묻고 답해요

loggerMiddleware로 morgan처럼 로깅하기 수업에서 @Injectable 부분 에러

미해결

Slack 클론 코딩[백엔드 with NestJS + TypeORM]

LoggerMiddleware class를 작성하던 도중 @Injectable 데코레이터에서 2개의 에러가 납니다. 하나는 Decorator function return type 'ClassDecorator' is not assignable to type 'void | typeof LoggerMiddleware'. 데코레이터 함수 반환 유형 'ClassDecorator'은(는) 'void | typeof LoggerMiddleware' 유형에 할당할 수 없습니다. 라는 에러와 Type 'typeof LoggerMiddleware' has no properties in common with type 'ScopeOptions'. 'typeof LoggerMiddleware' 유형에 'ScopeOptions' 유형과 공통적인 속성이 없습니다. 라는 에러가 납니다 첫번째 에러는 검색하니 주로 데코레이터를 직접 만드신 분들이 내는 에러고 두번째 에러는 타입스크립트 버전을 올리신 분들이 겪는 에러로 보이는데 어떻게 해결할지 모르겠습니다.. 타입스크립트 버전을 내려야 할까요? 현재코드는 이렇습니다. import { NestMiddleware , Logger , Injectable } from ' @nestjs/common '; import { Request , Response , NextFunction } from ' express '; @ Injectable export class LoggerMiddleware implements NestMiddleware { private logger = new Logger ( ' HTTP ' ) use ( req : Request , res : Response , next : NextFunction ) : void { const { ip , method , originalUrl } = req ; const userAgent = req . get ( ' user-agent ' ) || '' ; res . on ( ' finish ' , () => { const { statusCode } = res ; const contentLength = res . get ( " content-length " ) this . logger . log ( `${ method } ${ originalUrl } ${ statusCode } ${ contentLength } ${ userAgent } ${ ip }` ) } ) next () } }

• @injectable
• 에러
• NestJS
• TypeORM
• express
• nodejs

그래픽스 포폴 관련해서

해결됨

게임 프로그래머 취업 전략 가이드

그래픽스 포폴로 왜 DirectX를 많이 사용하는지 궁금합니다 . 오픈지엘이 크로스 플랫폼이라 배워두면 범용성이 더 좋을거 같아서요 단순히 성능의 차이가 심해서 인가요 ? PC게임은 오픈지엘로 못 만들 수준이라던지.. 다른 이유가 있는 것인지 .. 아직 둘다 얕게 아는정도라 잘 모르겠습니다. 만약에 오픈지엘과 다렉 둘 중 하나만 만들수 있는 시간이라면 무조건 다렉 포폴이라 생각하시나요 ?

• 기술면접
• 포트폴리오

[질문] roboflow 사이트에서 public Datasets에서 원하는 데이터가 다운이 안됩니다.

미해결

[개정판] 딥러닝 컴퓨터 비전 완벽 가이드

YOLOv5 Custom Training - GPU 를 위하여 roboflow 사이트에서 public Datasets 에 가서 Synthetic Fruit Dataset 이미지를 다운로드 할려고 하는데 우측 상단에 있는 Download 아이콘이 눌러지지도 않고 팝업창이 뜨지를 않습니다 . 이유를 모르겠습니다 ,

• python
• 딥러닝
• keras
• 컴퓨터-비전
• 머신러닝 배워볼래요?
• tensorflow

취약한 웹 뷰 구현 필터링 코드 질문입니다!

미해결

안드로이드 모바일 앱 모의해킹과 시큐어코딩

다른 코드들은 이해가 가는데 밑의 두가지 유형의 코드는 이해 하기가 좀 힘듭니다 replace가 문자열들을 치환한다 했는데 밑의 두개는 무엇을 치환하는지 모르겠습니다..! f romText =fromText.replaceAll("eval\\((.*)\\)",""); fromText =fromText.replaceAll("[\\\"\\\'][\\s]*javascript:(.*[\\\"\\\']","\"\"");

• android
• 모의해킹

질문있습니다

미해결

it 취업을 위한 알고리즘 문제풀이 입문 (with C/C++) : 코딩테스트 대비

DFS 함수에서 if ( L == n+1) { } 안에 cnt 조건문을 끝내고 그 다음 줄에 따로 return;을 안적어도 문제없이 리턴되는데 더이상 실행할 문장이 남아있지 않기에 자동으로 리턴되어 돌아가는 건가요??

• C++
• 코테 준비 같이 해요!

OSIV 질문드립니다.

미해결

실전! 스프링 부트와 JPA 활용2 - API 개발과 성능 최적화

OSIV가 false일때, createOrder의 param으로 맴버넣을경우에 맴버를 member.findBy로 조회하고 넣잖아요? 이때 Member엔티티에서 @OneToMany(mappedBy = "member") private List<Order> orders = new ArrayList<>(); 이렇게 오더가 프록시로 들어가있으니 선생님은 어떤식으로 초기화를 하시나요? orders could not initialize proxy - no Session 요렇게 나오는데.. 다른 Member는 겟네임으로 초기화한다고하는데 orders는 리스트니까.. 궁금해서요 ㅠㅠ

• spring
• java
• JPA
• spring-boot

클린 아키텍처에 대해

미해결

Flutter 중급 1편 - 클린 아키텍처

안녕하세요. 항상 친절한 답변 감사합니다. 이번 강의를 보다가 궁금증이 몇 개 생겨 질문드립니다. 1. Use Case가 MVC 패턴의(?) service와 비슷하다는 느낌을 받았는데 비슷한 개념인지 궁금합니다. 2. 기능에 따라 (강의에서는 5개(업데이트, 추가, 삭제 등)) class 별로 각자 구현하였는데 이 방식이 call 함수를 쓰기 위함인지 궁금합니다. MVC에서는 Service 계층에서 하나의 클래스 안에 필요한 메서드(Use Case)를 생성했었는데 기능 하나마다 클래스를 만드니 메모리면에서(큰 프로젝트에서는) 낭비가 되지 않나 싶습니다. 3. 모델이 여러개인 경우 use_case 안에 여러개의 모델 디렉토리를 만들어 그 모델 디렉토리 안에서 dart파일을 보통 관리하나요? 4. note_repository에서 추상클래스를 만들고, 그를 구현하는 NoteRepositoryImpl를 만들고, 구현한 그를 사용하는 db_helper를 또 만들었습니다. Impl과 helper를 합칠만하다고 생각하는데 따로 관리하는 이유가 궁금합니다.

• android
• Flutter
• ios

command prompt 문의

미해결

[신규 개정판] 이것이 진짜 크롤링이다 - 기본편

- 학습 관련 질문을 남겨주세요. 상세히 작성하면 더 좋아요! - 먼저 유사한 질문이 있었는지 검색해보세요. - 서로 예의를 지키며 존중하는 문화를 만들어가요. - 잠깐! 인프런 서비스 운영 관련 문의는 1:1 문의하기를 이용해주세요. 맥북이용중입니다. command prompt가 안나오는데 이 부분은 어떻게 해결 할 수 있을까요?

• 웹-크롤링
• python

학습 실행 오류

미해결

Google 공인! 텐서플로(TensorFlow) 개발자 자격증 취득

Epoch 1/100 --------------------------------------------------------------------------- UnimplementedError Traceback (most recent call last) <ipython-input-31-4fdf12fa2786> in <module> 2 validation_data=(validation_set), 3 epochs=epochs, ----> 4 callbacks = [checkpoint] ) 1 frames /usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs) 65 except Exception as e: # pylint: disable=broad-except 66 filtered_tb = _process_traceback_frames(e.__traceback__) ---> 67 raise e.with_traceback(filtered_tb) from None 68 finally: 69 del filtered_tb /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 53 ctx.ensure_initialized() 54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, ---> 55 inputs, attrs, num_outputs) 56 except core._NotOkStatusException as e: 57 if name is not None:

• keras
• 딥러닝
• tensorflow
• python

콘스트레인트 사용할 때

미해결

피그마(Figma)를 활용한 UI디자인 입문부터 실전까지 A to Z

스케일은 거의 사용하지 않나요? 여백 비율보다는 여백을 유지하는게 더 중요하지 않을까 싶어서요.. 그리고 이건 다른 질문인데, XD인 경우 도형을 만들고 이미지를 드래그하여 도형에 넣으면 도형 사이즈대로 이미지가 들어가지는데 피그마는 그런 기능이 없을까여?

• figma
• 웹 디자인
• 모바일-디자인

RNN 출력 수정 방법(Many to one)

해결됨

[파이토치] 실전 인공지능으로 이어지는 딥러닝 - 기초부터 논문 구현까지

강의를 듣고 직접 RNN을 구현하는 것을 시도했습니다. 하나의 물체를 다른 위치에서 촬영하고 각 사진마다 물체 좌표값(x,y)을 추출했습니다. 얻은 좌표값을 이용해서 실제 3D 좌표(x,y,z)로 예측하는 것을 만들려고 합니다. 제가 생각한 모델구성은 아래와 같습니다. 하지만, 모델을 구현해보니 좌표값이 1개만 출력되고 출력된 값이 무엇을 예측하고 나왔는지 모르겠습니다. 어디를 수정해야 x,y,z값이 출력되나요? 아래는 사용된 소스 및 데이터 일부입니다. import numpy as np import pandas as pd import torch from sklearn.model_selection import train_test_split import torch.nn as nn import torch.optim as optim import matplotlib.pyplot as plt #데이터 불러오기 path = r"redball_Data.csv" df = pd.read_csv(path) df_suffled = df.sample(frac=1).reset_index() #텐서 데이터 만들기 x = df_suffled[['x1','y1','x2','y2','x3','y3','x4','y4','x5','y5','x6','y6','x7','y7','x8','y8']].values y = df_suffled[['x','y','z']].values print(x.shape) x = x.reshape(len(x),8,2) #2d to 3d print(x.shape) print(y.shape) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") xt = torch.FloatTensor(x).to(device) yt = torch.FloatTensor(y).to(device) x_train, x_test, y_train, y_test = train_test_split(xt,yt,test_size=0.2) print(f"x_train {x_train.shape} | x_test {x_test.shape} | y_train {y_train.shape} | y_test {y_test.shape}") train = torch.utils.data.TensorDataset(x_train, y_train) test = torch.utils.data.TensorDataset(x_test, y_test) batch_size = 1000 train_loader = torch.utils.data.DataLoader(dataset=train, batch_size=batch_size, shuffle=False) test_loader = torch.utils.data.DataLoader(dataset=test, batch_size=batch_size, shuffle=False) #Hyperparameter setting # RNN print(f"input_size : {xt.size(2)}") input_size = xt.size(2) num_layers = 2 hidden_size = 8 sequence_length = xt.size(1) #Create Model class VanillaRNN(nn.Module): def __init__(self, input_size, hidden_size, sequence_length, num_layers, device): super(VanillaRNN, self).__init__() self.device = device self.hidden_size = hidden_size self.num_layers = num_layers self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True) self.fc = nn.Sequential(nn.Linear(hidden_size * sequence_length, 3), nn.ReLU()) #1 => 3으로 수정 def forward(self, x): h0 = torch.zeros(self.num_layers, x.size()[0], self.hidden_size).to(self.device) # 초기 hidden state 설정 out, _ = self.rnn(x, h0) # out: RNN의 마지막 레이어로 부터 나온 output feature 반환, hn: hidden state 반환 out = out.reshape(out.shape[0], -1) # many to many 전략 out = self.fc(out) return out model = VanillaRNN(input_size=input_size, hidden_size=hidden_size, sequence_length=sequence_length, num_layers=num_layers, device=device).to(device) criterion = nn.MSELoss() lr = 1e-3 num_epochs = 800 optimizer = optim.Adam(model.parameters(), lr=lr) #Learn Model loss_graph = [] n = len(train_loader) for epoch in range(num_epochs): running_loss = 0.0 for data in train_loader: seq, target = data # 배치 데이터 out = model(seq) loss = criterion(out, target) optimizer.zero_grad() loss.backward() optimizer.step() running_loss += loss.item() loss_graph.append(running_loss / n) if epoch % 50 == 0: print('[epoch: %d] loss: %.4f' % (epoch, running_loss / n)) plt.figure(figsize=(20,10)) plt.plot(loss_graph) plt.show() def plotting(train_loader, test_loader, actual): with torch.no_grad(): train_pred = [] test_pred = [] for data in train_loader: seq, target = data # 배치 데이터 # print(seq.size()) out = model(seq) train_pred += out.cpu().numpy().tolist() for data in test_loader: seq, target = data # 배치 데이터 # print(seq.size()) out = model(seq) test_pred += out.cpu().numpy().tolist() total = train_pred + test_pred plt.figure(figsize=(20, 10)) plt.plot(np.ones(100) * len(train_pred), np.linspace(0, 1, 100), '--', linewidth=0.6) plt.plot(actual, '--') plt.plot(total, 'b', linewidth=0.6) plt.legend(['train boundary', 'actual', 'prediction']) plt.show() plotting(train_loader, test_loader, y[:500,0]) x1,y1,x2,y2,x3,y3,x4,y4,x5,y5,x6,y6,x7,y7,x8,y8,x,z,y 0,0,1554.5,639.5,1022,561.5,0,0,0,0,0,0,516,580,291.5,778,2,1.5,-8.5 0,0,1478.5,630.5,975,567.5,0,0,0,0,0,0,465.5,591,303.5,811.5,3,1.5,-8.5 0,0,1407,621.5,926,573,0,0,0,0,0,0,410.5,603,317.5,849,4,1.5,-8.5 0,0,1338.5,614,874.5,579,0,0,0,0,0,0,351,615.5,333.5,893,5,1.5,-8.5 0,0,1274,606,821.5,585,0,0,0,0,1865.5,679.5,286.5,629.5,352.5,945,6,1.5,-8.5 0,0,1212,599,765,592,0,0,1516.5,1073.5,1780.5,661,216,644.5,375,1006.5,7,1.5,-8.5 0,0,1154,592,707,599,0,0,1544,1006.5,1703,644.5,138.5,661,403,1073.5,8,1.5,-8.5 0,0,1097.5,585,645,606,0,0,1566.5,945,1632.5,629.5,53.5,679.5,0,0,9,1.5,-8.5 0,0,1044.5,579,580.5,614,0,0,1585.5,893,1568,615.5,0,0,0,0,10,1.5,-8.5 0,0,993,573,512,621.5,0,0,1601.5,849,1508.5,603,0,0,0,0,11,1.5,-8.5 0,0,944,567.5,440.5,630.5,0,0,1615.5,811.5,1453.5,591,0,0,0,0,12,1.5,-8.5 0,0,897,561.5,364.5,639.5,0,0,1627,778,1403,580,0,0,0,0,13,1.5,-8.5 0,0,882,566.5,368.5,647.5,0,0,1572.5,768.5,1384,583.5,0,0,0,0,13,1.5,-8 0,0,929.5,572.5,446,638,0,0,1557,800,1434.5,595,0,0,0,0,12,1.5,-8 0,0,979.5,578,519,629.5,0,0,1539,836,1489,607,0,0,0,0,11,1.5,-8 0,0,1031,584.5,588.5,620.5,0,0,1519,878,1548,619.5,0,0,0,0,10,1.5,-8 0,0,1085,591,654.5,613,0,0,1495,927,1612.5,634.5,71.5,686.5,0,0,9,1.5,-8 0,0,1142,598,717,605,0,0,1466,985,1683.5,650,157.5,667,487.5,1055.5,8,1.5,-8 0,0,1202,605,777,598,0,0,1431.5,1055.5,1761.5,667,235.5,650,453,985,7,1.5,-8 0,0,1264.5,613,833.5,591,0,0,0,0,1847.5,686.5,306.5,634.5,424,927,6,1.5,-8 0,0,1330.5,620.5,888,584.5,0,0,0,0,0,0,371,619.5,400,878,5,1.5,-8 0,0,1400,629.5,939.5,578,0,0,0,0,0,0,430,607,380,836,4,1.5,-8 0,0,1473,638,989.5,572.5,0,0,0,0,0,0,484.5,595,362,800,3,1.5,-8 0,0,1550.5,647.5,1037,566.5,0,0,0,0,0,0,535,583.5,346.5,768.5,2,1.5,-8 0,0,1546.5,656,1052.5,571.5,0,0,0,0,0,0,555,587,399,759,2,1.5,-7.5 0,0,1467.5,646,1004.5,577.5,0,0,0,0,0,0,504.5,598.5,418,789,3,1.5,-7.5 0,0,1392.5,637,954.5,584,0,0,0,0,0,0,450,611,439.5,824,4,1.5,-7.5 0,0,1322,628.5,901.5,590,0,0,0,0,1916.5,714,391,624.5,463.5,863.5,5,1.5,-7.5 0,0,1254.5,619.5,846.5,597,29.5,1072.5,0,0,1828.5,692.5,326.5,639,492.5,910.5,6,1.5,-7.5 0,0,1191,611.5,789,604,0,0,1352,1031,1742,673,255,655,526.5,965,7,1.5,-7.5 0,0,1130,604,728,611.5,0,0,1392.5,965,1664,655,177,673,567,1031,8,1.5,-7.5 1889.5,1072.5,1072,597,664.5,619.5,0,0,1426.5,910.5,1592.5,639,90.5,692.5,0,0,9,1.5,-7.5 0,0,1017.5,590,597,628.5,0,0,1455.5,863.5,1528,624.5,2.5,714,0,0,10,1.5,-7.5 0,0,964.5,584,526.5,637,0,0,1479.5,824,1469,611,0,0,0,0,11,1.5,-7.5 0,0,914.5,577.5,451.5,646,0,0,1501,789,1414.5,598.5,0,0,0,0,12,1.5,-7.5 0,0,866.5,571.5,372.5,656,0,0,1520,759,1364,587,0,0,0,0,13,1.5,-7.5 0,0,850,576.5,377,665,0,0,1469,750,1344,591,0,0,0,0,13,1.5,-7 0,0,899,583,457.5,655,0,0,1447.5,779,1394,602.5,0,0,0,0,12,1.5,-7 0,0,950,589,534,645,0,0,1423,812.5,1448.5,615.5,0,0,0,0,11,1.5,-7 1882,997,1003,596,606,635.5,0,0,1395,850,1507.5,629,14,722,0,0,10,1.5,-7 1802.5,1038.5,1059,603,674.5,626.5,0,0,1362,894,1572.5,644,110.5,699.5,698.5,1074.5,9,1.5,-7 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842.5,632.5,419,974.5,1401.5,1013.5,1142,641,341.5,678.5,781.5,895.5,1079,947.5,1656.5,699,8,1.5,5.5 903.5,623.5,330.5,940,1290.5,1057,1210,651,412,660,732,851.5,1009,1009.5,1745,721.5,9,1.5,5.5 962.5,615.5,251,908.5,0,0,1282.5,661,477,644,689,813,924.5,1075.5,1843,746.5,10,1.5,5.5 1018,607.5,179,880,0,0,1360,671.5,536,628.5,652,780,0,0,0,0,11,1.5,5.5 1071,600,113.5,854,0,0,1441.5,682.5,589.5,615,619.5,750.5,0,0,0,0,12,1.5,5.5 1121,593,53.5,830,0,0,1528.5,695,639,602,590.5,725,0,0,0,0,13,1.5,5.5 1103,587.5,2,849.5,0,0,1533.5,684.5,617,598,545.5,733,0,0,0,0,13,1.5,6 1053,594,48.5,876,0,0,1449,673,567.5,611,572,759.5,0,0,0,0,12,1.5,6 1001,601,114,904.5,0,0,1368.5,662,513.5,624.5,602.5,790,0,0,0,0,11,1.5,6 946,608.5,186,935,0,0,1293.5,652,454.5,638.5,637,825,0,0,1865,738.5,10,1.5,6 888.5,616.5,266,969.5,0,0,1222,642.5,390,655,676.5,864.5,1075.5,1033,1767,714,9,1.5,6 828.5,624.5,355,1007.5,1464,1050.5,1155,633.5,318.5,672.5,723,911.5,1141,966.5,1679,692.5,8,1.5,6 764,633.5,455,1050.5,1564,1007.5,1090.5,624.5,240,692.5,778,966.5,1196,911.5,1600.5,672.5,7,1.5,6 697,642.5,0,0,1653,969.5,1030.5,616.5,152,714,844,1033,1242.5,864.5,1529,655,6,1.5,6 625.5,652,0,0,1733,935,973,609,54,738.5,0,0,1282,825,1464.5,638.5,5,1.5,6 550.5,662,0,0,1805,904.5,918,601,0,0,0,0,1316.5,790,1405.5,624.5,4,1.5,6 470,673,0,0,1870.5,876,866,594,0,0,0,0,1347,760,1351.5,611,3,1.5,6 385.5,684.5,0,0,1917,849.5,816,587.5,0,0,0,0,1373.5,733,1302,598,2,1.5,6 381,674.5,0,0,0,0,833.5,582,0,0,0,0,1420.5,741.5,1323,594,2,1.5,6.5 464,664,0,0,0,0,882.5,588,0,0,0,0,1396,769.5,1373,606.5,3,1.5,6.5 541.5,653,0,0,1875.5,931,934,595,0,0,0,0,1368.5,800.5,1427,619.5,4,1.5,6.5 616,643.5,0,0,1803.5,964.5,988,602,34,730,0,0,1337.5,837,1486.5,633.5,5,1.5,6.5 685.5,634.5,0,0,1724,1002.5,1045,609.5,131,706.5,773,1057.5,1300.5,879,1551,649.5,6,1.5,6.5 751.5,625.5,389,1079,1634.5,1044.5,1104.5,617.5,218,685.5,711.5,987,1258,928.5,1622.5,666.5,7,1.5,6.5 814.5,617.5,284.5,1044.5,1530,1079,1167.5,625.5,296.5,666.5,661,928.5,1207.5,987,1701,685.5,8,1.5,6.5 874,609.5,195,1002.5,0,0,1233.5,634.5,368,649.5,618.5,879,1146,1057.5,1788,706.5,9,1.5,6.5 931,602,115.5,964.5,0,0,1303,643.5,432.5,633.5,581.5,837,0,0,1885,730,10,1.5,6.5 985,595,43.5,931,0,0,1377.5,653,492,619.5,550.5,800.5,0,0,0,0,11,1.5,6.5 1036.5,588,0,0,0,0,1455,664,546,606.5,523,769.5,0,0,0,0,12,1.5,6.5 1085.5,582,0,0,0,0,1538,674.5,596,594,498.5,741.5,0,0,0,0,13,1.5,6.5 1069,576.5,0,0,0,0,1542,665,575,591,450,750.5,0,0,0,0,13,1.5,7 1020,583,0,0,0,0,1461.5,655,525,602.5,471.5,779,0,0,0,0,12,1.5,7 969,589,0,0,0,0,1385,645,470.5,615.5,496,812.5,0,0,0,0,11,1.5,7 916,596,37,997,0,0,1313,635.5,411.5,629,524,850,0,0,1905,722,10,1.5,7 860,603,116.5,1038.5,0,0,1244.5,626.5,346.5,644,557,894,1221.5,1074.5,1808.5,699.5,9,1.5,7 801.5,610.5,208,1075.5,0,0,1179.5,618.5,275.5,661,595.5,946,1277,1008.5,1722,679.5,8,1.5,7 739.5,618.5,0,0,1711,1075.5,1117.5,610.5,197,679.5,642,1008.5,1323.5,946,1643.5,661,7,1.5,7 674.5,626.5,0,0,1802.5,1038.5,1059,603,110.5,699.5,698.5,1074.5,1362,894,1572.5,644,6,1.5,7 606,635.5,0,0,1882,997,1003,596,14,722,0,0,1395,850,1507.5,629,5,1.5,7 534,645,0,0,0,0,950,589,0,0,0,0,1423,812.5,1448.5,615.5,4,1.5,7 457.5,655,0,0,0,0,899,583,0,0,0,0,1447.5,779,1394,602.5,3,1.5,7 377,665,0,0,0,0,850,576.5,0,0,0,0,1469,750,1344,591,2,1.5,7 372.5,656,0,0,0,0,866.5,571.5,0,0,0,0,1520,759,1364,587,2,1.5,7.5 451.5,646,0,0,0,0,914.5,577.5,0,0,0,0,1501,789,1414.5,598.5,3,1.5,7.5 526.5,637,0,0,0,0,964.5,584,0,0,0,0,1479.5,824,1469,611,4,1.5,7.5 597,628.5,0,0,0,0,1017.5,590,2.5,714,0,0,1455.5,863.5,1528,624.5,5,1.5,7.5 664.5,619.5,0,0,1889.5,1072.5,1072,597,90.5,692.5,0,0,1426.5,910.5,1592.5,639,6,1.5,7.5 728,611.5,0,0,0,0,1130,604,177,673,567,1031,1392.5,965,1664,655,7,1.5,7.5 789,604,0,0,0,0,1191,611.5,255,655,526.5,965,1352,1031,1742,673,8,1.5,7.5 846.5,597,29.5,1072.5,0,0,1254.5,619.5,326.5,639,492.5,910.5,0,0,1828.5,692.5,9,1.5,7.5 901.5,590,0,0,0,0,1322,628.5,391,624.5,463.5,863.5,0,0,1916.5,714,10,1.5,7.5 954.5,584,0,0,0,0,1392.5,637,450,611,439.5,824,0,0,0,0,11,1.5,7.5 1004.5,577.5,0,0,0,0,1467.5,646,504.5,598.5,418,789,0,0,0,0,12,1.5,7.5 1052.5,571.5,0,0,0,0,1546.5,656,555,587,399,759,0,0,0,0,13,1.5,7.5 1037,566.5,0,0,0,0,1550.5,647.5,535,583.5,346.5,768.5,0,0,0,0,13,1.5,8 989.5,572.5,0,0,0,0,1473,638,484.5,595,362,800,0,0,0,0,12,1.5,8 939.5,578,0,0,0,0,1400,629.5,430,607,380,836,0,0,0,0,11,1.5,8

• 인공신경망
• 머신러닝 배워볼래요?
• pytorch
• 딥러닝
• python

생성자에서 비동기처리

미해결

타입스크립트 입문 - 기초부터 실전까지

- 학습 관련 질문을 남겨주세요. 상세히 작성하면 더 좋아요! - 먼저 유사한 질문이 있었는지 검색해보세요. - 서로 예의를 지키며 존중하는 문화를 만들어가요. - 잠깐! 인프런 서비스 운영 관련 문의는 1:1 문의하기를 이용해주세요. 안녕하세요 캡틴판교님, 강의 잘 듣고있습니다. 실습에서 구현한 클래스를 보면 생성자에서 비동기처리를 수행하는 함수가 실행되고(fetchData())있는데요, 다음과같이 만들고 메소드를 실행했을 때 비동기처리때문인지 결과 값이 빈 배열이 나오는 것을 볼 수 있었습니다... 그래서 질문은 해당 실습코드처럼 클래스의 생성자에서 비동기처리를 하는 함수를 실행하는 방식을 많이 이용하나요? 뭔가 제 생각으로는 생성자에서 비동기로 데이터를 받아와 멤버변수에 값을 넣는 경우 멤버함수(메소드)를 외부에서 이용할 경우 위처럼 문제가 생길 것 같아서요. 실습에서 구현한 클래스를 어떻게 잘 사용할 수 있을까요? 제가 이해한 실습코드의 클래스 로직은 이렇습니다. 클래스가 만들어지고(new AddressBook()) 클래스 생성자에서 fetchData() - fetchContracts()실행 -> 비동기로 처리됨 1이 처리되어 멤버변수 contracts에 받아온 데이터가 할당되기 전 외부의 코드 실행됨(위 스샷) 1이 처리되기 전에 실행되었기 때문에 빈배열 출력 --클래스의 메소드를 사용하고 싶은 경우 생성자가 실행된 이후 메소드들을 실행할 수 있도록 async-await나 프로미스를 이용해 처리를 해줘야 할 것으로 생각되는데, 어떻게 구현할 수 있을지 감이 오지 않습니다... 도와주실 수 있으실까요?

• javascript
• es6
• typescript

state is assigned a value but never used

미해결

따라하며 배우는 리액트 A-Z[19버전 반영]

이렇게 오류가 뜨는데 실행은 됩니다 이건 어떠한 경우인가요?

• Next.js
• react
• redux
• typescript
• tdd

코드를 동일하게 한 것 같은데, 드래그 기능 작동이 안됩니다. 이유가 궁금합니다.

미해결

따라하며 배우는 리액트 A-Z[19버전 반영]

코드를 동일하게 한 것 같은데, 드래그 기능 작동이 안됩니다. 이유가 궁금합니다. import React from "react"; import { DragDropContext, Draggable, Droppable } from "react-beautiful-dnd"; export default function List({ todoData, setTodoData }) { const handleClick = (id) => { let newTodoData = todoData.filter((data) => data.id !== id); console.log("newTodoData", newTodoData); //this.setState({ todoData: newTodoData }); setTodoData(newTodoData); }; const handleCompleteChange = (id) => { let newTodoData = todoData.map((data) => { if (data.id === id) { data.complited = !data.complited; } return data; }); //this.setState({ todoData: newTodoData }); setTodoData(newTodoData); }; return ( <div> <DragDropContext> <Droppable droppableId="todo"> {(provided) => ( <div {...provided.droppableProps} ref={provided.innerRef}> {todoData.map((data, index) => ( <Draggable key={data.id} draggableId={data.id.toString()} index={index} > {(provided, snapshot) => ( <div key={data.id} {...provided.draggableProps} ref={provided.innerRef} {...provided.dragHandleProps} className={`${ snapshot.isDragging ? "bg-gray-400" : "bg-gray-100" } flex items-center justify-between w-full px-4 py-1 my-2 text-gray-600 border rounded`} > <div className="items-center"> <input type="checkbox" defaultChecked={data.complited} onChange={() => handleCompleteChange(data.id)} />{" "} <span className={ data.complited ? "line-through" : undefined } > {data.title} </span> </div> <div className="items-center"> <button className="px-4 py-2 float-right" onClick={() => handleClick(data.id)} > x </button> </div> </div> )} </Draggable> ))} {provided.placeholder} </div> )} </Droppable> </DragDropContext> </div> ); }

• typescript
• tdd
• redux
• react
• Next.js

TDD 코드 미 인식 문의

미해결

Do It! 장고+부트스트랩: 파이썬 웹개발의 정석

강사님 안녕하세요. 지난 번 TDD 오류가 났고 문제 해결했는데 그 이후로 계속해서 오류가 나지 않고 ok만 뜨고 있네요. 이럴 땐 어떻게 문제를 해결하면 좋을까요? 깃허브에 커밋해주신 코드를 살펴보면서 수정하면 좋을까요..? 접근 방법을 잘 몰라서 문의 드립니다.

• javascript
• aws
• tdd
• HTML/CSS
• docker
• django
• bootstrap
• python

localhost8080에서 localhost가 연결을 거부합니다.

미해결

스프링 입문 - 코드로 배우는 스프링 부트, 웹 MVC, DB 접근 기술

학습하는 분들께 도움이 되고, 더 좋은 답변을 드릴 수 있도록 질문전에 다음을 꼭 확인해주세요. 1. 강의 내용과 관련된 질문을 남겨주세요. 2. 인프런의 질문 게시판과 자주 하는 질문(링크)을 먼저 확인해주세요. (자주 하는 질문 링크: https://bit.ly/3fX6ygx) 3. 질문 잘하기 메뉴얼(링크)을 먼저 읽어주세요. (질문 잘하기 메뉴얼 링크: https://bit.ly/2UfeqCG) 질문 시에는 위 내용은 삭제하고 다음 내용을 남겨주세요. ========================================= [질문 템플릿] 1. 강의 내용과 관련된 질문인가요? (예/아니오) y 2. 인프런의 질문 게시판과 자주 하는 질문에 없는 내용인가요? (예/아니오) n 3. 질문 잘하기 메뉴얼을 읽어보셨나요? (예/아니오) y [질문 내용] 여기에 질문 내용을 남겨주세요. 안녕하세요 강의를 보고 memberlist와 다른 파일들을 알맞은 package밑에 잘 넣은 것 같은데 localhost:8080/members에서 계속 연결 거부가 뜹니다.. 이런 식으로 뜨는데 구글링해서 찾은 방법으로 해결이 안되어서 질문 남겨봅니다 감사합니다 h2콘솔창은 이러합니다 터미널에서 포트를 열어보니 8080은 검색이 안나오는걸로 확인됩니다

• java
• spring-boot
• MVC
• spring

Springboot 스케줄러를 이용한 db 데이터 자동 삭제

미해결

스프링부트스케줄러를 이용해서 mysql에 있는 데이터를 자동삭제 하고 싶습니다. 블로그들을 찾아봐도 딱히 쓸만한 정보를 얻지 못해서 이 글을 작성하게 됩니다. 따롴 클래스를 만들던지 해서 controller에서 처리하고 싶습니다. db에 데이터가 삽입된 기준으로 하던지 현재 시간을 기준으로 최근 1달 정도 데이터를 유지하던지 그런 방식으로 진행하고 싶습니다. 알려주십쇼 ..

• springboot
• 스케줄러
• mysql
• db자동삭제
• cron

오류

미해결

Google 공인! 텐서플로(TensorFlow) 개발자 자격증 취득

실습 자료를 다운받아 그대로 실행 시켰는데 오류가 뜹니다. 어떤게 문제인지 모르겠습니다. history = model.fit(train_padded, train_labels, validation_data=(validation_padded, validation_labels), callbacks=[checkpoint], epochs=epochs) 오류 내용은 UnknownError Traceback (most recent call last) <ipython-input-38-db48157b102c> in <module> 2 validation_data=(validation_padded, validation_labels), 3 callbacks=[checkpoint], ----> 4 epochs=epochs) 1 frames /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 53 ctx.ensure_initialized() 54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, ---> 55 inputs, attrs, num_outputs) 56 except core._NotOkStatusException as e: 57 if name is not None: UnknownError: Graph execution error: Fail to find the dnn implementation. [[{{node CudnnRNN}}]] [[sequential/bidirectional/forward_lstm/PartitionedCall]] [Op:__inference_train_function_9113]

• tensorflow
• keras
• 딥러닝
• python

localstorage 질문드립니다.

미해결

Vue.js 중급 강좌 - 웹앱 제작으로 배워보는 Vue.js, ES6, Vuex

안녕하세요 학습중에 질문드립니다. 다름이 아니라 궁금증이 있습니다. localstorage에 있는key값을 받아와 List에 뿌려주는 방식인데 TodoList.vue내에서 키값 중복에 대한 유효성검사를 따로 하지 않았는데 중복값이 들어가지 않습니다. localStorage내부에서 자체적으로 중복값 유효성검사를 하는건가요? 알고싶습니다.

• javascript
• vuex
• vuejs
• es6

화면 글씨가 잘 안보여요

미해결

[입문자를 위한] 캐글로 시작하는 머신러닝 • 딥러닝 분석

df = pd.read.csv(' /kaggle/input/london-bike-sharing-dataset/london_merged.csv',arse_dates * ['timestamp']) df.head() 이거 맞나요?

• 딥러닝
• kaggle
• 머신러닝 배워볼래요?