z,debugPrefix) = {
22
22
 
    let expNegZ = expApprox(-(z))
23
-
    let onePlusExpNegZ = (1000000 + expNegZ)
24
-
    let sigValue = fraction(1000000000000, onePlusExpNegZ, 1)
23
+
    let onePlusExpNegZ = (1000 + expNegZ)
24
+
    let sigValue = fraction(1000, 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] * 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
30
+
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + biases[0])
31
+
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + biases[1])
32
+
    let $t012851338 = sigmoid(sum0, "Layer1N0")
33
+
    let debugEntries0 = $t012851338._1
34
+
    let sig0 = $t012851338._2
35
+
    let $t013431396 = sigmoid(sum1, "Layer1N1")
36
+
    let debugEntries1 = $t013431396._1
37
+
    let sig1 = $t013431396._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] * 1000000))
46
-
    let $t017511804 = sigmoid(sum0, "Layer2N0")
47
-
    let debugEntries0 = $t017511804._1
48
-
    let sig0 = $t017511804._2
45
+
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + biases[0])
46
+
    let $t016971750 = sigmoid(sum0, "Layer2N0")
47
+
    let debugEntries0 = $t016971750._1
48
+
    let sig0 = $t016971750._2
49
49
 
    let debugInfo = debugEntries0
50
50
 
    let output = sig0
51
51
 
    $Tuple2(debugInfo, output)
55
55
 
@Callable(i)
56
56
 
func predict (input1,input2) = {
57
57
 
    let scaledInput1 = if ((input1 == 1))
58
-
        then 1000000
58
+
        then 1
59
59
 
        else 0
60
60
 
    let scaledInput2 = if ((input2 == 1))
61
-
        then 1000000
61
+
        then 1
62
62
 
        else 0
63
63
 
    let inputs = [scaledInput1, scaledInput2]
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
64
+
    let $t020502148 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
65
+
    let debugLayer1 = $t020502148._1
66
+
    let layer1Output = $t020502148._2
67
+
    let $t021532257 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
68
+
    let debugLayer2 = $t021532257._1
69
+
    let layer2Output = $t021532257._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
4
 
let layer1Weights = [[600497, 600733], [414197, 414253]]
5
5
 
6
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
-
    let scaledX = fraction(x, 1, 1000000)
14
-
    let scaledX2 = fraction(scaledX, scaledX, 1000000)
15
-
    let term1 = (1000000 - scaledX)
16
-
    let term2 = fraction(scaledX2, 500000, 1)
13
+
    let scaledX = fraction(x, 1, 1000)
14
+
    let scaledX2 = fraction(scaledX, scaledX, 1000)
15
+
    let term1 = (1000 - scaledX)
16
+
    let term2 = fraction(scaledX2, 500, 1)
17
17
 
    (term1 + term2)
18
18
 
    }
19
19
 
20
20
 
21
21
 
func sigmoid (z,debugPrefix) = {
22
22
 
    let expNegZ = expApprox(-(z))
23
-
    let onePlusExpNegZ = (1000000 + expNegZ)
24
-
    let sigValue = fraction(1000000000000, onePlusExpNegZ, 1)
23
+
    let onePlusExpNegZ = (1000 + expNegZ)
24
+
    let sigValue = fraction(1000, 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] * 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
30
+
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + biases[0])
31
+
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + biases[1])
32
+
    let $t012851338 = sigmoid(sum0, "Layer1N0")
33
+
    let debugEntries0 = $t012851338._1
34
+
    let sig0 = $t012851338._2
35
+
    let $t013431396 = sigmoid(sum1, "Layer1N1")
36
+
    let debugEntries1 = $t013431396._1
37
+
    let sig1 = $t013431396._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] * 1000000))
46
-
    let $t017511804 = sigmoid(sum0, "Layer2N0")
47
-
    let debugEntries0 = $t017511804._1
48
-
    let sig0 = $t017511804._2
45
+
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + biases[0])
46
+
    let $t016971750 = sigmoid(sum0, "Layer2N0")
47
+
    let debugEntries0 = $t016971750._1
48
+
    let sig0 = $t016971750._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
-
        then 1000000
58
+
        then 1
59
59
 
        else 0
60
60
 
    let scaledInput2 = if ((input2 == 1))
61
-
        then 1000000
61
+
        then 1
62
62
 
        else 0
63
63
 
    let inputs = [scaledInput1, scaledInput2]
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
64
+
    let $t020502148 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
65
+
    let debugLayer1 = $t020502148._1
66
+
    let layer1Output = $t020502148._2
67
+
    let $t021532257 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
68
+
    let debugLayer2 = $t021532257._1
69
+
    let layer2Output = $t021532257._2
70
70
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
71
71
 
    }
72
72
 
73
73
 

github/deemru/w8io/6500d08 
60.33 ms ◑