tx · CSKB8iCwwXfruTvzLC9tm5jdhrri1eMoeLApwdfSEHCE

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.05.26 19:19 [3123188] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "CSKB8iCwwXfruTvzLC9tm5jdhrri1eMoeLApwdfSEHCE",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1716740411880,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "4ZZ9x1GumaFS7MJ9S1NGPoBN9X4xAmfEuCNagn9YkKBgaSMmpYYDeVarG2YLBwo1MyVKRaRi59MdVCWAMikYCWtb"
    ],
    "script": "base64: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",
    "height": 3123188,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Diff: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 7 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let weights_layer_1 = [[60050, 60073], [41420, 41425]]
4
+
let weights_layer_1 = [[60049, 60073], [41419, 41425]]
5
5
 
6
-
let biases_layer_1 = [-25905, -63564]
6
+
let biases_layer_1 = [-25905, -63563]
7
7
 
8
-
let weights_layer_2 = [[83297, -89714]]
8
+
let weights_layer_2 = [[83296, -89714]]
9
9
 
10
-
let biases_layer_2 = [-38118]
10
+
let biases_layer_2 = [-38117]
11
11
 
12
12
 
func linear_forward (input,weights,biases) = {
13
13
 
    let weighted_sum1 = ((((input[0] * weights[0][0]) + (input[1] * weights[0][1])) / 10000) + biases[0])
28
28
 
29
29
 
@Callable(i)
30
30
 
func predict (x1,x2) = {
31
-
    let inputs = [(x1 * 10000), (x2 * 10000)]
31
+
    let x1_scaled = (x1 * 10000)
32
+
    let x2_scaled = (x2 * 10000)
33
+
    let inputs = [x1_scaled, x2_scaled]
32
34
 
    let z1 = linear_forward(inputs, weights_layer_1, biases_layer_1)
33
35
 
    let a1 = sigmoid_activation(z1)
34
-
    let debug_z1_1 = IntegerEntry("debug_z1_1", z1[0])
35
-
    let debug_z1_2 = IntegerEntry("debug_z1_2", z1[1])
36
-
    let debug_a1_1 = IntegerEntry("debug_a1_1", a1[0])
37
-
    let debug_a1_2 = IntegerEntry("debug_a1_2", a1[1])
38
-
    let z2 = ((((a1[0] * weights_layer_2[0][0]) + (a1[1] * weights_layer_2[0][1])) / 10000) + biases_layer_2[0])
39
-
    let a2 = sigmoid(z2)
36
+
    let z2 = linear_forward(a1, weights_layer_2, biases_layer_2)
37
+
    let a2 = sigmoid(z2[0])
40
38
 
    let result = (a2 / 10000)
41
-
    let debug_z2 = IntegerEntry("debug_z2", z2)
42
-
    let debug_a2 = IntegerEntry("debug_a2", a2)
43
-
    let debug_result = IntegerEntry("debug_result", result)
44
-
    $Tuple2([debug_z1_1, debug_z1_2, debug_a1_1, debug_a1_2, debug_z2, debug_a2, debug_result], result)
39
+
    let debug_outputs = [IntegerEntry("debug_z1_1", z1[0]), IntegerEntry("debug_a1_1", a1[0]), IntegerEntry("debug_z1_2", z1[1]), IntegerEntry("debug_a1_2", a1[1]), IntegerEntry("debug_z2_1", z2[0]), IntegerEntry("debug_a2", a2), IntegerEntry("debug_result", result)]
40
+
    $Tuple2(debug_outputs, result)
45
41
 
    }
46
42
 
47
43
 

Full: 
Old
New
Differences
1
1
 
{-# STDLIB_VERSION 7 #-}
2
2
 
{-# SCRIPT_TYPE ACCOUNT #-}
3
3
 
{-# CONTENT_TYPE DAPP #-}
4
-
let weights_layer_1 = [[60050, 60073], [41420, 41425]]
4
+
let weights_layer_1 = [[60049, 60073], [41419, 41425]]
5
5
 
6
-
let biases_layer_1 = [-25905, -63564]
6
+
let biases_layer_1 = [-25905, -63563]
7
7
 
8
-
let weights_layer_2 = [[83297, -89714]]
8
+
let weights_layer_2 = [[83296, -89714]]
9
9
 
10
-
let biases_layer_2 = [-38118]
10
+
let biases_layer_2 = [-38117]
11
11
 
12
12
 
func linear_forward (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 sigmoid (input) = if ((-10000 > input))
20
20
 
    then 0
21
21
 
    else if ((input > 10000))
22
22
 
        then 10000
23
23
 
        else (5000 + (input / 2))
24
24
 
25
25
 
26
26
 
func sigmoid_activation (inputs) = [sigmoid(inputs[0]), sigmoid(inputs[1])]
27
27
 
28
28
 
29
29
 
@Callable(i)
30
30
 
func predict (x1,x2) = {
31
-
    let inputs = [(x1 * 10000), (x2 * 10000)]
31
+
    let x1_scaled = (x1 * 10000)
32
+
    let x2_scaled = (x2 * 10000)
33
+
    let inputs = [x1_scaled, x2_scaled]
32
34
 
    let z1 = linear_forward(inputs, weights_layer_1, biases_layer_1)
33
35
 
    let a1 = sigmoid_activation(z1)
34
-
    let debug_z1_1 = IntegerEntry("debug_z1_1", z1[0])
35
-
    let debug_z1_2 = IntegerEntry("debug_z1_2", z1[1])
36
-
    let debug_a1_1 = IntegerEntry("debug_a1_1", a1[0])
37
-
    let debug_a1_2 = IntegerEntry("debug_a1_2", a1[1])
38
-
    let z2 = ((((a1[0] * weights_layer_2[0][0]) + (a1[1] * weights_layer_2[0][1])) / 10000) + biases_layer_2[0])
39
-
    let a2 = sigmoid(z2)
36
+
    let z2 = linear_forward(a1, weights_layer_2, biases_layer_2)
37
+
    let a2 = sigmoid(z2[0])
40
38
 
    let result = (a2 / 10000)
41
-
    let debug_z2 = IntegerEntry("debug_z2", z2)
42
-
    let debug_a2 = IntegerEntry("debug_a2", a2)
43
-
    let debug_result = IntegerEntry("debug_result", result)
44
-
    $Tuple2([debug_z1_1, debug_z1_2, debug_a1_1, debug_a1_2, debug_z2, debug_a2, debug_result], result)
39
+
    let debug_outputs = [IntegerEntry("debug_z1_1", z1[0]), IntegerEntry("debug_a1_1", a1[0]), IntegerEntry("debug_z1_2", z1[1]), IntegerEntry("debug_a1_2", a1[1]), IntegerEntry("debug_z2_1", z2[0]), IntegerEntry("debug_a2", a2), IntegerEntry("debug_result", result)]
40
+
    $Tuple2(debug_outputs, result)
45
41
 
    }
46
42
 
47
43
 

github/deemru/w8io/169f3d6 
33.90 ms ◑