tx · 5iNUFVUiuLxLyD9zsZtxLmY1BS5THCkkmFMT6kNw969S

3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY:  -0.01000000 Waves

2024.05.24 17:59 [3120241] smart account 3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "5iNUFVUiuLxLyD9zsZtxLmY1BS5THCkkmFMT6kNw969S",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1716562750825,
    "version": 2,
    "chainId": 84,
    "sender": "3N3n75UqB8G1GKmXFr4zPhKCjGcqJPRSuJY",
    "senderPublicKey": "2AWdnJuBMzufXSjTvzVcawBQQhnhF1iXR6QNVgwn33oc",
    "proofs": [
        "5M6rTHi4g7gfyNcQVYJcP6UtgzaUtjxtoXuqSZp8rjWdq5VzzLk4cbBeXQHxy9FuvpZPTU15mMbp6JJvbMPrknHJ"
    ],
    "script": "base64:BwIICAISBAoCAQEHAA93ZWlnaHRzX2xheWVyXzEJAMwIAgkAzAgCAJLVAwkAzAgCAKnVAwUDbmlsCQDMCAIJAMwIAgDMwwIJAMwIAgDRwwIFA25pbAUDbmlsAA5iaWFzZXNfbGF5ZXJfMQkAzAgCAM+1/v///////wEJAMwIAgC0j/z///////8BBQNuaWwAD3dlaWdodHNfbGF5ZXJfMgkAzAgCCQDMCAIA4YoFCQDMCAIAjsP6////////AQUDbmlsBQNuaWwADmJpYXNlc19sYXllcl8yCQDMCAIAmtb9////////AQUDbmlsAQ5saW5lYXJfZm9yd2FyZAMFaW5wdXQHd2VpZ2h0cwZiaWFzZXMEDXdlaWdodGVkX3N1bTEJAGQCCQBpAgkAZAIJAGgCCQCRAwIFBWlucHV0AAAJAJEDAgkAkQMCBQd3ZWlnaHRzAAAAAAkAaAIJAJEDAgUFaW5wdXQAAQkAkQMCCQCRAwIFB3dlaWdodHMAAAABAJBOCQCRAwIFBmJpYXNlcwAABA13ZWlnaHRlZF9zdW0yCQBkAgkAaQIJAGQCCQBoAgkAkQMCBQVpbnB1dAAACQCRAwIJAJEDAgUHd2VpZ2h0cwABAAAJAGgCCQCRAwIFBWlucHV0AAEJAJEDAgkAkQMCBQd3ZWlnaHRzAAEAAQCQTgkAkQMCBQZiaWFzZXMAAQkAzAgCBQ13ZWlnaHRlZF9zdW0xCQDMCAIFDXdlaWdodGVkX3N1bTIFA25pbAEHc2lnbW9pZAEFaW5wdXQDCQBmAgDwsf////////8BBQVpbnB1dAAAAwkAZgIFBWlucHV0AJBOAJBOCQBkAgCIJwkAaQIFBWlucHV0AAIBEnNpZ21vaWRfYWN0aXZhdGlvbgEGaW5wdXRzCQDMCAIJAQdzaWdtb2lkAQkAkQMCBQZpbnB1dHMAAAkAzAgCCQEHc2lnbW9pZAEJAJEDAgUGaW5wdXRzAAEFA25pbAEBaQEHcHJlZGljdAICeDECeDIEBmlucHV0cwkAzAgCCQBoAgUCeDEAkE4JAMwIAgkAaAIFAngyAJBOBQNuaWwEAnoxCQEObGluZWFyX2ZvcndhcmQDBQZpbnB1dHMFD3dlaWdodHNfbGF5ZXJfMQUOYmlhc2VzX2xheWVyXzEEAmExCQESc2lnbW9pZF9hY3RpdmF0aW9uAQUCejEECmRlYnVnX3oxXzEJAQxJbnRlZ2VyRW50cnkCAgpkZWJ1Z196MV8xCQCRAwIFAnoxAAAECmRlYnVnX3oxXzIJAQxJbnRlZ2VyRW50cnkCAgpkZWJ1Z196MV8yCQCRAwIFAnoxAAEECmRlYnVnX2ExXzEJAQxJbnRlZ2VyRW50cnkCAgpkZWJ1Z19hMV8xCQCRAwIFAmExAAAECmRlYnVnX2ExXzIJAQxJbnRlZ2VyRW50cnkCAgpkZWJ1Z19hMV8yCQCRAwIFAmExAAEEAnoyCQBkAgkAaQIJAGQCCQBoAgkAkQMCBQJhMQAACQCRAwIJAJEDAgUPd2VpZ2h0c19sYXllcl8yAAAAAAkAaAIJAJEDAgUCYTEAAQkAkQMCCQCRAwIFD3dlaWdodHNfbGF5ZXJfMgAAAAEAkE4JAJEDAgUOYmlhc2VzX2xheWVyXzIAAAQCYTIJAQdzaWdtb2lkAQUCejIEBnJlc3VsdAkAaQIFAmEyAJBOBAhkZWJ1Z196MgkBDEludGVnZXJFbnRyeQICCGRlYnVnX3oyBQJ6MgQIZGVidWdfYTIJAQxJbnRlZ2VyRW50cnkCAghkZWJ1Z19hMgUCYTIEDGRlYnVnX3Jlc3VsdAkBDEludGVnZXJFbnRyeQICDGRlYnVnX3Jlc3VsdAUGcmVzdWx0CQCUCgIJAMwIAgUKZGVidWdfejFfMQkAzAgCBQpkZWJ1Z196MV8yCQDMCAIFCmRlYnVnX2ExXzEJAMwIAgUKZGVidWdfYTFfMgkAzAgCBQhkZWJ1Z196MgkAzAgCBQhkZWJ1Z19hMgkAzAgCBQxkZWJ1Z19yZXN1bHQFA25pbAUGcmVzdWx0AIn6qtg=",
    "height": 3120241,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Diff: 
Old
New
Differences
16
16
 
    }
17
17
 
18
18
 
19
-
func relu (input) = {
20
-
    let out1 = if ((input[0] > 0))
21
-
        then input[0]
22
-
        else 0
23
-
    let out2 = if ((input[1] > 0))
24
-
        then input[1]
25
-
        else 0
26
-
[out1, out2]
27
-
    }
28
-
29
-
30
19
 
func sigmoid (input) = if ((-10000 > input))
31
20
 
    then 0
32
21
 
    else if ((input > 10000))
34
23
 
        else (5000 + (input / 2))
35
24
 
36
25
 
26
+
func sigmoid_activation (inputs) = [sigmoid(inputs[0]), sigmoid(inputs[1])]
27
+
28
+
37
29
 
@Callable(i)
38
30
 
func predict (x1,x2) = {
39
31
 
    let inputs = [(x1 * 10000), (x2 * 10000)]
40
32
 
    let z1 = linear_forward(inputs, weights_layer_1, biases_layer_1)
41
-
    let a1 = relu(z1)
33
+
    let a1 = sigmoid_activation(z1)
42
34
 
    let debug_z1_1 = IntegerEntry("debug_z1_1", z1[0])
43
35
 
    let debug_z1_2 = IntegerEntry("debug_z1_2", z1[1])
44
36
 
    let debug_a1_1 = IntegerEntry("debug_a1_1", a1[0])
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 = [[60050, 60073], [41420, 41425]]
5
5
 
6
6
 
let biases_layer_1 = [-25905, -63564]
7
7
 
8
8
 
let weights_layer_2 = [[83297, -89714]]
9
9
 
10
10
 
let biases_layer_2 = [-38118]
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
-
func relu (input) = {
20
-
    let out1 = if ((input[0] > 0))
21
-
        then input[0]
22
-
        else 0
23
-
    let out2 = if ((input[1] > 0))
24
-
        then input[1]
25
-
        else 0
26
-
[out1, out2]
27
-
    }
28
-
29
-
30
19
 
func sigmoid (input) = if ((-10000 > input))
31
20
 
    then 0
32
21
 
    else if ((input > 10000))
33
22
 
        then 10000
34
23
 
        else (5000 + (input / 2))
35
24
 
36
25
 
26
+
func sigmoid_activation (inputs) = [sigmoid(inputs[0]), sigmoid(inputs[1])]
27
+
28
+
37
29
 
@Callable(i)
38
30
 
func predict (x1,x2) = {
39
31
 
    let inputs = [(x1 * 10000), (x2 * 10000)]
40
32
 
    let z1 = linear_forward(inputs, weights_layer_1, biases_layer_1)
41
-
    let a1 = relu(z1)
33
+
    let a1 = sigmoid_activation(z1)
42
34
 
    let debug_z1_1 = IntegerEntry("debug_z1_1", z1[0])
43
35
 
    let debug_z1_2 = IntegerEntry("debug_z1_2", z1[1])
44
36
 
    let debug_a1_1 = IntegerEntry("debug_a1_1", a1[0])
45
37
 
    let debug_a1_2 = IntegerEntry("debug_a1_2", a1[1])
46
38
 
    let z2 = ((((a1[0] * weights_layer_2[0][0]) + (a1[1] * weights_layer_2[0][1])) / 10000) + biases_layer_2[0])
47
39
 
    let a2 = sigmoid(z2)
48
40
 
    let result = (a2 / 10000)
49
41
 
    let debug_z2 = IntegerEntry("debug_z2", z2)
50
42
 
    let debug_a2 = IntegerEntry("debug_a2", a2)
51
43
 
    let debug_result = IntegerEntry("debug_result", result)
52
44
 
    $Tuple2([debug_z1_1, debug_z1_2, debug_a1_1, debug_a1_2, debug_z2, debug_a2, debug_result], result)
53
45
 
    }
54
46
 
55
47
 

github/deemru/w8io/169f3d6 
31.25 ms ◑