잠토의 잠망경

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)..

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_..

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..

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..

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(..

https://github.com/yiwonjae/Project_Book_012/blob/master/sample/sample001.py 불러오는 중입니다... 사전 준비 ① instant-client download ② cx-oracle download [사전 준비] instant-client download https://www.oracle.com/database/technologies/instant-client/downloads.html Oracle Instant Client Downloads No results found Your search did not match any results. We suggest you try the following to help find what you're lo..

1. CUDA Toolkit 설치 (Base: 10.1) 2. cuDNN 설치 (Base: 10.1) 1. CUDA Toolkit 설치(10.1) https://developer.nvidia.com/cuda-10.1-download-archive-update2?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal CUDA Toolkit 10.1 update2 Archive Select Target Platform Click on the green buttons that describe your target platform. Only supported platforms will be shown. Operating System..

역순으로 만드는 법 1. sliding 기법 a[::-1] print(a[::-1]) print(type(a)) 위와 같이 할 경우 역순으로만들 수 있다.