tx · Dt43XLUUf8BCJnkLcq3JC7kCQ4a6N3PTGwv5jeCvkDY2

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.03.20 11:57 [3026133] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "Dt43XLUUf8BCJnkLcq3JC7kCQ4a6N3PTGwv5jeCvkDY2",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1710925050740,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "5r3Ze6ajut3FYg7orxxoTxwUfLKZFR7FwCegLni1fgP1zJ82pRSXodbKuUDXvPuVAesBuiWMgpFCrZLZeAGsUo3X"
    ],
    "script": "base64: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",
    "height": 3026133,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

View: original | compacted
Prev: DrftWca7J6bqUwVfecQjir8mFkKk6zDrPUzysY89nBUr
Next: FPJLJBHCFpWbureEe13BzNSfQbZFGFP6nVQzTvxGcaVB

Diff: 
Old
New
Differences
30
30
 
    let sum1 = (dotProduct(input, weights[1]) + biases[1])
31
31
 
    let sig0 = sigmoid(sum0)
32
32
 
    let sig1 = sigmoid(sum1)
33
-
[sig0, sig1]
33
+
[sig0, sig1, sum0, sum1]
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
-
    let outputLayerSum = (dotProduct(hiddenLayerOutput, layer2Weights[0]) + layer2Biases[0])
40
+
    let outputLayerSum = (dotProduct([hiddenLayerOutput[0], hiddenLayerOutput[1]], layer2Weights[0]) + layer2Biases[0])
41
41
 
    let output = sigmoid(outputLayerSum)
42
-
[IntegerEntry("hiddenLayerOutput1", hiddenLayerOutput[0]), IntegerEntry("hiddenLayerOutput2", hiddenLayerOutput[1]), IntegerEntry("outputLayerSum", outputLayerSum), IntegerEntry("finalOutput", output)]
42
+
[output, outputLayerSum, hiddenLayerOutput[2], hiddenLayerOutput[3]]
43
43
 
    }
44
44
 
45
45
 
51
51
 
    let scaledInput2 = if ((input2 == 1))
52
52
 
        then 1000000
53
53
 
        else 0
54
-
    xorNeuralNetwork(scaledInput1, scaledInput2)
54
+
    let networkOutputs = xorNeuralNetwork(scaledInput1, scaledInput2)
55
+
    let result = networkOutputs[0]
56
+
    let outputLayerSum = networkOutputs[1]
57
+
    let hiddenLayerOutput1Sum = networkOutputs[2]
58
+
    let hiddenLayerOutput2Sum = networkOutputs[3]
59
+
[IntegerEntry("result", result), IntegerEntry("outputLayerSum", outputLayerSum), IntegerEntry("hiddenLayerOutput1Sum", hiddenLayerOutput1Sum), IntegerEntry("hiddenLayerOutput2Sum", hiddenLayerOutput2Sum)]
55
60
 
    }
56
61
 
57
62
 

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
-
[sig0, sig1]
33
+
[sig0, sig1, sum0, sum1]
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
-
    let outputLayerSum = (dotProduct(hiddenLayerOutput, layer2Weights[0]) + layer2Biases[0])
40
+
    let outputLayerSum = (dotProduct([hiddenLayerOutput[0], hiddenLayerOutput[1]], layer2Weights[0]) + layer2Biases[0])
41
41
 
    let output = sigmoid(outputLayerSum)
42
-
[IntegerEntry("hiddenLayerOutput1", hiddenLayerOutput[0]), IntegerEntry("hiddenLayerOutput2", hiddenLayerOutput[1]), IntegerEntry("outputLayerSum", outputLayerSum), IntegerEntry("finalOutput", output)]
42
+
[output, outputLayerSum, hiddenLayerOutput[2], hiddenLayerOutput[3]]
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
-
    xorNeuralNetwork(scaledInput1, scaledInput2)
54
+
    let networkOutputs = xorNeuralNetwork(scaledInput1, scaledInput2)
55
+
    let result = networkOutputs[0]
56
+
    let outputLayerSum = networkOutputs[1]
57
+
    let hiddenLayerOutput1Sum = networkOutputs[2]
58
+
    let hiddenLayerOutput2Sum = networkOutputs[3]
59
+
[IntegerEntry("result", result), IntegerEntry("outputLayerSum", outputLayerSum), IntegerEntry("hiddenLayerOutput1Sum", hiddenLayerOutput1Sum), IntegerEntry("hiddenLayerOutput2Sum", hiddenLayerOutput2Sum)]
55
60
 
    }
56
61
 
57
62
 

github/deemru/w8io/6500d08 
20.88 ms ◑