tx · BQxK6Q9RxFkuvKPme7hUqZ728mZQD9T4F4MFJRiKaPvb

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.28 13:49 [3082618] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "BQxK6Q9RxFkuvKPme7hUqZ728mZQD9T4F4MFJRiKaPvb",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714301410024,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "FgWK3zuzQ4YT5ZMvpRZhqRQx4BUT6YS4o32eUH1eBXzTGrh5pCPiRYnsuL4sPicGaYkvqc2advPkQCuyPpFJDsV"
    ],
    "script": "base64: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",
    "height": 3082618,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

View: original | compacted
Prev: EUk6SP8KWsjHF1LQH8TKBsex2FAUFa7uDWqcmJfKSMsf
Next: 5K3SesFjoRF1HkRqSncDmVU95ew5cKw5DxSrJdSM3sXu

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 = [[600496, 600732], [414196, 414252]]
5
5
 
6
-
let layer1Biases = [-259050, -635637]
6
+
let layer1Biases = [-259051, -635637]
7
7
 
8
-
let layer2Weights = [[832966, -897142]]
8
+
let layer2Weights = [[832965, -897141]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
18
18
 
19
19
 
func exp_approx (x) = {
20
20
 
    let scaled_x = (x / 10000)
21
-
    let scaled_x2 = fraction(scaled_x, scaled_x, 1)
22
-
    let scaled_x3 = fraction(scaled_x2, scaled_x, 1)
23
-
    let exp_result = (((1000000 - scaled_x) + fraction(scaled_x2, 500000, 1, DOWN)) - fraction(scaled_x3, 6000000, 1, DOWN))
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
24
 
    if ((0 > x))
25
-
        then (1000000 + exp_result)
26
-
        else (1000000 - exp_result)
25
+
        then (10000 + exp_result)
26
+
        else (10000 - exp_result)
27
27
 
    }
28
28
 
29
29
 
33
33
 
        then -(z)
34
34
 
        else z
35
35
 
    let expValue = exp_approx(-(positiveZ))
36
-
    let sigValue = fraction(1000000, (1000000 + expValue), 1)
36
+
    let sigValue = fraction(10000, (10000 + expValue), 1, DOWN)
37
37
 
    $Tuple2([IntegerEntry((debugPrefix + "inputZ"), z), IntegerEntry((debugPrefix + "clampedZ"), clampedZ), IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
38
38
 
    }
39
39
 
41
41
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
42
42
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
43
43
 
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
44
-
    let $t019802033 = sigmoid(sum0, "Layer1N0")
45
-
    let debugEntries0 = $t019802033._1
46
-
    let sig0 = $t019802033._2
47
-
    let $t020382091 = sigmoid(sum1, "Layer1N1")
48
-
    let debugEntries1 = $t020382091._1
49
-
    let sig1 = $t020382091._2
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
50
50
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
51
51
 
    let output = [sig0, sig1]
52
52
 
    $Tuple2(debugInfo, output)
55
55
 
56
56
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
57
57
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
58
-
    let $t024012454 = sigmoid(sum0, "Layer2N0")
59
-
    let debugEntries0 = $t024012454._1
60
-
    let sig0 = $t024012454._2
58
+
    let $t024762529 = sigmoid(sum0, "Layer2N0")
59
+
    let debugEntries0 = $t024762529._1
60
+
    let sig0 = $t024762529._2
61
61
 
    let debugInfo = debugEntries0
62
62
 
    let output = sig0
63
63
 
    $Tuple2(debugInfo, output)
73
73
 
        then 1000000
74
74
 
        else 0
75
75
 
    let inputs = [scaledInput1, scaledInput2]
76
-
    let $t027662864 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
77
-
    let debugLayer1 = $t027662864._1
78
-
    let layer1Output = $t027662864._2
79
-
    let $t028692973 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
80
-
    let debugLayer2 = $t028692973._1
81
-
    let layer2Output = $t028692973._2
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
82
82
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
83
83
 
    }
84
84
 

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 = [[600496, 600732], [414196, 414252]]
5
5
 
6
-
let layer1Biases = [-259050, -635637]
6
+
let layer1Biases = [-259051, -635637]
7
7
 
8
-
let layer2Weights = [[832966, -897142]]
8
+
let layer2Weights = [[832965, -897141]]
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
20
 
    let scaled_x = (x / 10000)
21
-
    let scaled_x2 = fraction(scaled_x, scaled_x, 1)
22
-
    let scaled_x3 = fraction(scaled_x2, scaled_x, 1)
23
-
    let exp_result = (((1000000 - scaled_x) + fraction(scaled_x2, 500000, 1, DOWN)) - fraction(scaled_x3, 6000000, 1, DOWN))
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
24
 
    if ((0 > x))
25
-
        then (1000000 + exp_result)
26
-
        else (1000000 - exp_result)
25
+
        then (10000 + exp_result)
26
+
        else (10000 - exp_result)
27
27
 
    }
28
28
 
29
29
 
30
30
 
func sigmoid (z,debugPrefix) = {
31
31
 
    let clampedZ = clampZ(z, 100000)
32
32
 
    let positiveZ = if ((0 > z))
33
33
 
        then -(z)
34
34
 
        else z
35
35
 
    let expValue = exp_approx(-(positiveZ))
36
-
    let sigValue = fraction(1000000, (1000000 + expValue), 1)
36
+
    let sigValue = fraction(10000, (10000 + expValue), 1, DOWN)
37
37
 
    $Tuple2([IntegerEntry((debugPrefix + "inputZ"), z), IntegerEntry((debugPrefix + "clampedZ"), clampedZ), IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expValue"), expValue), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
38
38
 
    }
39
39
 
40
40
 
41
41
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
42
42
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
43
43
 
    let sum1 = (((input[0] * weights[1][0]) + (input[1] * weights[1][1])) + (biases[1] * 100000))
44
-
    let $t019802033 = sigmoid(sum0, "Layer1N0")
45
-
    let debugEntries0 = $t019802033._1
46
-
    let sig0 = $t019802033._2
47
-
    let $t020382091 = sigmoid(sum1, "Layer1N1")
48
-
    let debugEntries1 = $t020382091._1
49
-
    let sig1 = $t020382091._2
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
50
50
 
    let debugInfo = (debugEntries0 ++ debugEntries1)
51
51
 
    let output = [sig0, sig1]
52
52
 
    $Tuple2(debugInfo, output)
53
53
 
    }
54
54
 
55
55
 
56
56
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
57
57
 
    let sum0 = (((input[0] * weights[0][0]) + (input[1] * weights[0][1])) + (biases[0] * 100000))
58
-
    let $t024012454 = sigmoid(sum0, "Layer2N0")
59
-
    let debugEntries0 = $t024012454._1
60
-
    let sig0 = $t024012454._2
58
+
    let $t024762529 = sigmoid(sum0, "Layer2N0")
59
+
    let debugEntries0 = $t024762529._1
60
+
    let sig0 = $t024762529._2
61
61
 
    let debugInfo = debugEntries0
62
62
 
    let output = sig0
63
63
 
    $Tuple2(debugInfo, output)
64
64
 
    }
65
65
 
66
66
 
67
67
 
@Callable(i)
68
68
 
func predict (input1,input2) = {
69
69
 
    let scaledInput1 = if ((input1 == 1))
70
70
 
        then 1000000
71
71
 
        else 0
72
72
 
    let scaledInput2 = if ((input2 == 1))
73
73
 
        then 1000000
74
74
 
        else 0
75
75
 
    let inputs = [scaledInput1, scaledInput2]
76
-
    let $t027662864 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
77
-
    let debugLayer1 = $t027662864._1
78
-
    let layer1Output = $t027662864._2
79
-
    let $t028692973 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
80
-
    let debugLayer2 = $t028692973._1
81
-
    let layer2Output = $t028692973._2
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
82
82
 
    (([IntegerEntry("result", layer2Output)] ++ debugLayer1) ++ debugLayer2)
83
83
 
    }
84
84
 
85
85
 

github/deemru/w8io/026f985 
41.71 ms ◑