tx · CS21i4va4bE4wpJLLVcMuLnJmUSs6ayZzKEjCtJzd3f3

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.04.15 23:00 [3064333] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "CS21i4va4bE4wpJLLVcMuLnJmUSs6ayZzKEjCtJzd3f3",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1713211229821,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "33ijew3F8tZZB8zxehKgwkJDBSrdxVQ1TsTrbr4pfietpGz5BVH5QV6kTxWMrGiWb2VKdWspQEAFwTXjj4igx2cE"
    ],
    "script": "base64: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",
    "height": 3064333,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Diff: 
Old
New
Differences
35
35
 
36
36
 
37
37
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
38
-
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[0], weights[0][1], 1000000)) + biases[0])
39
-
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[0], weights[1][1], 1000000)) + biases[1])
40
-
    let $t016531699 = sigmoid(sum0, "Layer2N0")
41
-
    let debug0 = $t016531699._1
42
-
    let sig0 = $t016531699._2
43
-
    let $t017041750 = sigmoid(sum1, "Layer2N1")
44
-
    let debug1 = $t017041750._1
45
-
    let sig1 = $t017041750._2
46
-
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
38
+
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
39
+
    let $t015381584 = sigmoid(sum0, "Layer2N0")
40
+
    let debug0 = $t015381584._1
41
+
    let sig0 = $t015381584._2
42
+
    $Tuple2([sig0], debug0)
47
43
 
    }
48
44
 
49
45
 
56
52
 
        then 1000000
57
53
 
        else 0
58
54
 
    let inputs = [scaledInput1, scaledInput2]
59
-
    let $t020192117 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
60
-
    let layer1Output = $t020192117._1
61
-
    let debugLayer1 = $t020192117._2
62
-
    let $t021222226 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
63
-
    let layer2Output = $t021222226._1
64
-
    let debugLayer2 = $t021222226._2
55
+
    let $t018371935 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
56
+
    let layer1Output = $t018371935._1
57
+
    let debugLayer1 = $t018371935._2
58
+
    let $t019402044 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
59
+
    let layer2Output = $t019402044._1
60
+
    let debugLayer2 = $t019402044._2
65
61
 
    (([IntegerEntry("result", layer2Output[0])] ++ debugLayer1) ++ debugLayer2)
66
62
 
    }
67
63
 

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, -635637]
7
7
 
8
8
 
let layer2Weights = [[832966, -897142]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
12
 
func sigmoid (z,debugPrefix) = {
13
13
 
    let e = 2718281
14
14
 
    let base = 1000000
15
15
 
    let positiveZ = if ((0 > z))
16
16
 
        then -(z)
17
17
 
        else z
18
18
 
    let expPart = fraction(e, base, positiveZ)
19
19
 
    let sigValue = fraction(base, base, (base + expPart))
20
20
 
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
21
21
 
    }
22
22
 
23
23
 
24
24
 
func forwardPassLayer1 (input,weights,biases,debugPrefix) = {
25
25
 
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
26
26
 
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[1], weights[1][1], 1000000)) + biases[1])
27
27
 
    let $t011721218 = sigmoid(sum0, "Layer1N0")
28
28
 
    let debug0 = $t011721218._1
29
29
 
    let sig0 = $t011721218._2
30
30
 
    let $t012231269 = sigmoid(sum1, "Layer1N1")
31
31
 
    let debug1 = $t012231269._1
32
32
 
    let sig1 = $t012231269._2
33
33
 
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
34
34
 
    }
35
35
 
36
36
 
37
37
 
func forwardPassLayer2 (input,weights,biases,debugPrefix) = {
38
-
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[0], weights[0][1], 1000000)) + biases[0])
39
-
    let sum1 = ((fraction(input[0], weights[1][0], 1000000) + fraction(input[0], weights[1][1], 1000000)) + biases[1])
40
-
    let $t016531699 = sigmoid(sum0, "Layer2N0")
41
-
    let debug0 = $t016531699._1
42
-
    let sig0 = $t016531699._2
43
-
    let $t017041750 = sigmoid(sum1, "Layer2N1")
44
-
    let debug1 = $t017041750._1
45
-
    let sig1 = $t017041750._2
46
-
    $Tuple2([sig0, sig1], (debug0 ++ debug1))
38
+
    let sum0 = ((fraction(input[0], weights[0][0], 1000000) + fraction(input[1], weights[0][1], 1000000)) + biases[0])
39
+
    let $t015381584 = sigmoid(sum0, "Layer2N0")
40
+
    let debug0 = $t015381584._1
41
+
    let sig0 = $t015381584._2
42
+
    $Tuple2([sig0], debug0)
47
43
 
    }
48
44
 
49
45
 
50
46
 
@Callable(i)
51
47
 
func predict (input1,input2) = {
52
48
 
    let scaledInput1 = if ((input1 == 1))
53
49
 
        then 1000000
54
50
 
        else 0
55
51
 
    let scaledInput2 = if ((input2 == 1))
56
52
 
        then 1000000
57
53
 
        else 0
58
54
 
    let inputs = [scaledInput1, scaledInput2]
59
-
    let $t020192117 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
60
-
    let layer1Output = $t020192117._1
61
-
    let debugLayer1 = $t020192117._2
62
-
    let $t021222226 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
63
-
    let layer2Output = $t021222226._1
64
-
    let debugLayer2 = $t021222226._2
55
+
    let $t018371935 = forwardPassLayer1(inputs, layer1Weights, layer1Biases, "Layer1")
56
+
    let layer1Output = $t018371935._1
57
+
    let debugLayer1 = $t018371935._2
58
+
    let $t019402044 = forwardPassLayer2(layer1Output, layer2Weights, layer2Biases, "Layer2")
59
+
    let layer2Output = $t019402044._1
60
+
    let debugLayer2 = $t019402044._2
65
61
 
    (([IntegerEntry("result", layer2Output[0])] ++ debugLayer1) ++ debugLayer2)
66
62
 
    }
67
63
 
68
64
 

github/deemru/w8io/169f3d6 
47.44 ms ◑