tx · 6E7zaZ4wjSB9fjAqTL9Py14raWfez4jogsN994iBCpPg

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.05.26 22:28 [3123386] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "6E7zaZ4wjSB9fjAqTL9Py14raWfez4jogsN994iBCpPg",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1716751767080,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "3fUwDM74WpQDcMTaJkJyPiUTSmDSfe7aZuvvMyc5Z3nJr8WEAy1EtC2t74KJ74ALnyMe4TLy4nJrwvhcnDHP8KJf"
    ],
    "script": "base64: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",
    "height": 3123386,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Diff: 
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Differences
29
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        else (5000 + (input / 2))
30
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31
31
 
32
-
func sigmoid_activation (inputs) = [sigmoid(inputs[0])]
32
+
func sigmoid_activation (inputs,num_outputs) = [sigmoid(inputs[0]), sigmoid(inputs[1])]
33
33
 
34
34
 
35
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@Callable(i)
38
38
 
    let x2_scaled = (x2 * 10000)
39
39
 
    let inputs = [x1_scaled, x2_scaled]
40
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    let z1 = linear_forward_1(inputs, weights_layer_1, biases_layer_1)
41
-
    let a1 = sigmoid_activation(z1)
41
+
    let a1 = sigmoid_activation(z1, 2)
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    let z2 = linear_forward_2(a1, weights_layer_2, biases_layer_2)
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    let a2 = sigmoid(z2[0])
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    let result = (a2 / 10000)
Full: 
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Differences
1
1
 
{-# STDLIB_VERSION 7 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
4
 
let weights_layer_1 = [[60049, 60073], [41419, 41425]]
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5
 
6
6
 
let biases_layer_1 = [-25905, -63563]
7
7
 
8
8
 
let weights_layer_2 = [[83296, -89714]]
9
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10
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let biases_layer_2 = [-38117]
11
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12
12
 
func linear_forward_1 (input,weights,biases) = {
13
13
 
    let weighted_sum1 = ((((input[0] * weights[0][0]) + (input[1] * weights[0][1])) / 10000) + biases[0])
14
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    let weighted_sum2 = ((((input[0] * weights[1][0]) + (input[1] * weights[1][1])) / 10000) + biases[1])
15
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[weighted_sum1, weighted_sum2]
16
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    }
17
17
 
18
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19
19
 
func linear_forward_2 (input,weights,biases) = {
20
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    let weighted_sum1 = ((((input[0] * weights[0][0]) + (input[1] * weights[0][1])) / 10000) + biases[0])
21
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[weighted_sum1]
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    }
23
23
 
24
24
 
25
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func sigmoid (input) = if ((-10000 > input))
26
26
 
    then 0
27
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    else if ((input > 10000))
28
28
 
        then 10000
29
29
 
        else (5000 + (input / 2))
30
30
 
31
31
 
32
-
func sigmoid_activation (inputs) = [sigmoid(inputs[0])]
32
+
func sigmoid_activation (inputs,num_outputs) = [sigmoid(inputs[0]), sigmoid(inputs[1])]
33
33
 
34
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35
35
 
@Callable(i)
36
36
 
func predict (x1,x2) = {
37
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    let x1_scaled = (x1 * 10000)
38
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    let x2_scaled = (x2 * 10000)
39
39
 
    let inputs = [x1_scaled, x2_scaled]
40
40
 
    let z1 = linear_forward_1(inputs, weights_layer_1, biases_layer_1)
41
-
    let a1 = sigmoid_activation(z1)
41
+
    let a1 = sigmoid_activation(z1, 2)
42
42
 
    let z2 = linear_forward_2(a1, weights_layer_2, biases_layer_2)
43
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    let a2 = sigmoid(z2[0])
44
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    let result = (a2 / 10000)
45
45
 
    let debug_outputs = nil
46
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    $Tuple2(debug_outputs, result)
47
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    }
48
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49
49
 

github/deemru/w8io/169f3d6 
28.88 ms ◑