tx · HHerL6LJ9qpFpNLuhDtMDN5gutXSHW4tx8eUaoGnaiPv

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.06.09 12:05 [3142991] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "HHerL6LJ9qpFpNLuhDtMDN5gutXSHW4tx8eUaoGnaiPv",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1717923983241,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "33oghp6tiQBD2SusffYqPVMsUxtZ5U3dx9ya7MLaFV3xxxf4j69RkvKX8Ah2VgSBQtMCRRfbrQYBTiJpXwPz3W2W"
    ],
    "script": "base64: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",
    "height": 3142991,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Diff: 
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Differences
43
43
 
                                    else 10000
44
44
 
45
45
 
46
-
func sigmoid_activation (inputs,num_outputs) = [sigmoid(inputs[0]), sigmoid(inputs[1])]
46
+
func sigmoid_activation (inputs,num_outputs) = if ((num_outputs == 1))
47
+
    then [sigmoid(inputs[0])]
48
+
    else [sigmoid(inputs[0]), sigmoid(inputs[1])]
47
49
 
48
50
 
49
51
 
@Callable(i)
Full: 
Old
New
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]]
5
5
 
6
6
 
let biases_layer_1 = [-25905, -63563]
7
7
 
8
8
 
let weights_layer_2 = [[83296, -89714]]
9
9
 
10
10
 
let biases_layer_2 = [-38117]
11
11
 
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
14
 
    let weighted_sum2 = ((((input[0] * weights[1][0]) + (input[1] * weights[1][1])) / 10000) + biases[1])
15
15
 
[weighted_sum1, weighted_sum2]
16
16
 
    }
17
17
 
18
18
 
19
19
 
func linear_forward_2 (input,weights,biases) = {
20
20
 
    let weighted_sum1 = ((((input[0] * weights[0][0]) + (input[1] * weights[0][1])) / 10000) + biases[0])
21
21
 
[weighted_sum1]
22
22
 
    }
23
23
 
24
24
 
25
25
 
func sigmoid (input) = if ((-80000 > input))
26
26
 
    then 0
27
27
 
    else if ((-60000 > input))
28
28
 
        then fraction((input + 80000), 125, 10000)
29
29
 
        else if ((-40000 > input))
30
30
 
            then fraction((input + 60000), 100, 10000)
31
31
 
            else if ((-20000 > input))
32
32
 
                then fraction((input + 40000), 75, 10000)
33
33
 
                else if ((0 > input))
34
34
 
                    then fraction((input + 20000), 50, 10000)
35
35
 
                    else if ((20000 > input))
36
36
 
                        then (fraction(input, 50, 10000) + 5000)
37
37
 
                        else if ((40000 > input))
38
38
 
                            then (fraction((input - 20000), 75, 10000) + 7500)
39
39
 
                            else if ((60000 > input))
40
40
 
                                then (fraction((input - 40000), 100, 10000) + 8750)
41
41
 
                                else if ((80000 > input))
42
42
 
                                    then (fraction((input - 60000), 125, 10000) + 9375)
43
43
 
                                    else 10000
44
44
 
45
45
 
46
-
func sigmoid_activation (inputs,num_outputs) = [sigmoid(inputs[0]), sigmoid(inputs[1])]
46
+
func sigmoid_activation (inputs,num_outputs) = if ((num_outputs == 1))
47
+
    then [sigmoid(inputs[0])]
48
+
    else [sigmoid(inputs[0]), sigmoid(inputs[1])]
47
49
 
48
50
 
49
51
 
@Callable(i)
50
52
 
func predict (x1,x2) = {
51
53
 
    let x1_scaled = (x1 * 10000)
52
54
 
    let x2_scaled = (x2 * 10000)
53
55
 
    let inputs = [x1_scaled, x2_scaled]
54
56
 
    let z1 = linear_forward_1(inputs, weights_layer_1, biases_layer_1)
55
57
 
    let a1 = sigmoid_activation(z1, 2)
56
58
 
    let z2 = linear_forward_2(a1, weights_layer_2, biases_layer_2)
57
59
 
    let a2 = sigmoid_activation(z2, 1)
58
60
 
    let result0 = a2[0]
59
61
 
    let debug_outputs = nil
60
62
 
    ([IntegerEntry("move_prediction_0", result0)] ++ debug_outputs)
61
63
 
    }
62
64
 
63
65
 

github/deemru/w8io/169f3d6 
25.72 ms ◑