tx · Caw5hteY6VE5wJWUtbXE6eTkgJhAS89NUjyTEgyWD7vy

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 15:03 [3082685] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "Caw5hteY6VE5wJWUtbXE6eTkgJhAS89NUjyTEgyWD7vy",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714305832290,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "4VHqqiNJ6rQpQmyU7qtsUDCNjoVgwnXgbEa4h8qXjSjtbAGTaSQorPzdaGWujYTtvKyzP7mC9rzbn94v7XFxmgiw"
    ],
    "script": "base64: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",
    "height": 3082685,
    "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, 600733], [414197, 414253]]
4
+
let layer1Weights = [[600497, 600732], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259050, -635638]
6
+
let layer1Biases = [-259050, -635637]
7
7
 
8
8
 
let layer2Weights = [[832966, -897142]]
9
9
 
29
29
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
30
30
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + biases[0])
31
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
32
+
    let $t012951348 = sigmoid(sum0, "Layer1N0")
33
+
    let debugEntries0 = $t012951348._1
34
+
    let sig0 = $t012951348._2
35
+
    let $t013531406 = sigmoid(sum1, "Layer1N1")
36
+
    let debugEntries1 = $t013531406._1
37
+
    let sig1 = $t013531406._2
38
38
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
39
39
 
    let output = [sig0, sig1]
40
40
 
    $Tuple2(debugInfo, output)
43
43
 
44
44
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
45
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
46
+
    let $t017071760 = sigmoid(sum0, "Layer2N0")
47
+
    let debugEntries0 = $t017071760._1
48
+
    let sig0 = $t017071760._2
49
49
 
    let debugInfo = debugEntries0
50
50
 
    let output = sig0
51
51
 
    $Tuple2(debugInfo, output)
61
61
 
        then 1
62
62
 
        else 0
63
63
 
    let inputs = [scaledInput1, scaledInput2]
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
64
+
    let $t020602158 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
65
+
    let debugLayer1 = $t020602158._1
66
+
    let layer1Output = $t020602158._2
67
+
    let $t021632267 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
68
+
    let debugLayer2 = $t021632267._1
69
+
    let layer2Output = $t021632267._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, 600733], [414197, 414253]]
4
+
let layer1Weights = [[600497, 600732], [414197, 414253]]
5
5
 
6
-
let layer1Biases = [-259050, -635638]
6
+
let layer1Biases = [-259050, -635637]
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, 1000)
14
14
 
    let scaledX2 = fraction(scaledX, scaledX, 1000)
15
15
 
    let term1 = (1000 - scaledX)
16
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
23
 
    let onePlusExpNegZ = (1000 + expNegZ)
24
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
30
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + biases[0])
31
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
32
+
    let $t012951348 = sigmoid(sum0, "Layer1N0")
33
+
    let debugEntries0 = $t012951348._1
34
+
    let sig0 = $t012951348._2
35
+
    let $t013531406 = sigmoid(sum1, "Layer1N1")
36
+
    let debugEntries1 = $t013531406._1
37
+
    let sig1 = $t013531406._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
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
46
+
    let $t017071760 = sigmoid(sum0, "Layer2N0")
47
+
    let debugEntries0 = $t017071760._1
48
+
    let sig0 = $t017071760._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 1
59
59
 
        else 0
60
60
 
    let scaledInput2 = if ((input2 == 1))
61
61
 
        then 1
62
62
 
        else 0
63
63
 
    let inputs = [scaledInput1, scaledInput2]
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
64
+
    let $t020602158 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
65
+
    let debugLayer1 = $t020602158._1
66
+
    let layer1Output = $t020602158._2
67
+
    let $t021632267 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
68
+
    let debugLayer2 = $t021632267._1
69
+
    let layer2Output = $t021632267._2
70
70
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
71
71
 
    }
72
72
 
73
73
 

github/deemru/w8io/169f3d6 
29.36 ms ◑