목록CNN (8)

잠토의 잠망경

[ML] CNN - Multiple Parallel Input and Multi-step Output

GITHUB https://github.com/yiwonjae/Project_Lotto/blob/master/Book_001/p119.py 0. 목표 [ 10 15 25] [ 20 25 45] [ 30 35 65] [ 40 45 85] [ 50 55 105] [ 60 65 125] [ 70 75 145] [ 80 85 165] [ 90 95 185] input [ 10 15 25] [ 20 25 45] [ 30 35 65] output [ 40 45 85] [ 50 55 105] 1. DATA in_seq1 = np.asarray([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = np.asarray([15, 25, 35, 45, 55, 65, 75, 85, 95]) o..
공부/Python 2020. 2. 16. 16:47
[ML] Multiple Input Multi-step Output

GITHUB https://github.com/yiwonjae/Project_Lotto/blob/master/Book_001/p113.py 0. 목표 input 多, output 多step 1. DATA in_seq1 = np.asarray([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = np.asarray([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = np.asarray([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) in_seq1:ndarray = in_seq1.reshape((len(in_seq1), 1)) in_seq2:ndarray = in_seq2.reshape((len..
공부/Python 2020. 2. 16. 10:12
[ML] Multi-step CNN Models

GITHUB https://github.com/yiwonjae/Project_Lotto/blob/master/Book_001/p111.py 0. 목표 output이 여러단계를 원할 때 1. DATA raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] 2. DATA 정제 X from numpy import ndarray import numpy as np def split_sequence(sequence:list, n_steps_in:int, n_steps_out:int)->(ndarray, ndarray): x = [] y = [] for i in range(len(sequence)): if(i+n_steps_in+n_steps_out > len(sequence)): bre..
공부/Python 2020. 2. 15. 21:46
[ML] Multi-output Parallel Series

GITHUB https://github.com/yiwonjae/Project_Lotto/blob/master/Book_001/p107.py 0. 목표 feature의 수에 맞추서 output을 산출하는 방법이다. 1. DATA in_seq1 = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = np.array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = np.array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2)..
공부/Python 2020. 2. 15. 21:29
[ML] Multiple Parallel Series

GITHUB https://github.com/yiwonjae/Project_Lotto/blob/master/Book_001/p104.py 0. 목표 이전 값들을 갖고 한단계 앞 값을 예측한다. 1. DATA in_seq1 = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = np.array([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = np.array([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) in_seq1 = in_seq1.reshape((len(in_seq1), 1)) in_seq2 = in_seq2.reshape((len(in_seq2), 1)) out_..
공부/Python 2020. 2. 15. 17:11
[ML] Multiple Input Series - Multi-headed CNN Model

GITHUB https://github.com/yiwonjae/Project_Lotto/blob/master/Book_001/p100.py 0. 목표 이전 형태에서 model만 바꾼 것으로 input 값의 종류에 따라 layer를 변경한 것이다. 1. DATA # data sample in_seq1 = np.asarray([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = np.asarray([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = np.asarray([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) # sample용 의미 없음 in_seq1 = in_seq1.reshape((in..
공부/Python 2020. 2. 15. 11:46
[ML] Multiple Input Series - Multivariate CNN Models

GITHUB https://github.com/yiwonjae/Project_Lotto/blob/master/Book_001/p096.py 0. 목표 data가 두가지 종류가 있다. 현재 포함 과거 data 총 3단계를 바탕으로 예측되는 data를 산출한다. input data 10, 15 20, 25 30, 35 prediction data 65 1. DATA in_seq1 = np.asarray([10, 20, 30, 40, 50, 60, 70, 80, 90]) in_seq2 = np.asarray([15, 25, 35, 45, 55, 65, 75, 85, 95]) out_seq = np.asarray([in_seq1[i]+in_seq2[i] for i in range(len(in_seq1))]) i..
공부/Python 2020. 2. 15. 11:20
[ML] Univariate CNN Models

GITHUB https://github.com/yiwonjae/Project_Lotto/blob/master/Book_001/p091.py 0. 목표 연속된 하나의 sequcence에서 다음 하나의(univariate) 값을 예측한다. data : [10, 20, 30, 40, 50, 60, 70, 80, 90] x y 10 20 30 40 20 30 40 50 30 40 50 60 1. DATA raw_seq = [10, 20, 30, 40, 50, 60, 70, 80, 90] n_steps = 3 2. DATA 정제 X def splite_sequence(sequence:list, n_steps:int)->(ndarray, ndarray): x, y = [], [] for i in range(len(..
공부/Python 2020. 2. 15. 10:43
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