tx · CrFqEGY8DWvwsYq3yExJteW66i3hdNFg277CA3AXDGFm

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.05.04 11:41 [3091156] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "CrFqEGY8DWvwsYq3yExJteW66i3hdNFg277CA3AXDGFm",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1714812142861,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "4vsrxw6zxrmHWGFf3ymbpdr2gD2nVUAYg8k9JBr2kmbJKg8fB4AjELMPV8w8Uu1dcyjmjkLdJpvyZSJytdd62ebj"
    ],
    "script": "base64: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",
    "height": 3091156,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Diff: 
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New
Differences
14
14
 
    else 0
15
15
 
16
16
 
17
+
func sigmoid_approx (x) = if ((-5000 > x))
18
+
    then 0
19
+
    else if ((0 > x))
20
+
        then 5000
21
+
        else if ((5000 > x))
22
+
            then 9500
23
+
            else 10000
24
+
25
+
17
26
 
func dotProduct (v1,v2) = {
18
27
 
    let sum1 = ((v1[0] * v2[0]) / 10000)
19
28
 
    let sum2 = ((v1[1] * v2[1]) / 10000)
22
31
 
23
32
 
24
33
 
func feedforward (inputs) = {
25
-
    let layer1Result1 = relu((dotProduct(inputs, layer1Weights[0]) + layer1Biases[0]))
26
-
    let layer1Result2 = relu((dotProduct(inputs, layer1Weights[1]) + layer1Biases[1]))
34
+
    let layer1Result1 = sigmoid_approx((dotProduct(inputs, layer1Weights[0]) + layer1Biases[0]))
35
+
    let layer1Result2 = sigmoid_approx((dotProduct(inputs, layer1Weights[1]) + layer1Biases[1]))
27
36
 
    let layer2Inputs = [layer1Result1, layer1Result2]
28
-
    (dotProduct(layer2Inputs, layer2Weights[0]) + layer2Biases[0])
37
+
    sigmoid_approx((dotProduct(layer2Inputs, layer2Weights[0]) + layer2Biases[0]))
29
38
 
    }
30
39
 
31
40
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 5 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
4
 
let layer1Weights = [[600496, 600733], [414197, 414253]]
5
5
 
6
6
 
let layer1Biases = [-259050, -635637]
7
7
 
8
8
 
let layer2Weights = [[832966, -897141]]
9
9
 
10
10
 
let layer2Biases = [-381179]
11
11
 
12
12
 
func relu (x) = if ((x > 0))
13
13
 
    then x
14
14
 
    else 0
15
15
 
16
16
 
17
+
func sigmoid_approx (x) = if ((-5000 > x))
18
+
    then 0
19
+
    else if ((0 > x))
20
+
        then 5000
21
+
        else if ((5000 > x))
22
+
            then 9500
23
+
            else 10000
24
+
25
+
17
26
 
func dotProduct (v1,v2) = {
18
27
 
    let sum1 = ((v1[0] * v2[0]) / 10000)
19
28
 
    let sum2 = ((v1[1] * v2[1]) / 10000)
20
29
 
    (sum1 + sum2)
21
30
 
    }
22
31
 
23
32
 
24
33
 
func feedforward (inputs) = {
25
-
    let layer1Result1 = relu((dotProduct(inputs, layer1Weights[0]) + layer1Biases[0]))
26
-
    let layer1Result2 = relu((dotProduct(inputs, layer1Weights[1]) + layer1Biases[1]))
34
+
    let layer1Result1 = sigmoid_approx((dotProduct(inputs, layer1Weights[0]) + layer1Biases[0]))
35
+
    let layer1Result2 = sigmoid_approx((dotProduct(inputs, layer1Weights[1]) + layer1Biases[1]))
27
36
 
    let layer2Inputs = [layer1Result1, layer1Result2]
28
-
    (dotProduct(layer2Inputs, layer2Weights[0]) + layer2Biases[0])
37
+
    sigmoid_approx((dotProduct(layer2Inputs, layer2Weights[0]) + layer2Biases[0]))
29
38
 
    }
30
39
 
31
40
 
32
41
 
@Callable(i)
33
42
 
func predict (input1,input2) = {
34
43
 
    let inputs = [input1, input2]
35
44
 
    let prediction = feedforward(inputs)
36
45
 
[IntegerEntry("prediction", prediction)]
37
46
 
    }
38
47
 
39
48
 

github/deemru/w8io/6500d08 
41.18 ms ◑