tx · DrftWca7J6bqUwVfecQjir8mFkKk6zDrPUzysY89nBUr

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.03.20 11:48 [3026124] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "DrftWca7J6bqUwVfecQjir8mFkKk6zDrPUzysY89nBUr",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1710924548566,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "2QJyVUoazWmKB2J1s7UofFsuXfwfDzyLCdGgVAB42Gm5BSMaV42Hu1ay3G597XCGiZLPRDQRyWtuEMQp9KUbJx8E"
    ],
    "script": "base64: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",
    "height": 3026124,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Diff: 
Old
New
Differences
39
39
 
    let hiddenLayerOutput = forwardPass(input, layer1Weights, layer1Biases)
40
40
 
    let outputLayerSum = (dotProduct(hiddenLayerOutput, layer2Weights[0]) + layer2Biases[0])
41
41
 
    let output = sigmoid(outputLayerSum)
42
-
    output
42
+
[IntegerEntry("hiddenLayerOutput1", hiddenLayerOutput[0]), IntegerEntry("hiddenLayerOutput2", hiddenLayerOutput[1]), IntegerEntry("outputLayerSum", outputLayerSum), IntegerEntry("finalOutput", output)]
43
43
 
    }
44
44
 
45
45
 
51
51
 
    let scaledInput2 = if ((input2 == 1))
52
52
 
        then 1000000
53
53
 
        else 0
54
-
    let result = xorNeuralNetwork(scaledInput1, scaledInput2)
55
-
[IntegerEntry("result", 43)]
54
+
    xorNeuralNetwork(scaledInput1, scaledInput2)
56
55
 
    }
57
56
 
58
57
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
4
 
let layer1Weights = [[4721113, -5002107], [6226846, -6353789]]
5
5
 
6
6
 
let layer1Biases = [-2521378, 3389498]
7
7
 
8
8
 
let layer2Weights = [[8109936, -7559760]]
9
9
 
10
10
 
let layer2Biases = [3490942]
11
11
 
12
12
 
func sigmoid (z) = {
13
13
 
    let e = 2718281
14
14
 
    let base = 1000000
15
15
 
    let negativeZ = (-1 * z)
16
16
 
    let expPart = fraction(e, negativeZ, base)
17
17
 
    fraction(base, base, (base + expPart))
18
18
 
    }
19
19
 
20
20
 
21
21
 
func dotProduct (a,b) = {
22
22
 
    let product0 = fraction(a[0], b[0], 1000000)
23
23
 
    let product1 = fraction(a[1], b[1], 1000000)
24
24
 
    (product0 + product1)
25
25
 
    }
26
26
 
27
27
 
28
28
 
func forwardPass (input,weights,biases) = {
29
29
 
    let sum0 = (dotProduct(input, weights[0]) + biases[0])
30
30
 
    let sum1 = (dotProduct(input, weights[1]) + biases[1])
31
31
 
    let sig0 = sigmoid(sum0)
32
32
 
    let sig1 = sigmoid(sum1)
33
33
 
[sig0, sig1]
34
34
 
    }
35
35
 
36
36
 
37
37
 
func xorNeuralNetwork (input1,input2) = {
38
38
 
    let input = [input1, input2]
39
39
 
    let hiddenLayerOutput = forwardPass(input, layer1Weights, layer1Biases)
40
40
 
    let outputLayerSum = (dotProduct(hiddenLayerOutput, layer2Weights[0]) + layer2Biases[0])
41
41
 
    let output = sigmoid(outputLayerSum)
42
-
    output
42
+
[IntegerEntry("hiddenLayerOutput1", hiddenLayerOutput[0]), IntegerEntry("hiddenLayerOutput2", hiddenLayerOutput[1]), IntegerEntry("outputLayerSum", outputLayerSum), IntegerEntry("finalOutput", output)]
43
43
 
    }
44
44
 
45
45
 
46
46
 
@Callable(i)
47
47
 
func predict (input1,input2) = {
48
48
 
    let scaledInput1 = if ((input1 == 1))
49
49
 
        then 1000000
50
50
 
        else 0
51
51
 
    let scaledInput2 = if ((input2 == 1))
52
52
 
        then 1000000
53
53
 
        else 0
54
-
    let result = xorNeuralNetwork(scaledInput1, scaledInput2)
55
-
[IntegerEntry("result", 43)]
54
+
    xorNeuralNetwork(scaledInput1, scaledInput2)
56
55
 
    }
57
56
 
58
57
 

github/deemru/w8io/6500d08 
23.03 ms ◑