w8 · tx · 3yX···bQT

tx · 3yXAAVQ7hMyV4mpM7j8V8iJYvsLoYdyi3DGY5qod2bQT

3Moz6HJhucpFh4V3VScXhd9efei4Curytfj:  -0.01000000 Waves

2023.10.28 17:42 [2818620] smart account 3Moz6HJhucpFh4V3VScXhd9efei4Curytfj > SELF 0.00000000 Waves

{
    "type": 13,
    "id": "3yXAAVQ7hMyV4mpM7j8V8iJYvsLoYdyi3DGY5qod2bQT",
    "fee": 1000000,
    "feeAssetId": null,
    "timestamp": 1698504164952,
    "version": 2,
    "chainId": 84,
    "sender": "3Moz6HJhucpFh4V3VScXhd9efei4Curytfj",
    "senderPublicKey": "Ec1VLg3r1E7mwHmaNidmoKrFAUJ55UVJFq3J1YKRy5Zm",
    "proofs": [
        "pC6E8bsVhuGeFs3hA6w4qPcGPH6vc9cNsGEbZKz4tTvMCoYXVWyvzEAxe8CCN2KBiY9imVXZjzU8z4MKxVnz1Tq"
    ],
    "script": "base64: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",
    "height": 2818620,
    "applicationStatus": "succeeded",
    "spentComplexity": 0
}

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Diff: 
Old New Differences
1 1  {-# STDLIB_VERSION 6 #-}
2 2  {-# SCRIPT_TYPE ACCOUNT #-}
3 3  {-# CONTENT_TYPE DAPP #-}
4 + let species = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
5 + 
4 6  let weight1 = [[6157, -3066, 12102, 17305], [-3936, -2569, -2816, 392], [6633, 300, 11435, 11685], [4149, -4959, -3121, 917], [6310, -9286, 8772, 266], [-527, 5610, -2987, -12595], [6988, -5565, 11513, 14717], [2688, 5935, -9544, -8824], [2346, 6692, -6381, -13268], [2916, 10874, -10078, -11116], [-3257, 18970, -13738, -18644], [10669, -7058, 16831, 17339]]
5 7  
6 8  let biases1 = [-2287, -3248, -5442, -3810, 3699, 11759, -1281, 11270, 12675, 12008, 10765, -2116]
58 60      }
59 61  
60 62  
63 + func find_pred (output) = {
64 +     let max1 = if ((output[0] > output[1]))
65 +         then 0
66 +         else 1
67 +     let max2 = if ((max1 > output[2]))
68 +         then max1
69 +         else 2
70 +     max2
71 +     }
72 + 
73 + 
61 74  @Callable(i)
62 75  func prediction (input) = {
63 76      let output = forward_prop(input)
64 77      let callerAddress = toString(i.caller)
65 - [IntegerEntry((callerAddress + "_1"), output[0]), IntegerEntry((callerAddress + "_2"), output[1]), IntegerEntry((callerAddress + "_3"), output[2])]
78 +     let pred = find_pred(output)
79 + [IntegerEntry((callerAddress + "_1"), output[0]), IntegerEntry((callerAddress + "_2"), output[1]), IntegerEntry((callerAddress + "_3"), output[2]), StringEntry((callerAddress + "_p"), species[pred])]
66 80      }
67 81  
68 82  
Full: 
Old New Differences
1 1  {-# STDLIB_VERSION 6 #-}
2 2  {-# SCRIPT_TYPE ACCOUNT #-}
3 3  {-# CONTENT_TYPE DAPP #-}
4 + let species = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
5 + 
4 6  let weight1 = [[6157, -3066, 12102, 17305], [-3936, -2569, -2816, 392], [6633, 300, 11435, 11685], [4149, -4959, -3121, 917], [6310, -9286, 8772, 266], [-527, 5610, -2987, -12595], [6988, -5565, 11513, 14717], [2688, 5935, -9544, -8824], [2346, 6692, -6381, -13268], [2916, 10874, -10078, -11116], [-3257, 18970, -13738, -18644], [10669, -7058, 16831, 17339]]
5 7  
6 8  let biases1 = [-2287, -3248, -5442, -3810, 3699, 11759, -1281, 11270, 12675, 12008, 10765, -2116]
7 9  
8 10  let weight2 = [[-14019, -170, -13032, 2440, -11741, 13771, -15437, 12736, 13684, 14834, 18289, -12514], [-787, 525, -5546, -28, 3778, 14674, 330, 15426, 13747, 10007, -21208, 465], [6177, 1093, 9648, 1825, 1335, -20733, 6854, -25641, -25315, -18382, -8672, 7714]]
9 11  
10 12  let bias2 = [6583, 6472, -4596]
11 13  
12 14  func relu (x) = if ((x > 0))
13 15      then x
14 16      else 0
15 17  
16 18  
17 19  func calc (input,weight,bias) = {
18 20      let calc = (((((input[0] * weight[0]) + (input[1] * weight[1])) + (input[2] * weight[2])) + (input[3] * weight[3])) + bias)
19 21      calc
20 22      }
21 23  
22 24  
23 25  func calc_second_layer (input,weight,bias) = {
24 26      let calc_second = (((((((((((((input[0] * weight[0]) + (input[1] * weight[1])) + (input[2] * weight[2])) + (input[3] * weight[3])) + (input[4] * weight[4])) + (input[5] * weight[5])) + (input[6] * weight[6])) + (input[7] * weight[7])) + (input[8] * weight[8])) + (input[9] * weight[9])) + (input[10] * weight[10])) + (input[11] * weight[11])) + bias)
25 27      calc_second
26 28      }
27 29  
28 30  
29 31  func calculateFirstLayer (input) = {
30 32      let ouput_layer1 = relu(calc(input, weight1[0], biases1[0]))
31 33      let ouput_layer2 = relu(calc(input, weight1[1], biases1[1]))
32 34      let ouput_layer3 = relu(calc(input, weight1[2], biases1[2]))
33 35      let ouput_layer4 = relu(calc(input, weight1[3], biases1[3]))
34 36      let ouput_layer5 = relu(calc(input, weight1[4], biases1[4]))
35 37      let ouput_layer6 = relu(calc(input, weight1[5], biases1[5]))
36 38      let ouput_layer7 = relu(calc(input, weight1[6], biases1[6]))
37 39      let ouput_layer8 = relu(calc(input, weight1[7], biases1[7]))
38 40      let ouput_layer9 = relu(calc(input, weight1[8], biases1[8]))
39 41      let ouput_layer10 = relu(calc(input, weight1[9], biases1[9]))
40 42      let ouput_layer11 = relu(calc(input, weight1[10], biases1[10]))
41 43      let ouput_layer12 = relu(calc(input, weight1[11], biases1[11]))
42 44  [ouput_layer1, ouput_layer2, ouput_layer3, ouput_layer4, ouput_layer5, ouput_layer6, ouput_layer7, ouput_layer8, ouput_layer9, ouput_layer10, ouput_layer11, ouput_layer12]
43 45      }
44 46  
45 47  
46 48  func calculateSecondLayer (input) = {
47 49      let output_layer1 = calc_second_layer(input, weight2[0], bias2[0])
48 50      let output_layer2 = calc_second_layer(input, weight2[1], bias2[1])
49 51      let output_layer3 = calc_second_layer(input, weight2[2], bias2[2])
50 52  [output_layer1, output_layer2, output_layer3]
51 53      }
52 54  
53 55  
54 56  func forward_prop (input) = {
55 57      let first_layer = calculateFirstLayer(input)
56 58      let second_layer = calculateSecondLayer(first_layer)
57 59      second_layer
58 60      }
59 61  
60 62  
63 + func find_pred (output) = {
64 +     let max1 = if ((output[0] > output[1]))
65 +         then 0
66 +         else 1
67 +     let max2 = if ((max1 > output[2]))
68 +         then max1
69 +         else 2
70 +     max2
71 +     }
72 + 
73 + 
61 74  @Callable(i)
62 75  func prediction (input) = {
63 76      let output = forward_prop(input)
64 77      let callerAddress = toString(i.caller)
65 - [IntegerEntry((callerAddress + "_1"), output[0]), IntegerEntry((callerAddress + "_2"), output[1]), IntegerEntry((callerAddress + "_3"), output[2])]
78 +     let pred = find_pred(output)
79 + [IntegerEntry((callerAddress + "_1"), output[0]), IntegerEntry((callerAddress + "_2"), output[1]), IntegerEntry((callerAddress + "_3"), output[2]), StringEntry((callerAddress + "_p"), species[pred])]
66 80      }
67 81  
68 82

Old	New		Differences
1	1		{-# STDLIB_VERSION 6 #-}
2	2		{-# SCRIPT_TYPE ACCOUNT #-}
3	3		{-# CONTENT_TYPE DAPP #-}
	4	+	let species = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
	5	+
4	6		let weight1 = [[6157, -3066, 12102, 17305], [-3936, -2569, -2816, 392], [6633, 300, 11435, 11685], [4149, -4959, -3121, 917], [6310, -9286, 8772, 266], [-527, 5610, -2987, -12595], [6988, -5565, 11513, 14717], [2688, 5935, -9544, -8824], [2346, 6692, -6381, -13268], [2916, 10874, -10078, -11116], [-3257, 18970, -13738, -18644], [10669, -7058, 16831, 17339]]
5	7
6	8		let biases1 = [-2287, -3248, -5442, -3810, 3699, 11759, -1281, 11270, 12675, 12008, 10765, -2116]

58	60		}
59	61
60	62
	63	+	func find_pred (output) = {
	64	+	let max1 = if ((output[0] > output[1]))
	65	+	then 0
	66	+	else 1
	67	+	let max2 = if ((max1 > output[2]))
	68	+	then max1
	69	+	else 2
	70	+	max2
	71	+	}
	72	+
	73	+
61	74		@Callable(i)
62	75		func prediction (input) = {
63	76		let output = forward_prop(input)
64	77		let callerAddress = toString(i.caller)
65		-	[IntegerEntry((callerAddress + "_1"), output[0]), IntegerEntry((callerAddress + "_2"), output[1]), IntegerEntry((callerAddress + "_3"), output[2])]
	78	+	let pred = find_pred(output)
	79	+	[IntegerEntry((callerAddress + "_1"), output[0]), IntegerEntry((callerAddress + "_2"), output[1]), IntegerEntry((callerAddress + "_3"), output[2]), StringEntry((callerAddress + "_p"), species[pred])]
66	80		}
67	81
68	82

Old	New		Differences
1	1		{-# STDLIB_VERSION 6 #-}
2	2		{-# SCRIPT_TYPE ACCOUNT #-}
3	3		{-# CONTENT_TYPE DAPP #-}
	4	+	let species = ["Iris-setosa", "Iris-versicolor", "Iris-virginica"]
	5	+
4	6		let weight1 = [[6157, -3066, 12102, 17305], [-3936, -2569, -2816, 392], [6633, 300, 11435, 11685], [4149, -4959, -3121, 917], [6310, -9286, 8772, 266], [-527, 5610, -2987, -12595], [6988, -5565, 11513, 14717], [2688, 5935, -9544, -8824], [2346, 6692, -6381, -13268], [2916, 10874, -10078, -11116], [-3257, 18970, -13738, -18644], [10669, -7058, 16831, 17339]]
5	7
6	8		let biases1 = [-2287, -3248, -5442, -3810, 3699, 11759, -1281, 11270, 12675, 12008, 10765, -2116]
7	9
8	10		let weight2 = [[-14019, -170, -13032, 2440, -11741, 13771, -15437, 12736, 13684, 14834, 18289, -12514], [-787, 525, -5546, -28, 3778, 14674, 330, 15426, 13747, 10007, -21208, 465], [6177, 1093, 9648, 1825, 1335, -20733, 6854, -25641, -25315, -18382, -8672, 7714]]
9	11
10	12		let bias2 = [6583, 6472, -4596]
11	13
12	14		func relu (x) = if ((x > 0))
13	15		then x
14	16		else 0
15	17
16	18
17	19		func calc (input,weight,bias) = {
18	20		let calc = (((((input[0] * weight[0]) + (input[1] * weight[1])) + (input[2] * weight[2])) + (input[3] * weight[3])) + bias)
19	21		calc
20	22		}
21	23
22	24
23	25		func calc_second_layer (input,weight,bias) = {
24	26		let calc_second = (((((((((((((input[0] * weight[0]) + (input[1] * weight[1])) + (input[2] * weight[2])) + (input[3] * weight[3])) + (input[4] * weight[4])) + (input[5] * weight[5])) + (input[6] * weight[6])) + (input[7] * weight[7])) + (input[8] * weight[8])) + (input[9] * weight[9])) + (input[10] * weight[10])) + (input[11] * weight[11])) + bias)
25	27		calc_second
26	28		}
27	29
28	30
29	31		func calculateFirstLayer (input) = {
30	32		let ouput_layer1 = relu(calc(input, weight1[0], biases1[0]))
31	33		let ouput_layer2 = relu(calc(input, weight1[1], biases1[1]))
32	34		let ouput_layer3 = relu(calc(input, weight1[2], biases1[2]))
33	35		let ouput_layer4 = relu(calc(input, weight1[3], biases1[3]))
34	36		let ouput_layer5 = relu(calc(input, weight1[4], biases1[4]))
35	37		let ouput_layer6 = relu(calc(input, weight1[5], biases1[5]))
36	38		let ouput_layer7 = relu(calc(input, weight1[6], biases1[6]))
37	39		let ouput_layer8 = relu(calc(input, weight1[7], biases1[7]))
38	40		let ouput_layer9 = relu(calc(input, weight1[8], biases1[8]))
39	41		let ouput_layer10 = relu(calc(input, weight1[9], biases1[9]))
40	42		let ouput_layer11 = relu(calc(input, weight1[10], biases1[10]))
41	43		let ouput_layer12 = relu(calc(input, weight1[11], biases1[11]))
42	44		[ouput_layer1, ouput_layer2, ouput_layer3, ouput_layer4, ouput_layer5, ouput_layer6, ouput_layer7, ouput_layer8, ouput_layer9, ouput_layer10, ouput_layer11, ouput_layer12]
43	45		}
44	46
45	47
46	48		func calculateSecondLayer (input) = {
47	49		let output_layer1 = calc_second_layer(input, weight2[0], bias2[0])
48	50		let output_layer2 = calc_second_layer(input, weight2[1], bias2[1])
49	51		let output_layer3 = calc_second_layer(input, weight2[2], bias2[2])
50	52		[output_layer1, output_layer2, output_layer3]
51	53		}
52	54
53	55
54	56		func forward_prop (input) = {
55	57		let first_layer = calculateFirstLayer(input)
56	58		let second_layer = calculateSecondLayer(first_layer)
57	59		second_layer
58	60		}
59	61
60	62
	63	+	func find_pred (output) = {
	64	+	let max1 = if ((output[0] > output[1]))
	65	+	then 0
	66	+	else 1
	67	+	let max2 = if ((max1 > output[2]))
	68	+	then max1
	69	+	else 2
	70	+	max2
	71	+	}
	72	+
	73	+
61	74		@Callable(i)
62	75		func prediction (input) = {
63	76		let output = forward_prop(input)
64	77		let callerAddress = toString(i.caller)
65		-	[IntegerEntry((callerAddress + "_1"), output[0]), IntegerEntry((callerAddress + "_2"), output[1]), IntegerEntry((callerAddress + "_3"), output[2])]
	78	+	let pred = find_pred(output)
	79	+	[IntegerEntry((callerAddress + "_1"), output[0]), IntegerEntry((callerAddress + "_2"), output[1]), IntegerEntry((callerAddress + "_3"), output[2]), StringEntry((callerAddress + "_p"), species[pred])]
66	80		}
67	81
68	82

github/deemru/w8io/3ef1775 
43.02 ms ◑