tx · 5K3SesFjoRF1HkRqSncDmVU95ew5cKw5DxSrJdSM3sXu

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 13:59 [3082630] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "5K3SesFjoRF1HkRqSncDmVU95ew5cKw5DxSrJdSM3sXu",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714301865213,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "2MeJbQyFn1ZSDpv2Pic5PwkEDF8QEPAGoSqmGPbspgDhALhYVZoabYY3jN5BQkEcsn9k4QZbY6gRrC3PJbPbph45"
    ],
    "script": "base64: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",
    "height": 3082630,
    "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 = [[600496, 600732], [414196, 414252]]
4
+
let layer1Weights = [[600496, 600733], [414197, 414252]]
5
5
 
6
-
let layer1Biases = [-259051, -635637]
6
+
let layer1Biases = [-259050, -635637]
7
7
 
8
-
let layer2Weights = [[832965, -897141]]
8
+
let layer2Weights = [[832965, -897142]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
17
17
 
18
18
 
19
19
 
func exp_approx (x) = {
20
-
    let scaled_x = (x / 10000)
21
-
    let scaled_x2 = fraction(scaled_x, scaled_x, 1, DOWN)
22
-
    let scaled_x3 = fraction(scaled_x2, scaled_x, 1, DOWN)
23
-
    let exp_result = (((10000 - fraction(scaled_x, 10, 1, DOWN)) + fraction(scaled_x2, 200, 1, DOWN)) - fraction(scaled_x3, 6000, 1, DOWN))
24
-
    if ((0 > x))
25
-
        then (10000 + exp_result)
26
-
        else (10000 - exp_result)
20
+
    let maxExp = 100000
21
+
    if ((-(maxExp) > x))
22
+
        then 1
23
+
        else if ((x > maxExp))
24
+
            then 1000000000
25
+
            else {
26
+
                let scaled_x = (x / 10000)
27
+
                let scaled_x2 = fraction(scaled_x, scaled_x, 10000, DOWN)
28
+
                let exp_result = ((10000 - scaled_x) + (scaled_x2 / 2))
29
+
                (10000 - exp_result)
30
+
                }
27
31
 
    }
28
32
 
29
33
 
30
34
 
func sigmoid (z,debugPrefix) = {
31
35
 
    let clampedZ = clampZ(z, 100000)
32
-
    let positiveZ = if ((0 > z))
33
-
        then -(z)
34
-
        else z
35
-
    let expValue = exp_approx(-(positiveZ))
36
+
    let expValue = exp_approx(-(clampedZ))
36
37
 
    let sigValue = fraction(10000, (10000 + expValue), 1, DOWN)
37
-
    $Tuple2([IntegerEntry((debugPrefix + "inputZ"), z), IntegerEntry((debugPrefix + "clampedZ"), clampedZ), IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
38
+
    $Tuple2([IntegerEntry((debugPrefix + "clampedZ"), clampedZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
38
39
 
    }
39
40
 
40
41
 
41
42
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
42
43
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
43
44
 
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
44
-
    let $t020552108 = sigmoid(sum0, "Layer1N0")
45
-
    let debugEntries0 = $t020552108._1
46
-
    let sig0 = $t020552108._2
47
-
    let $t021132166 = sigmoid(sum1, "Layer1N1")
48
-
    let debugEntries1 = $t021132166._1
49
-
    let sig1 = $t021132166._2
45
+
    let $t017651818 = sigmoid(sum0, "Layer1N0")
46
+
    let debugEntries0 = $t017651818._1
47
+
    let sig0 = $t017651818._2
48
+
    let $t018231876 = sigmoid(sum1, "Layer1N1")
49
+
    let debugEntries1 = $t018231876._1
50
+
    let sig1 = $t018231876._2
50
51
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
51
52
 
    let output = [sig0, sig1]
52
53
 
    $Tuple2(debugInfo, output)
55
56
 
56
57
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
57
58
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
58
-
    let $t024762529 = sigmoid(sum0, "Layer2N0")
59
-
    let debugEntries0 = $t024762529._1
60
-
    let sig0 = $t024762529._2
59
+
    let $t021862239 = sigmoid(sum0, "Layer2N0")
60
+
    let debugEntries0 = $t021862239._1
61
+
    let sig0 = $t021862239._2
61
62
 
    let debugInfo = debugEntries0
62
63
 
    let output = sig0
63
64
 
    $Tuple2(debugInfo, output)
73
74
 
        then 1000000
74
75
 
        else 0
75
76
 
    let inputs = [scaledInput1, scaledInput2]
76
-
    let $t028412939 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
77
-
    let debugLayer1 = $t028412939._1
78
-
    let layer1Output = $t028412939._2
79
-
    let $t029443048 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
80
-
    let debugLayer2 = $t029443048._1
81
-
    let layer2Output = $t029443048._2
77
+
    let $t025512649 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
78
+
    let debugLayer1 = $t025512649._1
79
+
    let layer1Output = $t025512649._2
80
+
    let $t026542758 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
81
+
    let debugLayer2 = $t026542758._1
82
+
    let layer2Output = $t026542758._2
82
83
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
83
84
 
    }
84
85
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let layer1Weights = [[600496, 600732], [414196, 414252]]
4
+
let layer1Weights = [[600496, 600733], [414197, 414252]]
5
5
 
6
-
let layer1Biases = [-259051, -635637]
6
+
let layer1Biases = [-259050, -635637]
7
7
 
8
-
let layer2Weights = [[832965, -897141]]
8
+
let layer2Weights = [[832965, -897142]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
12
 
func clampZ (z,limit) = if ((z > limit))
13
13
 
    then limit
14
14
 
    else if ((-(limit) > z))
15
15
 
        then -(limit)
16
16
 
        else z
17
17
 
18
18
 
19
19
 
func exp_approx (x) = {
20
-
    let scaled_x = (x / 10000)
21
-
    let scaled_x2 = fraction(scaled_x, scaled_x, 1, DOWN)
22
-
    let scaled_x3 = fraction(scaled_x2, scaled_x, 1, DOWN)
23
-
    let exp_result = (((10000 - fraction(scaled_x, 10, 1, DOWN)) + fraction(scaled_x2, 200, 1, DOWN)) - fraction(scaled_x3, 6000, 1, DOWN))
24
-
    if ((0 > x))
25
-
        then (10000 + exp_result)
26
-
        else (10000 - exp_result)
20
+
    let maxExp = 100000
21
+
    if ((-(maxExp) > x))
22
+
        then 1
23
+
        else if ((x > maxExp))
24
+
            then 1000000000
25
+
            else {
26
+
                let scaled_x = (x / 10000)
27
+
                let scaled_x2 = fraction(scaled_x, scaled_x, 10000, DOWN)
28
+
                let exp_result = ((10000 - scaled_x) + (scaled_x2 / 2))
29
+
                (10000 - exp_result)
30
+
                }
27
31
 
    }
28
32
 
29
33
 
30
34
 
func sigmoid (z,debugPrefix) = {
31
35
 
    let clampedZ = clampZ(z, 100000)
32
-
    let positiveZ = if ((0 > z))
33
-
        then -(z)
34
-
        else z
35
-
    let expValue = exp_approx(-(positiveZ))
36
+
    let expValue = exp_approx(-(clampedZ))
36
37
 
    let sigValue = fraction(10000, (10000 + expValue), 1, DOWN)
37
-
    $Tuple2([IntegerEntry((debugPrefix + "inputZ"), z), IntegerEntry((debugPrefix + "clampedZ"), clampedZ), IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
38
+
    $Tuple2([IntegerEntry((debugPrefix + "clampedZ"), clampedZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
38
39
 
    }
39
40
 
40
41
 
41
42
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
42
43
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
43
44
 
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
44
-
    let $t020552108 = sigmoid(sum0, "Layer1N0")
45
-
    let debugEntries0 = $t020552108._1
46
-
    let sig0 = $t020552108._2
47
-
    let $t021132166 = sigmoid(sum1, "Layer1N1")
48
-
    let debugEntries1 = $t021132166._1
49
-
    let sig1 = $t021132166._2
45
+
    let $t017651818 = sigmoid(sum0, "Layer1N0")
46
+
    let debugEntries0 = $t017651818._1
47
+
    let sig0 = $t017651818._2
48
+
    let $t018231876 = sigmoid(sum1, "Layer1N1")
49
+
    let debugEntries1 = $t018231876._1
50
+
    let sig1 = $t018231876._2
50
51
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
51
52
 
    let output = [sig0, sig1]
52
53
 
    $Tuple2(debugInfo, output)
53
54
 
    }
54
55
 
55
56
 
56
57
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
57
58
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
58
-
    let $t024762529 = sigmoid(sum0, "Layer2N0")
59
-
    let debugEntries0 = $t024762529._1
60
-
    let sig0 = $t024762529._2
59
+
    let $t021862239 = sigmoid(sum0, "Layer2N0")
60
+
    let debugEntries0 = $t021862239._1
61
+
    let sig0 = $t021862239._2
61
62
 
    let debugInfo = debugEntries0
62
63
 
    let output = sig0
63
64
 
    $Tuple2(debugInfo, output)
64
65
 
    }
65
66
 
66
67
 
67
68
 
@Callable(i)
68
69
 
func predict (input1,input2) = {
69
70
 
    let scaledInput1 = if ((input1 == 1))
70
71
 
        then 1000000
71
72
 
        else 0
72
73
 
    let scaledInput2 = if ((input2 == 1))
73
74
 
        then 1000000
74
75
 
        else 0
75
76
 
    let inputs = [scaledInput1, scaledInput2]
76
-
    let $t028412939 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
77
-
    let debugLayer1 = $t028412939._1
78
-
    let layer1Output = $t028412939._2
79
-
    let $t029443048 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
80
-
    let debugLayer2 = $t029443048._1
81
-
    let layer2Output = $t029443048._2
77
+
    let $t025512649 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
78
+
    let debugLayer1 = $t025512649._1
79
+
    let layer1Output = $t025512649._2
80
+
    let $t026542758 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
81
+
    let debugLayer2 = $t026542758._1
82
+
    let layer2Output = $t026542758._2
82
83
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
83
84
 
    }
84
85
 
85
86
 

github/deemru/w8io/026f985 
46.60 ms ◑