tx · Cz1bL3xdP7eKdZwGURLxJR4c2Z5n6VT2qikSTCDKNthw

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.05.28 00:54 [3124967] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "Cz1bL3xdP7eKdZwGURLxJR4c2Z5n6VT2qikSTCDKNthw",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1716846869831,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "52m3iDKP26HgovQiVdiAi4ewfyfgbbSDx7kgrmaXciWzvSccHLazE5CwzG2BR6nFMhjXXAcBKuxzfGQ5JtnV9TeQ"
    ],
    "script": "base64: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",
    "height": 3124967,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Diff: 
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Differences
29
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        else (5000 + (input / 2))
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31
31
 
32
-
func sigmoid_activation (inputs,num_outputs) = [sigmoid(inputs[0]), sigmoid(inputs[1])]
32
+
func sigmoid_activation_1 (inputs) = [sigmoid(inputs[0]), sigmoid(inputs[1])]
33
+
34
+
35
+
func sigmoid_activation_2 (inputs) = [sigmoid(inputs[0])]
33
36
 
34
37
 
35
38
 
@Callable(i)
38
41
 
    let x2_scaled = (x2 * 10000)
39
42
 
    let inputs = [x1_scaled, x2_scaled]
40
43
 
    let z1 = linear_forward_1(inputs, weights_layer_1, biases_layer_1)
41
-
    let a1 = sigmoid_activation(z1, 2)
44
+
    let a1 = sigmoid_activation_1(z1)
42
45
 
    let z2 = linear_forward_2(a1, weights_layer_2, biases_layer_2)
43
-
    let a2 = sigmoid_activation(z2, 1)
46
+
    let a2 = sigmoid_activation_2(z2)
44
47
 
    let results = [a2[0]]
45
48
 
    let result_entries = [IntegerEntry("result", results[0])]
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    let debug_outputs = nil
Full: 
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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 ((-10000 > input))
26
26
 
    then 0
27
27
 
    else if ((input > 10000))
28
28
 
        then 10000
29
29
 
        else (5000 + (input / 2))
30
30
 
31
31
 
32
-
func sigmoid_activation (inputs,num_outputs) = [sigmoid(inputs[0]), sigmoid(inputs[1])]
32
+
func sigmoid_activation_1 (inputs) = [sigmoid(inputs[0]), sigmoid(inputs[1])]
33
+
34
+
35
+
func sigmoid_activation_2 (inputs) = [sigmoid(inputs[0])]
33
36
 
34
37
 
35
38
 
@Callable(i)
36
39
 
func predict (x1,x2) = {
37
40
 
    let x1_scaled = (x1 * 10000)
38
41
 
    let x2_scaled = (x2 * 10000)
39
42
 
    let inputs = [x1_scaled, x2_scaled]
40
43
 
    let z1 = linear_forward_1(inputs, weights_layer_1, biases_layer_1)
41
-
    let a1 = sigmoid_activation(z1, 2)
44
+
    let a1 = sigmoid_activation_1(z1)
42
45
 
    let z2 = linear_forward_2(a1, weights_layer_2, biases_layer_2)
43
-
    let a2 = sigmoid_activation(z2, 1)
46
+
    let a2 = sigmoid_activation_2(z2)
44
47
 
    let results = [a2[0]]
45
48
 
    let result_entries = [IntegerEntry("result", results[0])]
46
49
 
    let debug_outputs = nil
47
50
 
    $Tuple2(debug_outputs, result_entries)
48
51
 
    }
49
52
 
50
53
 

github/deemru/w8io/169f3d6 
26.11 ms ◑