tx · 2M6HVceMCCSxMt5DCypi4Uye6KUcaMYd3fZxBfo1itFh

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.03.20 12:15 [3026153] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "2M6HVceMCCSxMt5DCypi4Uye6KUcaMYd3fZxBfo1itFh",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1710926227447,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "3Vadz6GAuEBs7M8Fw78k8efUnuiHaDFpM59ZhFvuEor8BggppepSsFeFZJ92o7GRZzHbrH1eSLkjHjnixLU7E9bg"
    ],
    "script": "base64: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",
    "height": 3026153,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Diff: 
Old
New
Differences
12
12
 
func sigmoid (z,debugPrefix) = {
13
13
 
    let e = 2718281
14
14
 
    let base = 1000000
15
-
    let negativeZ = (-1 * z)
16
-
    let expPart = fraction(e, negativeZ, base)
15
+
    let positiveZ = if ((0 > z))
16
+
        then -(z)
17
+
        else z
18
+
    let expPart = fraction(e, base, positiveZ)
17
19
 
    let sigValue = fraction(base, base, (base + expPart))
18
-
    $Tuple2([IntegerEntry((debugPrefix + "negativeZ"), negativeZ), IntegerEntry((debugPrefix + "expPart"), expPart)], sigValue)
20
+
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
19
21
 
    }
20
22
 
21
23
 
29
31
 
func forwardPass (input,weights,biases,layer) = {
30
32
 
    let sum0 = (dotProduct(input, weights[0]) + biases[0])
31
33
 
    let sum1 = (dotProduct(input, weights[1]) + biases[1])
32
-
    let $t010051062 = sigmoid(sum0, (layer + "L1N1"))
33
-
    let sigmoidDebug0 = $t010051062._1
34
-
    let sig0 = $t010051062._2
35
-
    let $t010671124 = sigmoid(sum1, (layer + "L1N2"))
36
-
    let sigmoidDebug1 = $t010671124._1
37
-
    let sig1 = $t010671124._2
34
+
    let $t013311388 = sigmoid(sum0, (layer + "L1N1"))
35
+
    let sigmoidDebug0 = $t013311388._1
36
+
    let sig0 = $t013311388._2
37
+
    let $t013931450 = sigmoid(sum1, (layer + "L1N2"))
38
+
    let sigmoidDebug1 = $t013931450._1
39
+
    let sig1 = $t013931450._2
38
40
 
    $Tuple2([sig0, sig1, sum0, sum1], (sigmoidDebug0 ++ sigmoidDebug1))
39
41
 
    }
40
42
 
41
43
 
42
44
 
func xorNeuralNetwork (input1,input2) = {
43
45
 
    let input = [input1, input2]
44
-
    let $t013021394 = forwardPass(input, layer1Weights, layer1Biases, "HL")
45
-
    let hiddenLayerOutput = $t013021394._1
46
-
    let hiddenDebug = $t013021394._2
47
-
    let $t013991534 = sigmoid((dotProduct([hiddenLayerOutput[0], hiddenLayerOutput[1]], layer2Weights[0]) + layer2Biases[0]), "OL")
48
-
    let outputDebug = $t013991534._1
49
-
    let output = $t013991534._2
46
+
    let $t016281720 = forwardPass(input, layer1Weights, layer1Biases, "HL")
47
+
    let hiddenLayerOutput = $t016281720._1
48
+
    let hiddenDebug = $t016281720._2
49
+
    let $t017251860 = sigmoid((dotProduct([hiddenLayerOutput[0], hiddenLayerOutput[1]], layer2Weights[0]) + layer2Biases[0]), "OL")
50
+
    let outputDebug = $t017251860._1
51
+
    let output = $t017251860._2
50
52
 
    $Tuple2([output, (dotProduct([hiddenLayerOutput[0], hiddenLayerOutput[1]], layer2Weights[0]) + layer2Biases[0]), hiddenLayerOutput[2], hiddenLayerOutput[3]], (hiddenDebug ++ outputDebug))
51
53
 
    }
52
54
 
59
61
 
    let scaledInput2 = if ((input2 == 1))
60
62
 
        then 1000000
61
63
 
        else 0
62
-
    let $t019192000 = xorNeuralNetwork(scaledInput1, scaledInput2)
63
-
    let networkOutputs = $t019192000._1
64
-
    let debugEntries = $t019192000._2
64
+
    let $t022452326 = xorNeuralNetwork(scaledInput1, scaledInput2)
65
+
    let networkOutputs = $t022452326._1
66
+
    let debugEntries = $t022452326._2
65
67
 
    ([IntegerEntry("result", networkOutputs[0]), IntegerEntry("outputLayerSum", networkOutputs[1]), IntegerEntry("hiddenLayerOutput1Sum", networkOutputs[2]), IntegerEntry("hiddenLayerOutput2Sum", networkOutputs[3])] ++ debugEntries)
66
68
 
    }
67
69
 

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,debugPrefix) = {
13
13
 
    let e = 2718281
14
14
 
    let base = 1000000
15
-
    let negativeZ = (-1 * z)
16
-
    let expPart = fraction(e, negativeZ, base)
15
+
    let positiveZ = if ((0 > z))
16
+
        then -(z)
17
+
        else z
18
+
    let expPart = fraction(e, base, positiveZ)
17
19
 
    let sigValue = fraction(base, base, (base + expPart))
18
-
    $Tuple2([IntegerEntry((debugPrefix + "negativeZ"), negativeZ), IntegerEntry((debugPrefix + "expPart"), expPart)], sigValue)
20
+
    $Tuple2([IntegerEntry((debugPrefix + "positiveZ"), positiveZ), IntegerEntry((debugPrefix + "expPart"), expPart), IntegerEntry((debugPrefix + "sigValue"), sigValue)], sigValue)
19
21
 
    }
20
22
 
21
23
 
22
24
 
func dotProduct (a,b) = {
23
25
 
    let product0 = fraction(a[0], b[0], 1000000)
24
26
 
    let product1 = fraction(a[1], b[1], 1000000)
25
27
 
    (product0 + product1)
26
28
 
    }
27
29
 
28
30
 
29
31
 
func forwardPass (input,weights,biases,layer) = {
30
32
 
    let sum0 = (dotProduct(input, weights[0]) + biases[0])
31
33
 
    let sum1 = (dotProduct(input, weights[1]) + biases[1])
32
-
    let $t010051062 = sigmoid(sum0, (layer + "L1N1"))
33
-
    let sigmoidDebug0 = $t010051062._1
34
-
    let sig0 = $t010051062._2
35
-
    let $t010671124 = sigmoid(sum1, (layer + "L1N2"))
36
-
    let sigmoidDebug1 = $t010671124._1
37
-
    let sig1 = $t010671124._2
34
+
    let $t013311388 = sigmoid(sum0, (layer + "L1N1"))
35
+
    let sigmoidDebug0 = $t013311388._1
36
+
    let sig0 = $t013311388._2
37
+
    let $t013931450 = sigmoid(sum1, (layer + "L1N2"))
38
+
    let sigmoidDebug1 = $t013931450._1
39
+
    let sig1 = $t013931450._2
38
40
 
    $Tuple2([sig0, sig1, sum0, sum1], (sigmoidDebug0 ++ sigmoidDebug1))
39
41
 
    }
40
42
 
41
43
 
42
44
 
func xorNeuralNetwork (input1,input2) = {
43
45
 
    let input = [input1, input2]
44
-
    let $t013021394 = forwardPass(input, layer1Weights, layer1Biases, "HL")
45
-
    let hiddenLayerOutput = $t013021394._1
46
-
    let hiddenDebug = $t013021394._2
47
-
    let $t013991534 = sigmoid((dotProduct([hiddenLayerOutput[0], hiddenLayerOutput[1]], layer2Weights[0]) + layer2Biases[0]), "OL")
48
-
    let outputDebug = $t013991534._1
49
-
    let output = $t013991534._2
46
+
    let $t016281720 = forwardPass(input, layer1Weights, layer1Biases, "HL")
47
+
    let hiddenLayerOutput = $t016281720._1
48
+
    let hiddenDebug = $t016281720._2
49
+
    let $t017251860 = sigmoid((dotProduct([hiddenLayerOutput[0], hiddenLayerOutput[1]], layer2Weights[0]) + layer2Biases[0]), "OL")
50
+
    let outputDebug = $t017251860._1
51
+
    let output = $t017251860._2
50
52
 
    $Tuple2([output, (dotProduct([hiddenLayerOutput[0], hiddenLayerOutput[1]], layer2Weights[0]) + layer2Biases[0]), hiddenLayerOutput[2], hiddenLayerOutput[3]], (hiddenDebug ++ outputDebug))
51
53
 
    }
52
54
 
53
55
 
54
56
 
@Callable(i)
55
57
 
func predict (input1,input2) = {
56
58
 
    let scaledInput1 = if ((input1 == 1))
57
59
 
        then 1000000
58
60
 
        else 0
59
61
 
    let scaledInput2 = if ((input2 == 1))
60
62
 
        then 1000000
61
63
 
        else 0
62
-
    let $t019192000 = xorNeuralNetwork(scaledInput1, scaledInput2)
63
-
    let networkOutputs = $t019192000._1
64
-
    let debugEntries = $t019192000._2
64
+
    let $t022452326 = xorNeuralNetwork(scaledInput1, scaledInput2)
65
+
    let networkOutputs = $t022452326._1
66
+
    let debugEntries = $t022452326._2
65
67
 
    ([IntegerEntry("result", networkOutputs[0]), IntegerEntry("outputLayerSum", networkOutputs[1]), IntegerEntry("hiddenLayerOutput1Sum", networkOutputs[2]), IntegerEntry("hiddenLayerOutput2Sum", networkOutputs[3])] ++ debugEntries)
66
68
 
    }
67
69
 
68
70
 

github/deemru/w8io/6500d08 
25.99 ms ◑